Model based analysis of fMRI-data: Applying the sSoTS framework to the neural basic of preview search.
The current work aims to unveil the neural circuits under- lying visual search over time and space by using a model-based analysis of behavioural and fMRI data. It has been suggested by Watson and Humphreys [31] that the prioritization of new stimuli presented in our visual field can be helped by th...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Book Section |
| Published: |
Springer
2009
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| Online Access: | https://eprints.nottingham.ac.uk/32326/ |
| _version_ | 1848794385225875456 |
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| author | Mavritsaki, Eirini Allen, Harriet A. Humphreys, Glyn W. |
| author2 | Paletta, Lucas |
| author_facet | Paletta, Lucas Mavritsaki, Eirini Allen, Harriet A. Humphreys, Glyn W. |
| author_sort | Mavritsaki, Eirini |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The current work aims to unveil the neural circuits under- lying visual search over time and space by using a model-based analysis of behavioural and fMRI data. It has been suggested by Watson and Humphreys [31] that the prioritization of new stimuli presented in our visual field can be helped by the active ignoring of old items, a process they termed visual marking. Studies using fMRI link the marking pro- cess with activation in superior parietal areas and the precuneus [4, 18, 27, 26]. Marking has been simulated previously using a neural-level ac- count of search, the spiking Search over Time and Space (sSoTS) model, which incorporates inhibitory as well as excitatory mechanisms to guide visual selection. Here we used sSoTS to help decompose the fMRI signals found in a preview search procedure, when participants search for a new target whilst ignoring old distractors. The time course of activity linked to inhibitory and excitatory processes in the model was used as a regres- sor for the fMRI data. The results showed that different neural networks were correlated with top-down excitation and top-down inhibition in the model, enabling us to fractionate brain regions previously linked to vi- sual marking. We discuss the contribution of model-based analysis for decomposing fMRI data. |
| first_indexed | 2025-11-14T19:15:21Z |
| format | Book Section |
| id | nottingham-32326 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:15:21Z |
| publishDate | 2009 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-323262020-05-04T20:26:54Z https://eprints.nottingham.ac.uk/32326/ Model based analysis of fMRI-data: Applying the sSoTS framework to the neural basic of preview search. Mavritsaki, Eirini Allen, Harriet A. Humphreys, Glyn W. The current work aims to unveil the neural circuits under- lying visual search over time and space by using a model-based analysis of behavioural and fMRI data. It has been suggested by Watson and Humphreys [31] that the prioritization of new stimuli presented in our visual field can be helped by the active ignoring of old items, a process they termed visual marking. Studies using fMRI link the marking pro- cess with activation in superior parietal areas and the precuneus [4, 18, 27, 26]. Marking has been simulated previously using a neural-level ac- count of search, the spiking Search over Time and Space (sSoTS) model, which incorporates inhibitory as well as excitatory mechanisms to guide visual selection. Here we used sSoTS to help decompose the fMRI signals found in a preview search procedure, when participants search for a new target whilst ignoring old distractors. The time course of activity linked to inhibitory and excitatory processes in the model was used as a regres- sor for the fMRI data. The results showed that different neural networks were correlated with top-down excitation and top-down inhibition in the model, enabling us to fractionate brain regions previously linked to vi- sual marking. We discuss the contribution of model-based analysis for decomposing fMRI data. Springer Paletta, Lucas Tsotsos, John K. 2009 Book Section PeerReviewed Mavritsaki, Eirini, Allen, Harriet A. and Humphreys, Glyn W. (2009) Model based analysis of fMRI-data: Applying the sSoTS framework to the neural basic of preview search. In: Attention in Cognitive Systems : 5th International Workshop on Attention in Cognitive Systems, WAPCV 2008, Fira, Santorini, Greece, May 12, 2008 : revised selected papers. Lecture Notes in Computer Science (5395). Springer, Berlin, pp. 124-138. ISBN 9783642005817, 9783642005824 http://link.springer.com/chapter/10.1007%2F978-3-642-00582-4_10 doi:10.1007/978-3-642-00582-4_10 doi:10.1007/978-3-642-00582-4_10 |
| spellingShingle | Mavritsaki, Eirini Allen, Harriet A. Humphreys, Glyn W. Model based analysis of fMRI-data: Applying the sSoTS framework to the neural basic of preview search. |
| title | Model based analysis of fMRI-data: Applying the sSoTS framework
to the neural basic of preview search. |
| title_full | Model based analysis of fMRI-data: Applying the sSoTS framework
to the neural basic of preview search. |
| title_fullStr | Model based analysis of fMRI-data: Applying the sSoTS framework
to the neural basic of preview search. |
| title_full_unstemmed | Model based analysis of fMRI-data: Applying the sSoTS framework
to the neural basic of preview search. |
| title_short | Model based analysis of fMRI-data: Applying the sSoTS framework
to the neural basic of preview search. |
| title_sort | model based analysis of fmri-data: applying the ssots framework
to the neural basic of preview search. |
| url | https://eprints.nottingham.ac.uk/32326/ https://eprints.nottingham.ac.uk/32326/ https://eprints.nottingham.ac.uk/32326/ |