Matteo Colombo

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms

The specific regions in the brain that are active when some behaviour is observed is a kind of information that may be interesting for neuroscientists, but how could it be fruitful for economic theory? The thesis defended in the essay is that the brain matters to prediction. By using the Ultimatum Game as a benchmark, it is argued that if the goal of a model of human behaviour is to yield good predictions about important classes of choices, then models that incorporate neurobiological variables may have some advantages over alternative models.The essay includes two parts. Part I first analyses the Ultimatum Game and illustrates some of its experimental results. Then, it evaluates in detail the merits and shortcomings of Cristina Bicchieri’s model based on social norms. It centres on the predictive power of the model, and articulates some challenges it faces. Part II begins with a review of some neurobiological findings which suggest a different approach to construct predictive models of human behaviour. Drawing upon these findings, it gives some reasons why the predictions of a neurobiologically‐informed model have some advantage over those of Bicchieri’s model. A critical discussion of the thesis against possible objections terminates the essay. The conclusion follows that one way in which the study of the neurobiological foundations of decision‐making might be fruitful for economic modelling is in enhancing the predictive quality of its models

How should the information that a proposition p is a mystery impact your credence in p? To answer this question, we first provide a taxonomy of mysteries; then, we develop a test to distinguish two types of mysteries. When faced with mysteries of the first type, rational epistemic agents should lower their credence in p upon learning that p is a mystery. The same information should not impact agents’ credence in p, when they face mysteries of the second type. Our account of mystery complements existing explications of abduction. We illustrate its significance by examining the mystery of consciousness.

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory.

Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study elucidates this claim and argues that the main challenge this era is facing is not the lack of biological realism. The challenge lies in identifying general neurocomputational principles for the design of artificial systems, which could display the robust flexibility characteristic of biological intelligence.

Current explanatory frameworks for social norms pay little attention to why and how brains might carry out computational functions that generate norm compliance behavior. This paper expands on existing literature by laying out the beginnings of a neurocomputational framework for social norms and social cognition, which can be the basis for advancing our understanding of the nature and mechanisms of social norms. Two neurocomputational building blocks are identified that might constitute the core of the mechanism of norm compliance. They consist of Bayesian and reinforcement learning systems. It is sketched why and how the concerted activity of these systems can generate norm compliance by minimization of three specific kinds of prediction-errors

The “father of skyscrapers” and “father of modernist architecture” Louis Henry Sullivan (1856–1924) wrote that “[a]ll things in nature have a shape, … a form, an outward semblance, that tells us what they are, that distinguishes them from ourselves and from each other,” adding “Form follows from function.” But structure shapes function too.The biological world offers a myriad of examples where this is apparent. One such example, perhaps not the most intuitive, is the brain: a network with a complex architecture made of billions of neurons connected by trillions of synapses that shape such functions as movement, perception, imagination, volition, emotion, reasoning, and so on. Olaf Sporns’s Discovering the Human Connectome is an enthusiastic and honest journey through the forms, structures, and network architecture that can ground our understanding of how brains work, and how they produce cognition and behavior.In his previous book, Networks of the Brain, Sporns documented the encounter ..

There continues to be significant confusion about the goals, scope, and nature of modelling practice in neuroeconomics. This article aims to dispel some such confusion by using one of the most recent critiques of neuroeconomic modelling as a foil. The article argues for two claims. First, currently, for at least some economic model of choice behaviour, the benefits derivable from neurally informing an economic model do not involve special tractability costs. Second, modelling in neuroeconomics is best understood within Marr’s three-level of analysis framework and in light of a co-evolutionary research ideology. The first claim is established by elucidating the relationship between the tractability of a model, its descriptive accuracy, and its number of variables. The second claim relies on an explanation of what it can take to neurally inform an economic model of choice behaviour. 1 Introduction2 Neurally Informed Models of Choice: A Case Study2.1 A case study on risk-sensitive choice2.1.1 Target and modelling framework2.1.2 Research question and hypotheses2.1.3 Competitive models of risk-sensitive behaviour2.1.4 Model-based fMRI: from economics to brains and back2.2 Neurally informed modelling3 Tractability: When Does Size Matter?4 Neural Integration and the Co-evolutionary Research Ideology5 Conclusion.

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