Abstract
Modularity assumption is central to most theoretical and empirical approaches in cognitive science. The Bayesian Brain (BB) models are a class of neuro-computational models that aim to ground perception, cognition, and action under a single computational principle of prediction-error minimization. It is argued that the proposals of BB models contradict the modular nature of mind as the modularity assumption entails computational separation of individual modules. This review examines how BB models address the assumption of modularity. Empirical evidences of top-down influence on early sensory processes is often cited as a case against the modularity thesis. In the modularity thesis, such top-down effects are attributed to attentional modulation of the output of an early impenetrable stage of sensory processing. The attentional-mediation argument defends the modularity thesis. We analyse this argument using the novel conception of attention in the BB models. We attempt to reconcile classical bottom-up vs. top-down dichotomy of information processing, within the information passing scheme of the BB models. Theoretical considerations and empirical findings associated with BB models that address the modularity assumption is reviewed. Further, we examine the modularity of perceptual and motor systems.
Highlights
The modularity of cognitive processes is a fundamental principle of the representationalist paradigm (Fodor, 1983)
We explored the nature of information processing in the Bayesian Brain (BB) models and its implications on the assumption of modularity
Recent empirical findings question the classic notion that bottom-up units are invariant to topdown influences
Summary
The modularity of cognitive processes is a fundamental principle of the representationalist paradigm (Fodor, 1983). Higher-order cognitive states, such as desires (Balcetis and Dunning, 2006), morality (Gantman and Van Bavel, 2014), and racial category (Levin and Banaji, 2006) is shown to affect perceptual processing Such instances of top-down effects on perception are argued as evidence for cognitive penetration. The strictest form of modularity thesis, known as Massive Modularity, ascribes absolute information encapsulation between all modules of cognition, including the central systems Central systems, such as reasoning and decision-making involve the integration of domain-general representations, violating modularity. The Feature Integration Theory (FIT) (Treisman and Gelade, 1980), a widely accepted model of attention, proposes a dichotomy between bottomup and top-down processing In this account, the bottom-up processing involves the computation of fundamental featural dimensions, such as color and orientation by domain-specific units. We review how attention is defined in BB models and place the bottom-up/top-down dichotomy within the information passing scheme of the BB models
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