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Nicholas DeWind, Marty Woldorff, Elizabeth Brannon; Automatic Encoding of Visual Numerosity. Journal of Vision 2018;18(10):316. doi: https://doi.org/10.1167/18.10.316.
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The ability to approximately enumerate without counting emerges early in human development and is shared with non-human animals. Single-neuron recordings in monkeys and functional magnetic resonance imaging (fMRI) in humans have demonstrated that the intraparietal sulcus (IPS) and superior parietal lobule (SPL) are involved in representing the number of items in a display. Some of these experiments have required estimation or comparison of number, while others have only required participants to maintain attention without engaging in an explicit numerical task. There have also been reports of number encoding in early visual cortex (EVC), without a numerical task. Here we used an event-related fMRI design to test the role of explicit number comparison in shaping the cortical representations of number in parietal and early visual regions. Ten participants viewed arrays of dots that varied in both number and color. On half the trials in a run, participants made a decision about the color of the items and on the other half about their number. Participants also performed a localizer task to identify the parietal regions associated with numerical cognition. We used a support vector machine (SVM) to decode stimulus number from voxel-level signal intensity. The SVM was applied to the parietal ROI defined by our subject-specific localizer, as well as by atlas-defined ROIs estimating the location of retinotopic maps in V1, V2, V3, and IPS/SPL. The results showed that we could decode number in all 5 ROIs during both the number and color discrimination tasks. We found that there was no significant effect of task (number vs. color) on this decoding in any ROI. These findings indicate that number is encoded automatically in both early visual and parietal areas when people attend to an array, regardless of the feature they are discriminating.
Meeting abstract presented at VSS 2018
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