How neural specificity for distinct conceptual knowledge categories arises is central for understanding the organization of semantic memory in the human brain. Although there is a large body of research on the neural processing of distinct object categories, the organization of action categories remains largely unknown. In particular, it is unknown whether different action categories follow a specific topographical organization on the cortical surface analogously to the category-specific organization of object knowledge. Here, we tested whether the neural representation of action knowledge is organized in terms of nonsocial versus social and object-unrelated versus object-related actions (sociality and transitivity, respectively, hereafter). We hypothesized a major distinction of sociality and transitivity along dorsal and ventral lateral occipitotemporal cortex (LOTC), respectively. Using fMRI-based multivoxel pattern analysis, we identified neural representations of action information associated with sociality and transitivity in bilateral LOTC. Representational similarity analysis revealed a dissociation between dorsal and ventral LOTC. We found that action representations in dorsal LOTC are segregated along features of sociality, whereas action representations in ventral LOTC are segregated along features of transitivity. In addition, representations of sociality and transitivity features were found more anteriorly in LOTC than representations of specific subtypes of actions, suggesting a posterior-anterior gradient from concrete to abstract action features. These findings elucidate how the neural representations of perceptually and conceptually diverse actions are organized in distinct subsystems in the LOTC.

Brain regions that mediate action understanding must contain representations that are action specific and at the same time tolerate a wide range of perceptual variance. Whereas progress has been made in understanding such generalization mechanisms in the object domain, the neural mechanisms to conceptualize actions remain unknown. In particular, there is ongoing dissent between motor-centric and cognitive accounts whether premotor cortex or brain regions in closer relation to perceptual systems, i.e., lateral occipitotemporal cortex, contain neural populations with such mapping properties. To date, it is unclear to which degree action-specific representations in these brain regions generalize from concrete action instantiations to abstract action concepts. However, such information would be crucial to differentiate between motor and cognitive theories. Using ROI-based and searchlight-based fMRI multivoxel pattern decoding, we sought brain regions in human cortex that manage the balancing act between specificity and generality. We investigated a concrete level that distinguishes actions based on perceptual features (e.g., opening vs closing a specific bottle), an intermediate level that generalizes across movement kinematics and specific objects involved in the action (e.g., opening different bottles with cork or screw cap), and an abstract level that additionally generalizes across object category (e.g., opening bottles or boxes). We demonstrate that the inferior parietal and occipitotemporal cortex code actions at abstract levels whereas the premotor cortex codes actions at the concrete level only. Hence, occipitotemporal, but not premotor, regions fulfill the necessary criteria for action understanding. This result is compatible with cognitive theories but strongly undermines motor theories of action understanding.

Both temporal and frontoparietal brain areas are associated with the representation of knowledge about the world, in particular about actions. However, what these brain regions represent and precisely how they differ remains unknown. Here, we reveal distinct functional profiles of lateral temporal and frontoparietal cortex using fMRI-based MVPA. Frontoparietal areas encode representations of observed actions and corresponding written sentences in an overlapping way, but these representations do not generalize across stimulus type. By contrast, only left lateral posterior temporal cortex (LPTC) encodes action representations that generalize across observed action scenes and written descriptions. The representational organization of stimulus-general action information in LPTC can be predicted from models that describe basic agent-patient relations (object- and person-directedness) and the general semantic similarity between actions. Our results suggest that LPTC encodes general, conceptual aspects of actions whereas frontoparietal representations appear to be tied to specific stimulus types.

Categorizing and understanding other people’s actions is a key human capability. Whereas there exists a growing literature regarding the organization of objects, the representational space underlying the organization of observed actions remains largely unexplored. Here we examined the organizing principles of a large set of actions and the corresponding neural representations. Using multiple regression representational similarity analysis of fMRI data, in which we accounted for variability due to major action components (body parts, scenes, movements, objects, sociality, transitivity) and three control models (distance between observer and actor, number of people, HMAX-C1), we found that the semantic dissimilarity structure was best captured by patterns of activation in the lateral occipitotemporal cortex (LOTC). Together, our results demonstrate that the organization of observed actions in the LOTC resembles the organizing principles used by participants to classify actions behaviorally, in line with the view that this region is crucial for accessing the meaning of actions.

During movement planning, brain activity within parietofrontal networks encodes information about upcoming actions that can be driven either externally (e.g., by a sensory cue) or internally (i.e., by a choice/decision). Here we used multivariate pattern analysis (MVPA) of fMRI data to distinguish between areas that represent (1) abstract movement plans that generalize across the way in which these were driven, (2) internally driven movement plans, or (3) externally driven movement plans. In a delayed-movement paradigm, human volunteers were asked to plan and execute three types of nonvisually guided right-handed reaching movements toward a central target object: using a precision grip, a power grip, or touching the object without hand preshaping. On separate blocks of trials, movements were either instructed via color cues (Instructed condition), or chosen by the participant (Free-Choice condition). Using ROI-based and whole-brain searchlight-based MVPA, we found abstract representations of planned movements that generalize across the way these movements are selected (internally vs externally driven) in parietal cortex, dorsal premotor cortex, and primary motor cortex contralateral to the acting hand. In addition, we revealed representations specific for internally driven movement plans in contralateral ventral premotor cortex, dorsolateral prefrontal cortex, supramarginal gyrus, and in ipsilateral posterior parietotemporal regions, suggesting that these regions are recruited during movement selection. Finally, we observed representations of externally driven movement plans in bilateral supplementary motor cortex and a similar trend in presupplementary motor cortex, suggesting a role in stimulus-response mapping.

No abstract

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

No abstract