Schubotz et al. (2014)

Schubotz, R.I., Wurm, M.F., Wittmann, M., & von Cramon, D.Y. (2014). Objects tell us what action we can expect: Dissociating brain areas for retrieval and exploitation of action knowledge during action observation in fMRI. Frontiers in Psychology, 5, 636. doi: 10.3389/fpsyg.2014.00636.

Objects are reminiscent of actions often performed with them: knife and apple remind us on peeling the apple or cutting it. Mnemonic representations of object-related actions (action codes) evoked by the sight of an object may constrain and hence facilitate recognition of unrolling actions. The present fMRI study investigated if and how action codes influence brain activation during action observation. The average number of action codes (NAC) of 51 sets of objects was rated by a group of n = 24 participants. In an fMRI study, different volunteers were asked to recognize actions performed with the same objects presented in short videos. To disentangle areas reflecting the storage of action codes from those exploiting them, we showed object-compatible and object-incompatible (pantomime) actions. Areas storing action codes were considered to positively co-vary with NAC in both object-compatible and object-incompatible action; due to its role in tool-related tasks, we here hypothesized left anterior inferior parietal cortex (aIPL). In contrast, areas exploiting action codes were expected to show this correlation only in object-compatible but not incompatible action, as only object-compatible actions match one of the active action codes. For this interaction, we hypothesized ventrolateral premotor cortex (PMv) to join aIPL due to its role in biasing competition in IPL. We found left anterior intraparietal sulcus (IPS) and left posterior middle temporal gyrus (pMTG) to co-vary with NAC. In addition to these areas, action codes increased activity in object-compatible action in bilateral PMv, right IPS, and lateral occipital cortex (LO). Findings suggest that during action observation, the brain derives possible actions from perceived objects, and uses this information to shape action recognition. In particular, the number of expectable actions quantifies the activity level at PMv, IPL, and pMTG, but only PMv reflects their biased competition while observed action unfolds.

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