Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism.
Research shows that semantics, activated by words, impacts object detection. Skocypec & Peterson (2022) indexed object detection via correct reports of where figures lie in bipartite displays depicting familiar objects on one side of a border. They reported 2 studies with intermixed Valid and In...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012269 |
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| author | Jingming Xue Robert C Wilson Mary A Peterson |
| author_facet | Jingming Xue Robert C Wilson Mary A Peterson |
| author_sort | Jingming Xue |
| collection | DOAJ |
| description | Research shows that semantics, activated by words, impacts object detection. Skocypec & Peterson (2022) indexed object detection via correct reports of where figures lie in bipartite displays depicting familiar objects on one side of a border. They reported 2 studies with intermixed Valid and Invalid labels shown before test displays and a third, control, study. Valid labels denoted display objects. Invalid labels denoted unrelated objects in a different or the same superordinate-level category in studies 1 & 2, respectively. We used drift diffusion modeling (DDM) to elucidate the mechanisms of their results. DDM revealed that, following Valid labels, drift rate toward the correct decision increased, i.e., SNR increased. Invalid labels do not affect drift rate directly, but they create a context that diminishes the facilitative effect of valid labels on evidence accumulation. Threshold was higher in study 2 than control, but not in study 1. That more evidence must be accumulated from displays that follow labels denoting objects in the same-superordinate category as the object in the display indicates that more evidence from the display is needed to resolve semantic uncertainty regarding which object is present. These results support the view that semantic networks are engaged in object detection. |
| format | Article |
| id | doaj-art-aa3bd114c2414b8b802fd776ba578ef2 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-aa3bd114c2414b8b802fd776ba578ef22025-08-20T02:38:21ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-06-01216e101226910.1371/journal.pcbi.1012269Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism.Jingming XueRobert C WilsonMary A PetersonResearch shows that semantics, activated by words, impacts object detection. Skocypec & Peterson (2022) indexed object detection via correct reports of where figures lie in bipartite displays depicting familiar objects on one side of a border. They reported 2 studies with intermixed Valid and Invalid labels shown before test displays and a third, control, study. Valid labels denoted display objects. Invalid labels denoted unrelated objects in a different or the same superordinate-level category in studies 1 & 2, respectively. We used drift diffusion modeling (DDM) to elucidate the mechanisms of their results. DDM revealed that, following Valid labels, drift rate toward the correct decision increased, i.e., SNR increased. Invalid labels do not affect drift rate directly, but they create a context that diminishes the facilitative effect of valid labels on evidence accumulation. Threshold was higher in study 2 than control, but not in study 1. That more evidence must be accumulated from displays that follow labels denoting objects in the same-superordinate category as the object in the display indicates that more evidence from the display is needed to resolve semantic uncertainty regarding which object is present. These results support the view that semantic networks are engaged in object detection.https://doi.org/10.1371/journal.pcbi.1012269 |
| spellingShingle | Jingming Xue Robert C Wilson Mary A Peterson Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism. PLoS Computational Biology |
| title | Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism. |
| title_full | Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism. |
| title_fullStr | Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism. |
| title_full_unstemmed | Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism. |
| title_short | Semantic influences on object detection: Drift diffusion modeling provides insights regarding mechanism. |
| title_sort | semantic influences on object detection drift diffusion modeling provides insights regarding mechanism |
| url | https://doi.org/10.1371/journal.pcbi.1012269 |
| work_keys_str_mv | AT jingmingxue semanticinfluencesonobjectdetectiondriftdiffusionmodelingprovidesinsightsregardingmechanism AT robertcwilson semanticinfluencesonobjectdetectiondriftdiffusionmodelingprovidesinsightsregardingmechanism AT maryapeterson semanticinfluencesonobjectdetectiondriftdiffusionmodelingprovidesinsightsregardingmechanism |