In their attempt to connect the workings of the human mind with their neural realizers, cognitive neuroscientists often bracket out individual differences to build a single, abstract model that purportedly represents (almost) every human being’s brain. In this paper I first examine the rationale behind this model, which I call ‘Platonic Brain Model’. Then I argue that it is to be surpassed in favor of multiple models allowing for patterned inter-individual differences. I introduce the debate on legitimate (and illegitimate) ways (...) of mapping neural structures and cognitive functions, endorsing a view according to which function-structure mapping is context-sensitive. Building on the discussion of the ongoing debate on the function(s) of the so-called Fusiform “Face” Area, I show the necessity of indexing function-structure mappings to some populations of subjects, clustered on the basis of factors such as their expertise in a given domain. (shrink)

By the late 1990s, several converging trends in economics, psychology, and neuroscience had set the stage for the birth of a new scientific field known as “neuroeconomics”. Without the availability of an extensive variety of experimental designs for dealing with individual and social decision-making provided by experimental economics and psychology, many neuroeconomics studies could not have been developed. At the same time, without the significant progress made in neuroscience for grasping and understanding brain functioning, neuroeconomics would have never seen the (...) light of day. The paper is an overview of the main significant advances in the knowledge of brain functioning by neuroscience that have contributed to the emergence of neuroeconomics and its rise over the past two decades. These advances are grouped over three non-independent topics referred to as the “emo-rational” brain, “social” brain, and “computational” brain. For each topic, it emphasizes findings considered as critical to the birth and development of neuroeconomics while highlighting some of prominent questions about which knowledge should be improved by future research. In parallel, it shows that the boundaries between neuroeconomics and several recent sub-fields of cognitive neuroscience, such as affective, social, and more generally, decision neuroscience, are rather porous. (shrink)

À la fin des années 1990, plusieurs tendances convergentes en économie, psychologie et neuroscience ont préparé le terrain pour la naissance d’un nouveau champ scientifique qualifié de « neuroéconomie ». Comme pour toute discipline émergente – pensons par exemple à l’économie mathématique, l’économétrie ou l’économie expérimentale en d’autres temps – la neuroéconomie est plutôt controversée en économie. Elle soulève un grand nombre de questions d’ordre empirique, méthodologique et philosophiques donnant lieu à des débats et controverses que l’article identifie et discute (...) à travers trois questions majeures relatives aux connaissances produites : Sont-elles fiables? Sont-elles scientifiquement légitimes? Sont-elles utiles pour les économistes? Sans prétendre à l’exhaustivité, ce cadre analytique permet de comprendre l’essence des principales critiques adressées à la neuroéconomie. Dans le même temps, l’article éclaire la nature des réponses susceptibles d’être apportées à ces critiques au vu des progrès conceptuels et empiriques qu’a connus ce champ de recherche depuis son émergence et à la lumière du tournant apparu dans l’agenda de la neuroéconomie au cours de la dernière décennie. JEL Codes : A12, B41, C51, C81, C9, D03, D87. (shrink)

Reverse inference is a crucial inferential strategy used in cognitive neuroscience to derive conclusions about the engagement of cognitive processes from patterns of brain activation. While widely employed in experimental studies, it is now viewed with increasing scepticism within the neuroscience community. One problem with reverse inference is that it is logically invalid, being an instance of abduction in Peirce’s sense. In this paper, we offer the first systematic analysis of reverse inference as a form of abductive reasoning and highlight (...) some relevant implications for the current debate. We start by formalising an important distinction that has been entirely neglected in the literature, namely the distinction between weak and strong reverse inference. Then, we rely on case studies from recent neuroscientific research to systematically discuss the role and limits of both strong and weak reverse inference; in particular, we offer the first exploration of weak reverse inference as a discovery strategy within cognitive neuroscience. (shrink)