2. Daugman, J. G. Brain metaphor and brain theory 3. Mundale, J. Neuroanatomical Foundations of Cognition: Connecting the Neuronal Level with the Study of Higher Brain Areas.

Second, this collection of cognitive programs evolved in the Pleistocene to solve the adaptive problems regularly faced by our hunter-gatherer ancestors-...

My charge in this chapter is to set out the positive case supporting massively modular models of the human mind.1 Unfortunately, there is no generally accepted understanding of what a massively modular model of the mind is. So at least some of our discussion will have to be terminological. I shall begin by laying out the range of things that can be meant by ‘modularity’. I shall then adopt a pair of strategies. One will be to distinguish some things that (...) ‘modularity’ definitely can’t mean, if the thesis of massive modularity is to be even remotely plausible. The other will be to look at some of the arguments that have been offered in support of massive modularity, discussing what notion of ‘module’ they might warrant. It will turn out that there is, indeed, a strong case in support of massively modular models of the mind on one reasonably natural understanding of ‘module’. But what really matters in the end, of course, is the substantive question of what sorts of structure are adequate to account for the organization and operations of the human mind, not whether or not the components appealed to in that account get described as ‘modules’. So the more interesting question before us is what the arguments that have been offered in support of massive modularity can succeed in showing us about those structures, whatever they get called. (shrink)

When Fodor titled his (1983) book the _Modularity of Mind_, he overstated his position. His actual view is that the mind divides into systems some of which are modular and others of which are not. The book would have been more aptly, if less provocatively, called _The Modularity of Low-Level Peripheral Systems_. High-level perception and cognitive systems are non-modular on Fodor’s theory. In recent years, modularity has found more zealous defenders, who claim that the entire mind divides into highly specialized (...) modules. This view has been especially popular among Evolutionary Psychologists. They claim that the mind is massively modular (Cosmides and Tooby, 1994; Sperber, 1994; Pinker, 1997; see also Samuels, 1998). Like a Swiss Army Knife, the mind is an assembly of specialized tools, each of which has been designed for some particular purpose. My goal here is to raise doubts about both peripheral modularity and massive modularity. To do that, I will rely on the criteria for modularity laid out by Fodor (1983). I will argue that neither input systems, nor central systems are modular on any of these criteria. (shrink)

Some theorists who emphasize the complexity of biological and cognitive systems and who advocate the employment of the tools of dynamical systems theory in explaining them construe complexity and reduction as exclusive alternatives. This paper argues that reduction, an approach to explanation that decomposes complex activities and localizes the components within the complex system, is not only compatible with an emphasis on complexity, but provides the foundation for dynamical analysis. Explanation via decomposition and localization is nonetheless extremely challenging, and an (...) analysis of recent cognitive neuroscience research on memory is used to illustrate what is involved. Memory researchers split between advocating memory systems and advocating memory processes, and I argue that it is the latter approach that provides the critical sort of decomposition and localization for explaining memory. The challenges of linking distinguishable functions with brain processes is illustrated by two examples: competing hypotheses about the contribution of the hippocampus and competing attempts to link areas in frontal cortex with memory processing. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus.

trying to explain these reactions in terms of changes in ele- began trying to characterize physiological processes in.

In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide...

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

A great deal of philosophy of mind in the modern era has been driven by an intense aversion to Cartesian dualism. In the 1950s, materialists claimed to have succeeded once and for all in exorcising the Cartesian ghost by identifying the mind with the brain. In subsequent decades, cognitive science put scientific meat on this metaphysical skeleton by explicating mental processes as digital computation implemented in the brain's hardware.

William Uttal is concerned that in an effort to prove itself a hard science, psychology may have thrown away one of its most important methodological tools—a critical analysis of the fundamental assumptions that underlie day-to-day empirical research. In this book Uttal addresses the question of localization: whether psychological processes can be defined and isolated in a way that permits them to be associated with particular brain regions. New, noninvasive imaging technologies allow us to observe the brain while it is actively (...) engaged in mental activities. Uttal cautions, however, that the excitement of these new research tools can lead to a neuroreductionist wild goose chase. With more and more cognitive neuroscientific data forthcoming, it becomes critical to question their limitations as well as their potential. Uttal reviews the history of localization theory, presents the difficulties of defining cognitive processes, and examines the conceptual and technical difficulties that should make us cautious about falling victim to what may be a "neo-phrenological" fad. (shrink)

This paper will sketch a model of the human mind according to which the mind's structure is massively, but by no means wholly, modular. Modularity views in general will be motivated, elucidated, and defended, before the thesis of moderately massive modularity is explained and elaborated.

Neuroscience and cognitive science seek to explain behavioral regularities in terms of underlying mechanisms. An important element of a mechanistic explanation is a characterization of the operations of the parts of the mechanism. The challenge in characterizing such operations is illustrated by an example from the history of physiological chemistry in which some investigators tried to characterize the internal operations in the same terms as the overall physiological system while others appealed to elemental chemistry. In order for biochemistry to become (...) successful, researchers had to identify a new level of operations involving operations over molecular groups. Existing attempts at mechanistic explanation of behavior are in a situation comparable to earlier approaches to physiological chemistry, drawing their inspiration either from overall psychology activities or from low-level neural processes. Successful mechanistic explanations of behavior require the discovery of the appropriate component operations. Such discovery is a daunting challenge but one on which success will be beneficial to both behavioral scientists and cognitive and neuroscientists. (shrink)

A spate of recent anti-localizationist publications have re-ignited the old debate about the localization of function. Many of the recent attacks on localization, however, are directed at what I will argue to be a narrow and outmoded view of localization, and thus have little conceptual or empirical impact. What I hope to present here is an analysis of functional localization that more adequately reflects the sophistication and complexity of its use in neuroscientific research, both historically and recently. Proceeding first by (...) way of contrast, I examine theanti-localizationist positions of holism andequipotentiationism. Then, I present a four-fold analysis of localization according to physical scope, physical kind, functional scope, and functional kind. Next, I turn to a discussion of the heuristic value oflocalization in deciphering structure-functionrelationships. Finally, I hope to show that the overall view of functional localization that emerges from these considerations constitutes a much more elusive target than its critics assume. It serves to mitigate, and insome instances even defeat, some forms ofanti-localizationist criticisms. (shrink)

This chapter investigates the extent to which claims of massive modular organization of the mind (espoused by some members of the evolutionary psychology research program) are consistent with the main elements of the simple heuristics research program. A number of potential sources of conflict between the two programs are investigated and defused. However, the simple heuristics program turns out to undermine one of the main arguments offered in support of massive modularity, at least as the latter is generally understood by (...) philosophers. So one result of the argument will be to force us to re-examine the way in which the notion of modularity in cognitive science should best be characterized, if the thesis of massive modularity isn’t to be abandoned altogether. What is at stake in this discussion, is whether there is a well-motivated notion of ‘module’ such that we have good reason to think that the human mind must be massively modular in its organization. I shall be arguing (in the end) that there is. (shrink)

This paper defends cognitive neuroscience’s project of developing mechanistic explan- ations of cognitive processes through decomposition and localization against objections raised by William Uttal in The New Phrenology. The key issue between Uttal and researchers pursuing cognitive neuroscience is that Uttal bets against the possibility of decomposing mental operations into component elementary operations which are localized in distinct brain regions. The paper argues that it is through advancing and revising what are likely to be overly simplistic and incorrect decompositions that (...) the goals of cognitive neuroscience are likely to be achieved. (shrink)

Jerry Fodor argues against the widely held view that mental processes are largely computations, that the architecture of cognition is massively modular, and...

In my book How the Mind Works, I defended the theory that the human mind is a naturally selected system of organs of computation. Jerry Fodor claims that 'the mind doesn't work that way'(in a book with that title) because (1) Turing Machines cannot duplicate humans' ability to perform abduction (inference to the best explanation); (2) though a massively modular system could succeed at abduction, such a system is implausible on other grounds; and (3) evolution adds nothing to our understanding (...) of the mind. In this review I show that these arguments are flawed. First, my claim that the mind is a computational system is different from the claim Fodor attacks (that the mind has the architecture of a Turing Machine); therefore the practical limitations of Turing Machines are irrelevant. Second, Fodor identifies abduction with the cumulative accomplishments of the scientific community over millennia. This is very different from the accomplishments of human common sense, so the supposed gap between human cognition and computational models may be illusory. Third, my claim about biological specialization, as seen in organ systems, is distinct from Fodor's own notion of encapsulated modules, so the limitations of the latter are irrelevant. Fourth, Fodor's arguments dismissing of the relevance of evolution to psychology are unsound. (shrink)

This paper will sketch a model of the human mind according to which the mind’s structure is massively, but by no means wholly, modular. Modularity views in general will be motivated, elucidated, and defended, before the thesis of moderately massive modularity is explained and elaborated.

For more than a century the paradigm inspiringcognitive neuroscience has been modular and localist.Contemporary research in functional brain imaginggenerally relies on methods favorable to localizingparticular functions in one or more specific brainregions. Meanwhile, connectionist cognitive scientistshave celebrated the computational powers ofdistributed processing, and pioneered methods forinterpreting distributed representations. This papertakes a connectionist approach to functionalneuroimaging. A tabulation of 35 PET (positronemission tomography) experiments strongly indicatesdistributed function for at least the ''medium sized''anatomical units, the cortical Brodmann areas. Moreimportant, when these PET (...) experiments were interpretedas distributed representations, multidimensionalscaling revealed a ''brain activation space'' with asalient structure organized primarily by the sensorymodality of the stimulus, and secondarily by the typeof motor response. These results suggest that currentanalytical techniques in functional neuroimagingshould be augmented by distributed processinganalyses, and that these analyses may lead to manydiscoveries about the structure of ''inner space.''. (shrink)