Abstract
Humans can rapidly discriminate complex scenarios as they unfold in real time, for example during law enforcement or, more prosaically, driving and sport. Such decision-making improves with experience, as new sources of information are exploited. For example, sports experts are able to predict the outcome of their opponent's next action (e.g., a tennis stroke) based on kinematic cues “read” from preparatory body movements. Here, we explore the use of psychophysical classification-image techniques to reveal how participants interpret complex scenarios. We used sport as a test case, filming tennis players serving and hitting ground strokes, each with two possible directions. These videos were presented to novices and club-level amateurs, running from 0.8 s before to 0.2 s after racquet-ball contact. During practice, participants anticipated shot direction under a time limit targeting 90% accuracy. Participants then viewed videos through Gaussian windows (“bubbles”) placed at random in the temporal, spatial or spatiotemporal domains. Comparing bubbles from correct and incorrect trials revealed how information from different regions contributed toward a correct response. Temporally, only later frames of the videos supported accurate responding (from ~0.05 s before ball contact to 0.1 s afterwards). Spatially, information was accrued from the ball's trajectory and from the opponent's head. Spatiotemporal bubbles again highlighted ball trajectory information, but seemed susceptible to an attentional cuing artifact, which may caution against their wider use. Overall, bubbles proved effective in revealing regions of information accrual, and could thus be applied to help understand choice behavior in a range of ecologically valid situations.
Highlights
Imagine yourself driving your car one evening
We selected a larger temporal bubble width in spatiotemporal compared to temporal sessions because a larger value allowed us to utilise fewer bubbles, and this proved important in terms of the time taken to generate each trial of the experiment. 4Motion in each video was detected via algorithm, and the estimated regions were expanded slightly to ensure that no body motion was missed. 5In principal, this reframing can maximise power to detect information accrual at multiple points of interest in a series of analyses, but here we present data from a single coordinate transform for a relatively simple demonstration
We set out to evaluate whether the bubbles variant of classification-image analysis (Gosselin and Schyns, 2001) could be an effective and practical tool for revealing the information extracted from real-world video stimuli to inform a speeded discrimination
Summary
As you turn a bend, a cat appears in your headlights. Without your conscious intervention, your body has decided, and you are relieved to find that your reaction has avoided the cat without causing a more dangerous collision. Successful speeded decision-making of this kind has been fundamental to our survival as a species, and continues to pervade everyday life. It is not always obvious what particular information is exploited to make speeded choices, and which potentially relevant cues are left unused. When avoiding the cat, was the upcoming curvature of the road or the presence of another vehicle in the rear-view mirror taken into account? When avoiding the cat, was the upcoming curvature of the road or the presence of another vehicle in the rear-view mirror taken into account? If not, might a better driver have exploited these cues?
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