A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures

Goertzel, Ben; Lian, Ruiting; Arel, Itamar; Garis, Hugo de; Chen, Shuo

doi:10.1016/j.neucom.2010.08.012

Cited by 120 publications

(56 citation statements)

References 20 publications

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“…These processing strategies involve different neural mechanisms (Debaere, Wenderoth, Sunaert, Van Hecke, & Swinnen, 2003;Jueptner et al, 1997), but this often is ignored when interpreting neural activity. It should be noted that even cognitive architectures like ACT-R (Anderson et al, 2004), SOAR (J. E. Laird, Newell, & Rosenbloom, 1987), and EPIC (Meyer & Kieras, 1997; for an overview, see, e.g., Goertzel et al, 2010) attribute serial skill to just one mechanism (namely increasing the rate of selecting responses, e.g., Lebiere & Wallach, 2001), rather than taking into account the ability to switch to other processing strategies, and using different types of representations.…”

Section: The Dual Processor Modelmentioning

confidence: 99%

“…Simultaneously, the availability of increasingly powerful computers has enabled computational modeling of both cognitive and neural processes (Anderson, 1983;Anderson, Bothell, Byrne, Douglass, Lebiere, & Qin, 2004;De Garis, Shuo, Goertzel, & Ruiting, 2010;Goertzel, Lian, Arel, de Garis, & Chen, 2010;Kandel, Markram, Matthews, Yuste, & Koch, 2013;J. E. Laird, 2012).…”

Section: Introductionmentioning

confidence: 99%

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A cognitive framework for explaining serial processing and sequence execution strategies

2014

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Behavioral research has produced many taskspecific cognitive models that do not say much about the underlying information-processing architecture. Such an architecture is badly needed to better understand how cognitive neuroscience can benefit from existing cognitive models. This problem is especially pertinent in the domain of sequential behavior where behavioral research suggests a diversity of cognitive processes, processing modes and representations. Inspired by decades of reaction time (RT) research with the Additive Factors Method, the Psychological Refractory Period paradigm, and the Discrete Sequence Production task, we propose the Cognitive framework for Sequential Motor Behavior (C-SMB). We argue that C-SMB accounts for cognitive models developed for a range of sequential motor tasks (like those proposed by Keele et al., Psychological Review, 110(2), 316-339, 2003; Rosenbaum et al., Journal of Experimental Psychology: Human Perception and Performance, 9(1), 86-102, 1983, Journal of Memory and Language, 25, 710-725, 1986, Psychological Review, 102, 28-67, 1995 Schmidt, Psychological Review, 82(4), 225-260, 1975;Sternberg et al., 1978, Phonetica, 45, 177-197, 1988. C-SMB postulates that sequence execution can be controlled by a central processor using central-symbolic representations, and also by a motor processor using sequencespecific motor representations. On the basis of this framework, we present a classification of the sequence execution strategies that helps researchers to better understand the cognitive and neural underpinnings of serial movement behavior.

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Section: The Dual Processor Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cognitive framework for explaining serial processing and sequence execution strategies

2014

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“…Recent efforts to build artificial brains 1 employ both approaches [6,7]. Largescale brain simulations attempt to model in a realistic fashion the details of the brain organisation, i.e.…”

Section: Top-down and Bottom-up Approachesmentioning

confidence: 99%

“…In the survey [6,7] it was concluded that the two approaches display very different strengths. While bottom-up brain simulations are confined to syntactic aspects like how collections of neurons synchronize their electrical discharges, they do not tell anything about semantics, i.e.…”

Section: Top-down and Bottom-up Approachesmentioning

confidence: 99%

Complex Neuro-Cognitive Systems

Schierwagen

2011

Lecture Notes in Computer Science

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Abstract.Cognitive functions such as a perception, thinking and acting are based on the working of the brain, one of the most complex systems we know. The traditional scientific methodology, however, has proved to be not sufficient to understand the relation between brain and cognition. The aim of this paper is to review an alternative methodology -nonlinear dynamical analysis -and to demonstrate its benefit for cognitive neuroscience in cases when the usual reductionist method fails.

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“…An application of a neuroscience model to a practical task would be significant in that it would help us understand if computational principles of low level sensorimotor brain structures can be utilized. The development and application of practical subcortical models is also significant for large-scale cortical models [33,34] as they will allow us to explore how such models behave when faced with real-world stimuli, aiding the analysis of causal flow, such as in Darwin X [35], and perhaps providing the hybrid architecture needed for us to bridge the gap between large-scale brain simulations [36] and cognitive architectures [37].…”

Section: Introductionmentioning

confidence: 99%

Audio-visual localization with hierarchical topographic maps: Modeling the superior colliculus

2012

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A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures

Cited by 120 publications

References 20 publications

A cognitive framework for explaining serial processing and sequence execution strategies

A cognitive framework for explaining serial processing and sequence execution strategies

Complex Neuro-Cognitive Systems

Audio-visual localization with hierarchical topographic maps: Modeling the superior colliculus

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