Challenging Minds Cognitive Scientists’ Guide to Interdisciplinary, Research- and Problem-Based Learning Urban Kordeš, Maruša Sirk, Toma Strle Challenging Minds Cognitive Scientists’ Guide to Interdisciplinary, Research- and Problem-Based Learning Urban Kordeš, Maruša Sirk, Toma Strle CHALLENGING MINDS: COGNITIVE SCIENTISTS’ GUIDE TO INTERDISCIPLINARY, RESEARCH- AND PROBLEM- BASED LEARNING Authors: Urban Kordeš, Maruša Sirk, Toma Strle Reviewers: Markus Peschl, Olga Markič Proofreading and content review: Aleš Oblak, Neva Zver Design: Maruša Sirk Photographs: ChatGPT, RStudio, Canva Published by: Faculty of Education, University of Ljubljana For the publisher: Karmen Pižorn, dean Available at (URL): https://zalozba.pef.uni-lj.si/index.php/zalozba/catalog/ book/246 Financed by: EUTOPIA Published in Ljubljana in 2025 1st electronic edition Acknowledgments We dedicate this booklet to prof. dr. Julijana Kristl, Polonca Miklavc The publication is free. Valenčič and prof. dr. Maja Zalaznik. Three very special women, who took the time to understand our ideas, embraced them as their own, and put content above bureaucratic “this cannot be done” challenges. We thank them for being the first within University of Ljubljana to recognise the importance of interdisciplinary teaching. Kataložni zapis o publikaciji (CIP) pripravili v Narodni in univerzitetni knjižnici v Ljubljani We also dedicate this booklet to all students willing to make a leap and dare to play gracefully with ideas. COBISS.SI-ID 263029251 And to EUTOPIA, who supported the creation of the booklet. ISBN 978-961-253-351-9 (PDF) Challenge: An experiment on paradigms of studying emotions . . . . . . . . . . . . . . . . .52 Second part: The problem of emotion elicitation . . . . . . . . . . . . . . . . . . . . . . . . 64 Challenge: The quality of emotion elicitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65 Mini challenge: Ecological validity in emotion research – The case of curious emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67 Third part: Problems in measuring and reporting emotions . . . . . . . . . . . . . . . 70 Challenge: Reflecting on the challenges in measuring and reporting emotions . . . . . 71 Table of contents Mini challenge: Demand characteristics – What do we measure when we report emotions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75 Conclusion: Ethical reassessment of the stimulus-to-emotion model from a Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 lived experience perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 International Joint Interdisciplinary Master’s Programme in Cognitive Many Meanings of Decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Introductory lecture: A diversity of approaches to studying and The Purpose of This Booklet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 understanding decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Objectives of the Challenging Minds learning unit . . . . . . . . . . . . . . . . . . . .13 Collaborative Challenge-Solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Challenges in Challenging Minds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 First part: What is decision-making? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Objective 1: Integrating findings about selected cognitive phenomena from Mini challenge: Finding a definition of decision-making . . . . . . . . . . . . . . . . . . . . . 98 different disciplinary perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 Challenge: Comparing different conceptualisations of decision-making through the Objective 2: Acknowledging gaps in our knowledge and understanding of the lens of study design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .106 mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 Second part: Epistemic bubbles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Objective 3: Science of mind vs. lived experience . . . . . . . . . . . . . . . . . . . . . . . 24 Mini challenge: Cognitive scientists’ disciplinary bubbles . . . . . . . . . . . . . . . . . . . . 117 Objective 4: Gaining the Skill of Collaborative Research . . . . . . . . . . . . . . . . . 25 Challenge: How to change one’s epistemic bubble? . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Unravelling the mystery of decision-making? . . . . . . . . . . . . . . . . . . . . . . . . . .124 Objective 5: Gaining the Skill of Solving Complex Problems . . . . . . . . . . . . . . 26 Conclusion: The Bumpy Path from Student to Researcher . . . . . . . . . . . . . . . 127 The Structure of the Challenging Minds Learning Unit . . . . . . . . . . . . . . . . . . .31 Selection of Cognitive Phenomena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 A note on the creation process of the booklet . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Two Main Phases of the Learning Unit: Learning and Problem-Solving 33 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Examining Emotions: The Mystery of the Stimulus . . . . . . . . . . . . . . . . . . . . . . 37 List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Introductory lecture: The diversity of disciplinary perspectives on emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Collaborative challenge-solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 First part: Elicitation and measurement techniques . . . . . . . . . . . . . . . . . . . . . 49 Mini challenge: How emotions rise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 A fresh field of research demands fresh teaching approaches. The Middle European Interdisciplinary Master’s Programme in Cognitive Science, which we co-founded over 15 years ago, offered an opportunity for pedagogical innovation.1 It was built from the ground up, with a central goal in mind: to transcend old disciplinary constraints. At a time when many study programs were struggling with the mandated transition to the Bologna system of higher education (i.e., trying to keep their programs as close as possible to their pre-Bologna versions), we seized the opportunity to create a fundamentally different educational experience. Under the sponsorship of the then vice rector of the University of Vienna, a small group of enthusiasts (a.k.a. naïve idealists) from five European universities – Vienna, Bratislava, Budapest, Zagreb, and Ljubljana – came together. Unencumbered by existing teaching practices (simply because there were no established practices in cognitive science at these universities at the Foreword time), we asked ourselves: What would an ideal cognitive science program look like? How could we teach an interdisciplinary science of the mind in a way that would preserve the dynamic and emergent nature of this field of research? I particularly remember debates with professor Markus Peschl, who at the time led the Austrian Society for Cognitive Science (while I simultaneously led its sister organisation in Slovenia). We studied existing programs at major universities worldwide and tried to learn as much as possible from as cognitive science is an exciting and relatively new field of research. It T International Joint Interdisciplinary Master’s their mistakes. Most of the programs at the time (early 2000s) shared three characteristics that we believed our program needed to surpass: Programme in Cognitive Science First: No home for true interdisciplinarity he interdisciplinary project of the study of the mind known Although the cognitive science programs address an intrinsically interdisciplinary research field, they are mostly not truly interdisciplinary seeks to integrate knowledge from all the disciplines that touch upon the – nearly all are attached to an existing department at the university, most human mind and answer fundamental questions about human behaviour, commonly neuroscience, linguistics, philosophy, or computer science. experience, and the physiology that enables both. It would not be an This means that studying cognitive science often turns out to be studying exaggeration to say that cognitive science explores the very essence of who neuroscience – with potential additions of psychology and fragments we are. It addresses questions that are potentially even more existentially of philosophy; or studying linguistics – with an addition of cognitive significant than those posed by the natural sciences – after all, is it not neuroscience and artificial intelligence; or AI with additions of psychology necessary to first understand the characteristics of the observer in order to comprehend what is being observed, be it the self, the surrounding world 1 Interdisciplinary joint master’s in cognitive science, part of the 4-university consortium Middle or the universe? European interdisciplinary master’s programme in Cognitive Science (MEi:CogSci); https://www. meicogsci.eu and neuroscience, etc. The dependence on the organisational unit, from the It is crucial to present students not only with the knowledge we have gathered outset, prevents an equitable distribution of disciplines within the program. but also with its gaps. It is important that students understand science as of the contributing disciplines – independent of the organisational units of there are vast gaps in our understanding of the mind should be reframed: the host university and also independent of the currently most prominent or rather than being an unwelcome detail, mentioned briefly at the end of the “trendy” discipline (currently AI; at the time, neuroscience). Namely, a brief course, it should be viewed as an exciting opportunity – especially for young history of cognitive science reveals that while current brain research may be In our case, the vision of an “ideal” study program involved an equal distribution ignorance – not as a dogmatic collection of absolute truths. The fact that humanity’s (arguably most successful) way of confronting uncertainty and at the forefront, it has not always been central to understanding the mind – breaking discoveries. researchers, for whom these gaps represent immense potential for ground-the focus has historically shifted between artificial intelligence, linguistics, and even genetics. In our program, we wanted to reorient students – transforming them from We identified a few universities where cognitive science programs this reason, perhaps the greatest challenge of our program is to frame the passive photocopiers into active collaborators and co-researchers. For managed to break free from the organisational constraints and create truly confrontation with knowledge gaps as joyful puzzle-solving, rather than as interdisciplinary courses (such as those at UC Berkeley or Osnabrück an insurmountable void of uncertainty. University) – and we sought to follow suit. Third: The chasm between the science and lived experience Second: Knowledge is fluid, the curriculum is fixed students the material as if it were set in stone. The curriculum often conveys ourselves! We lose a great deal if, as researchers of the mind, we pretend the impression that we already have a definitive understanding of the to study something “out there” – something that can be treated as a purely brain, experience, and behaviour, leaving students with the primary task of laboratory problem, disconnected from our lives after 5 PM, when we close Most existing cognitive science programs – like other fields of study – teach overlook a fundamental aspect of the discipline: it is a science about Almost all researchers and lecturers in the field of investigating the mind2 memorising received wisdom. Even the research methods are taught as fixed, the lab door behind us. well-established algorithms. In reality, our knowledge of the mind is alive, rapidly changing, and deeply continuously test the taught theories against their own experience of what it We envisioned a program that encourages students (and lecturers) to incomplete. Certainly, the past century has brought immense progress, but means to be human. many new discoveries have opened even deeper questions. Moreover, the recent reproducibility crisis in psychology and biomedicine has shaken the scientific community, revealing that a significant portion of published findings might not be replicable – rendering them scientifically unusable. Because of this, the backbone of the “ideal” cognitive science program must depart from the traditional ex-cathedra teaching culture. Knowledge about the mind should be taught alongside scientific humility. This does not mean that the existing body of knowledge is insignificant or unimportant. Far from it! But when students are shown the winding path that is leading us to this knowledge, it becomes clear that we are at the heart of the process of understanding the mind – not at its conclusion. 2 In Slovenian: “duševnost”, which does not directly translate to mind, but encompasses it. The Purpose of This Booklet the end result. The twists and turns were many. Even the current form of the course – fifteen years in the making – is still a work in progress. are aiming for a truly interdisciplinary program that fosters a scientific spirit This booklet is written with the hope that our insights can be used by anyone Contemplating the above-mentioned challenges, it became apparent that we of inquiry, akin to solving puzzles. And a program that continually reminds tempted to replace ex cathedra teaching with challenging students. Anyone ready to take seriously the worn-out “truth” that it is important to teach how us that we ourselves are its object of inquiry. to solve problems and anyone unafraid to take the position of “I don’t know”. After an idyllic period of theoretical deliberations on the “ideal” structure The course in cognitive science is merely a concrete example – our findings, of a cognitive science program, we faced years of battling university I hope, transcend the field of exploring the mind. Today, I can imagine bureaucracies. Despite the Bologna Reform in higher education championing several scientific disciplines that desperately need interdisciplinarity and the desirability of programs like ours (interdisciplinary, collaborative, hands- engagement with the uncertainty of the unknown; fields that touch us very on, and rich in electives), the road to an accredited program was full of – personally, often existentially, such as ecology, economics, epidemiology, sometimes downright surreal – obstacles. As is often the case, individuals public mental health, etc. These are complex fields, full of unknowns and who took the time to understand our ideas, embraced them as their own, and misunderstandings. Such fields are ideal for unconventional teaching rose above bureaucratic “this cannot be done” came to the rescue. methods like those used in Introduction to Cognitive Science II. Fortunately, Ljubljana had enough such individuals – among both faculty The course – from now on referred to as learning unit – Introduction to and university leadership (thank you, Olga, Zvezdan, Andrej, Julijana, and Cognitive Science II was selected by the University of Ljubljana as an example Polonca). The program came to life, becoming one of the most successful of a modern teaching unit and served as a condensation nucleus for forming study programs by all conceivable evaluation parameters. a teaching community within the EUTOPIA project – a project aimed at I believe the program as a whole does. This is ensured by a few core courses of the learning unit (researching emotions) and, together with colleagues that aim to weave together narratives about the human mind, as told by from the University of Warwick and Gothenburg, adapted it into an online neuroscience, psychology, linguistics, philosophy, and artificial intelligence. version. Collaboration with colleagues from European universities further Unfortunately, not all courses in the program reflect the above goals. However, like. For the purposes of European collaboration, we selected a segment reflecting on what an ideal European university of the future might look One such course, with the rather dull title Introduction to Cognitive Science II, convinced us of the importance of the core idea underlying our learning unit will serve as an example of (hopefully) good practice and an illustration of – empowering students to confront the uncertainty of the unknown. The fresh approaches to teaching science. EUTOPIA project supported the preparation and publication of this guide. The purpose of this booklet is to show that interdisciplinarity, problem-oriented Our approach to writing this guide is to be brief, concrete, and free of teaching , engaged learning, and collaborative work need not merely be empty theoretical language acrobatics. Pedagogical literature is saturated with buzzwords written to appease bureaucrats allocating funds. These can be grand terms that, with every use, lose their meaning. We do not wish to vibrant characteristics of dynamic teaching at the academy. repeat this mistake in this text. This is a how-to guide, an example of best We aim to demonstrate that teaching methods can be completely immune practices. A description of experiences in designing and delivering a fairly to the negative interference of artificial intelligence – simply because they unconventional learning unit. Nothing more. cultivate skills that (current) AI cannot contribute. I hope you find it useful or – even better – thought-provoking. After all, The purpose of this booklet is not to describe the twists and turns of the path that’s the goal, isn’t it? to the current form of Introduction to Cognitive Science II, but rather to present Urban Kordeš Challenges I n c h a l l e n g i n g m i n d s Objectives of the Challenging Minds learning unit our learning unit. We believe that the master’s program in cognitive science Before we proceed, let us find a simpler and more fitting name for at the University of Ljubljana is pretty well thought of, it is perhaps even closer to the “ideal” we envisioned during the dreaming-up process than we dared to hope. However, it is not without flaws. One of these is the dull naming of courses – plenty of “introductions to” and numbers (like “1” and “2”). The title Introduction to Cognitive Science II is uninspiring, uninspired, and uninformative – it does not give away much about what students can expect. From now on, in this guide, we will call the learning unit Challenging Minds. Perhaps someday, we will successfully navigate the bureaucratic labyrinth Main Objectives of the Learning Unit and officially rename the learning unit.3 1. Integrating findings about selected cognitive phenomena from different Challenging Minds disciplinary perspectives. (CM) begins in the second semester of the program. By this point, students have already covered foundational understanding in the core 2. Acknowledging gaps in our knowledge and understanding of the mind. disciplines: cognitive neuroscience, cognitive psychology, philosophy, and 3. Connecting students’ everyday experiences with scientific understanding linguistics. (In parallel with CM, students also take a foundational course in of the discussed phenomena. artificial intelligence.) They are familiar with the key questions in the study 4. Gaining the skill of collaborative research. of the mind and some of the main theories answering these questions. They 5. Gaining the skill of solving complex problems. understand the history of cognitive science and how different models of the mind have evolved, along with associated shifts in research focus (e.g., Some objectives are self-explanatory, while others may require some emphasis on intelligence, brain function, embodiment, or computational clarification. processes). The foundation is therefore Objective 1: Integrating findings about selected cognitive set. This is where CM comes in. phenomena from different disciplinary perspectives recipient of information to lines between them do not entirely reflect the complex and ever-changing relationships between them. Furthermore, it would be incorrect to assume becoming an independent that the state-of-the-art knowledge of mental phenomena is simply the sum thinker. While it still includes of knowledge from some traditional lectures, the all the involved focus shifts to encouraging disciplines. Mere students to embark on their addition of the own paths of thinking and findings from research. It motivates them individual core to stop acting solely as science journey. The learning suggesting that the participating disciplines are evenly and harmoniously integrated in the study of the mind. These sorts of depictions are informative unit is structured to support in a sense that they enlist relevant areas of knowledge, but the balanced equal the transition from being a point in a student’s cognitive Cognitive science is often portrayed as a balanced polygon (as in Figure 1.2), It is designed to be a turning repositories of information disciplines is not enough to achieve and instead begin to grow a comprehensive into competent and critical understanding of peers to their teachers – the mind. 4 true partners in solving the Figure 1 .1 . The CM learning unit aims to facilitate the mysteries of the mind and transition from being a recipient of information to However, it is a consciousness. becoming an independent thinker and researcher. necessary starting Figure 1 .2 . Depiction of the participating disciplines evenly 3 In the context of an interdisciplinary joint program, “navigating bureaucracy” means approval point. While distributed and integrated in the study of the same subject – the mind. of changes at the level of four faculties at the University of Ljubljana and four partner universities – a significant investment of time and effort that might be better spent elsewhere. 4 Nearly a third of the CM learning unit is dedicated to tackling this challenge. various disciplinary courses (e.g., neuroscience, psychology, philosophy) may As if forgetting Richard Feynman’s definition of science as doubt about the mention a given cognitive phenomenon from their respective perspectives, expertise of experts (he has put it a bit more bluntly: “science is the belief this does not guarantee that students have formed appropriate connections in the ignorance of experts”), we fail to realise that it is precisely the false in their mental maps. For this reason, the integration of knowledge about pretence of omniscience that which ultimately undermines credibility. Such selected mental phenomena is the first objective of this learning unit. By pretence may be appropriate for spiritual teachers and religious leaders, but presenting insights from various disciplines on a given phenomenon within it certainly does not suit scientists. students to reorganise their mental maps of the mind: instead of structuring disagreements about which theory is more appropriate are presented as perhaps the most important element of the scientific method. Continuous their knowledge by disciplinary boundaries (where the limits are defined doubt in the results of colleagues and their ongoing verification is the engine by the disciplines), we want their mental geography to be organised around that drives the advancement of knowledge. We want to show students that from what they have learned across different courses. Our goal is to inspire In CM, we try to take the opposite approach: unexplained questions and a single lecture, we aim to encourage students to build a cohesive picture about a specific cognitive phenomenon, encompassing all relevant insights imperfect knowledge about the mind is good news for them: it means that phenomena. This means that the areas on the map are defined by knowledge regardless of their disciplinary origin. cognitive science has a long way to go and, consequently, many future job opportunities. phenomenon that students may not have encountered during discipline- Many students report that CM is the first opportunity in their academic path If necessary, we can also add additional information about the selected specific lectures. The goal is not to provide a detailed analysis of every where the hard problems of science are discussed in detail; while all of them report that it is the first time they are expected to attempt to solve them. aspect of the chosen phenomena but to establish a solid, interdisciplinary foundation that can support deeper exploration in the future. What do we mean by the term “hard problems of science”? For the purposes of this learning unit, we have divided the problems faced by scientists and – Objective 2: Acknowledging gaps in our knowledge and more broadly – science into five types. This division is not perfect, as some understanding of the mind problems are hard to categorise into just one type; it is, however, clear enough to allow us to present to students the current state of knowledge and – more As mentioned in the introduction, we believe that our message to students importantly – ignorance. about what science is supposed to do is inadequate. It almost always gives the impression that scientifically acquired knowledge is fixed, thus sweeping Types of problems encountered in science: under the rug some of the fundamental characteristics of the scientific 1 . Mundane problems related to research professions. method: continuous verification and, through that, constant renewal and 2 . Problems of choosing and applying “the right” methodology. improvement of knowledge. 3 . Paradigmatic and epistemic problems. It seems as though lecturers are afraid that science will lose credibility if 4 . Major methodological problems. we emphasise the transient and imperfect nature of its knowledge. This fear 5 . Problems related to interdisciplinarity. was, for example, clearly reflected in the public sphere during the COVID-19 pandemic – there was a fear that acknowledging uncertainty would be seen The first type of problems in acquiring and constructing scientific as an admission of scientific impotence. We fear that answers such as “I don’t knowledge is, of course, the mundane everyday tasks of a scientist’s work: know” or “The current hypothesis is X, but it is very likely to be replaced securing funding, writing reports, dealing with increasingly burdensome by a new and different hypothesis soon” will raise doubts about the value of bureaucracy... Although we call these challenges “mundane,” they are likely scientific projects. the most common and profoundly shape a researcher’s life: what would I like to research vs. what research can I get funding for; how can I – amidst tasks, problems, etc.). Parts of the mind that are removed from this sort of growing bureaucracy – find time for creative thinking and research; how can processing remain unnoticed. a significant impact on the development of science. Nevertheless, we believe it makes sense to conduct research by assigning “tasks” to experimental subjects and then observing their responses (whether neural or behavioural). that the master’s level is still a bit too early for students to confront this level This is precisely how most experimental cognitive psychology research is of challenges. conducted: the subject is presented with a task, and we observe what happens of my research organisation? These “mundane” problems can therefore have If we assume that the function of the mind is to respond to the environment, I open a new research area that does not fit within any existing department The second category includes challenges that could be grouped under the – calling this a “response”. Most good study programs cover these types of challenges within their We can imagine the surprise of cognitive neuroscientists when, around the question, “How can we optimally respond to a given research question?” methodological courses, so we can avoid them in the CM learning unit. year 2000, they came across brain activity that occurs exclusively when a person is not exposed to any stimulus or task. This activity, which involves CM addresses types of scientific problems categorised under points 3, 4, and the coordinated functioning of several relatively distant brain regions, was 5 (“hard problems”). Let us define these in more detail: named the default mode network (Reichle et al., 2001). An even greater surprise assumptions or models of the field we are studying. Type 4 encompasses blind common mental process. Some studies suggest that we might spend up to 70% spots in research that arise due to the limited scope of research techniques of our waking time wandering in thought. How is it possible that psychology – limitations that are often the result of assumptions underlying our and the entire field of cognitive science were, until the (accidental) discovery Type 3 includes questions that we cannot solve because we have incorrect associated with functioning without externally imposed tasks – is the most likely followed when they found that mind-wandering – a mental activity understanding of the field. Consequently, type 4 is frequently a consequence of the default mode network, almost entirely blind to the most common of type 3 and there is no clear boundary between the two. mental phenomenon? Example: Cognitive science long operated on the behaviourist assumption The story of the “discovery” of mind-wandering serves as a useful illustration that cognitive systems could be treated as “devices” whose function is to of several of the points mentioned above. find (or “compute”) the most appropriate response to environmental events (the so-called “stimuli”). As a result, psychology was for a long time blind First, it’s important to recognise that most major scientific problems are to “internal” experiential states – it was concerned with reaction times and only identified in hindsight. For example, when at the end of the 19th century solutions to behavioural tests but not with subjective experiences.5 physics couldn’t explain why, as the illumination of a photosensitive metal the research subject as it truly is, this is not the case. Measurements reveal through improved measurement techniques or greater technical precision in fragments of the research subject that align with the assumptions underlying experimental setups. Only a few imagined that its explanation would upend our current paradigm. Research approaches enable the testing of hypotheses, the entire field of physics – and with it, our understanding of reality. Even though it may initially seem that measurements allow us to observe most physicists saw it as a minor issue – something that could be resolved is increased, voltage increased in discrete jumps rather than continuously, 6 but they simultaneously obscure parts of the research field that our assumptions do not encompass. The same applies to the “discovery” of mind-wandering – psychology and neuroscience were unaware of a massive gap in their knowledge.7 It was only If we treat the mind as a device for responding to stimuli, we narrow our focus to only the aspects that can be related to outside signals (stimuli, 6 Einstein’s explanation of the “quantum” nature of the photoelectric effect – along with a few other discoveries – paved the way for the most unusual and counterintuitive scientific theory to date: quantum 5 One legacy of this era is that the Anglo-Saxon school of psychology still treats phenomena like physics. emotions as “behaviour”! 7 This is not entirely true. At the beginning of the 20th century, Hans Berger, the inventor of recently, as mind-wandering became central to cognitive science research, Challenges of interdisciplinary that we could retrospectively acknowledge this gap as a significant problem. Let us conclude the enumeration of scientific problems we present to our In general, major problems only reveal their true scale after they have been students with a type of problem that is widely named but rarely understood solved. Before that, the associated open questions tend to appear as “technical” and almost never acted upon: challenges arising from attempts to integrate deficiencies that can be resolved with “more of the same” – slightly better multiple, diverse strands of research (type 3 from the above list). potential major scientific problems when confronted with them. They are lie in the spaces between disciplines – or require knowledge from more than one discipline to address them effectively. These include ecology, tackling accustomed to solutions like “it would be good to increase the sample size” climate change, a comprehensive understanding of the effects of infectious always working and often feel lost when we reject such “solutions” (which disease spread, etc., and, of course, questions related to the human mind and statistical methods. Interestingly, this is often how students approach We are convinced that most existentially significant issues of modern society measurement techniques, improved instrument precision, or enhanced ChatGPT often advocates as well). consciousness. Judging by the vocabulary used by bureaucrats responsible for For the CM learning unit, we selected problems that are excellent candidates scientific and educational policies, one might conclude that the importance for being considered as “hard”. However, it is always possible that we are of interdisciplinarity is widely recognised. The word is almost a mandatory mistaken, and future hindsight might show that they were simply resolved at component of “strategies”, “guidelines”, and “values”. or not, they help reframe students’ thinking – shifting from finding the right The problem, however, lies in the vast discrepancy between the use of some point. Regardless of whether the chosen examples turn out to be “hard” procedure to inventing one and thus attempting creative problem-solving. this term in strategic documents and actual attempts at interdisciplinary research. Despite broad consensus that addressing complex scientific issues Such reframing is not easy. Clear, universally accepted problem-solving would benefit from combining knowledge from different disciplines, this procedures provide a sense of security – a feeling that the foundation is rarely happens in practice. uncertainty (or disbelief) as would, before the year 2000, an attempt to The reasons for this are twofold: 1) Because it is genuinely difficult, and 2) solid, known, and reliable. Any suggestion to the contrary evokes a similar convince a cognitive neuroscientist that we lack fundamental knowledge Because it is nobody’s business. about mental phenomena and that this knowledge lies beyond research Combining knowledge from different specialised disciplines is hard. The techniques based on measuring responses to tasks. challenge lies in the fact that the farther apart two disciplines are in terms The described case of the huge mind-wandering-shaped hole in our knowledge of methodology, terminology, and research assumptions, the harder it is to integrate their insights. Interdisciplinary connections between closely related is also revealing from another perspective. It highlights how much cognitive disciplines are important and interesting, but they do not belong to the same science loses when disconnected from everyday experience. If we were to tell category as “strong” interdisciplinary connections. Fields like mathematical someone today – when mind-wandering is one of the most studied topics physics, biochemistry, neuropsychology, or the intersection between – that for much of the history of psychology and later cognitive science, anthropology and ethnology, share the advantage that researchers in both neither field noticed a process that occupies most of our waking lives, we involved disciplines do not need a translator or additional methodological could rightfully expect some ridicule. When we study the mind in ways that training to understand each other’s work. On the other hand, connections disregard everyday experiences, we do so at our own peril. 8 between sociology and epidemiology, economics and the physics of climate change, or between neurology and phenomenology, attempt to integrate the EEG technique for measuring brain activity, pointed out the blind spot of this research approach. fields that share neither terminology, nor methodology, nor – often even However, this warning was forgotten for over 80 years. – epistemic assumptions about the nature of the domain they are studying. 8 We discuss this challenge in the next objective. Yet, precisely these “strong” connections are needed to answer most of the (i.e., the first-person, qualitative study of subjectivity) with neuroscience. Such endeavours aim to bridge the so-called explanatory gap. pressing questions of contemporary society. In the CM learning unit, we aim to draw students’ attention to the intriguing There are only a few examples where very diverse disciplines have successfully “assembled” their insights into a new, better, or more holistic understanding challenges that arise when attempting to merge disciplines. For example, how of the area under investigation. However, this does not mean that attempts different disciplines use the same terms (such as the word “decision-making”) at collaboration between vastly different disciplines are futile. to describe very different processes. In such cases, attempts to integrate knowledge are doomed to fail – a frequent occurrence in cognitive science The early period of cognitive science stands as proof of the immense potential that happens because none of the participating researchers realise they are of thinking beyond disciplinary boundaries. In 1941, Frank Fremont-Smith, discussing different phenomena. the executive secretary of the Macy Foundation, began developing a problem- Equally fascinating and common is the opposite problem: due to the epistemic solving, multidisciplinary conference format, later known as the Macy Conferences . Fremont-Smith believed that the best way to advance knowledge bubbles in which individual research fields are typically enclosed, multiple was through interdisciplinary collaboration among diverse fields. Indeed, groups of researchers (whether in different disciplines or within the same the Macy Conferences, which brought together physicists, neurologists, discipline) may be working on the same problem without knowing about anthropologists, philosophers, mathematicians, economists, biologists, each other. Because of differing vocabularies and the fact that they operate and scientists from many other disciplines, sparked a scientific revolution within distinct research communities, they fail to recognise that they are whose benefits we still enjoy today. Participants in these conferences tackling similar issues. noted that certain abstract models effectively described phenomena across many otherwise unrelated fields. Together, they identified and described structures like the feedback loop. They mathematically modelled this structure and discovered that it could explain phenomena in systems that maintain themselves in equilibrium. Such structures were termed negative feedback loops or control systems. Biologists found such organisation in organisms (e.g., the sensorimotor loop), engineers in devices like thermostats, economists in markets, anthropologists in cultures, and so on. This research occupied the space between disciplines! This type of research came to be called cybernetics. While today the term is often associated with computing or robotics, these are just the two – probably most successful – fields that emerged from cybernetics. Other fields that emerged include systemic and family psychotherapy, communication theory, Figure 1 .3 . Different scientists working on the same problem, without knowing about each other. systems theories in economics, artificial intelligence, and, most notably, the This poses a great challenge and opportunity for interdisciplinary collaboration. entirety of cognitive science. The entire early period of cognitive science The challenges of interdisciplinarity only begin here. Once again, it is crucial was transdisciplinary – many disciplines came together on “no man’s land,” to emphasise that truly interesting and novel insights are often found in the exploring patterns of functioning (in addition to the already mentioned connections between disciplines that do not share common methodologies feedback loops, also information or symbolic processing) that later enriched or conceptual frameworks. Within cognitive science, some of the most the development of knowledge about the mind on many levels. promising connections are those attempting to integrate phenomenology Unfortunately, disciplinary tribalism triumphed over collective research efforts. The question of how to return cognitive science to transdisciplinarity may well be the most critical issue in contemporary cognitive science. The will may not exist, and your sense of being able to make free choices is real mission of CM is to encourage students to view the knowledge they acquire (and incredibly useful). The feeling of free will should be regarded as a user during their studies not merely as detailed information about specific interface that helps us navigate life and not as an indication of what is (or is research areas but also as patterns of functioning. We believe that fostering not) objectively true. As such it should be acknowledged as an essential part an abstract, mathematical, and/or philosophical perspective on individual of experiential landscape. processes can contribute to a new, more holistic understanding of the mind. We aim to remind students not to forget that, by studying brain activity, Objective 3: Science of mind vs. lived experience behavioural patterns, and theoretical models of consciousness, they are also studying themselves. It is crucial that the next generation of cognitive Connecting scientific insights about the mind with students’ everyday lived scientists considers the practical relevance of understanding oneself as one of experiences is another crucial objective of the CM learning unit. the benchmarks for evaluating the quality of emerging science. Science often prides itself on revealing realities that lie beyond our simple Objective 4: Gaining the Skill of Collaborative Research ideas about how the world works. The science of the mind is no exception, offering numerous insights that seemingly contradict our intuitions about The remarkable success of the Macy Conferences highlights an intriguing how we perceive, think, and act. characteristic of interdisciplinary research: it is not so much a methodological However, we also believe that elevating scientific discoveries to the pedestal revolutionary scientific insights, he acted as the catalyst for an extraordinary of ultimate truth is not the best approach. Dismissing our “naive” ideas series of discoveries, some of which opened the doors not only to new about the mind as inadequate and insisting on their swift replacement with scientific understandings but to entirely new scientific disciplines. Fremont- We believe it is essential to address these contradictions with students. one of the forgotten heroes of science. While he was not the author of any problem as it is a matter of social organisation. Frank Fremont-Smith is scientific ones can sever (or sweep under the rug) our ingrained sense of self, Smith was an organiser – he arranged the conditions and convinced the the world, and our place within it. leading scientists of his time to dedicate a week or two each year to meetings A better strategy, we think, is to demonstrate to students that these are two not solely focused on their specialised fields of research. distinct domains – despite often using the same terminology. On the one Undoubtedly, interdisciplinary collaboration demands thorough hand, we have scientific theories; on the other, our own meaningful lived methodological reflection. The question is, who will undertake it? The experience. These are complementary domains that are interconnected but organisational structures of universities and research institutions are not fundamentally not the same. Meaningful lived experience is the medium in conducive to the development of new interdisciplinary fields. Typically, those which we exist and navigate our lives. Scientists often forget that our feelings who excel in monodisciplinary environments lack both the motivation and of the self exist primarily because they work – they help us survive. the proper competencies for integrative research. Additionally, it is difficult Take, for example, the concept of free will. Neuroscientist Benjamin Libet’s to be sufficiently literate in more than one scientific discipline. experiments demonstrated that brain activity initiating finger movement Although there is a general consensus that interdisciplinary research and occurs before the conscious decision to move the finger. Such findings teaching are valuable, dedicated collaborative research rarely occurs. Finding seriously challenge the concept of free will. Yet, on the other hand, the sense funding, space, and – most importantly – mutually available times for all of being able to make free choices is inherent to (almost) every human being participants – who will take care of that? It’s nobody’s business. in virtually all moments of life. We believe that the role of scientific education is not to deny this lived experience. Instead, we should encourage students Interdisciplinary work (whether research or teaching) requires a diverse to think in parallel about both truths: it is true that experiments suggest free group of researchers willing to dedicate at least part of their energy to exploring distant disciplinary “bubbles”. It requires individuals willing to disciplinary lectures and felt isolated because neither professors nor peers invest significant time and personal resources into a project that – even if addressed these significant gaps in knowledge. exceptionally successful – will not bring them much recognition. The other (much more common) response is disinterest. Mentions of major It is no exaggeration to say that the foundation of any interdisciplinary open questions fail to resonate with students. This lack of engagement project is collaborative work. The goal of the CM learning unit is for students becomes clear the moment we stop merely describing problems and challenge to experience first-hand the dynamics of problem-solving in a diverse students to solve them. It turns out that, up to this point, students have group. Fortunately, cognitive science students come from a wide variety treated problem descriptions as just another lecture topic – another set of of disciplinary backgrounds, making it easy to form groups that include “study material” to be learned. Only when they have skin in the game, when representatives from both sides of the explanatory gap. It is crucial for they are expected to provide solutions, do they begin to feel uncertainty, students to understand the essential role of soft skills in achieving significant sometimes even anxiety. research results. These soft skills include: • Attempting to understand the perspectives and terminologies of anxiety or confusion into curiosity and demonstrate that the scientific The role of the CM learning unit teachers is to try to channel this potential colleagues from “distant” disciplinary bubbles. problem-solving attitude is an excellent way to confront uncertainty. As • The ability to articulate one’s own viewpoint in a sufficiently mentioned, this cannot be achieved except through a trial by fire – by inviting comprehensible way. students to solve challenges as equal peers; as co-researchers. • Observing and understanding the group dynamics that emerge in various configurations and reflecting on one’s role within them. This confrontation is conducted as gently as possible: we begin with simple, fun, mini-challenges (which we call “icebreakers”). Then, once students feel a In the CM learning unit, the goal of training in collaborative work is treated bit more confident in their role as problem-solvers, we present them with the as equally important as all other, more “scientific-sounding” goals. We address “big” challenges – those that represent the “hard” problems of contemporary it not as mere lip service to currently fashionable phrases, but as an essential cognitive science. convincing students of the intrinsic importance of social skills in the research Most students approach solving challenges by trying to find an appropriate research technique. Perhaps the most challenging task of our learning unit is process within the field of interdisciplinary cognitive science. solution – either online, in the vague and murky waters of AI, or by searching through lecture materials and notes. This approach works for some icebreakers Objective 5: Gaining the Skill of Solving Complex but not for most challenges (recall that many of the grand challenges are drawn from the pool of unsolved scientific problems). Problems By “appropriate solving”, we describe a specific attitude toward problem- It is not enough for students to merely become aware of the major barriers solving – what could be called a student mindset. Students assume that an to advancing knowledge about the mind (even though simply confronting appropriate solution already exists somewhere – their task is merely to recall the existence of these challenges can sometimes be difficult for students). We or find where it is. also want them to actively engage in solving these issues. All the goals of the CM learning unit described thus far lay the groundwork for achieving this Under the label of “appropriate solving” falls another common instinct of objective. students: if they cannot find the solution, they at least attempt to find the correct or appropriate way of answering – such that satisfies the evaluator. In Students respond to the challenges of contemporary cognitive science in this case, the CM learning unit teacher. various ways. Some (a rare few) feel relieved. They may have noticed these or similar problems during their undergraduate studies or within our program’s The goal of the CM learning unit is to demonstrate that there is another This opens the door to the next crucial step: understanding that and why approach or attitude – one that shifts from seeking the appropriate solution something is a problem. We ask students to revisit the problem description. to seeking the right or good solution. In other words, instead of looking for They are encouraged to describe the problem in their own words, summarise answers that will satisfy the professor, students aim to find solutions (or them, and extract the essence. We hope that this way, they will place the gist solution processes) that genuinely address the problem. of the problem on their own mental maps and recognise the challenge as important, or – even better – interesting. The step from seeking appropriate solutions to seeking good ones is immense. The entire educational system conditions students to become If all that is achieved, the students can enter the trial and error process experts in finding appropriate solutions. Along the way, the distinction is of puzzle-solving. They are encouraged to jot down ideas as they arise – rarely emphasised. A student might occasionally admit, with some honesty, regardless of how unusual or unacceptable they might seem at first glance. that they wrote something they thought “Kordeš would like”. But for the This does not mean that anything goes: the next critical step is to establish most part, they themselves believe that mastering the skill of providing a feedback loop of validation. First, we encourage all kinds of ideas, no appropriate solutions to academic tasks equates to expertise in the field. matter how unconventional; then, they are immediately subjected to critical The result of this confusion is that schools produce individuals who excel examination and pruning. This is the last, but essential step. Namely, if at gauging the expectations of authority figures (and meeting them) but are students feel critique as a threat instead of support, that means that they still quite poor at finding effective or right solutions. In other words, instead of haven’t internalised the essence of the scientific processes of conjecture and fostering curious researchers, schools produce bureaucrats. refutation. So, how can we shift students’ mindset from seeking appropriate solutions to We want students to approach problem-solving with a playful attitude – pursuing the right ones? freely exploring ideas without becoming too attached to them. The ideal First, we must acknowledge that this goal can never be guaranteed. All we in identifying flaws in their own arguments. problem-solving atmosphere would be one where students find satisfaction can do is create an optimal learning environment and provide students with maximum encouragement – and then hope for the best. The challenge lies In summary, we aim to create an environment where students feel safe to play in avoiding, at all costs, the impression that instructors already know the with ideas. In more poetic terms, we can borrow from Oscar Wilde and say correct way to solve a problem or the “right” solution.9 If we show students that the primary goal of CM is for students to develop the “Oxford temper”. one possible solution path, most will take it as a new “appropriate” procedure, This term comes from a letter Wilde wrote from prison to his lover and Oxford and any hope of reframing the focus from appropriateness to truth-seeking colleague, Alfred “Bosie” Douglas. Wilde bitterly notices that Douglas “[…] will be lost. Instead, students need to be convinced that the solution is had not yet been able to acquire the ‘Oxford temper’ in intellectual matters, not nearly as important as choosing a meaningful approach to solving the never, I mean, been one who could play gracefully with ideas but had arrived problem. The path – not the destination – is important. at violence of opinion merely” (Oscar Wilde, De Profundis). How can we create an environment that fosters curiosity-driven puzzle- This is what we want: for students to be able to play gracefully with ideas. If solving instead of bureaucratic thinking? that is too much to expect, then at least for them to be able to suspend the A key step is to clearly shift the emphasis of evaluation from the final answer them heavily. violence of opinions – a violence towards which the current zeitgeist is luring to the problem-solving process. It is already a significant achievement if a student merely entertains the possibility that alternative approaches to problem-solving exist. 9 After a few attempts, students often ask, “So how would you solve this?” The structure of the Challenging Minds learning unit for students to gain a broad, integrated, and interdisciplinary overview of As we have seen, the goals of Challenging Minds are ambitious: we aim selected mental phenomena. We want them to understand the relationship between scientific knowledge of these phenomena and their own lived experience . Most importantly, we want them to grasp that scientific knowledge is not a carved-in-stone deal but rather unpredictable and ever- changing. And – as a cherry on top of a cake – we hope they experience the joy of forging new paths in solving hard scientific problems. Introduction to Cognitive Science II (which we termed for the purposes of this guide Challenging Minds) is an inherently interdisciplinary learning unit. It forms the backbone of the second semester of the Middle European Interdisciplinary Master Programme in Cognitive Science. Approximately 25 first-year master’s students with diverse disciplinary backgrounds – such as psychology, linguistics, life sciences, computer science, and philosophy – attend the learning unit. Admission to the cognitive science a substantial part of the vast territory of knowledge about the mind. program is competitive (the acceptance rate is about 1:2), meaning most of Additionally, students have already encountered these three phenomena to our students are highly motivated.10 By the time they begin this learning some extent (mainly in cognitive neuroscience, psychology, and artificial unit, students have already acquired foundational knowledge in psychology, intelligence). A possible exception is decision-making, a large part of which neuroscience, some research methods, as well as cognitive science more falls under behavioural science, which is poorly covered in our program – broadly. addressing decision-making in Challenging Minds thus provides an opportunity to cover this gap. In this section of the guide, we will aim to provide a concrete description of the Challenging Minds learning unit. Rather than detailing the entire As mentioned, these three phenomena are the result of a multi-year “natural curriculum, we will focus on elements we believe are transferable beyond selection” process. Over the years of running Challenging Minds, we have the disciplinary scope of cognitive science. Specifically, we will emphasise learned that the learning unit’s goals are better achieved by working in-depth elements that foster interdisciplinarity, collaborative problem-solving, on a small number of selected phenomena. Throughout the history of the exploration within a diverse team of colleagues, and the comparison of learning unit, we have reduced the number of phenomena without increasing students’ personal experiences with scientific findings. At the same time, the number of challenges. Instead, now for all challenges, we allow at least we will provide enough descriptions of the specific content on mental one iteration of trial and error problem-solving. In this way, we aim to enact phenomena discussed in the learning unit to ensure that the context of the the feedback loop and show students that incorrect solutions are part of the challenges is understandable, even to readers without prior knowledge. scientific process – every scientific discovery is the result of a trial and error process. In recent years, we have even experimented with focusing on only Selection of Cognitive Phenomena two phenomena per year – but when we omitted visual perception, students clearly expressed their desire to have it back. We still regret having to let go It is, of course, impossible to cover the entire body of knowledge about the of many phenomena throughout the history of the learning unit (such as mind in a semester-long learning unit. It is also clear that the objectives language and consciousness), so we have to constantly remind ourselves that of the learning unit cannot be addressed merely as theoretical knowledge. the learning unit’s goal is not to be a comprehensive cognitive science course. Hence, we need a handful of the most illustrative examples. After years of trial and error, we have built the entire learning unit Two Main Phases of the Learning Unit: Learning around three cognitive phenomena: decision-making, emotions, and visual and Problem-Solving perception. The learning unit consists of two phases: The term “phenomena” may not be the most apt, but it is the broadest. The dictionary defines “phenomenon” as “a fact or situation that is observed to Phase 1 consists of a series of lectures providing an overview of basic exist or happen, especially one whose cause or explanation is in question.” knowledge about the selected phenomena from different disciplinary In this sense, we speak of phenomena as overarching terms that cover all perspectives. With this, we aim to show students, in one place, the different elements of the mind whose explanations are in question – elements that are disciplinary perspectives of the three selected phenomena. In addition to sometimes called events, processes, constructs, etc. the ex-cathedra lectures, this phase also includes reading articles. Students must familiarise themselves with some seminal works from the history of We selected decision-making, emotions, and visual perception for several research on the selected phenomena, as well as read several state-of-the-art reasons: they are very different types of phenomena, allowing us to cover articles to get a sense of the current research trends. This phase amounts to 10 approximately one-third of the learning unit. Sometimes, they can even be too motivated – interested in a very specific aspect of cognitive science to the point where they see unrelated elements of the learning unit as unwelcome distractions. Phase 2 is carried out in an active learning format. Each phenomenon is used to illustrate one or more of the major problems in researching the mind. The teaching process is based on working in groups, where particular problems encountered in researching the selected phenomena are tackled in the form of collaborative challenge/riddle-solving. The treatment of each phenomenon begins with a so-called icebreaker challenge – an engaging short puzzle that introduces the problem we will be working on in the coming weeks. This is followed by larger, more complex challenges. Through examining these particular problems, students confront fundamental issues in studying the mind. For example, in solving a challenge where students experiment with different approaches to eliciting emotions, students learn not only about emotion research, but also about the more general issue of task-dependent research designs in cognitive science. They also learn about the concept of demand characteristics in psychological research and the problem of relating measurements of behavioural and physiological parameters to participants’ lived experience. We encourage students to critically examine the existing understanding of the selected phenomena, acknowledging gaps in knowledge and identifying areas of research that present a challenge to the collaboration of different research disciplines. Through the described group-based collaborative work, students are challenged to understand the examined cognitive phenomena not only as objects of scientific research, but also as lived and socially embedded phenomena, relevant both for their everyday experiences and for the workings and understanding of society more broadly. Each group-based challenge is accompanied by a short lecture on the relevant Figure 2 .1 . Two main phases of the CM learning unit: the ex-cathedra theories and methods. This phase amounts to approximately two-thirds of introductory lecture format, followed by collaborative challenge solving. the learning unit. Students’ work in this phase is assessed through the reports The disciplines shown do not constitute an exhaustive list of all disciplines covered in the learning unit. on attempts at solving the given challenges. PREVIEW ONLY Examining emotions The mystery of the stimulus emotion are you experiencing right now? Is it tiredness from your Before we start, we invite you to take a deep breath. What everyday turmoil? Or perhaps happiness from starting to read this chapter? Maybe you feel emotional, because someone said something to you? But, what do you think – did this emotion really arise because of an external event? Did perhaps some event just trigger something that was already brewing inside? Or, did the emotion in question arise from the depths of your consciousness unannounced, completely by itself? Whatever the reasons for you feeling as you do right now, let the curiosity about the origin of emotion guide you through the story of emotions in this chapter. Emotions are phenomena that are richly represented both in scientific research and everyday lived experience. This makes them an ideal subject for investigation in our Challenging Minds (CM) learning unit, in which we want to introduce students to studying complex problems Russell Hurlburt later additionally contributed to the phenomenological – problems that interact in difficult-to-predict ways and change as a function understanding of emotions. approaches the study of emotions using its own methods that emerge from fundamental aspects. For instance, there is broad agreement that emotions are positive or negative responses to external or internal stimuli, that they their specific theories and presumptions. In this part of the learning unit, involve physiological, behavioural, and phenomenological changes, and that we want to encourage students to critically reflect on what we are actually they differ from moods. However, in the CM learning unit, we challenge disciplines come with significant methodological challenges. Each discipline At this point, there are several theories of emotions that converge on certain of time. Both the nature of complex problems and the inclusion of multiple what do we gain with each method? And ultimately, are we truly measuring almost all of these beliefs. measuring when we attempt to measure emotions. What do we sacrifice, and emotions – and emotions only? One of the most significant issues, which we address in the CM learning unit, To explore this topic, we must start with a fundamental question: What even is the problem of eliciting and measuring emotions. Most theories – and, by extension, most measurement methods – assume that emotions are responses are emotions? to a specific stimulus. While emotions may have indeed evolved as adaptive Modern psychology has a long history of studying this phenomenon, with its responses to external circumstances, our everyday experiences suggest that roots in the work of Charles Darwin and William James. Darwin proposed emotions often arise without an obvious stimulus. Moreover, the same PREVIEW ONLY the idea of universality of emotions among both humans and animals, stimulus or event can elicit significantly different emotional reactions in while James suggested that emotions arise as a consequence of physiological different individuals. Even when people are exposed to an identical stimulus, reactions to stimuli, an idea that later became known as the James-Lange their emotional responses often vary, and the emotion they experience Theory (after Carl Lange independently reached similar conclusions). may not even be directly caused by the stimulus itself. Despite this, most In neuroscience, the study of this phenomenon was facilitated with an this variability. experimental techniques designed to measure emotions fail to account for “accidental” case study – of Phineas Gage. While working on a railroad, a metal rod pierced Gage’s head. He suffered a severe brain injury that damaged the At first glance, this challenge may not seem particularly daunting. However, frontal part of his brain. This injury altered his personality, making him it quickly becomes apparent when we consider a hypothetical conversation impulsive, impatient, disrespectful, and unreliable. His case provided key between individuals from different disciplinary backgrounds12 trying to insights into the role of different brain regions in emotional processing. Since design an experiment to study emotions. This hypothetical conversation then, further discoveries in neuroscience led to various theories on emotional vividly illustrates the interdisciplinary challenges in studying emotions. Each processing and identified multiple brain regions involved in the processing field brings its own assumptions and methodologies, yet they all struggle of emotions, one such region being the limbic system.11 with the fundamental question: What exactly are we measuring when we measure emotions? Additionally, Heidegger’s notion on moods [Stimmung] and finding oneself in a world through the moods as Befindlichkeit marked a milestone in the field Student 1 (background in psychology): “It’s simple – let’s give participants a validated Student 1 (background in psychology): “It’s simple – let’s give participants a validated of phenomenology of emotions. His body of work emphasises that emotions and standardised questionnaire, and then make them watch standardised image sets, and standardised questionnaire, and then make them watch standardised image sets, are not merely internal reactions to external stimuli, but rather that they for which we already anticipate the emotional response they will evoke. Participants for which we already anticipate the emotional response they will evoke. Participants play an important role in how reality discloses itself to us, orienting us will then report which emotions they experienced on the questionnaire. The only will then report which emotions they experienced on the questionnaire. The only within our lived environment. Other scholars, such as Matthew Ratcliffe and 12 The purpose of this section is not to present stereotypes about specific professions or disciplines 11 Readers interested in a more in-depth exploration of the evolution of affective neuroscience can but rather to illustrate the challenges of eliciting and measuring emotions based on the methods most refer to the work of Dalgleish (2004). See the literature section for the full reference. commonly used in different disciplinary fields. thing that we need to agree upon is which questionnaire and which standardised set thing that we need to agree upon is which questionnaire and which standardised set The CM learning unit aims to encourage students from different disciplines The CM learning unit aims to encourage students from different disciplines of images to use.” of images to use.” to engage in precisely the kinds of conversations described above, without to engage in precisely the kinds of conversations described above, without Student 2 (background in neuroscience): “Showing images might be the right Student 2 (background in neuroscience): “Showing images might be the right students’ diverse disciplinary backgrounds and their prior knowledge of the putting a preference on one discipline over another. The combination of putting a preference on one discipline over another. The combination of approach, but I disagree with the method of measurement. Let’s present the images to approach, but I disagree with the method of measurement. Let’s present the images to phenomenon forms a solid foundation for embarking on the quest to solve students’ diverse disciplinary backgrounds and their prior knowledge of the phenomenon forms a solid foundation for embarking on the quest to solve participants in an fMRI scanner and measure their brain activity to determine which participants in an fMRI scanner and measure their brain activity to determine which the mysteries of the phenomenon of emotions. the mysteries of the phenomenon of emotions. regions activate in response to a given image. Since previous studies have already regions activate in response to a given image. Since previous studies have already standardised these images and identified the emotions they elicit, we don’t need to standardised these images and identified the emotions they elicit, we don’t need to concern ourselves with that now, and thus we don’t need a questionnaire to validate concern ourselves with that now, and thus we don’t need a questionnaire to validate the findings of which emotion the participants experienced. The brain region that the findings of which emotion the participants experienced. The brain region that activates for a particular image is involved in processing the corresponding emotion activates for a particular image is involved in processing the corresponding emotion and with that we know which emotion was experienced.” and with that we know which emotion was experienced.” Student 3 (background in computer science and informatics): “What if we take it a Student 3 (background in computer science and informatics): “What if we take it a step further? Let’s consider the knowledge we already have. We possess standardised step further? Let’s consider the knowledge we already have. We possess standardised images of emotions with known effects, and we also have substantial knowledge images of emotions with known effects, and we also have substantial knowledge PREVIEW ONLY about the brain regions activated by these emotions. What if we used this existing about the brain regions activated by these emotions. What if we used this existing data to train a neural network? We could teach it to predict which image a person data to train a neural network? We could teach it to predict which image a person is viewing based on known brain activity patterns. Then, we could test whether the is viewing based on known brain activity patterns. Then, we could test whether the neural network correctly identifies images it hasn’t been trained on. This could mark neural network correctly identifies images it hasn’t been trained on. This could mark a breakthrough in mind reading!” a breakthrough in mind reading!” Student 4 (background in anthropology): “Hold on, everyone – it seems to me that Student 4 (background in anthropology): “Hold on, everyone – it seems to me that these ideas stem from the assumption that there is such a thing as a universal stimulus. these ideas stem from the assumption that there is such a thing as a universal stimulus. But let me remind you that some studies show emotions are deeply shaped by culture, But let me remind you that some studies show emotions are deeply shaped by culture, time, and individual experience. A facial expression, a gesture, an animal… pictures time, and individual experience. A facial expression, a gesture, an animal… pictures in general might evoke completely different emotions in different people. We can’t just in general might evoke completely different emotions in different people. We can’t just blindly rely on standardised tools and pretend as if context does not matter.” blindly rely on standardised tools and pretend as if context does not matter.” Student 5 (background in phenomenology): “These ideas are fascinating and exciting Student 5 (background in phenomenology): “These ideas are fascinating and exciting for scientific progress, but I urge you, dear colleagues, to return to the things for scientific progress, but I urge you, dear colleagues, to return to the things Figure 3 .1 . A depiction of the interdisciplinary debate between students from different 13 themselves. themselves. 13 Do we truly know that a specific image evokes a targeted emotion? And Do we truly know that a specific image evokes a targeted emotion? And backgrounds, trying to come up with an experiment to study emotions. does brain activity genuinely tell us how a person feels while viewing an image? Let does brain activity genuinely tell us how a person feels while viewing an image? Let me remind you of one of the most fundamental statistical principles: correlation does me remind you of one of the most fundamental statistical principles: correlation does not imply causation. Does brain activity associated with viewing an image – an not imply causation. Does brain activity associated with viewing an image – an image that is assumed to trigger a particular emotion – actually tell us that the image that is assumed to trigger a particular emotion – actually tell us that the person experienced that emotion? In our research process, are we not losing sight of person experienced that emotion? In our research process, are we not losing sight of the very thing we set out to study: the emotion itself?” the very thing we set out to study: the emotion itself?” 13 “Back to the things themselves!” Is a call to go back to the way things are actually experienced and not merely theorised. The call was made by the famous phenomenologist Edmund Husserl. Main objectives of the phenomenon of emotions learning unit approaches scientific research (and also everyday problems) from a assumptions of experimental cognitive psychology is that stimulus always and epistemological point of view. It is important to recall this issue when precedes an emotion. We need only consult the first paragraph of any number addressing the problem of eliciting and measuring emotions. Each discipline of introductory textbooks on psychology or neuroscience. Thus Kandel et al. has its established methods for investigating phenomena, so we can imagine (2012): As we have seen above, one of the core methodological (if not conceptual) ways of understanding this phenomenon from a different methodological particular disciplinary perspective thereby becoming unaware of different disciplines as enclosed in their own bubbles. Nevertheless, all these disciplines Emotions are transient responses to specific stimuli in the environment (e.g., investigate the same phenomenon, and they arrive at different insights about danger), the body (e.g., pain) or, for humans, the mind (e.g., train of thought). it. The bubbles of disciplines intersect in the sense that they investigate the same phenomenon, but they remain confined within their own paradigmatic And Gazzaniga et al. (2019): and methodological frameworks, hence closed in their own bubbles. [Emotions] are triggered by emotionally salient stimuli, which are highly relevant for the well-being and survival of the observer. This assumption is also implied in Banich and Compton (2023): PREVIEW ONLY Emotional experiences often include bodily changes, such as an increased heart rate or sweaty palms, that are considered to be part of the body’s fight-or-flight response. If these scientists are correct, then measuring emotions should not be particularly difficult: we simply identify a suitable stimulus, present it to participants in a lab, and the corresponding emotional reaction should reliably occur. But, as already discussed above, things are not necessarily that straightforward. While many people might feel fear when shown a picture of a snake, some do not. Others may even respond with curiosity or excitement. And, perhaps most strikingly: many feel nothing at all. Therefore, it is interesting to present and investigate the methodological problems of eliciting and measuring emotions that follow from the standard postulate Figure 3.2. A stimulus (the wolf), waiting for each discipline to figure out its method of measuring of a stimulus always preceding an emotion, which is the main goal of this the emotion it elicited. The picture depicts the closedness of each discipline in its own bubble. part of the learning unit. In doing so, all five learning objectives as described When researching emotions, most disciplines try to quantify them and in the introduction chapter are addressed. However, the emphasis is placed make measurements as replicable and reliable as possible, which means on the second objective, which is to acknowledge the gaps in our knowledge that if the test were repeated in the future, we would get the same results. and understanding of the mind However, in real life, it seems that emotions are not experienced as numbers, . It also importantly addresses the objective of connecting students’ everyday experiences with the scientific understanding of the and most experiments are conducted in the laboratory, which means they discussed phenomena (most probably) have low ecological validity – we do not know whether a . person would experience the same emotion in a real-world situation. When In the chapter on decision-making, we will be introduced to the problem we look deeper and explore individual descriptions of emotions and their of epistemic bubbles, which describes the situation when an individual phenomenology, we see that their quantification is insufficient, as we lose the essence of the emotional experience when we reduce them to numbers. Introductory lecture: The diversity of disciplinary Nevertheless, descriptions of emotions are not always suitable for scientific perspectives on emotions research because they are highly subjective and non-replicable, but they tend to be closer to the described phenomena or at least of how they feel like to We begin the quest of “finding” emotions, with an introductory lecture on an individual The problem, therefore, lies in whether we choose to prioritise the disciplinary perspectives of emotion research. The goal is to provide a more reliable or more valid data. comprehensive overview of what different disciplines already know about In this part of the learning unit, we will tackle the challenge of eliciting and the phenomenon at hand. This helps us establish a foundation for “solving” measuring emotions, while trying to connect the scientific knowledge and the challenges that students will face in the next part of the learning unit. methods to the implications that the findings have on the lived experience of Students are given some basic textbooks to read, as well as more recent the phenomenon of emotions in everyday lives of people. articles that present contemporary findings in the field. A comprehensive list of the literature is provided in the literature section. Learning unit overview for the phenomenon of emotions The first problem we encounter when presenting the state-of-the-art research Introductory lecture – The diversity of disciplinary perspectives on emotions in the field of emotions is the definition of what emotions actually are. When Collaborative challenge-solving while there are some dimensions that mostly overlap, there is differing 1. evidence regarding the basic claims of what emotions are (see e.g., Izard, 2011). First part: Elicitation and measurement techniques • Mini challenge: How emotions arise The main dimensions that usually overlap include the idea that emotions are a response to a stimulus, involving several different dimensions, such as • Challenge: An experiment on paradigms of studying emotions PREVIEW ONLY searching for a unified definition of emotions, one quickly discovers that, behavioural and experiential changes, etc. However, theories are generally 2. Second part: The problem of emotion elicitation not unified regarding which components constitute emotions, how they are • Challenge: The quality of emotion elicitation connected, related, and manifested (see e.g., Gazzaniga et al., 2019). 3. • Mini challenge: Ecological validity in emotion research – The case of Universality of emotions. One key debate in emotion research concerns curious emotions whether basic emotions are universal or socially constructed. The idea Third part: Problems in measuring and reporting emotions that emotions are universal – not only across individuals but also across • Challenge: Reflecting on the challenges in measuring and reporting other species – was first proposed by Darwin (1872). His claims were later emotions expanded and deepened, particularly through the work of Paul Ekman, who • Mini challenge: Demand characteristics – What do we measure when provided substantial evidence for emotional universality. Through extensive • Conclusion: Ethical reassessment of the stimulus-to-emotion model found cross-cultural consistency in facial expressions associated with basic emotions (Ekman & Friesen, 1971). On the other hand, researchers such as from a lived experience perspective we report emotions research, including studies on communities from isolated cultures, Ekman Lisa Feldman Barrett have challenged this perspective, providing substantial evidence that the way emotion experiments are structured greatly influences their outcomes. By employing different techniques to study emotions in secluded cultures, Barrett found results that support the idea that emotions are shaped by culture and context rather than being biologically hardwired (Barrett, 2017). these changes as emotions (James, 1884; Lange, 1922). Cannon-Bard Theory challenges this view, arguing that emotions and physiological responses occur simultaneously and independently, rather than one causing the other (Cannon, 1927). Schachter-Singer Two-Factor Theory introduces a cognitive component, proposing that when we experience physiological arousal, we interpret it using environmental cues to determine what emotion we are feeling (Schachter & Singer, 1962). LeDoux’s Dual-Pathway Model adds brain circuits in this equation, identifying two pathways for emotional processing: a fast, subconscious pathway from the thalamus to the amygdala, triggering a rapid response to danger, and a slower, more deliberate pathway involving the cortex, which allows for a more detailed evaluation of the situation (LeDoux, 1996). For a broader overview of different emotion theories, see, Figure 3.3. Darwin-inspired illustration of universal emotional expressions (fear, happiness and anger, e.g., Dalgleish (2004). respectively), across humans and animals. The image represents some of the basic human emotions, in line with Darwin’s ideas, which were later adopted and expanded by researchers such as Ekman. Structure of emotions. The debate over what emotions are is also reflected PREVIEW ONLY in differing views on their structure. Emotions are generally understood through either a categorical or dimensional framework. The categorical approach treats emotions as distinct categories, classifying them into basic and complex emotions. Fear, anger, sadness, happiness, disgust, and surprise are normally conveyed as basic emotions, while complex emotions such as jealousy and love are a composition of specific basic emotions (Gazzaniga et al., 2019). In contrast, the dimensional approach views emotions as existing along a continuum rather than as discrete categories. A prominent example is Russell’s circumplex model of emotions, which organises emotions along two dimensions: valence (ranging from pleasant to unpleasant) and arousal (ranging from low to high). Based on numerous studies using this model, emotions have been mapped onto these two dimensions according to how individuals subjectively rate them (Posner et al., 2005). Emotion generation theories. Another key topic in the introductory lecture is Figure 3.4. An overview of some emotion generation theories. the exploration of theories on the origin of emotions. These theories provide stimulus is necessary for an emotion to emerge, they differ in their proposed multiple brain structures and neural pathways. However, identifying which specific brain areas activate in response to particular emotions is a complex mechanisms and components of emotion generation. James-Lange Theory challenge. A foundational theory in the neuroscience of emotions is the Papez for example suggests that emotions result from physiological reactions to emotions arise after a stimulus is perceived. While they all agree that a Neuroscience of emotions. The process of generating emotions involves different layers of explanation, primarily focusing on the sequence in which events. According to this theory, we first experience bodily changes (e.g., Circuit theory, which proposed that a network involving the hippocampus, hypothalamus, and cingulate cortex is responsible for emotional experiences increased heart rate) in response to a stimulus, and then we interpret PREVIEW ONLY Many meanings of Decision-making as the process of choosing between alternative options or courses What is decision-making? Typically, decision-making is defined of action by a wide range of scientific disciplines – psychology, economics, philosophy, neuroscience, artificial intelligence, ethology, etc. However, although all explicitly define decision-making in a very similar manner, they study the phenomenon from quite different perspectives and use a diverse range of research approaches and methodologies to study the phenomenon. This implies however, as we shall see, that different research approaches often reveal rather different aspects of decision-making and, consequently, form quite a diverse range of theories and models of the phenomenon. Classical normative economic models of decision-making mostly focus on creating formal (mathematical) descriptions of the phenomenon that delineate conditions for optimal choices or choices that best satisfy one’s preferences. The philosophical take on decision-making similarly choices, tries to understand what role the brain’s reward system plays in concerns itself with theoretical understanding of decision-making: it explores, decision-making, how sensory information is integrated within the decision- for instance, what it means for an agent to make rational choices, analyses making processes, etc. It must be pointed out that neuroscience, which started concepts, relevant for understanding decision-making (e.g., beliefs, desires, exploring decision-making on a larger scale much later than psychology or volition, free will), theoretically explores what kind of reasonings could behavioural economics, relies heavily on using behavioural study designs. As underlie agents’ choices, compares different theories of decision-making and such, it focuses mostly on identifying neural correlates of behaviours people the presuppositions on which they are based. Psychology and behavioural exhibit when solving laboratory decision tasks. Contemporary artificial economics, on the other hand, take an empirical approach to studying (mostly) intelligence is, on the other hand, less concerned with how humans make behavioural aspects of judgment and decision-making (judgment is generally decisions. Its interest rather lies in developing systems that aid human conceived as an integral part of the process of decision-making, where people decision-making (e.g., assisting airline pilots in navigating stormy weather evaluate, estimate and infer what uncertain events (e.g., outcomes) will occur conditions) or systems that themselves make choices instead of humans (such and what consequences they will have). Contrary to classical normative as self-driving cars). formal, normative models of rational choice. One of the most influential settings. However, some approaches do focus on studying decision-making “in the wild” – i.e., in natural environments. For instance, naturalistic approaches to empirical study decision-making framework in psychology emphasises researching decision- of judgment and decision- making in demanding real-world situations (e.g., how expert firefighters people actually decide and whether their decisions align with the “rules” of Most empirical research on decision-making is conducted in laboratory models, psychology and behavioural economics aim at understanding how (in their seminal and quite decisions about treatment, etc.). Empirical phenomenology also investigates decision-making as it unfolds in natural environments and everyday lives of famous 1974 and 1979 papers), people – however, it focuses on exploring lived experience of decision-making demonstrates, for instance, (for instance, how parents experience the process of deciding about their that people mostly do not sick infant’s medical treatment). Ethology (the study of animal behaviour), make decisions rationally. moreover, explores how various types of organisms make choices – from Instead, they rely on mental small worms like C. elegans to primates such as chimpanzees (its interest shortcuts (heuristics) which, lies, for instance, in investigating how primates’ choices are embedded in under certain circumstances, Kahneman and Amos Tversky arrive at their decisions in critical situations; how doctors and patients make making, established by Daniel PREVIEW ONLY lead to systematic errors their wider social environment or in studying group decision-making of bees – e.g., how bees collaboratively decide for a new hive or choose profitable (biases) in judgment and food sources. 25 (The reader will, towards the end of the chapter on decision- poor choices. Neuroscience, making, discover that understanding bees’ behaviour can be quite useful for moreover, focuses on tackling one of the challenges.) identifying neural processes and mechanisms involved in A brief examination of the diverse field of decision-making research reveals decision-making. It explores, that decision-making is, in fact, a multifaceted, complex, and a highly diverse for instance, how the brain phenomenon, which can be studied from very different perspectives. To represents values of options or outcomes, how it assesses 25 Here, we mostly consider core disciplines and research approaches to studying decision-making the probabilities of outcomes that generally focus on decision-making of individuals, omitting, for instance, political, management, Figure 4 .1 . A person wondering which path to take – it’s and organisational sciences, which are mostly concerned with group or institutional aspects of decision- or subjective consequences of a tough decision! making. illustrate this claim, let us examine a few quite different decision-making indicate our decision by pressing the left or right button. In a variation of study designs. the task, we are looking at a screen and must repeatedly decide whether the First illustration: Behavioural and neural correlates of decision-making under risk – cannot clearly consciously determine). This type of decision-making task is presented ambiguous and noisy image depicts a house or a face (which we a financial decision-making task primarily used in the context of studying perceptual decision-making – the We are looking at a screen displaying an initial sum of money (“You receive area of investigating the neural basis of how organisms gather and integrate 50 euro.”; pounds are stated in the study). Researchers inform us that we will sensory information and how this informs their choices (Heekeren, Marrett not be able to keep the entire amount. We must then choose one of the two & Ungerleider, 2008). presented options – one carries risk (it’s called a gamble), the other carries no Third illustration: Lived experience of decision-making in a natural setting risk (it’s a sure option bringing gain with certainty). In the first scenario, if we choose option A we keep 20 euro with certainty but if we choose the risky In the empirical phenomenological study, van Manen (2014) examined the option B we keep all 50 euro with a probability of 0.4 and lose all 50 euro with lived experience of parents of premature infants who required intensive a probability of 0.6. In the second scenario, if we choose option A we lose 30 hospital care (life-support therapy such as mechanical ventilation or euro with certainty but if we choose option B we again keep all 50 euro with cardiovascular support) due to extreme prematurity, complex congenital a probability of 0.4 and lose all 50 euro with a probability of 0.6. We repeat defects, neurological injuries, etc. The parents were faced with an “impossible PREVIEW ONLY the task multiple times. Up to this point, the study is purely behavioural, choice” (posed by the doctors), unimaginable to those of us who are lucky measuring reaction times, and responses (choices) of participants; if we are enough never to have been faced with such a predicament. The parents had interested in the neural correlates of participants’ behavioural responses, the option to discontinue their infant’s life-support (which almost certainly functional magnetic resonance imaging (fMRI), for instance, can be added meant the infant would not survive) or to undertake a high-risk treatment to the study design. This allows us to identify, among other things, which (e.g., heart surgery, organ transplantation, or tracheostomy). While the latter brain regions are involved in making such choices under different conditions. offered the possibility of the infant’s survival, it carried a high risk that The task described here is taken from De Martino et al. (2006) who studied the prematurely born child would be severely disabled in later life. As the the neural basis of the framing effect – how framing or presenting options parents worked through their existential choices (which in some cases lasted in terms of gains (keep 20 euro with certainty) or losses (lose 30 euro with for days), they documented their lived experience and reported it to the certainty) influences our choices – individual differences in susceptibility researcher. The study showed that different parents navigated the decision- to framing effect, and the role of emotions in mediating biases stemming making process in very different ways. Some oscillated between alternatives, from framing choice problems in terms of gains or losses. Framing effects imagining different possible consequences; others remained trapped in were first discovered and systematically described by Kahneman and Tversky indecision; still others could not help but constantly return to a decision (1979) and Tversky and Kahneman (1981). they had already made. Some parents, however, reported that they never Second illustration: Behavioural and neural correlates of perceptual decision-making answer to the question of how to proceed in this extremely difficult situation experienced anything like a decision-making process or choice at all, for the We are lying in an fMRI scanner and looking at a screen displaying what had always been clear to them – they immediately felt they must give their appears to be a chaotic movement of many small dots. Some dots are moving infant a chance to survive, regardless of how small that chance was or what to the left, others to the right. The “sum” of the movement of the dots (the consequences it might have brought. collective motion of the dots) is left or right but the dots are moving in Although many disciplines and research approaches explicitly define such a way that we cannot clearly determine the direction. Even though we decision-making in a very similar manner, the three examples of research cannot see (consciously determine) in which direction the cluster of dots is clearly reveal quite different aspects of what researchers call decision- moving, we must repeatedly choose the overall direction of movement. We making. In the above illustrations, the first two studies investigate decision- making from the third-person perspective, studying behavioural and neural practice research and gain knowledge about the phenomenon of interest. aspects of the phenomenon, whereas the third explores the lived experience And, as we shall see, it is not as simple as it might seem to look beyond of decision-making from the first-person perspective. Moreover, the our ever-present epistemic perspective that co-determines our perception, first study is concerned with risky choices involving monetary gains and knowledge and behaviour. We address this aspect of the first objective in the losses, representing a typical type of a laboratory decision problem where Epistemic bubbles section. choices that resemble pattern recognition rather than what we might call aspects of the decision-making with its scientific understanding. How do we experience decision-making in our daily lives and does this differ from decision-making in everyday lives. While the first two studies exemplify what science has to say about it? Moreover, how does our own epistemic the predominant study of decision-making in laboratory settings, the third examines how the brain integrates sensory information to make simple Secondly, this part of the booklet also tries to relate everyday lived experience alternatives, outcomes, and probabilities are all known. The second study study, on the other hand, serves as an illustration of how decision-making perspective as ordinary human beings shape the way we see the world, others, and ourselves – bearing in mind that scientists, after all, are human too? can be investigated in natural environments where the decision situation is Posing such questions is essential, for cognitive science is, after all, a science full of uncertainty. 26 As such, decision-making seems to be quite a diverse of living, experiencing beings. and broadly understood phenomenon. When we consider more examples and approaches later in the first challenge, the diversity and broadness only Despite the many perspectives and approaches to studying and understanding PREVIEW ONLY become more apparent – to the degree that we will perhaps have to start decision-making – and the challenges they pose – we have to start the learning doubting the claim that these different research approaches investigate the unit somewhere. In the introductory lecture, we first look at what individual same phenomenon. disciplines know about decision-making, how they study it, and how (un) Main objectives of the decision-making part of the learning unit connected this knowledge is across disciplinary approaches and perspectives. This serves as a starting point for playful reflection on the challenges of This part of the booklet highlights two core objectives of the Challenging interdisciplinary research and the (dis)connection between lived experience Minds (CM) learning unit. Firstly, it addresses the challenge of integrating and scientific understanding of the human mind in the second phase of the findings about cognitive phenomena from different disciplinary perspectives. learning unit. described in the following way. In the early days of cognitive science, we Learning unit overview for the phenomenon of decision-making We tackle this challenge from two different sides. The first can be briefly somewhat naively imagined that we could integrate knowledge from different Introductory lecture – A diversity of approaches to studying and disciplinary perspectives by simply adding all knowledge together. However, understanding decision-making decision-making serves as a good example that quickly teaches us that this is not so simple. The diversity of disciplinary perspectives and approaches to Collaborative challenge-solving integration cannot simply be a matter of summing up existing knowledge 1. First part: What is decision-making? studying and understanding decision-making clearly demonstrates that the of individual disciplines. We address this aspect of the first objective in the • Mini challenge: Finding a definition of decision-making What is decision-making? section. The second side tells a similar story but from • Challenge: Comparing different conceptualisations of decision-making an “inverted” perspective. It often happens that different disciplines and through the lens of study design are unaware of each other’s ideas – they often “live” in their own epistemic • Mini challenge: Cognitive scientists’ disciplinary bubbles bubbles, relatively stable and closed cognitive “worlds”, through which they • research approaches do study the same or closely related phenomena but 2. Second part: Epistemic bubbles Challenge: How to change one’s epistemic bubble? 26 Descriptions of the three illustrations are adapted after Strle and Markič (2021). Introductory lecture: A diversity of approaches to most influential work on decision-making to this day. Other theories are addressed as well: the fast and frugal heuristics , decision-field theory , dual-process studying and understanding decision-making theories , naturalistic decision-making , etc. Then, we delve into the differences In the introductory lecture, students are introduced to basic disciplinary between intuitive and deliberate decision-making. We spend some time on answering the question of whether, and under which conditions, intuition perspectives and methodological approaches to studying decision-making, leads to correct judgments and advantageous choices, and in which it does get acquainted with main theories of the phenomenon, and learn about not. Some ways of how to aid and/or improve decision-making are moreover interesting and important studies. Moreover, cases of (dis)connectedness of presented (with a focus on decision nudges). Here, we also touch upon artificial methodologies, findings, and theories of individual disciplinary approaches intelligence approach to modelling decision-making. From discussing are presented. For example, we consider how insights into experiential aspects intuitive judgment and decision-making, we turn to considering the role of of decision-making remain largely ignored within third-person sciences of emotions in decision-making. From there, we slowly move into the realm of decision-making. Moreover, in a journal club setting, students engage with neuroscience of decision-making – where research on the role of emotions is certain seminal and contemporary studies on decision-making. As part of quite richly represented, notably by the seminal work of Antonio Damasio, this activity, they write summaries of selected research, comment on their Antoine Bechara and others. Students get insight into some of the main peers’ work, and present the studies to each other. Through this process, they neuroscientific approaches and findings on decision-making. For instance, gain a deeper understanding of some of the key studies in the field, learn to we look into neuroscience’s understanding of how the brain represents and identify and express the core ideas of research reported in scientific papers, evaluates probabilities, uncertainty and values, how it evaluates and deals and develop insight into how our knowledge and theories about a given with gains and losses, what role emotions play in this process, and what role phenomenon evolved over time. PREVIEW ONLY the brain’s reward systems plays in decision-making. Here, we introduce the The introductory lecture first provides a basic definition of decision-making, basic ideas of the predictive processing account of how the brain processes presents the components of the decision-making process, different types rewards. It turns out the brain is not much concerned with rewards per se of decision problems (e.g., decision-making under risk and uncertainty), (as was theorised in the past), but with what it expects and the errors it and various (disciplinary) perspectives from which decision-making can be makes in its expectations or prediction – this is also one of the findings that examined. The lecture continues with a short introduction to the prehistory will turn out to be important for solving the second main challenge of the of decision-making – students get to know, for instance, the basic ideas of decision-making part of the learning unit. We moreover look into the area of Blaise Pascal, Daniel Bernoulli and Jeremy Bentham. Early classical economic perceptual decision-making where the interest lies in how organisms gather theories and views on decision-making are then presented – students get and combine sensory information to inform their choices. subjective expected utility theory The last part of the lecture is dedicated to discussing some important aspects acquainted with classical normative theories of rational choice, such as the Oskar Morgenstern, and Maurice Allais. of studying decision-making that have traditionally been neglected. First, , and authors such as John von Neumann, we discuss differences between studying decision-making in the laboratory The notion of bounded rationality, proposed by Herbert Simon, is then and in natural environments – some evidence from the area of psychology introduced to serve as a bridge between normative theories of rational choice and neuroscience is presented to show that findings from laboratory and empirical research that tries to understand, mostly through laboratory studies cannot always be generalised to the natural environment. Secondly, experimentation, how we really decide (and how human choice deviates from we introduce students to the phenomenology of decision-making. Some postulates of normative models). Seminal theories and empirical approaches key on lived experience of decision-making reveal that behavioural and from the areas of psychology and behavioural economics are discussed in neuroscientific third-person approaches to studying the phenomenon have more detail. Most notably, the heuristics and biases approach of Daniel much to learn from phenomenological approaches. Since the area of decision- Kahneman and Amos Tversky, and their prospect theory are presented – the making research is very broad, we end the introductory lecture by making PREVIEW ONLY Conclusion T h e b u m p y p a t h f ro m s t u d e n t t o re s e a rc h e r CM learning unit: to allow students to at least get a taste of what In the introduction of this booklet, we described the goal of the it is like to be a researcher – free from the prison of the professor’s expectations, free to discover something new. We aim to spark students’ interest in solving a problem (rather than seeking the correct answer that will satisfy the evaluator). As CM instructors, we want to draw attention to the mistaken equation of the ability to perform well at exams with mastery in research. We do not wish to contribute to the inflation of professionals trained merely to reproduce broadly accepted “solutions”. Instead, we would much prefer that at least a certain percentage of our graduates be capable of identifying important gaps in existing knowledge and filling them creatively. The goal of CM is to shift the mindset of the student – from one focused on finding the correct response to the professor’s questions, to one where an intriguing challenge (i.e., a gap in knowledge) becomes the The attitude we want to encourage – the researcher attitude – is characterised centre of attention, and the aim of the work is to solve or at least address this by the desire to discover the true (real, meaningful…) answer. Not in order to challenge. We have labelled these two mindsets the student and the researcher satisfy an evaluator, but because we have noticed a grey area in our personal (or “explorer”) attitudes. model of the world – a gap in knowledge we want to fill. The researcher seeks we are not suggesting existing understanding (i.e. that the given problem will not be solved by that this attitude is merely locating the pre-existing answer) and that they will have to construct inferior or wrong. In the answer themselves. For this, they are prepared to engage creativity, By calling it “student”, for researchers to discover that the gap in knowledge cannot be filled with to find out what is true, rather than what is appropriate. It is not unusual fact, such an attitude is originality – and even bold speculation. quite necessary in order to become acquainted In the following pages, we will try to summarise some insights, working with the existing body of principles, and open questions that we have encountered in over a decade knowledge in a given field. of searching for a formula for the magical transformation of students into One characteristic of this researchers. PREVIEW ONLY it encounters a question The shape of the challenge: from the everyday and tangible to theory – and attitude is that – when without an immediate back again answer – it automatically The final challenge example in the chapter on decision-making – the question assumes the problem lies of how a young researcher might discover alternative models of decision- in not having yet learned making – can serve to illustrate the structure of a typical challenge, and, the material: surely the in a way, also exemplifies the general approach to content presentation answer is out there – in a throughout the CM learning unit. textbook, on the internet, or in the neural networks At first glance, the task appears simple and straightforward. One is asked of ChatGPT; it just needs to describe a possible narrative involving a researcher in the field of locating. This attitude computational modelling of decision systems, who realises that her own often proves to be internal, personal decision-making process (choosing the breed of a new pet) accurate and successful. Figure 5 .1 . A student, eager to learn how to become an equal did not unfold in the way the models she studies in her lab would predict. cases where new, non-existing solutions are needed – the cases that science Despite the friendly, everyday nature of the story, it is important for students to recognise that behind this tale of an AI scientist lies a broader issue of But it does not hold in to his teacher/researcher. and cutting-edge industrial innovation aim to address. contemporary science – the problem of the inflation of scientific efforts, The student attitude, then, sees problem-solving as the problem of locating which makes it impossible to maintain a comprehensive overview of the the appropriate answer. The criteria for “appropriateness” may be defined by body of research, even within relatively narrow domains. In other words: the the demands of the academic context (e.g., the professor’s expectations), but problem of epistemic bubbles. And closely related to it, the question of how one the mindset can also be viewed more broadly – as an attempt to meet the might escape the confines of their own bubble – how to extend knowledge expectations of an authoritative recipient (reader, evaluator, superior...). beyond the boundaries of the currently used paradigm. Students must therefore identify the deeper problem embedded within the playful framing of a challenge as a source of ambiguity (“why not simply the seemingly charming story about choosing a dog breed. They need to straightforwardly ask ‘how to model epistemic bubbles’?”). problem will not be solved simply by inventing a story that leads the scientist At the same time, the often humorous and everyday character of the challenge distinguish signal from noise. In this case, students must realise that the to phenomenological inquiries into decision-making. They must understand narratives led other students to dismiss them as mere entertaining riddles – amusing, but not worthy of deeper intellectual engagement and thus not that they will get lost in irrelevant details if they start by searching for specific worthy to dig into the core of the problem. steps. What is the signal? What is the real general problem, and what is just noise arising from the particularities of the case? Despite misunderstandings like that, we insist on maintaining the playful The challenge’s protagonist begins in the field of AI, with the anticipated character of the challenges and on wrapping profound problems of understanding the mind in everyday clothing. Partly because we want to end of her journey in empirical phenomenology or behavioural science. Yet keep the general playful tone of the learning unit, but more importantly, for understanding the broader narrative, all of this constitutes the noise because it reflects the reality of scientific inquiry. Real-life problems (i.e., – unimportant, secondary details. Even the issues of the dispersion of the those encountered in research) rarely present themselves as neatly formulated scientist’s knowledge, although still an important issue, is not the backbone theoretical questions (e.g., How can Bayesian statistics be used to model epistemic – the “signal” – of the task. Behind all these distracting “details” lies the true bubbles? ). Rather, they tend to be masked and cluttered with the noise of challenge: how can we model an epistemic bubble in a way that could indicate concreteness – without any subtitle labelling them as “big problems”. practical strategies for its expansion or transformation? PREVIEW ONLY Framing “big” questions in the form of playful stories only appears to make The presented problem must thus be abstracted from its specific context: things harder for students. The true advantage of this method is that it What is an epistemic bubble? Can we model it abstractly? The second step implicitly (yet clearly) signals that a different set of rules is in play. Questions is to describe the possibilities such a model offers. The final step – pleasant that directly articulate theoretical problems are easier to treat as “schoolwork” and preferably humorous – is to “dress” the solution in the details of the given and to approach with the student attitude. Problems wrapped in stories, on the story and to add even more playful, intriguing, and creative noise. other hand, hint that CM is not about finding correct answers or memorising Let us now take a closer look at the first two steps. established solutions – but rather about playfully constructing and testing First step in problem-solving: understanding the core (of the) problem uncertainties in science – as well as, perhaps, in everyday life. ideas. We wish to present playfulness as a basic method for confronting deep As previously stated, it is crucial that students sense the presence of an When students manage to identify the general within the specific – when important, “big” problem behind a challenge. We want them to recognise the they succeed in distinguishing signal from noise – this is a very promising general within the particular. sign. But the road to that point is often long. In the early stages of preparing this learning unit, we assumed that the No matter how colourful or unconventional a challenge may appear, students playfulness of the challenges would enhance students’ enjoyment and will often attempt to respond to it in the way they have been conditioned contribute to their engagement with the material in a curious and exploratory – by searching for the “correct” or established answer. Instead of trying to way. understand the problem, they focus on divining the expectations of the We were surprised to find that this playfulness often appeared to increase instructor. Because playfully formulated tasks do not resemble standard the difficulty for some students – particularly those accustomed to clear, academic questions, this guesswork becomes more difficult – but usually not exam-like questions with learnable, well-defined answers. They perceived difficult enough that they would abandon the attempt. An example of the student way of contemplating the solution (to a decision-making challenge): “Since we know the professor is interested in studying human experience, he moments of groundlessness, students stand on the threshold of a shift toward probably expects us to highlight the importance of experiential approaches a researcher mindset, or at least that they are open to such a shift. This is in decision-making research.” why it’s crucial to be sensitive to these opportunities. They present the best portals into a deeper engagement with the problem. The task of the CM instructor is to somehow convince students that they need not worry about his or her expectations. That they are free to engage with The engagement with the problem usually develops in a form of a circular, the problem itself, without concern for whether the result will be deemed trial-and-error process. It’s essential to maintain student motivation even “appropriate”. Not all students manage to make this leap. Overcoming when the feedback is simply: “You don’t quite understand the question yet – try this requires at least a certain level of motivation and inherent curiosity again.” As we will see in the next section, the trial-and-error loop becomes – something no curriculum can guarantee. The challenge text must be even more vital and strategic in the second stage of problem-solving, but even approached with as fresh a perspective as possible. One must try to set aside here, it is crucial that we allow the time and space that the circular process of the lens that frames it as just another assignment on the path to graduation. discovery requires – from both student and instructor. One way to support students is by emphasising the importance of the first Of course, not every CM student requires this kind of guidance when step in problem-solving: rereading and reflecting on the purpose of the encountering a challenge. Some are natural researchers who light up when challenge. We encourage students to express the challenge in their own words they realise they are finally allowed to play with ideas. For this group, all PREVIEW ONLY – to paraphrase it. This step often reveals the core of the difficulty that the they need is reassurance: “Yes, really, you can solve this your own way!” and students encounter in the attempt to solve the challenge. If a student cannot “Yes, you really can experiment with ideas!” The only task of the CM learning clearly articulate the challenge in their own words, it’s usually a sign that unit instructor in such cases is to keep these students (more accurately: these they haven’t fully understood it. It often happens that a student’s paraphrase researchers) on track – their curious nature can easily lead them off into actually conveys something entirely different – and only when they hear exploring tangential challenges. realise how much their expectations have reshaped the original challenge. Second step: establishing the loop of self-correction – creative and iterative themselves (or when their peers or instructor reflect it back to them) do they construction of a solution emotion research, the task was: If a student reaches the point where they grasp the “message” of the challenge For example, in an assignment exploring the concept of a stimulus in with the way the term is used in cognitive neuroscience.” (i.e., successfully completes the first step of problem-solving), we already “Compare the standard definition of a stimulus the task as: consider this a success (and evaluate it accordingly). Fortunately, this is rarely One group paraphrased students realise they had fundamentally misunderstood the challenge. (They challenge presents an engaging and meaningful problem – and if they realise that the goal is not a final, correct, or desired solution, but rather the testing explained that the task was not entirely clear to them, but they knew the of ideas along the path toward a solution – it is unlikely they will be content learning unit encourages critical evaluation of foundational assumptions with understanding of the problem alone. the instructor asked why they believed their task was to criticise, did the where students stop. If the student internalises the understanding that the “Our assignment is to criticise the concept of a stimulus.” Only after in cognitive science. So – almost unconsciously – they concluded that the instructor must be expecting a critical take on the concept of stimulus in The task of the learning unit instructors regarding the second step is twofold: experimental design.) 1. To prevent students from rushing into solution-mode too early – that is, Often, students recognise that a CM challenge is different from standard before they fully understand the challenge; exam-style assessments, but they struggle to situate this difference within 2. And once the deeper question hidden within the challenge is clear, to any familiar mode. Their prior study experience offers little to help them help the student establish and sustain a loop of self-checking. approach CM-style challenges. As instructors, we sense that in these Just like understanding the challenge, this loop of creatively constructing a (and not only students) when it comes to exploring something new, original, solution can occur at three different levels: and untested.45 • within the student’s internal dialogue with themselves, As instructors, we’re still scratching our heads, wondering how we didn’t • among peers, come up with the idea ourselves. Now that we have it, it seems so obvious • and between the student and the instructor. and self-evident. Following Nina’s suggestion, we added a new element CM instructors do everything possible to support all three levels of this loop solution to the challenge raises the final grade – regardless of how strange, to the assessment criteria: every iteration that does not produce a viable – encouraging solution-building and critical reflection on the usefulness of unconventional, or even silly the proposed solution might be. the ideas being tested. As already mentioned, it is crucial that the course module allocates time and space for this kind of feedback process.44 What we We opened a dedicated space in the online classroom for additions to the must guarantee is iterativeness. Nothing can replace the process of gradual main challenge reports. This optional supplementary report includes: mapping of the space of possible solutions. • a description of the attempted solution, One of the key difficulties we face as instructors (aside from the formal • a description of the feedback process that led to the realisation that the challenges of justifying repeated iterations and involving more than one proposed idea does not solve the problem, PREVIEW ONLY instructor per session) is the loss of motivation that students often feel due • a reflection on the insights gained from this “failed” attempt. often seen as a failure. A student’s quality is typically measured by how “unsuccessful” solutions will not harm their performance in the course – in fact, they will improve it. The supplementary report requires enough effort quickly they can find a correct answer (and move on to new material). Slow that we’ve effectively protected against the risk of students documenting meandering of ideas is not what the system expects from students. But from every minor attempt just to boost their grade. But even if that risk weren’t From a student’s perspective, repeatedly working on the same problem is In this way, students are rewarded for trying. They now know that to the feeling of failure at the end of each “unsuccessful” solution attempt. and error – or, in philosopher Karl Popper’s words, a string of conjectures fully prevented – wouldn’t it actually be interesting if a student started a research perspective, that is the job description. Science is a process of trial and refutations. inventing new and novel types of solutions, regardless of their motivation? After all, creativity is valuable – no matter what drives it. with the insight that it doesn’t work, and the decision to continue testing – is Individual tutorship So how do we convince students that discovering a “failed” solution – along an important and highly desirable component of the research process? And The “bonus points for failure” scheme described above can significantly aid in how do we keep them motivated to remain in that loop of experimentation establishing a self-correction loop during group challenges. In such settings, for as long as possible? the instructor has no direct insight into group dynamics, so any mechanism Bonus points for every failure that fosters the spontaneous emergence of reflective practices is welcome. For individual challenges, however, the main responsibility for guiding the There is a way to preserve the teaching environment while also encouraging student toward a research-oriented attitude naturally falls to the instructor a research-oriented approach. We wish we could claim authorship of this or mentor. she understood the core intention of the course, she simply asked: These days, it is not uncommon to hear that students feel threatened when method, but we cannot. The idea was proposed by a student – Nina. Once reward failure?” told that they’ve solved a task incorrectly or when weaknesses in their “Why not She realised that it is the fear of failure that paralyses students arguments are pointed out. This prevailing mindset is not particularly helpful 44 In CM, creating space for such processes was the main reason we opted to reduce the number of topics covered and instead focus on a more in-depth exploration of just two phenomena. 45 Thank you, Nina! are extremely grateful to Neva for providing us with last recommendations and edits for the text. The booklet’s design wouldn’t have been the same without Barbi Seme’s initial ideas and suggestions. For that, we are truly thankful. No good book is created alone, and this one is no exception. It is the product of collaborative, interdisciplinary work – many times messy, but driven by A note on the creation process of the booklet dedication, curiosity and a spirit of playful inquiry. We would like to once again thank the European EUTOPIA program and the EUTOPIA office at the University of Ljubljana for their financial support in the creation of this book, as well as for enabling us to build an international EUTOPIA learning community around the Challenging Minds learning unit. The authors of this book were the instructors of the learning unit in the academic year 2023/2024, namely Urban Kordeš, Maruša Sirk and Toma Strle. We divided the writing as follows: the introduction and conclusion (Foreword, Challenges in Challenging minds and Conclusion: The bumpy path from student to researcher) were prepared by Urban Kordeš, the section on emotions (Examining emotions: The mystery of the stimulus) by Maruša Sirk, and the section on decision-making (Many meanings of decision-making) by Toma Strle. Although each author prepared the initial draft of their respective section, the final version of all chapters is the result of collaborative work. Some chapters were originally written in Slovene, the initial English translation was done with the help of ChatGPT, while the final version of the text was edited and rewritten by the authors. Illustrations in the book were created using a combination of AI-generated imagery, which were generated via ChatGPT’s image tools, and manually produced visualisations developed in RStudio and Canva. If not stated otherwise under the picture, the pictures were created using ChatGPT. Linguistic and content editing was first provided by Aleš Oblak. We sincerely thank Aleš for his invaluable support in the making of this book. His comments and suggestions significantly improved the quality of the content, and we are deeply grateful for his contribution. Once the text was finished, the last grammar and content check was provided by Neva Zver. We Lange, C. G. (1922). The emotions. In K. Dunlap (Ed.), The emotions (pp. 33–90). Williams & Wilkins. LeDoux, J. E. (1996). 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An example of how the results of the GL questionnaire for one film clip using one of the emotion measurements should look like. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Figure 3 .16. An example of the conversion from the GL scale to the SAM scale for one film clip. The data is hypothetical and shows the answers from 6 students that were in the same group, using the GL scale to report the emotions in the film clips. The yellow point shows the average of all scores and the ellipse shows the standard deviation for the valence and arousal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 List of figures Figure 3 .17. An example of the conversion from the SAM scale to the GL scale for one film clip. The data is hypothetical and shows the answers from 6 students that were in the same group, using the SAM scale to report the emotions in the film clips. On the figure, some nearest emotions overlap with the scores of the students (fear, anxiety and guilt), making the yellow dots not clearly visible. . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Figure 1 .1 . Figure 3 .18 . This is a continuation of Figure 3.17 and shows the end result of the example of the translation The CM learning unit aims to facilitate the transition from being a recipient of information to from the SAM to the GL scale for one film clip. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 becoming an independent thinker and researcher. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Figure 3 .19 . An example of emotional categories with descriptive suggestions that could indicate the Figure 1 .2 . Depiction of the participating disciplines evenly distributed and integrated in the study of the presence of an emotion in the qualitative measures. The example shows suggestions for a few of the same subject – the mind. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 emotions of the GL scale and were inspired and adapted from previous students’ answers. . . . . . . . . . . . . . . 62 Figure 1.3. Different scientists working on the same problem, without knowing about each other. This Figure 3 .20 . A snippet of a fictional research paper about the psychometric properties of the Woo- poses a great challenge and opportunity for interdisciplinary collaboration. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 DiStefano-Ankler inflection scale that we present to the students. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Figure 2 .1 . Two main phases of the CM learning unit: the ex-cathedra introductory lecture format, followed Figure 3 .21 . The presence of individual emotions in response to the film clip intended to elicit “joy.” The by collaborative challenge solving. The disciplines shown do not constitute an exhaustive list of all data shows that, in reality, the emotion “interest” was reported more frequently. The data is fictional but disciplines covered in the learning unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 shows the trend we normally see during the CM learning unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Figure 3 .1 . A depiction of the interdisciplinary debate between students from different backgrounds, Figure 3 .22 . A hypothetical experiment environment for koi no yokan, as designed by students. The picture trying to come up with an experiment to study emotions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 illustrates an experimental environment, where koi no yokan is elicited through a speed-dating simulation Figure 3.2. A stimulus (the wolf), waiting for each discipline to figure out its method of measuring the and physiological responses are measured. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 emotion it elicited. The picture depicts the closedness of each discipline in its own bubble. . . . . . . . . . . . . . 43 Figure 3 .23 . An illustration of the distance between individual observed responses and the target emotion. Figure 3 .3 . Darwin-inspired illustration of universal emotional expressions (fear, happiness and anger, This was inspired by an example of students that used the Gross & Levenson scale to report their emotions. respectively), across humans and animals. The image represents some of the basic human emotions, in line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 with Darwin’s ideas, which were later adopted and expanded by researchers such as Ekman. . . . . . . . . . . . . 46 Figure 3 .24 . A depiction of average distances between participants’ observed responses and the target Figure 3 .4 . An overview of some emotion generation theories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 emotion. The data is inspired by previous generations. SAM = Self-Assessment Manikin, GL = Gross & Figure 3 .5 . A depiction of the limbic system and (some of) the brain regions involved in processing Levenson scale, expE = experiencing emotions, exp = overall experience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 emotions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Figure 3 .25. Answers from generation 2020/21. Note we had more answers this year, as the learning unit Figure 3 .6 . An illustration of Hafez. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 was taught as part of the Eutopia collaboration between various faculties around the world. The answers Figure 3 .7 . include some options regarding the online experimental situation, due to the Covid-19 times. This display The reason why most theories wouldn’t agree with Hafez’s description of happiness lies in the is in percentages, as we don’t have the data on the frequency of answers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 stimulus-to-emotion assumption – meaning a stimulus must always be present in order for an emotion to emerge. This assumption will be further challenged in later sections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51 Figure 3 .26. Answers from generation 2021/22. The answers include some options regarding the online Figure 3 .8 experimental situation, due to the Covid-19 times. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 . The Self-Assessment Manikin (Valence and Arousal Scales). Reprinted from Röggla, T. (2019). Licensed under the BSD 2-Clause License. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Figure 3 .27 . Answers from generation 2022/23. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Figure 3 .9 . An example of how the data for each of the four emotion measurements and reporting methods Figure 3 .28 . Answers from generation 2023/24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 look like. Each table on the figure corresponds to the answers of one student. The answers were inspired Figure 4 .1 . A person wondering which path to take – it’s a tough decision! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 by the answers of students, but do not correspond to specific ones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Figure 4 .2 . A forester running away from a falling tree (Illustrating the mini challenge Finding a definition Figure 3 .10 . An overview of the research protocol of the experiment and the task of the challenge presented of decision-making). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101 to students. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Figure 4 .3. Students’ graphic representation of the multidimensional space of four operational definitions Figure 3 .11 . Example of emotions placed on the circumplex model. Adapted from Russell, J. A. (1980). . . . . . 57 of decision-making. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112 Figure 3 .12 . The empty circumplex model on which students need to put each emotion based on ratings of Figure 4 .4. Students’ graphic representation of the multidimensional space of three operational definitions valence and arousal for each specific emotion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 of decision-making. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112 Figure 3.13. (left) and 3.14. (right) . On the left, an example of a table with valence and arousal scores for the 18 Figure 4 .5 . Students’ representation of the degree of similarity between operational definitions of decision- making in relation to the number of shared dimensions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .113 Figure 4 .6 . Illustration of epistemic bubbles; most are floating around disconnectedly but some are perhaps about to come into contact with each other. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .114 Figure 4 .7. AI scientist and her little Border Collie (illustrating the challenge How to change one’s epistemic bubble). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .118 Figure 4 .8 . Illustration of the ball which – in its movement across the landscape of a dynamic system – fell into a deep basin of attraction. Now it’s very difficult, if not impossible, for it to get out. The ball represents the state of the system – i.e., the way we perceive and understand our epistemic bubble, while the landscape along which it was moving before falling into the basin represents the epistemic possibilities of perceiving and knowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .122 Figure 4 .9. Bees’ foraging for food and their waggle dance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .123 Figure 4 .10 . This figure humorously illustrates how behavioural data in studies of decision-making and other mental phenomena can be misleading about the cognitive or experiential processes underlying the respective behaviour. Here, onlookers are impressed by the subject’s apparent decision-making prowess (decision-making is commonly quite stressful), even though he is simply flipping a coin and not deliberating or deciding at all. Adapted from Cartoonstock, https://www.cartoonstock.com/. . . . . . . . . . . .125 Figure 5 .1 . A student, eager to learn how to become an equal to his teacher/researcher. . . . . . . . . . . . . . . . . . 128 Figure 5 .2. A researcher taking a photograph of a colourful landscape using a green-only sensor, symbolising the limits of single-discipline approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .137 “In a world full of ambiguities and profound technological transformation, we as cognitive systems face the extremely challenging task of making sense of such an uncertain (VUCA-)environment. Nowhere is this challenge more acute than in cognitive science, an emerging field that is so closely related to our own lives. In their outstanding book, the authors present a truly transformative educational guide and roadmap in which a genuinely interdisciplinary approach is key to navigating this complexity. In addition to its interdisciplinary nature, what makes this work special is its radically enactivist and phenomenological perspective. This perspective effectively bridges the critical gap between objective science and our everyday lived experience. Moving beyond abstract and conceptual discussion, the authors translate these profound insights into concrete, problem-based teaching/learning settings, empowering students to become independent, autonomously thinking, and creative researchers.” — Univ. Prof. Dr. Markus F. Peschl, University of Vienna “How can we address the challenges facing education in the era of generative artificial intelligence?” This book presents an innovative approach to guiding students as explorers in the interdisciplinary field of cognitive science. Through playful stories that pose challenging questions, it encourages critical thinking, invites exploration of scientific methods across disciplines, and helps students connect these ideas to their own lived experiences. — Univ. Prof. Dr. Olga Markič, University of Ljubljana