In a recent article in this Journal, Fumagalli argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, (...) neural findings enable choice modellers to confirm the causal relevance of variables posited by competing economic models, identify causally relevant variables overlooked by existing models, and explain observed behavioural variability better than standard economic models. In this paper, I critically examine these three claims and respond to the related criticisms of Fumagalli’s argument. Moreover, I qualify and extend Fumagalli’s account of how trade-offs between distinct modelling desiderata hamper neuroeconomists’ attempts to improve economic models of choice. I then draw on influential neuroeconomic studies to argue that even the putatively best available neural findings fail to substantiate current calls for a neural enrichment of economic models. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by grounded representational content, that is, (...) content evaluated by appeal to its truth, falsity, accuracy, inaccuracy and other relational properties. (shrink)

According to Marr's theory of vision, computational processes of early vision rely for their success on certain "natural constraints" in the physical environment. I examine the implications of this feature of Marr's theory for the question whether psychological states supervene on neural states. It is reasonable to hold that Marr's theory is nonindividualistic in that, given the role of natural constraints, distinct computational theories of the same neural processes may be justified in different environments. But to avoid trivializing computational explanations, (...) theories must respect methodological solipsism in the sense that within a theory there cannot be differences in content without a corresponding difference in neural states. (shrink)

There are two distinct approaches to Bayesian modelling in cognitive science. Black-box approaches use Bayesian theory to model the relationship between the inputs and outputs of a cognitive system without reference to the mediating causal processes; while mechanistic approaches make claims about the neural mechanisms which generate the outputs from the inputs. This paper concerns the relationship between these two approaches. We argue that the dominant trend in the philosophical literature, which characterizes the relationship between black-box and mechanistic approaches to (...) Bayesian cognitive science in terms of the dichotomy between instrumentalism and realism, is misguided. We propose that the two distinctions are orthogonal: black-box and mechanistic approaches to Bayesian modelling can each be given either an instrumentalist or a realist interpretation. We argue that the current tendency to conflate black-box approaches with instrumentalism and mechanistic approaches with realism stems from unwarranted assumptions about the nature of scientific explanation, the ontological commitments of scientific theories, and the role of abstraction and idealization in scientific models. We challenge each of these assumptions to reframe the debates over Bayesian modelling in cognitive science. (shrink)

A central aspect of the computational theory of vision developed by Marr and his coworkers is the use made of contingent regularities in the physical environment to explain how the visual system determines the shape and location of objects in the world on the basis of the spatial organization of the retinal image. Marr (1982) refers to these environmental regularities as “natural constraints” and “physical assumptions.” In this paper I am concerned with recent arguments concerning the implications of this feature (...) of Marr’s theory for understanding psychological explanation. Burge (1986) claims that Marr’s use of natural constraints shows the theory to be nonindividualistic, in the sense that the individuation of psychological states depends essentially on the objects and conditions of the world external to the subject. While it does capture an important aspect of Marr’s theory, Burge’s account is misleadingly incomplete. To see this it is useful to compare the issue of individualism with the question whether the theory is compatible with methodological solipsism as a research strategy. (shrink)

The ‘received view’ about computation is that all computations must involve representational content. Egan and Piccinini argue against the received view. In this paper, I focus on Egan’s arguments, claiming that they fall short of establishing that computations do not involve representational content. I provide positive arguments explaining why computation has to involve representational content, and how that representational content may be of any type. I also argue that there is no need for computational psychology to be individualistic. Finally, I (...) draw out a number of consequences for computational individuation, proposing necessary conditions on computational identity and necessary and sufficient conditions on computational I/O equivalence of physical systems.Keywords: Computation; Representation; Computational identity; Explanation; Narrow content; Physical computation. (shrink)

While the research programs in early cognitive science and artificial intelligence aimed to articulate what cognition was in ideal terms, much research in contemporary computational neuroscience looks at how and why brains fail to function as they should ideally. This focus on impairment affects how we understand David Marr's hypothesized three levels of understanding. In this essay, we suggest some refinements to Marr's distinctions using a population activity model of cortico-striatal circuitry exploring impulsivity and behavioral inhibition as a case study. (...) In particular, we urge that Marr's computational level should be redefined to include a description of how systems break down. We also underscore that feed-forward processing, cognition disconnected from behavioral context, and representations do not always drive cognition in the way that Marr originally assumed. (shrink)

Marr’s celebrated contribution to cognitive science (Marr 1982, chap. 1) was the introduction of (at least) three levels of description/explanation. However, most contemporary research has relegated the distinction between levels to a rather dispensable remark. Ignoring such an important contribution comes at a price, or so we shall argue. In the present paper, first we review Marr’s main points and motivations regarding levels of explanation. Second, we examine two cases in which the distinction between levels has been neglected when considering (...) the structure of mental representations: Cummins et al.’s distinction between structural representation and encodings (Cummins in Journal of Philosophy, 93(12):591–614, 1996; Cummins et al. in Journal of Philosophical Research, 30:405–408, 2001) and Fodor’s account of iconic representation (Fodor 2008). These two cases illustrate the kind of problems in which researchers can find themselves if they overlook distinctions between levels and how easily these problems can be solved when levels are carefully examined. The analysis of these cases allows us to conclude that researchers in the cognitive sciences are well advised to avoid risks of confusion by respecting Marr’s old lesson. (shrink)

Horgan and Tienson do a wonderful job of explicating the dynamical system perspective and contrasting that view with classical AI approaches. However, their arguments for replacing a classical conception of connectionism with system dynamics rely on philosophical distinctions that do not make a difference. In particular, (1) their generalized version of Man's three levels of analysis collapses into itself; (2) their description of attractor dynamics works better than their metaphor of forces; and (3) their versions of “soft laws” and physical (...) laws amount to the same thing. (shrink)

Different explanations of color vision favor different philosophical positions: Computational vision is more compatible with objectivism (the color is in the object), psychophysics and neurophysiology with subjectivism (the color is in the head). Comparative research suggests that an explanation of color must be both experientialist (unlike objectivism) and ecological (unlike subjectivism). Computational vision's emphasis on optimally prespecified features of the environment (i.e., distal properties, independent of the sensory-motor capacities of the animal) is unsatisfactory. Conceiving of visual perception instead as the (...) visual guidance of activity in an environment that is determined largely by that very activity suggests new directions for research. (shrink)

Noam Chomsky and Frances Egan argue that David Marr.

According to Marr, a computational-level theory consists of two elements, the what and the why. This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: that the Why element plays an explanatory role in computational-level theories, that its goal is to explain why the computed function is appropriate for a given visual task, and that the explanation consists in showing that the functional relations between the representing cells are similar to (...) the “external” mathematical relations between the entities that these cells represent. (shrink)

Se comparan las teorías de Marr y Berkeley sobre la visión a partir de las cualidades de Descartes. La descripción de tres niveles de Marr, donde la conciencia está ausente, contrasta con el nivel único de Berkeley construido sobre la conciencia y la experiencia carece de importancia en los momentos esquemáticos y cobra protagonismo en el último paso del proceso de la visión de Marr mediante la noción de marcación. Bajo la premisa de que las descripciones puramente sincrónicas han de (...) ser forzosamente incompletas, se reformula el problema de Molyneux y se reivindica el papel explicativo de la conciencia. (shrink)

The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical (...) signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy. (shrink)