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Preface to the special issue on computational cognitive neuroscience
B RA IN RE S EA R CH 1 36 5 (2 0 1 0 ) 1 –2
available at www.sciencedirect.com
www.elsevier.com/locate/brainres
Introduction
Preface to the special issue on computational cognitive neuroscience Computational cognitive neuroscience is an emerging discipline that employs mathematical analysis and computational models to understand the neural bases of cognitive functions. The papers in this special issue are based on a selection of the best presentations at the fourth meeting of the Computational Cognitive Neuroscience (CCN) conference, which took place in Boston, Mass, November 19–22, 2009. The CCN conference focuses on research at the intersection of neuroscience, cognitive psychology, and computational modeling, where neuroscience-based computational models are used to simulate and understand cognitive functions such as learning, memory, attention, language, perception, decision making, and cognitive control. CCN research is having a growing impact in cognitive neuroscience because it complements traditional empirical approaches such as neuroimaging, cellular electrophysiology, and behavioural measurement. These traditional empirical methods span a wide range of levels of observation from the systems level to the cellular level. CCN research provides a means of bridging these disparate levels, by addressing the underlying neural mechanisms at the level of interacting neural circuits. Hence there is a growing appreciation of the value of combining theoretical models and empirical research. While empirical data tend to drive the initial design of models, theoretical models can in turn make powerful empirical predictions, leading to model refinement. Thus, a major goal of this conference is to encourage cross-disciplinary interactions between theoreticians and empiricists, across multiple levels of investigation within cognitive neuroscience. It is for this reason that the CCN conference has partnered with different host conferences each year, alternating between the annual meeting of the Psychonomics Society and that of the Society for Neuroscience. For this special issue, the CCN program committee reviewed all oral and poster presentations at the 2009 conference and invited a select group of authors to submit full-length papers for this special issue, based on their presentations. Selection criteria included a high quality presentation, a significant contribution to the field, a substantial computational modeling component, and a clear linkage between the neural and cognitive levels of explanation. All submitted papers underwent a rigorous reviewing process, receiving 2–3 reviews per article, resulting in five papers
finally being accepted for inclusion. The papers in this issue span several themes, including short-term and long-term memory, reward processing, decision-making, attention, and language. With the increasing maturation and sophistication of CCN as a discipline, many CCN researchers are now developing complex models encompassing multiple interacting brain and cognitive systems, and accounting for behaviour on multiple complex tasks. This trend is strongly reflected in the articles in this special issue. Shankar and Howard’s article “Timing using temporal context” extends Howard and Kahana's highly influential TCM model to address the issue of learning involving precise timing of events. Whereas TCM addressed contextual influences in serial order and free recall using a rather abstract mathematical framework, Shankar and Howard's new version of the model is further specified at a more neurobiologically relevant level of detail, and addresses the important problem of how neurons encode precise timing information, while linking cellular mechanisms to the systems level by addressing timing effects in Pavlovian conditioning. Cockburn and Holroyd's article “Focus on the positive: Computational simulations implicate asymmetrical reward prediction error signals in childhood Attention-Deficit/Hyperactivity Disorder” proposes a computational model of dopaminergic signaling in reward learning, and fits the model to several datasets from children with ADHD as well as animal models of ADHD. This allows the authors to assess the goodness-of-fit of several alternative accounts of altered dopaminergic signaling in ADHD. Contrary to several prominent theoretical accounts, the simulation results suggest that it is the asymmetric, relative scaling of positive versus negative reward prediction errors, rather than absolute levels of dopamine, which best explains observed behaviour. This article nicely highlights the key role that theoretical models can play in explaining complex behaviours at a mechanistic level, and in adjudicating between competing theories. Reinforcement learning also figures prominently in Sheynikhovich and Arleo’s article “A reinforcement learning approach to model interactions between landmarks and geometric cues during spatial learning,” where the contributions of multiple interacting brain systems to spatial learning is considered. A growing body of evidence links reward-based circuits in the dorsal striatum with response learning
2
B RA IN RE S EAR CH 1 36 5 (2 0 1 0) 1–2
strategies, while medial temporal lobe structures are linked to place-based locale strategies for spatial navigation. One issue emerging from these findings is whether these multiple systems interact competitively or co-operatively, with empirical data providing mixed support for both hypotheses. The model proposed by Sheynikovich and Arleo incorporates a prefrontal model capable of reinforcement-driven strategy learning that is able to account for these seemingly contradictory findings. While the models described so far have focused on tasks that span human and non-human animal behaviours, such as classical conditioning, reinforcement learning and spatial learning, the final two articles focus on human verbal memory and language representations respectively. Piquado, Cousins, Wingfield and Miller's article “Effects of degraded sensory input on memory for speech: Behavioral data and a test of biologically constrained computational models” investigates the ability of both short-term memory buffer models and longterm memory (“associative linking”) models to account for the effects of neighboring words in free recall, particularly in the case where some words are briefly presented and masked, and asymmetrically disrupt recall of words prior to but not subsequent to the masked word. This finding, first reported by Rabbit in 1968, is replicated here in a behavioural study. Detailed analyses of recall probabilities and transition probabilities from these data suggest several potential mechanisms that may explain the patterns of behavior observed, and are used to constrain model development. Simulations of these data with several alternative models support a hybrid working memory/long-term memory model combining both a short-term memory buffer and temporal associative linking. Finally, Dilkina and McClelland’s article “Are There Mental Lexicons? The Role of Semantics in Lexical Decision” addresses the issue of how words are represented, and whether there are two parallel routes to word recognition–a
lexical whole-word lookup route and a semantics route–or just a single system. Data from semantic dementia patients lend support to the latter view as they typically show deficits in lexical decision, and yet this is not consistently so. A singleroute model of word processing, similar to an earlier model developed by Plaut, is proposed that incorporates multiple, interactive levels of processing. Simulating damage to the semantic layer is shown to account for the diverse range of findings observed in patients with semantic dementia. For example, item-by-item analyses showed that, as in the SD patients, the model's lexical decision performance co-varied with word spelling consistency but not with performance on other semantic tasks, whereas the model's performance on semantic tasks co-varied with conceptual consistency. This small collection of articles provides a representative, if sparse, sample of the kinds of innovative contributions that computational modeling methods are making to our understanding of the biological basis of cognition. Taken together, these papers demonstrate the benefits that can be achieved by using formal mathematical and computational methods to characterize and assess theoretical positions in cognitive neuroscience. These benefits extend beyond precision and rigor to include truly novel insights, born of computational considerations, into the relationship between the brain and behavior. We hope that the work reported in this special issue will entice a broad range of experimentalists to further explore the exciting world of computational cognitive neuroscience.
Suzanna Becker David C. Noelle 0006-8993/$ - see front matter © 2010 Published by Elsevier B.V. doi:10.1016/j.brainres.2010.11.001
available at www.sciencedirect.com
www.elsevier.com/locate/brainres
Introduction
Preface to the special issue on computational cognitive neuroscience Computational cognitive neuroscience is an emerging discipline that employs mathematical analysis and computational models to understand the neural bases of cognitive functions. The papers in this special issue are based on a selection of the best presentations at the fourth meeting of the Computational Cognitive Neuroscience (CCN) conference, which took place in Boston, Mass, November 19–22, 2009. The CCN conference focuses on research at the intersection of neuroscience, cognitive psychology, and computational modeling, where neuroscience-based computational models are used to simulate and understand cognitive functions such as learning, memory, attention, language, perception, decision making, and cognitive control. CCN research is having a growing impact in cognitive neuroscience because it complements traditional empirical approaches such as neuroimaging, cellular electrophysiology, and behavioural measurement. These traditional empirical methods span a wide range of levels of observation from the systems level to the cellular level. CCN research provides a means of bridging these disparate levels, by addressing the underlying neural mechanisms at the level of interacting neural circuits. Hence there is a growing appreciation of the value of combining theoretical models and empirical research. While empirical data tend to drive the initial design of models, theoretical models can in turn make powerful empirical predictions, leading to model refinement. Thus, a major goal of this conference is to encourage cross-disciplinary interactions between theoreticians and empiricists, across multiple levels of investigation within cognitive neuroscience. It is for this reason that the CCN conference has partnered with different host conferences each year, alternating between the annual meeting of the Psychonomics Society and that of the Society for Neuroscience. For this special issue, the CCN program committee reviewed all oral and poster presentations at the 2009 conference and invited a select group of authors to submit full-length papers for this special issue, based on their presentations. Selection criteria included a high quality presentation, a significant contribution to the field, a substantial computational modeling component, and a clear linkage between the neural and cognitive levels of explanation. All submitted papers underwent a rigorous reviewing process, receiving 2–3 reviews per article, resulting in five papers
finally being accepted for inclusion. The papers in this issue span several themes, including short-term and long-term memory, reward processing, decision-making, attention, and language. With the increasing maturation and sophistication of CCN as a discipline, many CCN researchers are now developing complex models encompassing multiple interacting brain and cognitive systems, and accounting for behaviour on multiple complex tasks. This trend is strongly reflected in the articles in this special issue. Shankar and Howard’s article “Timing using temporal context” extends Howard and Kahana's highly influential TCM model to address the issue of learning involving precise timing of events. Whereas TCM addressed contextual influences in serial order and free recall using a rather abstract mathematical framework, Shankar and Howard's new version of the model is further specified at a more neurobiologically relevant level of detail, and addresses the important problem of how neurons encode precise timing information, while linking cellular mechanisms to the systems level by addressing timing effects in Pavlovian conditioning. Cockburn and Holroyd's article “Focus on the positive: Computational simulations implicate asymmetrical reward prediction error signals in childhood Attention-Deficit/Hyperactivity Disorder” proposes a computational model of dopaminergic signaling in reward learning, and fits the model to several datasets from children with ADHD as well as animal models of ADHD. This allows the authors to assess the goodness-of-fit of several alternative accounts of altered dopaminergic signaling in ADHD. Contrary to several prominent theoretical accounts, the simulation results suggest that it is the asymmetric, relative scaling of positive versus negative reward prediction errors, rather than absolute levels of dopamine, which best explains observed behaviour. This article nicely highlights the key role that theoretical models can play in explaining complex behaviours at a mechanistic level, and in adjudicating between competing theories. Reinforcement learning also figures prominently in Sheynikhovich and Arleo’s article “A reinforcement learning approach to model interactions between landmarks and geometric cues during spatial learning,” where the contributions of multiple interacting brain systems to spatial learning is considered. A growing body of evidence links reward-based circuits in the dorsal striatum with response learning
2
B RA IN RE S EAR CH 1 36 5 (2 0 1 0) 1–2
strategies, while medial temporal lobe structures are linked to place-based locale strategies for spatial navigation. One issue emerging from these findings is whether these multiple systems interact competitively or co-operatively, with empirical data providing mixed support for both hypotheses. The model proposed by Sheynikovich and Arleo incorporates a prefrontal model capable of reinforcement-driven strategy learning that is able to account for these seemingly contradictory findings. While the models described so far have focused on tasks that span human and non-human animal behaviours, such as classical conditioning, reinforcement learning and spatial learning, the final two articles focus on human verbal memory and language representations respectively. Piquado, Cousins, Wingfield and Miller's article “Effects of degraded sensory input on memory for speech: Behavioral data and a test of biologically constrained computational models” investigates the ability of both short-term memory buffer models and longterm memory (“associative linking”) models to account for the effects of neighboring words in free recall, particularly in the case where some words are briefly presented and masked, and asymmetrically disrupt recall of words prior to but not subsequent to the masked word. This finding, first reported by Rabbit in 1968, is replicated here in a behavioural study. Detailed analyses of recall probabilities and transition probabilities from these data suggest several potential mechanisms that may explain the patterns of behavior observed, and are used to constrain model development. Simulations of these data with several alternative models support a hybrid working memory/long-term memory model combining both a short-term memory buffer and temporal associative linking. Finally, Dilkina and McClelland’s article “Are There Mental Lexicons? The Role of Semantics in Lexical Decision” addresses the issue of how words are represented, and whether there are two parallel routes to word recognition–a
lexical whole-word lookup route and a semantics route–or just a single system. Data from semantic dementia patients lend support to the latter view as they typically show deficits in lexical decision, and yet this is not consistently so. A singleroute model of word processing, similar to an earlier model developed by Plaut, is proposed that incorporates multiple, interactive levels of processing. Simulating damage to the semantic layer is shown to account for the diverse range of findings observed in patients with semantic dementia. For example, item-by-item analyses showed that, as in the SD patients, the model's lexical decision performance co-varied with word spelling consistency but not with performance on other semantic tasks, whereas the model's performance on semantic tasks co-varied with conceptual consistency. This small collection of articles provides a representative, if sparse, sample of the kinds of innovative contributions that computational modeling methods are making to our understanding of the biological basis of cognition. Taken together, these papers demonstrate the benefits that can be achieved by using formal mathematical and computational methods to characterize and assess theoretical positions in cognitive neuroscience. These benefits extend beyond precision and rigor to include truly novel insights, born of computational considerations, into the relationship between the brain and behavior. We hope that the work reported in this special issue will entice a broad range of experimentalists to further explore the exciting world of computational cognitive neuroscience.
Suzanna Becker David C. Noelle 0006-8993/$ - see front matter © 2010 Published by Elsevier B.V. doi:10.1016/j.brainres.2010.11.001