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Tools such as pens, forks, and scissors play an important role in many daily-life activities, an importance underscored by the presence in visual cortex of a set of tool-selective brain regions. This review synthesizes decades of neuroimaging research that investigated the representational spaces in the visual ventral stream for objects, such as tools, that are specifically characterized by action-related properties. Overall, results reveal a dissociation between representational spaces in ventral and lateral occipito-temporal cortex (OTC). While lateral OTC encodes both visual (shape) and action-related properties of objects, distinguishing between objects acting as end-effectors (e.g., tools, hands) versus similar noneffector manipulable objects (e.g., a glass), ventral OTC primarily represents objects' visual features such as their surface properties (e.g., material and texture). These areas act in concert with regions outside of OTC to support object interaction and tool use. The parallel investigation of the dimensions underlying object representations in artificial neural networks reveals both the possibilities and the difficulties in capturing the action-related dimensions that distinguish tools from other objects. Although artificial neural networks offer promise as models of visual cortex computations, challenges persist in replicating the action-related dimensions that go beyond mere visual features. Taken together, we propose that regions in OTC support the representation of tools based on a behaviorally relevant action code and suggest future paths to generate a computational model of this object space. [ABSTRACT FROM AUTHOR]

In everyday life, we frequently engage in 'hybrid' visual and memory search, where we look for multiple items stored in memory (e.g., a mental shopping list) in our visual environment. Across three experiments, we used event-related potentials to better understand the contributions of visual working memory (VWM) and long-term memory (LTM) during the memory search component of hybrid search. Experiments 1 and 2 demonstrated that the FN400 (an index of LTM recognition) and the CDA (an index of VWM load) increased with memory set size (target load), suggesting that both VWM and LTM are involved in memory search, even when target load exceeds capacity limitations of VWM. In Experiment 3, we used these electrophysiological indices to test how categorical similarity of targets and distractors affects memory search. The CDA and FN400 were modulated by memory set size only if items resembled targets. This suggests that dissimilar distractor items can be rejected before eliciting a memory search. Together, our findings demonstrate the interplay of VWM and LTM processes during memory search for multiple targets. [ABSTRACT FROM AUTHOR]

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed taskspecific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing. [ABSTRACT FROM AUTHOR]