Dataset Information

Neural Representations of Observed Actions Generalize across Static and Dynamic Visual Input.

ABSTRACT: People interact with entities in the environment in distinct and categorizable ways (e.g., kicking is making contact with foot). We can recognize these action categories across variations in actors, objects, and settings; moreover, we can recognize them from both dynamic and static visual input. However, the neural systems that support action recognition across these perceptual differences are unclear. Here, we used multivoxel pattern analysis of fMRI data to identify brain regions that support visual action categorization in a format-independent way. Human participants were scanned while viewing eight categories of interactions (e.g., pulling) depicted in two visual formats: (1) visually controlled videos of two interacting actors and (2) visually varied photographs selected from the internet involving different actors, objects, and settings. Action category was decodable across visual formats in bilateral inferior parietal, bilateral occipitotemporal, left premotor, and left middle frontal cortex. In most of these regions, the representational similarity of action categories was consistent across subjects and visual formats, a property that can contribute to a common understanding of actions among individuals. These results suggest that the identified brain regions support action category codes that are important for action recognition and action understanding.SIGNIFICANCE STATEMENT Humans tend to interpret the observed actions of others in terms of categories that are invariant to incidental features: whether a girl pushes a boy or a button and whether we see it in real-time or in a single snapshot, it is still pushing Here, we investigated the brain systems that facilitate the visual recognition of these action categories across such differences. Using fMRI, we identified several areas of parietal, occipitotemporal, and frontal cortex that exhibit action category codes that are similar across viewing of dynamic videos and still photographs. Our results provide strong evidence for the involvement of these brain regions in recognizing the way that people interact physically with objects and other people.

SUBMITTER: Hafri A

PROVIDER: S-EPMC6596723 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Neural Representations of Observed Actions Generalize across Static and Dynamic Visual Input.

Hafri Alon A Trueswell John C JC Epstein Russell A RA

The Journal of neuroscience : the official journal of the Society for Neuroscience 20170216 11

People interact with entities in the environment in distinct and categorizable ways (e.g., <i>kicking</i> is <i>making contact with foot</i>). We can recognize these action categories across variations in actors, objects, and settings; moreover, we can recognize them from both dynamic and static visual input. However, the neural systems that support action recognition across these perceptual differences are unclear. Here, we used multivoxel pattern analysis of fMRI data to identify brain regions ...[more]

PMID: 28209734

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Project description:The present fMRI study investigated whether human observers spontaneously exploit the statistical structure underlying continuous action sequences. In particular, we tested whether two different statistical properties can be distinguished with regard to their neural correlates: an action step's predictability and its probability. To assess these properties we used measures from information theory. Predictability of action steps was operationalized by its inverse, conditional entropy, which combines the number of possible action steps with their respective probabilities. Probability of action steps was assessed using conditional surprisal, which increases with decreasing probability. Participants were trained in an action observation paradigm with video clips showing sequences of 9-33 s length with varying numbers of action steps that were statistically structured according to a Markov chain. Behavioral tests revealed that participants implicitly learned this statistical structure, showing that humans are sensitive toward these probabilistic regularities. Surprisal (lower probability) enhanced the BOLD signal in the anterior intraparietal sulcus. In contrast, high conditional entropy, i.e., low predictability, was correlated with higher activity in dorsomedial prefrontal cortex, orbitofrontal gyrus, and posterior intraparietal sulcus. Furthermore, we found a correlation between the anterior hippocampus' response to conditional entropy with the extent of learning, such that the more participants had learnt the structure, the greater the magnitude of hippocampus activation in response to conditional entropy. Findings show that two aspects of predictions can be dissociated: an action's predictability is reflected in a top-down modulation of attentional focus, evident in increased fronto-parietal activation. In contrast, an action's probability depends on the identity of the stimulus itself, resulting in bottom-up driven processing costs in the parietal cortex.

Dataset Information

Neural Representations of Observed Actions Generalize across Static and Dynamic Visual Input.

Publications

Neural Representations of Observed Actions Generalize across Static and Dynamic Visual Input.

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