Information processing, computation, and cognition

Gualtiero Piccinini, Andrea Scarantino
2010 Journal of biological physics (Print)  
This is a preprint of an article whose final and definitive form will be published in Journal of Biological Physics; Journal of Biological Physics is available online at: http://www.springerlink.com/content/102921/ Abstract: 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
more » ... h 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 and connectionism/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. the independent roles computation and information processing can fulfill in a theory of cognition. The notion of digital computation was imported from computability theory into neuroscience and psychology primarily for two reasons: first, it seemed to provide the right mathematics for modeling neural activity [43] ; second, it inherited mathematical tools (algorithms, computer program, formal languages, logical formalisms, and their derivatives, including many types of neural networks) that appeared to capture some aspects of cognition. These reasons are not sufficient to actually establish that cognition is digital computation. Whether cognition is digital computation is a difficult question, which lies outside the scope of this essay. The theory that cognition is computation became so popular that it progressively led to a stretching of the operative notion of computation. In many quarters, especially neuroscientific ones, the term 'computation' is used, more or less, for whatever internal processes explain cognition. Unlike 'digital computation,' which stands for a mathematical apparatus in search of applications, 'neural computation' is a label in search of a theory. Of course, the theory is quite well developed by now, as witnessed by the explosion of work in computational and theoretical neuroscience over the last decades [20, [44] [45] . The point is that such a theory need not rely on a previously existing and independently defined notion of computation, such as 'digital computation' or even 'analog computation' in its most straightforward sense. By contrast, the various notions of information (processing) have distinct roles to play. By and large, they serve to make sense of how organisms keep track of their environments and produce behaviors accordingly. Shannon's notion of information can serve to address quantitative problems of efficiency of communication in the presence of noise, including communication between the external (distal) environment and the nervous system. Other notions of information-specifically, semantic information-can serve to give specific semantic content to particular states or events. This may include cognitive or neural events that reliably correlate with events occurring in the organism's distal environment as well as mental representations, words, and the thoughts and sentences they constitute. Whether cognitive or neural events fulfill all or any of the job descriptions of computation and information processing is in part an empirical question and in part a conceptual one. It's a conceptual question insofar as we can mean different things by 'information' and 'computation', and insofar as there are conceptual relations between the various notions. It's an empirical question insofar as, once we fix the meanings of 'computation' and 'information', the extent to which computation and the processing of information are both instantiated in the brain depends on the empirical facts of the matter. Ok, but do these distinctions really matter? Why should a cognitive theorist care about the differences between computation and information processing? The main theoretical advantage of keeping them separate is to appreciate the independent contributions they can make to a theory of cognition. Conversely, the main cost of conflating computation and information processing is that the resulting mongrel concept may be too messy and vague to do all the jobs that are required of it. As a result, it becomes difficult to reach consensus on whether cognition involves either computation or information processing.
doi:10.1007/s10867-010-9195-3 pmid:22210958 pmcid:PMC3006465 fatcat:n37mygozufbhxpicabbcirdxxq