Computational Characterization of Decision Making During Trans-saccadic Visual Perception
<p dir="ltr">When sampling visual information from the environment, humans execute fast sequential saccadic eye movements and yet preserve stability in their visual percept. This ability relies on the continuous integration of external sensory information (e.g., visual displacements)...
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2025
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| _version_ | 1849927637195030528 |
|---|---|
| author | Pierre Gianferrara (22216921) |
| author2 | Wilsaan M. Joiner (4011668) |
| author2_role | author |
| author_facet | Pierre Gianferrara (22216921) Wilsaan M. Joiner (4011668) |
| author_role | author |
| dc.creator.none.fl_str_mv | Pierre Gianferrara (22216921) Wilsaan M. Joiner (4011668) |
| dc.date.none.fl_str_mv | 2025-11-25T01:32:40Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30090127.v5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Computational_Characterization_of_Decision_Making_During_Trans-saccadic_Visual_Perception/30090127 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neurosciences not elsewhere classified eye movements decision-making perception drift diffusion model |
| dc.title.none.fl_str_mv | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p dir="ltr">When sampling visual information from the environment, humans execute fast sequential saccadic eye movements and yet preserve stability in their visual percept. This ability relies on the continuous integration of external sensory information (e.g., visual displacements) and internal monitoring signals. At the time of the saccade subjects often experience saccadic suppression of displacement—a failure to notice changes in the location of visual objects slightly shifted during the eye movement. Although well studied, questions remain regarding <i>how </i>and <i>when</i> external sensory signals lead to perceptual judgment alterations following saccadic eye movements. Here we used a drift diffusion modeling (DDM) framework to systematically examine the extent to which sensory information biased perceptual judgments when detecting trans-saccadic shifts of visual targets. Healthy human participants (<i>N </i>= 30, 21 female) completed a visual perception task in which a visual target was shifted following a prompted saccadic eye movement (4<sup>o</sup> or 8<sup>o</sup>). Target displacements occurred up to <u>+</u>2.5<sup>o</sup> along the horizontal axis, and participants reported the shift direction with a button press response. Incorporating the perceptual response and visual error between the displaced target and end-movement fixation location, the DDM accounted for the visuospatial biasing effects of sensory processing on decision-making. The modelling framework based on the visual error could also capture individual positive and negative biasing effects across backward shifts (i.e., towards the initial fixation location) and forward shifts (i.e., further away from initial fixation). Furthermore, participants whose perceptual bias was more pronounced in either the backward or forward directions tended to show a greater visuospatial offset in evidence accumulation preceding perceptual judgments due to an overreliance on the visual error information. The DDM computational modeling approach described here shows promise as an explanatory framework to account for sensorimotor integration impairments based on perceptual deficits demonstrated in certain patient populations.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_5729aa617282cd8e1453dcc5d8ba2c48 |
| identifier_str_mv | 10.6084/m9.figshare.30090127.v5 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30090127 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Computational Characterization of Decision Making During Trans-saccadic Visual PerceptionPierre Gianferrara (22216921)Wilsaan M. Joiner (4011668)Neurosciences not elsewhere classifiedeye movementsdecision-makingperceptiondrift diffusion model<p dir="ltr">When sampling visual information from the environment, humans execute fast sequential saccadic eye movements and yet preserve stability in their visual percept. This ability relies on the continuous integration of external sensory information (e.g., visual displacements) and internal monitoring signals. At the time of the saccade subjects often experience saccadic suppression of displacement—a failure to notice changes in the location of visual objects slightly shifted during the eye movement. Although well studied, questions remain regarding <i>how </i>and <i>when</i> external sensory signals lead to perceptual judgment alterations following saccadic eye movements. Here we used a drift diffusion modeling (DDM) framework to systematically examine the extent to which sensory information biased perceptual judgments when detecting trans-saccadic shifts of visual targets. Healthy human participants (<i>N </i>= 30, 21 female) completed a visual perception task in which a visual target was shifted following a prompted saccadic eye movement (4<sup>o</sup> or 8<sup>o</sup>). Target displacements occurred up to <u>+</u>2.5<sup>o</sup> along the horizontal axis, and participants reported the shift direction with a button press response. Incorporating the perceptual response and visual error between the displaced target and end-movement fixation location, the DDM accounted for the visuospatial biasing effects of sensory processing on decision-making. The modelling framework based on the visual error could also capture individual positive and negative biasing effects across backward shifts (i.e., towards the initial fixation location) and forward shifts (i.e., further away from initial fixation). Furthermore, participants whose perceptual bias was more pronounced in either the backward or forward directions tended to show a greater visuospatial offset in evidence accumulation preceding perceptual judgments due to an overreliance on the visual error information. The DDM computational modeling approach described here shows promise as an explanatory framework to account for sensorimotor integration impairments based on perceptual deficits demonstrated in certain patient populations.</p>2025-11-25T01:32:40ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30090127.v5https://figshare.com/articles/dataset/Computational_Characterization_of_Decision_Making_During_Trans-saccadic_Visual_Perception/30090127CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300901272025-11-25T01:32:40Z |
| spellingShingle | Computational Characterization of Decision Making During Trans-saccadic Visual Perception Pierre Gianferrara (22216921) Neurosciences not elsewhere classified eye movements decision-making perception drift diffusion model |
| status_str | publishedVersion |
| title | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| title_full | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| title_fullStr | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| title_full_unstemmed | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| title_short | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| title_sort | Computational Characterization of Decision Making During Trans-saccadic Visual Perception |
| topic | Neurosciences not elsewhere classified eye movements decision-making perception drift diffusion model |