Inferring intentions from biological motion: A stimulus set of point-light communicative interactions

Manera, Valéria; Schouten, Ben; Becchio, Cristina; Bara, Bruno G.; Verfaillie, Karl

doi:10.3758/brm.42.1.168

Cited by 96 publications

(116 citation statements)

References 47 publications

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“…This bias may be due to historical accident (walking was the first action investigated using point-light stimuli; Johansson, 1973), as well as to the mathematical tractability of the combined joint movements in spatial-temporal space (Cutting, 1978;Troje, 2002). However, humans are able to perceive a wide range of action categories other than walking when presented as point-light displays (e.g., boxing, dancing, jumping jacks; Brown et al, 2005;Dittrich, 1993;Dittrich et al, 1996;Giese & Lappe, 2002;Ma, Paterson, & Pollick, 2006;Norman, Payton, Long, & Hawkes, 2004;Thurman & Grossman, 2008) and communicative interactions (Manera, Schouten, Becchio, Bara, & Verfaillie, 2010;Neri, Luu, & Levi, 2006). Comparing human performance for these different actions may help us understand how the visual system conducts an intelligent spatial-temporal analysis for action recognition (Giese & Lappe, 2002;Thurman & Grossman, 2008).…”

Section: Introductionmentioning

confidence: 95%

Visual search by action category

Boxtel

2011

Journal of Vision

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Humans are sensitive to different categories of actions due to their importance in social interactions. However, biological motion research has been heavily tilted toward the use of walking figures. Employing point-light animations (PLAs) derived from motion capture data, we investigated how different activities (boxing, dancing, running, and walking) related to each other during action perception, using a visual search task. We found that differentiating between actions requires attention in general. However, a search asymmetry was revealed between boxers and walkers, i.e., searching for a boxer among walkers is more efficient than searching for a walker among boxers, suggesting the existence of a critical feature for categorizing these two actions. The similarities among the various actions were derived from hierarchical clustering of search slopes. Walking and running proved to be most related, followed by dancing and then boxing. Signal detection theory was used to conduct a non-parametric ROC analysis, revealing that human performance in visual search is not fully explained by low-level motion information.

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Section: Introductionmentioning

confidence: 95%

Visual search by action category

Boxtel

2011

Journal of Vision

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“…graspingfor-drinking vs. grasping-for-cleaning, Iacoboni et al, 2005), even in conditions when the final part of the action is hidden from view (Umiltà et al, 2001). Similarly, behavioral experiments have shown that an agent's intention (e.g., to deceive) or affective state (e.g., happiness) can be reliably communicated to external observers in video or point-light depictions of actions, such as lifting a box (Grèzes, Frith, & Passingham, 2004;Runeson & Frykholm, 1981, 1983, basketball passing (Sebanz & Shiffrar, 2009), and in situations depicting various whole-body expressive gestures and movements, such as pointing (Manera, Schouten, Becchio, Bara, & Verfaillie, 2010), communicating (Clarke, Bradshaw, Field, Hampson, & Rose, 2005), walking (Chouchourelou, Matsuka, Harber, & Shiffrar, 2006;Roether, Omlor, Christensen, & Giese, 2009), or dancing (Dittrich, Troscianko, Lea, & Morgan, 1996).…”

Section: Introductionmentioning

confidence: 98%

Perceiving performer identity and intended expression intensity in point-light displays of dance

Sevdalis

Keller

2010

Psychological Research

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This study investigated agent and expression intensity recognition in point-light displays depicting dancing performances. In a first session, participants danced with two different expression intensities to music, solo or in dyads. In a subsequent session, they watched point-light displays of 1-5-s duration, depicting their own, their partner's or another participant's recorded actions, and were asked to identify the agent (self vs. partner vs. stranger) and the intended expression intensity (expressive vs. inexpressive) of the performer. The results indicate that performer identity and expression intensity could be discerned reliably from displays as short as 1 s. The accuracy in judgment increased with exposure duration and, for performer identification, with higher expression intensity. Judgment accuracy in agent and expression intensity recognition tasks correlated with self-report empathy indices. Accuracy correlated also with confidence in judgment, but only in the intensity recognition task. The results are discussed in relation to perceptual and neural mechanisms underlying action and intention recognition.

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“…For human actions, in perception research this subject matter is also often found in the context of "biological motion", with databases of motion capture data including [9], [10]. Another well-known database is the CMU Graphics Lab Motion Capture Database (available at mocap.cs.cmu.edu), which contains motion capture data of various actions in categories such as locomotions, pantomime, and expressions.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Whereas it contains a lot of data on locomotion patterns, longer activities as well as human interactions are very much underrepresented and in addition only very loosely organized. As far as humanhuman interactions are concerned, a recent motion-capture database [10] contains 20 elementary interactions (such as point to the ceiling, I am angry, pick up, etc.) each performed by one male and one female couple.…”

Section: Related Work and Motivationmentioning

confidence: 99%

The POETICON enacted scenario corpus — A tool for human and computational experiments on action understanding

Wallraven

Schultze

Mohler

et al. 2011

Face and Gesture 2011

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A good data corpus lies at the heart of progress in both perceptual/cognitive science and in computer vision. While there are a few datasets that deal with simple actions, creating a realistic corpus for complex, long action sequences that contains also human-human interactions has so far not been attempted to our knowledge. Here, we introduce such a corpus for (inter)action understanding that contains six everyday scenarios taking place in a kitchen / living-room setting. Each scenario was acted out several times by different pairs of actors and contains simple object interactions as well as spoken dialogue. In addition, each scenario was first recorded with several HD cameras and also with motion-capturing of the actors and several key objects. Having access to the motion capture data allows not only for kinematic analyses, but also allows for the production of realistic animations where all aspects of the scenario can be fully controlled. We also present results from a first series of perceptual experiments that show how humans are able to infer scenario classes, as well as individual actions and objects from computer animations of everyday situations. These results can serve as a benchmark for future computational approaches that begin to take on complex action understanding.

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Inferring intentions from biological motion: A stimulus set of point-light communicative interactions

Cited by 96 publications

References 47 publications

Visual search by action category

Visual search by action category

Perceiving performer identity and intended expression intensity in point-light displays of dance

The POETICON enacted scenario corpus — A tool for human and computational experiments on action understanding

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Inferring intentions from biological motion: A stimulus set of point-light communicative interactions

Cited by 96 publications

References 47 publications

Visual search by action category

Visual search by action category

Perceiving performer identity and intended expression intensity in point-light displays of dance

The POETICON enacted scenario corpus &#x2014; A tool for human and computational experiments on action understanding

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The POETICON enacted scenario corpus — A tool for human and computational experiments on action understanding