Probing the Role of Feature Dimension Maps in Visual Cognition: Impact of Salience Level (Eye-tracking Follow-up Study)
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 1.1 (Behavioral)
How does one know what to look at in a scene? Imagine a "Where's Waldo" game - it's challenging to find Waldo because there are many 'salient' locations in the picture, each vying for one's attention. One can only attend to a small location on the picture at a given moment, so to find Waldo, one needs to direct their attention to different locations. One prominent theory about how one accomplishes this claims that important locations are identified based on distinct feature types (for example, motion or color), with locations most unique compared to the background most likely to be attended. An important component of this theory is that individual feature dimensions (again, color or motion) are computed within their own 'feature maps', which are thought to be implemented in specific brain regions. However, whether and how specific brain regions contribute to these feature maps remains unknown.
The goal of this study is to determine how brain regions that respond strongly to different feature types (color and motion) and which encode spatial locations of visual stimuli extract 'feature dimension maps' based on stimulus properties, including feature contrast. The investigators hypothesize that feature-selective brain regions act as neural feature dimension maps, and thus encode representations of salient location(s) based on their preferred feature dimension. The investigators will collect eye-tracking data while participants view visual stimuli made salient based on different combinations of feature dimensions. From the eye-tracking data, the investigators will construct fixation heat maps on the feature dimensions for all levels of salience, allowing them to connect behavioral data to the latter fMRI dataset. Each participant will freely view the stimuli as they appear on the computer display. Across trials, the investigators will manipulate 1) the 'strength' of the salient locations based on how different the salient stimulus is compared to the background, 2) the number of salient locations, and 3) the feature value(s) used to make each location salient. Altogether, these manipulations will help the investigators fully understand these critical salience computations in the healthy human visual system.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Locations
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California
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Santa Barbara, California, United States, 93117
- University of California, Santa Barbara
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- between 18 and 55 years of age
- normal or corrected-to-normal vision
Exclusion Criteria:
- N/A
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Basic Science
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
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Experimental: Manipulations of graded feature salience (Expt 1.1)
Participants will view stimuli made salient based on feature contrast in one feature dimensions (color or motion direction; or checkerboard luminance contrast).
The degree to which a location is salient will be manipulated based on the feature contrast across multiple values
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The feature used to define a salient location will be varied across trials (checkerboard contrast; motion direction; color hue)
The magnitude of the salient location will be varied across trials independently from salience-defining feature (based on feature contrast)
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
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Gaze Position - Probability of Fixation at Salient Position
Time Frame: Through study completion, an average of one week
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For each condition, we compute the probability that participants instructed to free-view the display fixate the salient location on the screen (reported for highest contrast condition)
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Through study completion, an average of one week
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Tommy Sprague, University of California, Santa Barbara
Publications and helpful links
General Publications
- Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Gilmore AW, McDermott KB, Nelson SM, Dosenbach NU, Schlaggar BL, Mumford JA, Poldrack RA, Petersen SE. Functional System and Areal Organization of a Highly Sampled Individual Human Brain. Neuron. 2015 Aug 5;87(3):657-70. doi: 10.1016/j.neuron.2015.06.037. Epub 2015 Jul 23.
- Mackey WE, Winawer J, Curtis CE. Visual field map clusters in human frontoparietal cortex. Elife. 2017 Jun 19;6:e22974. doi: 10.7554/eLife.22974.
- Hallenbeck GE, Sprague TC, Rahmati M, Sreenivasan KK, Curtis CE. Working memory representations in visual cortex mediate distraction effects. Nat Commun. 2021 Aug 5;12(1):4714. doi: 10.1038/s41467-021-24973-1.
- Sprague TC, Itthipuripat S, Vo VA, Serences JT. Dissociable signatures of visual salience and behavioral relevance across attentional priority maps in human cortex. J Neurophysiol. 2018 Jun 1;119(6):2153-2165. doi: 10.1152/jn.00059.2018. Epub 2018 Feb 28.
- Sprague TC, Adam KCS, Foster JJ, Rahmati M, Sutterer DW, Vo VA. Inverted Encoding Models Assay Population-Level Stimulus Representations, Not Single-Unit Neural Tuning. eNeuro. 2018 Jun 5;5(3):ENEURO.0098-18.2018. doi: 10.1523/ENEURO.0098-18.2018. eCollection 2018 May-Jun. No abstract available.
- Sprague TC, Boynton GM, Serences JT. The Importance of Considering Model Choices When Interpreting Results in Computational Neuroimaging. eNeuro. 2019 Dec 20;6(6):ENEURO.0196-19.2019. doi: 10.1523/ENEURO.0196-19.2019. Print 2019 Nov/Dec.
- Allen EJ, St-Yves G, Wu Y, Breedlove JL, Prince JS, Dowdle LT, Nau M, Caron B, Pestilli F, Charest I, Hutchinson JB, Naselaris T, Kay K. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat Neurosci. 2022 Jan;25(1):116-126. doi: 10.1038/s41593-021-00962-x. Epub 2021 Dec 16.
- Naselaris T, Allen E, Kay K. Extensive sampling for complete models of individual brains. Current Opinion in Behavioral Sciences. 2021; 40:45-51.
- Poldrack RA. Diving into the deep end: a personal reflection on the MyConnectome study. Current Opinion in Behavioral Sciences. 2021; 40:1-4.
- Pritschet L, Taylor CM, Santander T, Jacobs EG. Applying dense-sampling methods to reveal dynamic endocrine modulation of the nervous system. Curr Opin Behav Sci. 2021 Aug;40:72-78. doi: 10.1016/j.cobeha.2021.01.012. Epub 2021 Feb 25.
- Gratton C, Nelson SM, Gordon EM. Brain-behavior correlations: Two paths toward reliability. Neuron. 2022 May 4;110(9):1446-1449. doi: 10.1016/j.neuron.2022.04.018.
- Smith PL, Little DR. Small is beautiful: In defense of the small-N design. Psychon Bull Rev. 2018 Dec;25(6):2083-2101. doi: 10.3758/s13423-018-1451-8.
- Sprague TC, Serences JT. Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices. Nat Neurosci. 2013 Dec;16(12):1879-87. doi: 10.1038/nn.3574. Epub 2013 Nov 10.
- Itthipuripat S, Vo VA, Sprague TC, Serences JT. Value-driven attentional capture enhances distractor representations in early visual cortex. PLoS Biol. 2019 Aug 9;17(8):e3000186. doi: 10.1371/journal.pbio.3000186. eCollection 2019 Aug.
- Poltoratski S, Tong F. Resolving the Spatial Profile of Figure Enhancement in Human V1 through Population Receptive Field Modeling. J Neurosci. 2020 Apr 15;40(16):3292-3303. doi: 10.1523/JNEUROSCI.2377-19.2020. Epub 2020 Mar 5.
- Poltoratski S, Ling S, McCormack D, Tong F. Characterizing the effects of feature salience and top-down attention in the early visual system. J Neurophysiol. 2017 Jul 1;118(1):564-573. doi: 10.1152/jn.00924.2016. Epub 2017 Apr 5.
- Fedorenko E. The early origins and the growing popularity of the individualsubject analytic approach in human neuroscience. Current Opinion in Behavioral Sciences. 2021; 40:105-112.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- 5-24-0700: Expt 1.1 Behavioral
- R01EY035300 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
- SAP
- ICF
- ANALYTIC_CODE
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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