SAL: an explicitly pluralistic cognitive architecture

Jilk, David J.; Lebière, Christian; O’Reilly, Randall C.; Anderson, John R.

doi:10.1080/09528130802319128

Cited by 90 publications

(65 citation statements)

References 26 publications

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“…A stark transition can be seen, in particular, in Anderson's work, where his initial ambitions for his ACT-R production system architecture (as a univocal model for cognition) have been replaced by the acknowledgement that hybrid architectures are more promising. In a recent paper, Anderson et al argue that "theories at different levels of detail and from different perspectives are mutually informative and constraining, and furthermore no single level can capture the full richness of cognition" (Jilk et al 2008). Similarly, Laird's most recent presentation of his Soar architecture advocates constraint by evidence from neuroscience (as well as from psychology and AI): "we have found that combining what is known in psychology, in neuroscience, and in AI is an effective strategy to building a comprehensive cognitive architecture" (Laird 2012, p. 22).…”

Section: How Cognitive Neuroscience Exhibits Multilevel Mechanistic Imentioning

confidence: 99%

The cognitive neuroscience revolution

2015

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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.

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Section: How Cognitive Neuroscience Exhibits Multilevel Mechanistic Imentioning

confidence: 99%

The cognitive neuroscience revolution

2015

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“…This is also the case in connectionism and DST, and honoring ideas therein, findings within SIT in fact open -in an explanatory or epistemological sense (cf. Jilk, Lebiere, O'Reilly, & Anderson, 2008) -a pluralist perspective on how the brain might arrive at simplest interpretations. This is explicated in the next given context of (a) processing in the visual hierarchy in the brain, and perhaps surprising (b) quantum computing.…”

Section: The Neural Realization Of Simplicitymentioning

confidence: 99%

Simplicity in Perceptual Organization

Helm

2014

Oxford Handbooks Online

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A long-standing debate in perception concerns the question of whether perceptual organization is guided by internal efficiency (the simplicity principle) or by external veridicality (the likelihood principle). This article focuses on the simplicity principle which is a modern information-theoretic version of Occam's razor, but also compares it to the likelihood principle which reflects classical information theory. Both principles can be modeled by Bayes' rule which combines quantifications of view-independent and view-dependent factors to predict stimulus interpretations. Whereas the likelihood principle relies on hardly quantifiable frequencies of occurrence of things in the world, the simplicity principle relies on better quantifiable structural complexities of individual things. The simplicity principle is further argued to be sufficiently veridical in everyday perception, and neurally realizable via cognitive architecture enabling transparallel processing. This quantum-like form of representational processing relates to connectionist modeling and complements dynamic-systems ideas about neuronal synchronization.

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“…have not yet explained where the symbols are in the human mind, or how the brain implements symbolic information processing (though see Jilk, Lebiere, O'Reilly & Anderson, 2008).…”

Section: Introductionmentioning

confidence: 99%

Conflict resolution and learning probability matching in a neural cell-assembly architecture

Belavkin¹,

Huyck²

2011

Cognitive Systems Research

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Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in the human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for all tasks, and that additional meta-control processes should be involved. We describe an earlier proposed architecture (Belavkin & Huyck, 2008) implementing an adaptive conflict resolution process between CAs, and then evaluate it by modelling the probability matching phenomenon in a classic two-choice task. The model and its results are discussed in view of mathematical theory of learning and existing cognitive architectures.

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SAL: an explicitly pluralistic cognitive architecture

Cited by 90 publications

References 26 publications

The cognitive neuroscience revolution

The cognitive neuroscience revolution

Simplicity in Perceptual Organization

Conflict resolution and learning probability matching in a neural cell-assembly architecture

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