Adaptation to Embodied Dynamics: Evidence from Bayes' Ball

Adaptation to Embodied Dynamics: Evidence from Bayes’ Ball Chris R. Sims (simsc@rpi.edu) & Wayne D. Gray (grayw@rpi.edu) Department of Cognitive Science Rensselaer Polytechnic Institute Troy, NY 12180 Abstract knowledge that is most likely to be relevant in future tasks. This perspective has important implications for research on skill acquisition, as it shifts the explanatory focus from the- ories of learning a new task, to theories of adapting and ex- ploiting existing internal models to changing environmental circumstances. The purpose of this research is to explore the extent to which interactive behavior in a novel task is sensitive to the performance dynamics and interactions among simple cogni- tive, perceptual, and motor processes. The work builds on a growing body of literature demonstrating remarkable sensi- tivity to low-level dynamics even in the most mundane activi- ties. For example, (Augustyn & Rosenbaum, 2005) presented subjects with two circular targets on a computer screen. The subjects were required to move the mouse cursor to a start- ing position anywhere along a line connecting the two tar- gets. After choosing a starting position, one of the two targets (randomly chosen) disappeared, and the task for the partici- pant was to move the mouse cursor into the remaining target within a short response interval. Across trials, the width of one of the targets was varied. By making one of the targets larger, it became easier and faster to move the cursor into that target, in accordance with Fitts law. Given the goal of maximizing performance in terms of landing the cursor in- side the target within the time period, optimal performance in the task requires accounting for Fitts law, as well as nu- merous low-level dynamics of the specific task (for example, subjects moved a small wooden disk on a table to control the movement of the cursor). In keeping with their predic- tions, Augustyn and Rosenbaum found that their participants demonstrated performance that was indistinguishable from an optimal performer. In a somewhat more complex task, Gray and colleagues (Gray, Sims, Fu, & Schoelles, 2006) examined behavior in a block copying paradigm, where subjects had to memorize and replicate a pattern of eight colored blocks seen in one location of the computer screen at another location. The original pat- tern of blocks and the workspace area where the pattern was to be reproduced were both normally covered by occluding boxes. To uncover the target or workspace area the subject needed to move the mouse cursor inside the occluding box. As a between-subjects manipulation, the investigators added a lockout time to the target window that varied between 0 and 3.2 seconds, such that the subject had to move the mouse cur- sor inside the box and wait the lockout duration before the target pattern could be viewed. Gray et al recorded the duration that subjects studied the target pattern, the number of blocks of the pattern that were In recent years researchers have begun to recognize the re- markable intelligence of even routine interactive behavior— the extent to which humans adapt to and exploit the low-level dynamics of cognitive processes, perception, and motor con- trol without conscious deliberation. We developed an experi- ment known as Bayes’ Ball, in which subjects must estimate short time intervals (< 1000 ms) in order to maximize a point score in a simple ball tracking task. Optimal performance in this task requires that subjects possess intricate knowledge of the dynamics of uncertainty in their estimates of elapsed time (the Weber law of interval timing). The results of our experi- ment show human performance that approaches, but does not fully match the predictions of an ideal performer. On average, humans differed from the ideal performer by less than 40 ms in their response timing. Keywords: embodied cognition; time estimation; uncertainty; ideal performer analysis Introduction Nearly all of human activity can be described as an intricately coordinated dance of low-level cognitive, perceptual, and mo- tor components, each taking small steps of progress towards a larger goal. The elementary operations performed by these components unfold on the timescale of 1/3 of a second and form the embodiment level of cognition (Ballard, Hayhoe, Pook, & Rao, 1997)–the level of analysis at which the con- straints of the physical components first have implications for the ability to achieve goals in the world. The field of psy- chology has a long history of studying the basic properties of each of these components in isolation, for example, the rate of memory decay as a function of time and practice (Ebbing- haus, 1913) or how movement duration scales with magni- tude (Fitts, 1954). However, a related and fascinating ques- tion has received considerably less attention. Given a lifetime of experience reaching for objects and remembering facts, to what extent are individuals already and implicitly aware of the performance characteristics of their own embodied cog- nitive processes? Given the multitude of varying tasks that humans face over the course of a lifetime, it is notable that the only stable components in these interactions are the internal components themselves. While tasks and demands on cognition are likely to change drastically from minute to minute, the dynamics of memory decay are fairly invariant (and thus predictable) across a wide range of tasks, as are the dynamics of simple reaching movements. Thus from the perspective of efficient skill acquisition, it seems rational that much of what should be learned in routine behavior is an accurate internal model of one’s own embodied dynamics—the performance character- istics and interactions among cognition, perception, and mo- tor control—rather than knowledge of the task, as this is the