Attention reorganizes as structure is detected in dynamic action

Abstract

Once one sees a pattern, it is challenging to "unsee" it; discovering structure alters processing. Precisely what changes as this happens is unclear, however. We probed this question by tracking changes in attention as viewers discovered statistical patterns within unfolding event sequences. We measured viewers' "dwell times" (e.g., Hard, Recchia, & Tversky, 2011) as they advanced at their own pace through a series of still-frame images depicting a sequence of event segments ("actions") that were discoverable only via sensitivity to statistical regularities among the component motion elements. "Knowledgeable" adults, who had had the opportunity to learn these statistical regularities prior to the slideshow viewing, displayed dwell-time patterns indicative of sensitivity to the statistically defined higher-level segmental structure; "naïve" adults, who lacked the opportunity for prior viewing, did not. These findings clarify that attention reorganizes in conjunction with statistically guided discovery of segmental structure within continuous human activity sequences. As patterns emerge in the mind, attention redistributes selectively to target boundary regions, perhaps because they represent highly informative junctures of "predictable unpredictability."

Neural bases of learning and recognition of statistical regularities

“…These results were interpreted within the framework of statistical learning . Besides, it has been suggested that the segmentation of actions, of continuous sensory input across modalities, and segmentation of speech into linguistic constituents like words and phrases—all rely on the same cognitive processes related to statistical learning …”

“…In humans, this includes splitting speech into words and phrases, 9 separating distinct rhythms and other musical properties (in musical compositions), [10][11][12] parsing sequences of events, as well as discerning discrete sequences of actions in a continuous series of human activities. 13,14 For example, while viewing a series of still images representing a continuous dynamic activity, viewers' gaze tends to dwell longer on those slides that illustrate the boundaries between unfolding events: dwell times are longer for slides that show the grasp of a glass has been completed than those that show the grasping action still unfolding. This segmentation takes place at various levels: slides that depict boundaries between distinct higher-level actions, for example, the boundary between emptying a dishwasher (which includes the lower-level action of grasping a glass to take it out of the machine) and starting a new sequence of sweeping the floor attracts even longer gazes.…”

“…16 Besides, it has been suggested that the segmentation of actions, of continuous sensory input across modalities, and segmentation of speech into linguistic constituents like words and phrases-all rely on the same cognitive processes related to statistical learning. 14,16 Statistical learning operates on a variety of cues, including (but not limited to) conditional regularities known as transitional probabilities (TPs). TPs refer to the probability of an event B happening given that event A has occurred.…”

“…Segmentation based on statistical cues contained in the sensory input operates across different domains. In humans, this includes splitting speech into words and phrases, separating distinct rhythms and other musical properties (in musical compositions), parsing sequences of events, as well as discerning discrete sequences of actions in a continuous series of human activities . For example, while viewing a series of still images representing a continuous dynamic activity, viewers’ gaze tends to dwell longer on those slides that illustrate the boundaries between unfolding events: dwell times are longer for slides that show the grasp of a glass has been completed than those that show the grasping action still unfolding.…”

How statistical learning interacts with the socioeconomic environment to shape children’s language development

“…On the other hand, it is likely that these two variables are measuring slightly different aspects of statistical learning. The ERPs are time-locked to the onset of the predictor stimuli before the target appears and therefore seem to reflect the recognition–i.e., a modulation of attention [ 11 , 71 ]—that certain predictors are cues for the occurrence of the target (i.e., a form of predictive processing). Alternatively, the reaction times are a measure of the behavioral responses following the occurrence of a target and therefore reflect the reaction to the target, and not a prediction that the target will occur.…”