Matteo Colombo

Climate change, nutrition, poverty and medical drugs are widely discussed and pressing issues in science, policy and society. Despite these issues being of great importance for the quality of our lives it remains unclear how well people understand them. Specifically, do particular demographic and socioeconomic factors explain variation in public understanding of these four concepts? To what extent are people's changes in understanding associated with changes in their behaviour? Do people judge scientific practices relying on the more descriptive concepts of climate change and effective medical drugs to be more objective (less controversial) than practices relying on the more value-laden concepts of poverty and healthy nutrition? To address these questions, an experimental survey and regression analyses are conducted using data collected from about one thousand participants across different continents. The study finds that public understanding of science is generally low. A smaller proportion of people were able to correctly identify the common explanation accepted internationally among the scientific community for climate change and effectiveness of medical drugs (42% and 43% of participants in the study, respectively) than for poverty and healthy nutrition (61% and 65% of participants, respectively). Older age and political non-conservativeness were the strongest predictors of correctly understanding these four concepts. Greater levels of education and political non-conservativeness were in turn the strongest predictors of people's reported changes in their behaviour based on their improved understanding of these concepts. Because climate change is among the least understood scientific concepts but is arguably the greatest challenge of our time, better efforts are needed to improve how media, awareness campaigns and education systems mediate information on the topic in order to tackle the large knowledge deficits that constrain behavioural change.

Thick terms like “courageous,” “smart,” and “tasty” combine description and evaluation, contrasting with purely evaluative terms like “good” and “bad,” and descriptive terms like “Italian” and “green.” Thick terms intuitively constitute a special class of evaluative language; but we currently do not know whether the psycholinguistic effects of these terms are reducible to known semantic dimensions. Here, we start to systematically explore this question by comparing the behavior of thick terms and non‐thick descriptive terms with similar affective valence, which is a strong candidate semantic dimension to account for differences between evaluative and non‐evaluative language. We study thick terms from English, Dutch, and Italian, combining behavioral data from the cancellability task, Cloze task, and free association networks, with natural language processing methods and psycholinguistic ratings of word valence. We find that thick and non‐thick descriptive terms are associated with different psycholinguistic effects, even when carefully matched for valence, suggesting that valence is insufficient to account for the difference between thick and non‐thick terms. Instead, we find no reliable difference between positive and negative thick terms, and between moral, epistemic, and aesthetic thick terms. Our findings indicate that thick terms form a homogeneous class of evaluative language whose psycholingusitic effects cannot be explained only in terms of affective valence.

Responding to recent concerns about the reliability of the published literature in psychology and other disciplines, we formed the X-Phi Replicability Project to estimate the reproducibility of experimental philosophy. Drawing on a representative sample of 40 x-phi studies published between 2003 and 2015, we enlisted 20 research teams across 8 countries to conduct a high-quality replication of each study in order to compare the results to the original published findings. We found that x-phi studies – as represented in our sample – successfully replicated about 70% of the time. We discuss possible reasons for this relatively high replication rate in the field of experimental philosophy and offer suggestions for best research practices going forward.

It has been argued that fungi have cognitive capacities, and even conscious experiences. While these arguments risk ushering in unproductive disputes about how words like “mind,” “cognitive,” “sentient,” and “conscious” should be used, paying close attention to key properties of fungal life can also be uncontroversially productive for cognitive science. Attention to fungal life can, for example, inspire new, potentially fruitful directions of research in cognitive science. Here, I introduce a concept of cognitive symbiosis whose significance for cognitive science becomes salient when we consider the centrality of symbioses in the life of fungi. Like fungi, virtually all cognitive systems live in close association with other kinds of cognitive systems, and this living together can have substantive psychological consequences. Expanding the scope of cognitive science to study a wide variety of cognitive symbioses underwrites the importance of biology and evolution in understanding minds.

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences.

The geometry of the environment can affect numerous psychological, social, and ecological processes. But its roles in social learning and the dynamics of descriptive norms remain unclear. Here we use agent-based modeling to explore how environments with different geometric shapes can constrain social learning to produce universally shared descriptive norms. Our simulations show that an environment with an irregular layout facilitates the emergence of multiple descriptive norms in a population, whereas an environment with a regular grid plan constrains social learning to produce a behaviorally homogeneous population.

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined.

Scientific literacy is an essential aspect of an undergraduate education. Recipes for Science responds to this need by providing an accessible introduction to the nature of science and scientific methods appropriate for any beginning college student. The book is adaptable to a wide variety of different courses, such as introductions to scientific reasoning, methods courses in scientific disciplines, science education, and philosophy of science. Recipes for Science ​​was first published in 2018, and a thoroughly revised second edition was published in 2024. Special features include contemporary and historical case studies from many fields of physical, life, and social sciences; visual aids to clarify and illustrate ideas; text boxes to explore related topics; plenty of exercises to support student recall and application of concepts; suggestions for further readings at the end of each chapter; a glossary with helpful definitions of key terms; and a companion website with course syllabi, PowerPoint presentations, additional exercises, and original short videos on key topics.

Perceptual Confidence is the thesis that perceptual experiences can be probabilistic. This thesis has been defended and criticised based on a variety of phenomenological, epistemological, and explanatory arguments. One gap in these arguments is that they neglect the question of whether perceptual experiences satisfy the formal conditions that define the notion of probability to which Perceptual Confidence is committed. Here, we focus on this underexplored question and argue that perceptual experiences do not satisfy such conditions. But if they do not, then ascriptions of perceptual confidence are undefined; and so, Perceptual Confidence cannot be true.

Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that enable cognitive agents to take uncertainty into account and weigh it appropriately in producing adaptive behaviour. The appeal of Bayesian cognitive science derives from its transparency, normativity, and unifying power. Resources from Bayesian statistics allow cognitive scientists to characterise transparently, in terms of random variables and probabilistic dependencies between them, the inductive problems that many mental capacities are presumed to solve. Given this characterisation, researchers can define an upper bound on one’s performance on that problem and use it as a benchmark for interpreting experimental results, formulating hypotheses about why and when deviations from optimal performance should be expected, and seeking explanations of mental capacities within one encompassing theoretical framework. Although critics question the testability, explanatory power, and plausibility of Bayesian models of mental capacities, Bayesian cognitive science has proved itself to be an incredibly fruitful research programme. Fuelled by empirical and theoretical results from psychology, neuroscience, philosophy, computer science, artificial intelligence, and evolution, Bayesian cognitive science has: advanced our understanding of why cognitive systems should keep track of uncertainty and use it to facilitate adaptive behaviour; clarified how the brain might encode information about the uncertainty of its stimuli and perform computations with probability distributions; and crystallised the insight that cognitive agents’ management of uncertainty might be grounded in predictive processes aimed at avoiding surprising exchanges with the environment.

In this volume, a range of high-profile researchers in philosophy of mind, philosophy of cognitive science, and empirical cognitive science, critically engage with Clark's work across the themes of: Extended, Embodied, Embedded, Enactive, and Affective Minds; Natural Born Cyborgs; and Perception, Action, and Prediction. Daniel Dennett provides a foreword on the significance of Clark's work, and Clark replies to each section of the book, thus advancing current literature with original contributions that will form the basis for new discussions, debates and directions in the discipline.

Where does a probabilistic language-of-thought (PLoT) come from? How can we learn new concepts based on probabilistic inferences operating on a PLoT? Here, I explore these questions, sketching a traditional circularity objection to LoT and canvassing various approaches to addressing it. I conclude that PLoT-based cognitive architectures can support genuine concept learning; but, currently, it is unclear that they enjoy more explanatory breadth in relation to concept learning than alternative architectures that do not posit any LoT.

Can facts about subpersonal states and events be constitutively relevant to personal-level phenomena? And can knowledge of these facts inform explanations of personal-level phenomena? Some philosophers, like Jennifer Hornsby and John McDowell, argue for two negative answers whereby questions about persons and their behavior cannot be answered by using information from subpersonal psychology. Knowledge of subpersonal states and events cannot inform personal-level explanation such that they cast light on what constitutes persons’ behaviors. In this paper I argue against this position. After having distinguished between enabling and constitutive relevance, I defend the claim that at least some facts about subpersonal states and events are constitutively relevant to some personal-level phenomenon, and therefore can, and sometimes should, inform personal-level explanations. I draw some of the possible consequences of my claim for our understanding of personal-level behavior by focusing on the phenomenon of addiction.

Analyses of the Sleeping Beauty Problem are polarised between those advocating the “1/2 view” (“halfers”) and those endorsing the “1/3 view” (“thirders”). The disagreement concerns the evidential relevance of self-locating information. Unlike halfers, thirders regard self-locating information as evidentially relevant in the Sleeping Beauty Problem. In the present study, we systematically manipulate the kind of information available in different formulations of the Sleeping Beauty Problem. Our findings indicate that patterns of judgment on different formulations of the Sleeping Beauty Problem do not fit either the “1/2 view” or the “1/3 view.” Human reasoners tend to acknowledge self-locating evidence as relevant, but discount its weight significantly. Accordingly, self-locating information may trigger more cautious judgments of confirmation than familiar kinds of statistical evidence. We also discuss how these results can advance the debate by providing a more nuanced and empirically grounded account or explication of the evidential impact of self-locating information.

This paper employs a case study from the history of neuroscience—brain reward function—to scrutinize the inductive argument for the so-called ‘Heuristic Identity Theory’ (HIT). The case fails to support HIT, illustrating why other case studies previously thought to provide empirical support for HIT also fold under scrutiny. After distinguishing two different ways of understanding the types of identity claims presupposed by HIT and considering other conceptual problems, we conclude that HIT is not an alternative to the traditional identity theory so much as a relabeling of previously discussed strategies for mechanistic discovery.

The subject matter of this thesis can be summarized by a triplet of questions and answers. Showing what these questions and answers mean is, in essence, the goal of my project. The triplet goes like this: Q: How can we make progress in our understanding of social norms and norm compliance? A: Adopting a neurocomputational framework is one effective way to make progress in our understanding of social norms and norm compliance. Q: What could the neurocomputational mechanism of social norm compliance be? A: The mechanism of norm compliance probably consists of Bayesian - Reinforcement Learning algorithms implemented by activity in certain neural populations. Q: What could information about this mechanism tell us about social norms and social norm compliance? A: Information about this mechanism tells us that: a1: Social norms are uncertainty-minimizing devices. a2: Social norm compliance is one trick that agents employ to interact coadaptively and smoothly in their social environment. Most of the existing treatments of norms and norm compliance consist in what Cristina Bicchieri refers to as “rational reconstructions.” A rational reconstruction of the concept of social norm “specifies in which sense one may say that norms are rational, or compliance with a norm is rational”. What sets my project apart from these types of treatments is that it aims, first and foremost, at providing a description of some core aspects of the mechanism of norm compliance. The single most original idea put forth in my project is to bring an alternative explanatory framework to bear on social norm compliance. This is the framework of computational cognitive neuroscience. The chapters of this thesis describe some ways in which central issues concerning social norms can be fruitfully addressed within a neurocomputational framework. In order to qualify and articulate the triplet above, my strategy consists firstly in laying down the beginnings of a model of the mechanism of norm compliance behaviour, and then zooming in on specific aspects of the model. Such a model, the chapters of this thesis argue, explains apparently important features of the psychology and neuroscience of norm compliance, and helps us to understand the nature of the social norms we live by.

Bruineberg and collaborators distinguish three philosophical positions about the status of Markov blankets in the context of active inference modelling, namely: literalism, realism, and instrumentalism. They criticize the first two positions and suggest that instrumentalism is “less problematic but also less interesting” (sect. 6.1.2, para. 5) Here, I sketch how literalists and realists might reply to Bruineberg et al.'s criticisms, and I explain why instrumentalism is more interesting and contentious than what Bruineberg and collaborators suggest.

Climate change, nutrition, poverty and medical drugs are widely discussed and pressing issues in science, policy and society. Despite these issues being of great importance for the quality of our lives it remains unclear how well people understand them. Specifically, do particular demographic and socioeconomic factors explain variation in public understanding of these four concepts? To what extent are people’s changes in understanding associated with changes in their behaviour? Do people judge scientific practices relying on the more descriptive concepts of climate change and effective medical drugs to be more objective (less controversial) than practices relying on the more value-laden concepts of poverty and healthy nutrition? To address these questions, an experimental survey and regression analyses are conducted using data collected from about one thousand participants across different continents. The study finds that public understanding of science is generally low. A smaller proportion of people were able to correctly identify the common explanation accepted internationally among the scientific community for climate change and effectiveness of medical drugs (42% and 43% of participants in the study, respectively) than for poverty and healthy nutrition (61% and 65% of participants, respectively). Older age and political non-conservativeness were the strongest predictors of correctly understanding these four concepts. Greater levels of education and political non-conservativeness were in turn the strongest predictors of people’s reported changes in their behaviour based on their improved understanding of these concepts. Because climate change is among the least understood scientific concepts but is arguably the greatest challenge of our time, better efforts are needed to improve how media, awareness campaigns and education systems mediate information on the topic in order to tackle the large knowledge deficits that constrain behavioural change.

Letheby’s "Philosophy of Psychedelics" relies on Predictive Processing to try and find unifying explanations relevant to understanding how serotonergic psychedelics work in psychiatric therapy, what subjective experiences are associated with their use and whether such experiences are epistemically defective. But if Predictive Processing lacks genuinely explanatory unifying power, Letheby’s account of psychedelic therapy risks being unwarranted. In this commentary, I motivate this worry and sketch an alternative interpretation of psychedelic therapy within the Reinforcement Learning framework.

According to the free energy principle, life is an “inevitable and emergent property of any random dynamical system at non-equilibrium steady state that possesses a Markov blanket” :20130475, 2013). Formulating a principle for the life sciences in terms of concepts from statistical physics, such as random dynamical system, non-equilibrium steady state and ergodicity, places substantial constraints on the theoretical and empirical study of biological systems. Thus far, however, the physics foundations of the free energy principle have received hardly any attention. Here, we start to fill this gap and analyse some of the challenges raised by applications of statistical physics for modelling biological targets. Based on our analysis, we conclude that model-building grounded in the free energy principle exacerbates a trade-off between generality and realism, because of a fundamental mismatch between its physics assumptions and the properties of actual biological targets.

What is the relationship between the concepts of the predictive processing theory of brain functioning and the everyday concepts with which people conduct and explain their mental lives? To answer this question, we focus on predictive processing explanations of mental disorder that appeal to false inference. After distinguishing two concepts of false inference, we survey four ways of understanding the relationship between explanations of mental phenomena at the personal and sub-personal level. We then argue that if predictive processing accurately accounts for psychological and socio-cultural dynamics, then personal and sub-personal concepts must display a looping relationship that can impact the nature of at least some mental phenomena. In particular, explaining the phenomenon of delusion in terms of the sub-personal concept of false inference should change the nature of delusional experiences themselves.