Online research in philosophy

The dynamical systems approach in cognitive science offers a potentially useful perspective on both brain and behavior. Indeed, the importation of formal tools from dynamical systems research has already paid off for our field in many ways. However, like some other theoretical perspectives in cognitive science, the dynamical systems approach comes in both moderate or pragmatic and “fundamentalist” varieties (Jones & Love, 2011). In the latter form, dynamical systems theory can rise to some stirring rhetorical heights. However, as argued here, it also triggers a number of serious and specific reservations.

Although cognitive psychology is still dominated by computational theories, there is an emerging emphasis on dynamical aspects of cognition. Examples are provided supporting the increased use of dynamically inspired models by psychologists. Despite measurement and model verification problems in the direct use of dynamical system theoretic technology, van Gelder's general approach to cognition is recommended.

De Finetti's representation theorem is a special case of the ergodic decomposition of stationary probability measures. The problems of the interpretation of probabilities centred around de Finetti's theorem are extended to this more general situation. The ergodic decomposition theorem has a physical background in the ergodic theory of dynamical systems. Thereby the interpretations of probabilities in the cases of de Finetti's theorem and its generalization and in ergodic theory are systematically connected to each other.

De Finetti's representation theorem is a special case of the ergodic decomposition of stationary probability measures. The problems of the interpretation of probabilities centred around de Finetti's theorem are extended to this more general situation. The ergodic decomposition theorem has a physical background in the ergodic theory of dynamical systems. Thereby the interpretations of probabilities in the cases of de Finetti's theorem and its generalization and in ergodic theory are systematically connected to each other.

Dynamics is not enough for cognition, nor it is a substitute for information-processing aspects of brain behavior. Moreover, dynamics and computation are not at odds, but are quite compatible. They can be synthesized so that any dynamical system can be analyzed in terms of its intrinsic computational components.

In this paper, I outline two strands of evidence for the conclusion that the dynamical approach to cognitive science both seeks and provides covering law explanations. Two of the most successful dynamical models—Kelso’s model of rhythmic finger movement and Thelen et al.’s model of infant perseverative reaching—can be seen to provide explanations which conform to the famous explanatory scheme first put forward by Hempel and Oppenheim. In addition, many prominent advocates of the dynamical approach also express the provision of this kind of explanation as a goal of dynamical cognitive science. I conclude by briefly outlining two consequences. First, dynamical cognitive science’s explanatory style may strengthen its links to the so-called “situated” approach to cognition, but, secondly, it may also undermine the widespread intuition that dynamics is related to emergentism in the philosophy of mind.

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors.

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.

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.