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 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

Evidence for Information Processing in the Brain

Many cognitive and neuroscientists attempt to assign biological functions to brain structures. To achieve this end, scientists perform experiments that relate the physical properties of brain structures to organism-level abilities, behaviors, and environmental stimuli. Researchers make use of various measuring instruments and methodological techniques to obtain this kind of relational evidence, ranging from single-unit electrophysiology and optogenetics to whole brain functional MRI. Each experiment is intended to identify brain function. However, seemingly independent of experimental evidence, many cognitive scientists, neuroscientists, and philosophers of science assume that the brain processes information as a scientific fact. In this work we analyze categories of relational evidence and find that although physical features of specific brain areas selectively covary with external stimuli and abilities, and that the brain shows reliable causal organization, there is no direct evidence supporting the claim that information processing is a natural function of the brain. We conclude that the belief in brain information processing adds little to the science of cognitive science and functions primarily as a metaphor for efficient communication of neuroscientific data

A new way of linking information theory with cognitive science

The relationship between the notion of *information* in information theory, and the notion of *information processing* in cognitive science, has long been controversial. But as the present paper shows, part of the disagreement arises from conflating different formulations of measurement. Clarifying distinctions reveals it is the context-free nature of Shannon's information average that is particular problematic from the cognitive point of view. Context-sensitive evaluation is then shown to be a way of addressing the problems that arise

Decentering Cognition

The neocortex figures importantly in human cognition, but it is not the only locus of cognitive activities or even at the top of a hierarchy of cognitive processing areas in the central nervous system. Moreover, the form of information processing employed in the neocortex is not representative of information processing elsewhere in the nervous system. In this paper, we articulate and argue against cortico-centrism in cognitive science, contending instead that the nervous system constitutes a heterarchical network of diverse types of information processing systems. To press this perspective, we examine neural information processing in both non-vertebrates and vertebrates, including examples of cognitive processing in the vertebrate hypothalamus and basal ganglia

Mapping dynamic interactions among cognitive biases in depression

Depression is theorized to be caused in part by biased cognitive processing of emotional information. Yet, prior research has adopted a reductionist approach that does not characterize how biases in cognitive processes such as attention and memory work together to confer risk for this complex multifactorial disorder. Grounded in affective and cognitive science, we highlight four mechanisms to understand how attention biases, working memory difficulties, and long-term memory biases interact and contribute to depression. We review evidence for each mechanism and highlight time- and context-dependent dynamics. We outline methodological considerations and recommendations for research in this area. We conclude with directions to advance the understanding of depression risk, cognitive training interventions, and transdiagnostic properties of cognitive biases and their interactions

Risk perception or self perception

In cognitive entrepreneurship research one main question is: Do entrepreneurs think differently than others in various ways? Especially in the area of risk perception cognition is thought of as information processing. In later streams of cognitive science it has developed from a state where cognition is seen as information processing to a state where cognition is mainly seen as an effective act, where experiences play an important role. We use risk perception as an indicator for information processing and self perception as an indicator for past experience. We found that past experience explains starting a real venture whereas risk information processing explains starting a case study venture

Representation recovers information

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation-use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is *necessary* for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation-use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing