Human medial parietal cortex (MPC) is implicated in multiple cognitive processes including memory recall, visual scene processing and navigation, and is a core component of the default mode network. Here, we demonstrate distinct subdivisions of MPC that are selectively recruited during memory recall of either specific people or places. First, distinct regions of MPC exhibited differential functional connectivity with medial and lateral regions of ventral temporal cortex (VTC). Second, these same medial regions showed selective, but negative, responses to the visual presentation of different stimulus categories, with clear preferences for scenes and faces. Finally, and most critically, these regions were differentially recruited during memory recall of either people or places with a strong familiarity advantage. Taken together, these data suggest that the organizing principle defining the medial-lateral axis of VTC is reflected in MPC, but in the context of memory recall.

In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly 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 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.

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.

Human retrosplenial complex (RSC), located in medial parietal cortex, has been implicated in numerous cognitive functions, including scene perception, spatial navigation, and autobiographical memory retrieval. Recently, a posterior-anterior distinction within RSC was proposed, such that posterior aspects process scene-related visual information (constituting a medial place area [MPA]), whereas anterior aspects process information that is vividly retrieved from memory, thereby supporting remembering and potentially navigation. Here, we tested this proposed distinction in a single group of participants (both male and female) using fMRI with both perceptual and mnemonic tasks. After completing a resting-state scan, participants performed a task that required constructing scenes from memory and completed a scene selectivity localizer task. We tested directly perceptual and mnemonic responses in MPA and an anterior, connectivity-defined region (CON), which showed strong functional connectivity with anterior parahippocampal place area. A double dissociation was observed, such that CON was more strongly activated during scene construction than was MPA, whereas MPA was more perceptually responsive than CON. Further, peak responses from the scene construction task were anterior to perceptual peaks in all but 1 participant and hemisphere. Finally, through analyses of the posterior-anterior response profiles, we identify the fundus of the parieto-occipital sulcus as a potential location for the crossover from perceptual to mnemonic representations and highlight a potential left-hemisphere advantage for mnemonic representations. Collectively, our results support a distinction between posterior and anterior aspects of the RSC, suggesting that more specific functional-anatomic terms should be used in its place in future work.