Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention
We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabl...
| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
American Psychological Association
2011
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| Online Access: | https://eprints.nottingham.ac.uk/27756/ |
| _version_ | 1848793430393618432 |
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| author | Mavritsaki, Eirini Heinke, Dietmar Allen, Harriet A. Deco, Gustavo Humphreys, Glyn W. |
| author_facet | Mavritsaki, Eirini Heinke, Dietmar Allen, Harriet A. Deco, Gustavo Humphreys, Glyn W. |
| author_sort | Mavritsaki, Eirini |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data. |
| first_indexed | 2025-11-14T19:00:10Z |
| format | Article |
| id | nottingham-27756 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:00:10Z |
| publishDate | 2011 |
| publisher | American Psychological Association |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-277562020-05-04T20:24:00Z https://eprints.nottingham.ac.uk/27756/ Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention Mavritsaki, Eirini Heinke, Dietmar Allen, Harriet A. Deco, Gustavo Humphreys, Glyn W. We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance—enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data. American Psychological Association 2011 Article PeerReviewed Mavritsaki, Eirini, Heinke, Dietmar, Allen, Harriet A., Deco, Gustavo and Humphreys, Glyn W. (2011) Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention. Psychological Review, 118 (1). pp. 3-41. ISSN 0033-295X http://dx.doi.org/10.1037/a0021868 doi:10.1037/a0021868 doi:10.1037/a0021868 |
| spellingShingle | Mavritsaki, Eirini Heinke, Dietmar Allen, Harriet A. Deco, Gustavo Humphreys, Glyn W. Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention |
| title | Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention |
| title_full | Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention |
| title_fullStr | Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention |
| title_full_unstemmed | Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention |
| title_short | Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention |
| title_sort | bridging the gap between physiology and behavior: evidence from the ssots model of human visual attention |
| url | https://eprints.nottingham.ac.uk/27756/ https://eprints.nottingham.ac.uk/27756/ https://eprints.nottingham.ac.uk/27756/ |