Process algebraic modelling of attentional capture and human electrophysiology in interactive systems

Su, Li; Bowman, Howard; Barnard, Philip; Wyble, Brad

doi:10.1007/s00165-008-0094-3

Cited by 16 publications

(9 citation statements)

References 44 publications

(26 reference statements)

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“…There has been a surge of interest in computational models of possible neural mechanisms underlying the AB (Bowman and Wyble, 2007; Dehaene et al, 2003; Fragopanagos et al, 2005; Nieuwenhuis et al, 2005; Olivers and Meeter, 2008; Shih, 2008; Su et al, in press; Taatgen et al, 2009; Wyble et al, 2009). Modelers have been able to frame questions that have motivated new experimental work ranging from the pharmacological underpinnings of the AB to an understanding of the neural correlates of consciousness.…”

Section: Promising New Avenues: Modelingmentioning

confidence: 99%

The attentional blink: Past, present, and future of a blind spot in perceptual awareness

Martens

Wyble

2010

Neuroscience & Biobehavioral Reviews

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A survey of the attention literature reveals the prominence of the attentional blink (AB)-a deficit in reporting the second of two targets when presented in close temporal succession. For two decades, this robust attentional phenomenon has been a major topic in attention research because it is informative about the rate at which stimuli can be encoded into consciously accessible representations. The pace of discovery and theoretical advancement concerning the AB has increased rapidly in the past few years with emphasis on new neurophysiological evidence and computational accounts of attentional processes. In this review we extract the central questions and the main lessons learnt from the past, and subsequently provide important directions for future research.

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Section: Promising New Avenues: Modelingmentioning

confidence: 99%

The attentional blink: Past, present, and future of a blind spot in perceptual awareness

Martens

Wyble

2010

Neuroscience & Biobehavioral Reviews

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306

282

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“…This suggests that a system of salience sensitive control exists, which trades goal-directed processing off against monitoring and responding to (potentially high salience) stimuli that are irrelevant to the current task. In previous work, we have proposed the glance–look model, which formally specifies mental representations and processes that support salience detection and attentional control in the context of temporal attention (Su et al, 2009 ; Bowman et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Glancing and Then Looking: On the Role of Body, Affect, and Meaning in Cognitive Control

Su¹,

Bowman²,

Barnard³

2011

Front. Psychology

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In humans, there is a trade-off between the need to respond optimally to the salient environmental stimuli and the need to meet our long-term goals. This implies that a system of salience sensitive control exists, which trades task-directed processing off against monitoring and responding to potentially high salience stimuli that are irrelevant to the current task. Much cognitive control research has attempted to understand these mechanisms using non-affective stimuli. However, recent research has emphasized the importance of emotions, which are a major factor in the prioritization of competing stimuli and in directing attention. While relatively mature theories of cognitive control exist for non-affective settings, exactly how emotions modulate cognitive processes is less well understood. The attentional blink (AB) task is a useful experimental paradigm to reveal the dynamics of both cognitive and affective control in humans. Hence, we have developed the glance–look model, which has replicated a broad profile of data on the semantic AB task and characterized how attentional deployment is modulated by emotion. Taking inspiration from Barnard’s Interacting Cognitive Subsystems, the model relies on a distinction between two levels of meaning: implicational and propositional, which are supported by two corresponding mental subsystems: the glance and the look respectively. In our model, these two subsystems reflect the central engine of cognitive control and executive function. In particular, the interaction within the central engine dynamically establishes a task filter for salient stimuli using a neurobiologically inspired learning mechanism. In addition, the somatic contribution of emotional effects is modeled by a body-state subsystem. We argue that stimulus-driven interaction among these three subsystems governs the movement of control between them. The model also predicts attenuation effects and fringe awareness during the AB.

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“…Formally specifying and analysing biological systems is an ongoing research area (e.g., [61,62,63,64,65,66,67,68]), including recent formal modeling of aspects of cognition [69,70]. As advocated by Fisher and Henzinger [18], formalising biological models opens the field up to rigorous validation and analysis, as well as providing a solid foundation for extending the models as more discoveries are made, and comparing or integrating different explanations for observed phenomena.…”

Section: Related Workmentioning

confidence: 99%

Modelling and analysing neural networks using a hybrid process algebra

Colvin

2016

Theoretical Computer Science

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Please cite this article in press as: R.J. Colvin, Modeling and analysing neural networks using a hybrid process algebra, Theoret. Comput. Sci. (2015), http://dx.doi.org/10.1016/j.tcs. 2015.08.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Modeling and analysing neural networks using a hybrid process algebra Robert J. ColvinThe University of Queensland, Queensland Brain Institute, Brisbane, Australia, 4072 AbstractResearch involving artificial neural networks has tended to be driven towards efficient computation, especially in the domain of pattern recognition, or towards elucidating biological processes in the brain. Models have become more detailed as our understanding of the biology of the brain has increased, incorporating real-time behaviour of individual neurons interacting within complex system structures and dynamics. There are few examples of abstract and fully formal models of biologically plausible neural networks: in the neural networks literature models are often presented as a mixture of mathematical equations and natural language, supported by simulation code and associated experimental results. The informality often hides or obscures important aspects of a particular model, and leaves a large conceptual gap between the model descriptions and the usually low-level programming code used to simulate them.The main contribution of this paper is formally modelling and analysing a biologically plausible neural network model from the literature that exhibits complex neuron-level behaviour and network-level structure. To achieve this a modelling language 'Pann' is developed, based on the process algebras CSP and Hybrid χ . It is designed to be convenient for mixing the behaviour of discrete events (such as a neuron spike) with mutable continuous and discrete variables (representing chemical properties of a neuron, for instance). Its behaviour is defined using an operational semantics, from which a set of general properties of the language is proved.The groundwork for the biological model is laid by first formalising some well-known concepts from the artificial neural networks domain, such as feedforward behaviour, backpropagation, and recurrent neural networks. The Pann model of a feedforward network, comprising a set of communicating processes representing individual neurons, is proved equivalent to the standard one-line calculation of feedforward behaviour.

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Process algebraic modelling of attentional capture and human electrophysiology in interactive systems

Cited by 16 publications

References 44 publications

The attentional blink: Past, present, and future of a blind spot in perceptual awareness

The attentional blink: Past, present, and future of a blind spot in perceptual awareness

Glancing and Then Looking: On the Role of Body, Affect, and Meaning in Cognitive Control

Modelling and analysing neural networks using a hybrid process algebra

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