Chen Yu and Dana H. Ballard
- Published in print:
- 2009
- Published Online:
- May 2010
- ISBN:
- 9780199553242
- eISBN:
- 9780191720444
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199553242.003.0010
- Subject:
- Linguistics, Semantics and Pragmatics, Psycholinguistics / Neurolinguistics / Cognitive Linguistics
We show in a series of three related studies that intentional cues encoded in body movements can provide very specific gains to language learning. A computational model is developed on the basis of ...
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We show in a series of three related studies that intentional cues encoded in body movements can provide very specific gains to language learning. A computational model is developed on the basis of machine learning techniques which can identify sound patterns of individual words from continuous speech using non‐linguistic contextual information and employ body movements as deictic references to discover word‐meaning associations.Less
We show in a series of three related studies that intentional cues encoded in body movements can provide very specific gains to language learning. A computational model is developed on the basis of machine learning techniques which can identify sound patterns of individual words from continuous speech using non‐linguistic contextual information and employ body movements as deictic references to discover word‐meaning associations.
Julia Trommershäuser, Konrad Kording, and Michael S. Landy (eds)
- Published in print:
- 2011
- Published Online:
- September 2012
- ISBN:
- 9780195387247
- eISBN:
- 9780199918379
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195387247.001.0001
- Subject:
- Psychology, Cognitive Neuroscience, Cognitive Psychology
This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, ...
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This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, across behavioral, electrophysiological, and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed. This book focuses on the emerging probabilistic way of thinking about these problems. These approaches derive from the realization that all our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The computational approaches described in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems, such as shape and depth perception. Other parts of the book ask how uncertainty may be represented in the nervous system and used for cue combination. The broadening scope of probabilistic approaches to cue combination is highlighted in the breadth of topics covered: the chapters summarize and discuss computational approaches and behavioral evidence aimed at understanding the combination of visual, auditory, proprioceptive, and haptic cues. Some chapters address the combination of cues within a single sensory modality while others address the combination across sensory modalities. Neural implementation, behavior, and theory are considered. The unifying aspect of this book is the focus on the uncertainty intrinsic to sensory cues and the underlying question of how the nervous system deals with this uncertainty.Less
This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, across behavioral, electrophysiological, and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed. This book focuses on the emerging probabilistic way of thinking about these problems. These approaches derive from the realization that all our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The computational approaches described in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems, such as shape and depth perception. Other parts of the book ask how uncertainty may be represented in the nervous system and used for cue combination. The broadening scope of probabilistic approaches to cue combination is highlighted in the breadth of topics covered: the chapters summarize and discuss computational approaches and behavioral evidence aimed at understanding the combination of visual, auditory, proprioceptive, and haptic cues. Some chapters address the combination of cues within a single sensory modality while others address the combination across sensory modalities. Neural implementation, behavior, and theory are considered. The unifying aspect of this book is the focus on the uncertainty intrinsic to sensory cues and the underlying question of how the nervous system deals with this uncertainty.
Todd S. Braver, Jonathan D. Cohen, and Deanna M. Barch
- Published in print:
- 2002
- Published Online:
- May 2009
- ISBN:
- 9780195134971
- eISBN:
- 9780199864157
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195134971.003.0027
- Subject:
- Neuroscience, Behavioral Neuroscience, Molecular and Cellular Systems
This chapter presents a theory of prefrontal cortex (PFC) function using the connectionist computational modeling framework. This modeling approach involves three components: (1) computational ...
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This chapter presents a theory of prefrontal cortex (PFC) function using the connectionist computational modeling framework. This modeling approach involves three components: (1) computational analysis of the critical processing mechanisms required for cognitive control; (2) use of neurobiologically plausible principles of information processing; and (3) implementation and simulation of cognitive tasks and behavioral performance. The chapter describes behavioral and neuroimaging data on healthy young adults that validate critical components of the model. It then summarizes the application of the model to the clinical domain. These studies highlight the power of the cognitive neuroscience approach by demonstrating how a single, integrated account of PFC function can capture a wide range of data from different methodologies and multiple populations.Less
This chapter presents a theory of prefrontal cortex (PFC) function using the connectionist computational modeling framework. This modeling approach involves three components: (1) computational analysis of the critical processing mechanisms required for cognitive control; (2) use of neurobiologically plausible principles of information processing; and (3) implementation and simulation of cognitive tasks and behavioral performance. The chapter describes behavioral and neuroimaging data on healthy young adults that validate critical components of the model. It then summarizes the application of the model to the clinical domain. These studies highlight the power of the cognitive neuroscience approach by demonstrating how a single, integrated account of PFC function can capture a wide range of data from different methodologies and multiple populations.
Alan Gilchrist
- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780195187168
- eISBN:
- 9780199786725
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195187168.003.0007
- Subject:
- Psychology, Clinical Psychology
The computer revolution produced two quite different streams of work on lightness perception. These streams are represented by decomposition models and brightness models, respectively. Perhaps the ...
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The computer revolution produced two quite different streams of work on lightness perception. These streams are represented by decomposition models and brightness models, respectively. Perhaps the sharpest difference between these streams concerns their treatment of physiology. If the brightness models were driven by physiology, the decomposition models were driven by veridicality. While the decomposition modelers saw in the computer revolution a chance to deduce, just like those working in machine vision, the logical steps necessary to derive veridical lightness percepts from proximal input without concern for physiological data, the brightness modelers saw it as a chance to create computer models of neural functions.Less
The computer revolution produced two quite different streams of work on lightness perception. These streams are represented by decomposition models and brightness models, respectively. Perhaps the sharpest difference between these streams concerns their treatment of physiology. If the brightness models were driven by physiology, the decomposition models were driven by veridicality. While the decomposition modelers saw in the computer revolution a chance to deduce, just like those working in machine vision, the logical steps necessary to derive veridical lightness percepts from proximal input without concern for physiological data, the brightness modelers saw it as a chance to create computer models of neural functions.
Alain Destexhe and Michelle Rudolph-Lilith
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780199235070
- eISBN:
- 9780191715778
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199235070.003.0009
- Subject:
- Mathematics, Biostatistics
In the cerebral cortex of awake animals, neurons are subject to tremendous fluctuating activity, mostly of synaptic origin, termed “synaptic noise”. Synaptic noise is the dominant source of membrane ...
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In the cerebral cortex of awake animals, neurons are subject to tremendous fluctuating activity, mostly of synaptic origin, termed “synaptic noise”. Synaptic noise is the dominant source of membrane potential fluctuations in neurons and can have a strong influence on their integrative properties. We review here the experimental measurements of synaptic noise, and its modelling by conductance-based stochastic processes. We then review the consequences of synaptic noise on neuronal integrative properties, as predicted by computational models and investigated experimentally using the dynamic clamp. We also review analysis methods such as spike-triggered average or conductance analysis, which are derived from the modelling of synaptic noise by stochastic processes. These different approaches aim at understanding the integrative properties of neocortical neurons in the intact brain.Less
In the cerebral cortex of awake animals, neurons are subject to tremendous fluctuating activity, mostly of synaptic origin, termed “synaptic noise”. Synaptic noise is the dominant source of membrane potential fluctuations in neurons and can have a strong influence on their integrative properties. We review here the experimental measurements of synaptic noise, and its modelling by conductance-based stochastic processes. We then review the consequences of synaptic noise on neuronal integrative properties, as predicted by computational models and investigated experimentally using the dynamic clamp. We also review analysis methods such as spike-triggered average or conductance analysis, which are derived from the modelling of synaptic noise by stochastic processes. These different approaches aim at understanding the integrative properties of neocortical neurons in the intact brain.
Geraint A. Wiggins
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199553426
- eISBN:
- 9780191731020
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199553426.003.0018
- Subject:
- Psychology, Music Psychology, Cognitive Psychology
This chapter adds further comments on the discussion in Chapter 16. It provides a detailed account of the theoretical underpinnings of the computational modelling of cognitive processes. It is ...
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This chapter adds further comments on the discussion in Chapter 16. It provides a detailed account of the theoretical underpinnings of the computational modelling of cognitive processes. It is critical of the modelling approach followed by the target authors, especially with respect to its hand-crafted representation, musical presumptions, and its generality. It suggests that unsupervised, domain-general learning models are more informative and offer greater explanatory potential for cognitive modelling of music processing.Less
This chapter adds further comments on the discussion in Chapter 16. It provides a detailed account of the theoretical underpinnings of the computational modelling of cognitive processes. It is critical of the modelling approach followed by the target authors, especially with respect to its hand-crafted representation, musical presumptions, and its generality. It suggests that unsupervised, domain-general learning models are more informative and offer greater explanatory potential for cognitive modelling of music processing.
Jonathan Bendor, Daniel Diermeier, David A. Siegel, and Michael M. Ting
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691135076
- eISBN:
- 9781400836802
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691135076.003.0006
- Subject:
- Political Science, American Politics
This chapter introduces a model of two-party elections that integrates the focused models of party competition, turnout, and voter choice. To address the complexity of this synthetic model, ...
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This chapter introduces a model of two-party elections that integrates the focused models of party competition, turnout, and voter choice. To address the complexity of this synthetic model, computation is used as the main way to generate results (predictions). The model yields a “general equilibrium” of the election game. It also allows for greater heterogeneity within each coalition while taking into account the link between payoffs and aspirations. The chapter first describes the proposed computational model for two parties before discussing some results of the basic integrated model. It also considers several new questions that the model can address, such as: who votes and who votes correctly. Finally, it examines the dynamics that lead to equilibrium behavior.Less
This chapter introduces a model of two-party elections that integrates the focused models of party competition, turnout, and voter choice. To address the complexity of this synthetic model, computation is used as the main way to generate results (predictions). The model yields a “general equilibrium” of the election game. It also allows for greater heterogeneity within each coalition while taking into account the link between payoffs and aspirations. The chapter first describes the proposed computational model for two parties before discussing some results of the basic integrated model. It also considers several new questions that the model can address, such as: who votes and who votes correctly. Finally, it examines the dynamics that lead to equilibrium behavior.
Denis Mareschal, Gert Westermann, and Nadja Althaus
- Published in print:
- 2012
- Published Online:
- September 2012
- ISBN:
- 9780199586059
- eISBN:
- 9780191741470
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199586059.003.0015
- Subject:
- Psychology, Cognitive Psychology, Developmental Psychology
This chapter asks how multisensory perception can develop. That is, what are the mechanisms by which separate modalities become differentiated and integrated during the first years of life? This ...
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This chapter asks how multisensory perception can develop. That is, what are the mechanisms by which separate modalities become differentiated and integrated during the first years of life? This chapter argues that computer models provide a powerful tool for answering these questions. The chapter begins by describing the role of computational modelling in understanding causal mechanisms of developmental change, going on to characterise connectionist neural network models as a family of modelling approaches that is particularly well suited for studying learning and development. In the next three sections the chapter illustrates how neural network models have been used to grapple with how pair-wise multisensory (and likewise, sensorimotor) integration develops beginning with auditory-visual, then auditory-motor, and then finally visual-motor coupling. Finally, the chapter reviews our findings and point to some challenges for future research.Less
This chapter asks how multisensory perception can develop. That is, what are the mechanisms by which separate modalities become differentiated and integrated during the first years of life? This chapter argues that computer models provide a powerful tool for answering these questions. The chapter begins by describing the role of computational modelling in understanding causal mechanisms of developmental change, going on to characterise connectionist neural network models as a family of modelling approaches that is particularly well suited for studying learning and development. In the next three sections the chapter illustrates how neural network models have been used to grapple with how pair-wise multisensory (and likewise, sensorimotor) integration develops beginning with auditory-visual, then auditory-motor, and then finally visual-motor coupling. Finally, the chapter reviews our findings and point to some challenges for future research.
Mark Tatham and Katherine Morton
- Published in print:
- 2003
- Published Online:
- September 2007
- ISBN:
- 9780199250677
- eISBN:
- 9780191719462
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199250677.003.0013
- Subject:
- Linguistics, Phonetics / Phonology
This chapter suggests that core studies of emotion can contribute importantly to speech models. Inadequacies of current speech models are pointed out and the usefulness of an integrated physical ...
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This chapter suggests that core studies of emotion can contribute importantly to speech models. Inadequacies of current speech models are pointed out and the usefulness of an integrated physical cognitive model is presented. A suitable model of emotion that can be tightly correlated with a speech model is proposed, and the resulting speech model can incorporate emotive content. The advantages of building the model of speech with expressive and emotive content are presented, as well as the case for building the model in computational terms; computational adequacy is highlighted.Less
This chapter suggests that core studies of emotion can contribute importantly to speech models. Inadequacies of current speech models are pointed out and the usefulness of an integrated physical cognitive model is presented. A suitable model of emotion that can be tightly correlated with a speech model is proposed, and the resulting speech model can incorporate emotive content. The advantages of building the model of speech with expressive and emotive content are presented, as well as the case for building the model in computational terms; computational adequacy is highlighted.
Michael S. C. Thomas, James L. McClelland, Fiona M. Richardson, Anna C. Schapiro, and Frank D. Baughman
- Published in print:
- 2009
- Published Online:
- September 2009
- ISBN:
- 9780195300598
- eISBN:
- 9780199867165
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195300598.003.0017
- Subject:
- Psychology, Cognitive Psychology, Developmental Psychology
A tension has existed between connectionism and dynamic systems theory (DST), and this chapter considers why this should be the case. The chapter argues that much of the tension arises from a tenet ...
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A tension has existed between connectionism and dynamic systems theory (DST), and this chapter considers why this should be the case. The chapter argues that much of the tension arises from a tenet that the two approaches share: they both rely on the explicit, quantitative instantiation of ideas in mathematical or computational models. The use of such models is responsible for much of the theoretical progress generated by connectionism and DST beyond the theories of good old-fashioned cognitive development (GOFCD). But the use of explicit, quantitative models brings with it a new set of problems. The chapter discusses several consequences of the use of such models and considers three points of apparent disagreement between connectionism and DST.Less
A tension has existed between connectionism and dynamic systems theory (DST), and this chapter considers why this should be the case. The chapter argues that much of the tension arises from a tenet that the two approaches share: they both rely on the explicit, quantitative instantiation of ideas in mathematical or computational models. The use of such models is responsible for much of the theoretical progress generated by connectionism and DST beyond the theories of good old-fashioned cognitive development (GOFCD). But the use of explicit, quantitative models brings with it a new set of problems. The chapter discusses several consequences of the use of such models and considers three points of apparent disagreement between connectionism and DST.
Joshua M. Epstein
- Published in print:
- 2014
- Published Online:
- October 2017
- ISBN:
- 9780691158884
- eISBN:
- 9781400848256
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691158884.003.0003
- Subject:
- Mathematics, Applied Mathematics
This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an ...
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This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.Less
This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.
Isabel Gauthier, Michael Tarr, and Daniel Bub (eds)
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195309607
- eISBN:
- 9780199865291
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195309607.001.0001
- Subject:
- Psychology, Cognitive Psychology, Vision
This book surveys the study of perceptual expertise in visual object recognition, introducing a variety of questions, research findings, and extant issues that have emerged from recent studies of ...
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This book surveys the study of perceptual expertise in visual object recognition, introducing a variety of questions, research findings, and extant issues that have emerged from recent studies of face, object, and letter recognition. The book also discusses a novel collaborative model, codified as the “Perceptual Expertise Network.” The central idea of this group effort is an emphasis on domain-general principles of high-level visual learning that can account for how different object categories are processed and come to be associated with spatially localized activity in the primate brain. The approach brings together different traditions and techniques critical to cognitive neuroscience, such as psychophysics, human brain imaging, monkey physiology, developmental work, neuropsychological studies, and computational modeling. In 12 chapters, members of the Perceptual Expertise Network and their collaborators review how face perception motivated the study of perceptual expertise with objects, how face expertise develops in children, how different kinds of experience result in different degrees of expertise, and how perceptual expertise can break down in individuals with autism or different forms of deficits in perception. They describe advances and challenges in developing models to account for expertise, including the need to account for competition between different domains of expertise and to specify the functional locus of effects of expertise. They introduce more recent directions in the study of expertise, including research on expertise with letters and research investigating the interactions between perception and conception. Finally, they discuss the difficulties in relating high-level perceptual impairments and brain-based evidence to normal performance.Less
This book surveys the study of perceptual expertise in visual object recognition, introducing a variety of questions, research findings, and extant issues that have emerged from recent studies of face, object, and letter recognition. The book also discusses a novel collaborative model, codified as the “Perceptual Expertise Network.” The central idea of this group effort is an emphasis on domain-general principles of high-level visual learning that can account for how different object categories are processed and come to be associated with spatially localized activity in the primate brain. The approach brings together different traditions and techniques critical to cognitive neuroscience, such as psychophysics, human brain imaging, monkey physiology, developmental work, neuropsychological studies, and computational modeling. In 12 chapters, members of the Perceptual Expertise Network and their collaborators review how face perception motivated the study of perceptual expertise with objects, how face expertise develops in children, how different kinds of experience result in different degrees of expertise, and how perceptual expertise can break down in individuals with autism or different forms of deficits in perception. They describe advances and challenges in developing models to account for expertise, including the need to account for competition between different domains of expertise and to specify the functional locus of effects of expertise. They introduce more recent directions in the study of expertise, including research on expertise with letters and research investigating the interactions between perception and conception. Finally, they discuss the difficulties in relating high-level perceptual impairments and brain-based evidence to normal performance.
Falk Fleischer and Martin A. Giese
- Published in print:
- 2012
- Published Online:
- January 2013
- ISBN:
- 9780195393705
- eISBN:
- 9780199979271
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195393705.003.0022
- Subject:
- Psychology, Cognitive Psychology
Computational models are fundamentally important for testing the feasibility of theories of the visual processing of body movements and for deriving well-defined theoretical predictions that can be ...
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Computational models are fundamentally important for testing the feasibility of theories of the visual processing of body movements and for deriving well-defined theoretical predictions that can be tested experimentally. A computational model is proposed for the recognition of transitive and nontransitive hand actions from real videos that reproduces several key neurophysiological properties of the action perception system. Limitations of the proposed model, along with novel predictions and areas for future research, are discussed.Less
Computational models are fundamentally important for testing the feasibility of theories of the visual processing of body movements and for deriving well-defined theoretical predictions that can be tested experimentally. A computational model is proposed for the recognition of transitive and nontransitive hand actions from real videos that reproduces several key neurophysiological properties of the action perception system. Limitations of the proposed model, along with novel predictions and areas for future research, are discussed.
Yuko Munakata, J. Bruce Morton, and Randall C. O'Reilly
- Published in print:
- 2008
- Published Online:
- March 2012
- ISBN:
- 9780195168648
- eISBN:
- 9780199847297
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195168648.003.0007
- Subject:
- Psychology, Cognitive Psychology
This chapter considers the working memory processes that contribute to the ability to handle tasks and differences that lead to variations in these working memory processes. Two processes are focused ...
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This chapter considers the working memory processes that contribute to the ability to handle tasks and differences that lead to variations in these working memory processes. Two processes are focused on: active maintenance and information updating. They are important processes that other working memory processes are likely to tap into or build on. Computational models and behavioral studies are described to test the mechanisms subserving active maintenance and information updating. The overaching theory of working memory focuses on the computational mechanisms underlying the active maintenance and updating of information. Simulations made demonstrate computational trade-offs in active maintenance. They provide a strong computational foundation for our overall theory of working memory function. The main theme emphasized is that the grounding of constructs in neural mechanisms can provide important constraints on interpretations of behavioral findings of individual variability.Less
This chapter considers the working memory processes that contribute to the ability to handle tasks and differences that lead to variations in these working memory processes. Two processes are focused on: active maintenance and information updating. They are important processes that other working memory processes are likely to tap into or build on. Computational models and behavioral studies are described to test the mechanisms subserving active maintenance and information updating. The overaching theory of working memory focuses on the computational mechanisms underlying the active maintenance and updating of information. Simulations made demonstrate computational trade-offs in active maintenance. They provide a strong computational foundation for our overall theory of working memory function. The main theme emphasized is that the grounding of constructs in neural mechanisms can provide important constraints on interpretations of behavioral findings of individual variability.
Nathaniel D. Daw
- Published in print:
- 2011
- Published Online:
- May 2011
- ISBN:
- 9780199600434
- eISBN:
- 9780191725623
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199600434.003.0001
- Subject:
- Psychology, Cognitive Psychology, Developmental Psychology
Researchers have recently begun to integrate computational models into the analysis of neural and behavioural data, particularly in experiments on reward learning and decision making. This chapter ...
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Researchers have recently begun to integrate computational models into the analysis of neural and behavioural data, particularly in experiments on reward learning and decision making. This chapter aims to review and rationalize these methods. It exposes these tools as instances of broadly applicable statistical techniques, considers the questions they are suited to answer, provides a practical tutorial and tips for their effective use, and, finally, suggests some directions for extension or improvement. The techniques are illustrated with fits of simple models to simulated datasets. Throughout, the chapter flags interpretational and technical pitfalls of which authors, reviewers, and readers should be aware.Less
Researchers have recently begun to integrate computational models into the analysis of neural and behavioural data, particularly in experiments on reward learning and decision making. This chapter aims to review and rationalize these methods. It exposes these tools as instances of broadly applicable statistical techniques, considers the questions they are suited to answer, provides a practical tutorial and tips for their effective use, and, finally, suggests some directions for extension or improvement. The techniques are illustrated with fits of simple models to simulated datasets. Throughout, the chapter flags interpretational and technical pitfalls of which authors, reviewers, and readers should be aware.
David H. Brainard, James M. Kraft, and Philippe Longère
- Published in print:
- 2003
- Published Online:
- March 2012
- ISBN:
- 9780198505006
- eISBN:
- 9780191686764
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198505006.003.0010
- Subject:
- Psychology, Vision
This chapter focuses on computational theories of colour constancy, which define particular ensembles of scenes where some degree of colour constancy is possible and express algorithms that achieve ...
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This chapter focuses on computational theories of colour constancy, which define particular ensembles of scenes where some degree of colour constancy is possible and express algorithms that achieve constancy for these ensembles. It demonstrates how computational models can be elaborated to make predictions about human performance. It also presents empirical measurements of human colour constancy, emphasizing on studies of performance for stimulus conditions closely related to natural viewing, and on measurements that connect to computational theory.Less
This chapter focuses on computational theories of colour constancy, which define particular ensembles of scenes where some degree of colour constancy is possible and express algorithms that achieve constancy for these ensembles. It demonstrates how computational models can be elaborated to make predictions about human performance. It also presents empirical measurements of human colour constancy, emphasizing on studies of performance for stimulus conditions closely related to natural viewing, and on measurements that connect to computational theory.
Joscha Bach
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780195370676
- eISBN:
- 9780199870721
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195370676.001.0001
- Subject:
- Psychology, Cognitive Models and Architectures
Although computational models of cognition have become very popular, these models are relatively limited in their coverage of cognition—they usually only emphasize problem solving and reasoning, or ...
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Although computational models of cognition have become very popular, these models are relatively limited in their coverage of cognition—they usually only emphasize problem solving and reasoning, or treat perception and motivation as isolated modules. The first architecture to cover cognition more broadly is the Psi theory, developed by Dietrich Dörner. By integrating motivation and emotion with perception and reasoning, and including grounded neuro-symbolic representations, the Psi contributes significantly to an integrated understanding of the mind. It provides a conceptual framework that highlights the relationships between perception and memory, language and mental representation, reasoning and motivation, emotion and cognition, autonomy and social behavior. So far, the Psi theory's origin in psychology, its methodology, and its lack of documentation have limited its impact. This book adapts the Psi theory to cognitive science and artificial intelligence, by elucidating both its theoretical and technical frameworks, and clarifying its contribution to how we have come to understand cognition.Less
Although computational models of cognition have become very popular, these models are relatively limited in their coverage of cognition—they usually only emphasize problem solving and reasoning, or treat perception and motivation as isolated modules. The first architecture to cover cognition more broadly is the Psi theory, developed by Dietrich Dörner. By integrating motivation and emotion with perception and reasoning, and including grounded neuro-symbolic representations, the Psi contributes significantly to an integrated understanding of the mind. It provides a conceptual framework that highlights the relationships between perception and memory, language and mental representation, reasoning and motivation, emotion and cognition, autonomy and social behavior. So far, the Psi theory's origin in psychology, its methodology, and its lack of documentation have limited its impact. This book adapts the Psi theory to cognitive science and artificial intelligence, by elucidating both its theoretical and technical frameworks, and clarifying its contribution to how we have come to understand cognition.
Denis Mareschal and Andrew J. Bremner
- Published in print:
- 2009
- Published Online:
- March 2012
- ISBN:
- 9780199216895
- eISBN:
- 9780191696039
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199216895.003.0010
- Subject:
- Psychology, Developmental Psychology
Computational models can be outlined into two distinct kinds: symbolic models and connectionist models. One particular focus of computational modeling research has been the development of object ...
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Computational models can be outlined into two distinct kinds: symbolic models and connectionist models. One particular focus of computational modeling research has been the development of object interactions during infancy. In this chapter, the authors describe a number of computational approaches to understand the developing object concept, focusing particularly on the contribution of such models to our understanding of the development of representations governing infant–object interactions across the first two years of life. The authors argue that connectionist models have an important role to play in directing theoretical and empirical research in this area, because they best enable researchers to understand the causal factors at play in the ontogeny of knowledge about objects and cognition in general. A brief review of connectionist modeling principles is also presented.Less
Computational models can be outlined into two distinct kinds: symbolic models and connectionist models. One particular focus of computational modeling research has been the development of object interactions during infancy. In this chapter, the authors describe a number of computational approaches to understand the developing object concept, focusing particularly on the contribution of such models to our understanding of the development of representations governing infant–object interactions across the first two years of life. The authors argue that connectionist models have an important role to play in directing theoretical and empirical research in this area, because they best enable researchers to understand the causal factors at play in the ontogeny of knowledge about objects and cognition in general. A brief review of connectionist modeling principles is also presented.
Timothy J. O’Donnell
- Published in print:
- 2015
- Published Online:
- May 2016
- ISBN:
- 9780262028844
- eISBN:
- 9780262326803
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262028844.003.0001
- Subject:
- Linguistics, Sociolinguistics / Anthropological Linguistics
This chapter introduces the problem of productivity: How do language learners determine which potential generalizations can actually be used to create novel expressions in their language and which ...
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This chapter introduces the problem of productivity: How do language learners determine which potential generalizations can actually be used to create novel expressions in their language and which only occur as parts of stored items? The first part of the chapter reviews historical approaches to this question, discussing previous unsuccessful attempts to reduce the problem of what is stored and what is computed to other properties. The second part of the chapter outlines a new theory of storage and computation based on the idea that the problem can be solved via a probabilistic inference which optimizes a tradeoff between fewer, simpler stored items, and simpler derivations of linguistic expressions. This inference-based model is contrasted with four other models to which it will be compared throughout the book: (i) the full-parsing model where all structure is always computed, (ii) the full-listing model where all structure is stored after the first time it is computed and (iii) two variants of the exemplar-based model which hypothesizes all possible mixtures of computation and storage in the derivation of every expression.Less
This chapter introduces the problem of productivity: How do language learners determine which potential generalizations can actually be used to create novel expressions in their language and which only occur as parts of stored items? The first part of the chapter reviews historical approaches to this question, discussing previous unsuccessful attempts to reduce the problem of what is stored and what is computed to other properties. The second part of the chapter outlines a new theory of storage and computation based on the idea that the problem can be solved via a probabilistic inference which optimizes a tradeoff between fewer, simpler stored items, and simpler derivations of linguistic expressions. This inference-based model is contrasted with four other models to which it will be compared throughout the book: (i) the full-parsing model where all structure is always computed, (ii) the full-listing model where all structure is stored after the first time it is computed and (iii) two variants of the exemplar-based model which hypothesizes all possible mixtures of computation and storage in the derivation of every expression.
Josef Nerb, Frank E. Ritter, and Pat Langley
- Published in print:
- 2007
- Published Online:
- April 2010
- ISBN:
- 9780195178845
- eISBN:
- 9780199893751
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195178845.003.0004
- Subject:
- Psychology, Cognitive Psychology
Science is concerned not only with data, but also with models or theories that explain those data. Because human cognition is dynamic and involves change over time, accounts of cognition often take ...
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Science is concerned not only with data, but also with models or theories that explain those data. Because human cognition is dynamic and involves change over time, accounts of cognition often take the form of process models, which are sometimes called cognitive models. This chapter reviews the form such models have taken and their relation to order effects in learning. It begins by discussing the connection between artificial intelligence (AI) systems, including those from machine learning and computational models of human behavior, including some illustrations of the latter. It presents a computational model of order effects on a cognitive task, cast within a particular but simplified theoretical framework. It then explores more broadly the possible sources of order effects within such models and then briefly considers an alternative approach that models human behavior at a more abstract level. The chapter closes with some open problems in the area of modeling order effects and a charge to new modelers.Less
Science is concerned not only with data, but also with models or theories that explain those data. Because human cognition is dynamic and involves change over time, accounts of cognition often take the form of process models, which are sometimes called cognitive models. This chapter reviews the form such models have taken and their relation to order effects in learning. It begins by discussing the connection between artificial intelligence (AI) systems, including those from machine learning and computational models of human behavior, including some illustrations of the latter. It presents a computational model of order effects on a cognitive task, cast within a particular but simplified theoretical framework. It then explores more broadly the possible sources of order effects within such models and then briefly considers an alternative approach that models human behavior at a more abstract level. The chapter closes with some open problems in the area of modeling order effects and a charge to new modelers.
