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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: Decoding stimuli (tool-hand) and viewpoint invariant grasp-type information.
    Author: Bergström F, Wurm M, Valério D, Lingnau A, Almeida J.
    Journal: Cortex; 2021 Jun; 139():152-165. PubMed ID: 33873036.
    Abstract:
    When we see a manipulable object (henceforth tool) or a hand performing a grasping movement, our brain is automatically tuned to how that tool can be grasped (i.e., its affordance) or what kind of grasp that hand is performing (e.g., a power or precision grasp). However, it remains unclear where visual information related to tools or hands are transformed into abstract grasp representations. We therefore investigated where different levels of abstractness in grasp information are processed: grasp information that is invariant to the kind of stimuli that elicits it (tool-hand invariance); and grasp information that is hand-specific but viewpoint-invariant (viewpoint invariance). We focused on brain areas activated when viewing both tools and hands, i.e., the posterior parietal cortices (PPC), ventral premotor cortices (PMv), and lateral occipitotemporal cortex/posterior middle temporal cortex (LOTC/pMTG). To test for invariant grasp representations, we presented participants with tool images and grasp videos (from first or third person perspective; 1pp or 3pp) inside an MRI scanner, and cross-decoded power versus precision grasps across (i) grasp perspectives (viewpoint invariance), (ii) tool images and grasp 1pp videos (tool-hand 1pp invariance), and (iii) tool images and grasp 3pp videos (tool-hand 3pp invariance). Tool-hand 1pp, but not tool-hand 3pp, invariant grasp information was found in left PPC, whereas viewpoint-invariant information was found bilaterally in PPC, left PMv, and left LOTC/pMTG. These findings suggest different levels of abstractness-where visual information is transformed into stimuli-invariant grasp representations/tool affordances in left PPC, and viewpoint invariant but hand-specific grasp representations in the hand network.
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