Joints and their relations as critical features in action discrimination: Evidence from a classification image method

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Classifying an action as a runner or a walker is a seemingly effortless process. However, it is difficult to determine which features are used with hypothesis-driven research, because biological motion stimuli generally consist of about a dozen joints, yielding an enormous number of potential relationships among them. Here, we develop a hypothesis-free approach based on a classification image method, using experimental data from relatively few trials (;1,000 trials per subject). Employing ambiguous actions morphed between a walker and a runner, we identified three types of features that play important roles in discriminating bipedal locomotion presented in a side view: (a) critical joint feature, supported by the finding that the similarity of the movements of feet and wrists to prototypical movements of these joints were most reliably used across all participants; (b) structural features, indicated by contributions from almost all other joints, potentially through a form-based analysis; and (c) relational features, revealed by statistical correlations between joint contributions, specifically relations between the two feet, and relations between the wrists/ elbow and the hips. When the actions were inverted, only critical joint features remained to significantly influence discrimination responses. When actions were presented with continuous depth rotation, critical joint features and relational features associated strongly with responses. Using a double-pass paradigm, we estimated that the internal noise is about twice as large as the external noise, consistent with previous findings. Overall, our novel design revealed a rich set of critical features that are used in action discrimination. The visual system flexibly selects a subset of features depending on viewing conditions.




van Boxtel, J. J. A., & Lu, H. (2015). Joints and their relations as critical features in action discrimination: Evidence from a classification image method. Journal of Vision, 15(1), 1–17.

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