Matteo Colombo

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

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix.

Modularity is one of the most important concepts used to articulate a theory of cognitive architecture. Over the last 30 years, the debate in many areas of the cognitive sciences and in philosophy of psychology about what modules are, and to what extent our cognitive architecture is modular, has made little progress. After providing a diagnosis of this lack of progress, this article suggests a remedy. It argues that the theoretical framework of network science can be brought to bear on the traditional modularity debate, facilitating our progress in articulating a good theory of the human cognitive architecture.

This paper brings together results from the philosophy and the psychology of explanation to argue that there are multiple concepts of explanation in human psychology. Specifically, it is shown that pluralism about explanation coheres with the multiplicity of models of explanation available in the philosophy of science, and it is supported by evidence from the psychology of explanatory judgment. Focusing on the case of a norm of explanatory power, the paper concludes by responding to the worry that if there is a plurality of concepts of explanation, one will not be able to normatively evaluate what counts as good explanation.

In a recent Analysis piece, John Shand (2014) argues that the Predictive Theory of Mind provides a unique explanation for why one cannot play chess against oneself. On the basis of this purported explanatory power, Shand concludes that we have an extra reason to believe that PTM is correct. In this reply, we first rectify the claim that one cannot play chess against oneself; then we move on to argue that even if this were the case, Shand’s argument does not give extra weight to the Predictive Theory of Mind.