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Kidder, A.

Publications and source records attributed to Kidder, A..

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Distinct subdivisions of human medial parietal cortex are recruited differentially for memory recall of places and people

Human medial parietal cortex (MPC) is implicated in multiple cognitive processes including memory recall, visual scene processing and navigation. It is also considered a core component of the default mode network. Here, we combine fMRI data across three independent experiments to demonstrate distinct subdivisions of MPC that are selectively recruited during memory recall of either specific places or specific people. First, distinct regions of MPC were identified on the basis of differential functional connectivity with medial and lateral regions of anterior ventral temporal cortex (VTC). Second, these same medial regions exhibited differential responses to the visual presentation of different stimulus categories, with clear preferences for scenes and faces, respectively. Third, and most critically, these regions were selectively recruited during either place or people memory recall. These subdivisions also showed a striking relationship with ventral and dorsal divisions of the default mode network. Taken together, these data reveal distinct subdivisions within MPC for the recall of places and people and moreover, suggest that the organizing principle defining the medial-lateral axis of VTC is reflected in MPC, but in the context of memory recall.

neuroscience

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

In recent years, the use of a large number of object concepts and naturalistic object images has been growing enormously in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually-curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of uncontrolled naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.

neuroscience