Gualtiero Piccinini

Integrating psychology and neuroscience: functional analyses as mechanism sketches

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms, in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated with multilevel mechanistic explanations.

Information Processing, Computation, and Cognition

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects.

Neural computation and the computational theory of cognition.

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism-neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.

Computational modelling vs. Computational explanation: Is everything a Turing Machine, and does it matter to the philosophy of mind?1

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.

THE FIRST COMPUTATIONAL THEORY OF MIND AND BRAIN: A CLOSE LOOK AT MCCULLOCH AND PITTS'S ''LOGICAL CALCULUS OF IDEAS IMMANENT IN NERVOUS ACTIVITY''

Despite its significance in neuroscience and computation, McCulloch and Pittss celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pittss paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pittss contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain.

Computational explanation in neuroscience

According to some philosophers, computational explanation is proprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors.

Functionalism, Computationalism, and Mental Contents

Almost no one cites Sellars, while reinventing his wheels with gratifying regularity. (Dennett 1987, 349)

The cognitive neuroscience revolution

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches.

The Physical Church–Turing Thesis: Modest or Bold?

This article defends a modest version of the Physical Church-Turing thesis (CT). Following an established recent trend, I distinguish between what I call Mathematical CT—the thesis supported by the original arguments for CT— and Physical CT. I then distinguish between bold formulations of Physical CT, according to which any physical process—anything doable by a physical system—is computable by a Turing machine, and modest formulations, according to which any function that is computable by a physical system is computable by a Turing machine. I argue that Bold Physical CT is not relevant to the epistemological concerns that motivate CT and hence not suitable as a physical analog of Mathematical CT. The correct physical analog of Mathematical CT is Modest Physical CT. I propose to explicate the notion of physical computability in terms of a usability constraint, according to which for a process to count as relevant to Physical CT, it must be usable by a finite observer to obtain the desired values of a function. Finally, I suggest that proposed counterexamples to Physical CT are still far from falsifying it because they have not been shown to satisfy the usability constraint. 1 The Mathematical Church–Turing Thesis 2 A Usability Constraint on Physical Computation 3 The Bold Physical Church–Turing Thesis   3.1 Lack of confluence   3.2 Unconstrained appeals to real-valued quantities   3.3 Falsification by irrelevant counterexamples 4 The Modest Physical Church–Turing Thesis   4.1 Hypercomputation: genuine and spurious   4.2 Relativistic hypercomputers   4.3 Other challenges to Modest Physical CT 5 Conclusion 1 The Mathematical Church–Turing Thesis 2 A Usability Constraint on Physical Computation 3 The Bold Physical Church–Turing Thesis   3.1 Lack of confluence   3.2 Unconstrained appeals to real-valued quantities   3.3 Falsification by irrelevant counterexamples   3.1 Lack of confluence   3.2 Unconstrained appeals to real-valued quantities   3.3 Falsification by irrelevant counterexamples 4 The Modest Physical Church–Turing Thesis   4.1 Hypercomputation: genuine and spurious   4.2 Relativistic hypercomputers   4.3 Other challenges to Modest Physical CT   4.1 Hypercomputation: genuine and spurious   4.2 Relativistic hypercomputers   4.3 Other challenges to Modest Physical CT 5 Conclusion

Functions Must Be Performed at Appropriate Rates in Appropriate Situations

We sketch a novel and improved version of Boorse’s biostatistical theory of functions. Roughly, our theory maintains that (i) functions are non-negligible contributions to survival or inclusive fitness (when a trait contributes to survival or inclusive fitness); (ii) situations appropriate for the performance of a function are typical situations in which a trait contributes to survival or inclusive fitness; (iii) appropriate rates of functioning are rates that make adequate contributions to survival or inclusive fitness (in situations appropriate for the performance of that function); and (iv) dysfunction is the inability to perform a function at an appropriate rate in appropriate situations. Based on our theory, we sketch solutions to three problems that have afflicted Boorse’s theory of function, namely, Kingma’s ([2010]) problem of the situation-specificity of functions, the problem of multi-functional traits, and the problem of how to distinguish between appropriate and inappropriate rates of functioning. 1 Functions Are Situation-Specific 2 A General Account of Biostatistical Functions   2.1 Functions   2.2 Appropriate situations for the performance of a function   2.3 Appropriate rates of functioning   2.4 Dysfunction 3 Performing Functions at Appropriate Rates in Appropriate Situations 4 Conclusion 1 Functions Are Situation-Specific 2 A General Account of Biostatistical Functions   2.1 Functions   2.2 Appropriate situations for the performance of a function   2.3 Appropriate rates of functioning   2.4 Dysfunction   2.1 Functions   2.2 Appropriate situations for the performance of a function   2.3 Appropriate rates of functioning   2.4 Dysfunction 3 Performing Functions at Appropriate Rates in Appropriate Situations 4 Conclusion