Biased competition in semantic representations across the human brain during category-based visual search
| buir.advisor | Çukur, Tolga | |
| dc.contributor.author | Shahdloo, Mohammad | |
| dc.date.accessioned | 2017-01-27T09:41:42Z | |
| dc.date.available | 2017-01-27T09:41:42Z | |
| dc.date.copyright | 2017-01 | |
| dc.date.issued | 2017-01 | |
| dc.date.submitted | 2017-01-23 | |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2017. | en_US |
| dc.description | Includes bibliographical references (leaves 49-55). | en_US |
| dc.description.abstract | Humans can perceive thousands of distinct object and action categories in the visual scene and successfully divide their attention among multiple target categories. It has been shown that object and action categories are represented in a continuous semantic map across the cortical surface and attending to a specific category causes broad shifts in voxel-wise semantic tuning profiles to enhance the representation of the target category. However, the effects of divided attention to multiple categories on semantic representation remain unclear. In line with predictions of the biased-competition model, recent evidence suggests that brain response to two objects presented simultaneously can be described as a weighted average of the responses to individual objects presented in isolation, and that attention biases these weights in favor of the target object. We question whether this biased-competition hypothesis can also account for attentional modulation of semantic representations. To address this question, we recorded participants’ BOLD responses while they performed category-based search in natural movies that contained 831 distinct objects and actions. Three different tasks were used: search for “humans”, search for “vehicles”, and search for “both humans and vehicles” (i.e. divided attention). Voxel-wise category models were fit separately under each task, and voxel-wise semantic tuning profiles were then obtained using a principal components analysis on the model weights. Semantic tuning profiles were compared across the single-target tasks and the divided-attention task. We find that in higher visual cortex a substantial portion of semantic tuning during divided attention can be expressed as a weighted average of the tuning profiles during attention to single targets. We also find that semantic tuning in categoryselective regions is biased towards the preferred object category. Overall, these results suggest that the biased-competition theory accounts for attentional modulation of semantic representations during natural visual search. | en_US |
| dc.description.provenance | Submitted by Betül Özen (ozen@bilkent.edu.tr) on 2017-01-27T09:41:42Z No. of bitstreams: 1 10136334.pdf: 34734578 bytes, checksum: 94089b0a39191dabda54170b9dc06969 (MD5) | en |
| dc.description.provenance | Made available in DSpace on 2017-01-27T09:41:42Z (GMT). No. of bitstreams: 1 10136334.pdf: 34734578 bytes, checksum: 94089b0a39191dabda54170b9dc06969 (MD5) Previous issue date: 2017-01 | en |
| dc.description.statementofresponsibility | by Mohammad Shahdloo. | en_US |
| dc.format.extent | xx, 66 leaves : illustrations, charts (some color) | en_US |
| dc.identifier.itemid | B155008 | |
| dc.identifier.uri | http://hdl.handle.net/11693/32622 | |
| dc.language.iso | English | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | FMRI | en_US |
| dc.subject | Visual perception | en_US |
| dc.subject | Attention | en_US |
| dc.subject | Biased-competition | en_US |
| dc.subject | Semantic representation | en_US |
| dc.title | Biased competition in semantic representations across the human brain during category-based visual search | en_US |
| dc.title.alternative | Kategori temelli görsel tarama esnasında beyindeki anlam temsillerinde oluşan taraflı rekabet etkileri | en_US |
| dc.type | Thesis | en_US |
| thesis.degree.discipline | Electrical and Electronic Engineering | |
| thesis.degree.grantor | Bilkent University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | MS (Master of Science) |