Abstract:Is THE STREAM OF BEHAVIOR seen as a continuum or as a sequence of discrete units? If the latter, do different people see the same units? People behave toward others, and they speak and write about their own and other's behavior as if they perceive behavior in units; and the degree of harmony with which interacting individuals guide their behavior suggests considerable agreement regarding the beginning and end-points of the behavior units they discern. The research reported in this chapter provides the first pr… Show more
“…Comparing the position of perceived and expected endpoints, subjects had a mean hit rate of 0.69 (standard error 0.05) within an accuracy window of less than 500 ms. These results are in accordance with previous findings about the agreement of human raters on boundary placing in movement sequences [Dickman 1963;Newtson and Engquist 1976;Zacks et al 2009]. …”
Section: Segmentation Results Of Human Observerssupporting
Natural body movements arise in the form of temporal sequences of individual actions. During visual action analysis, the human visual system must accomplish a temporal segmentation of the action stream into individual actions. Such temporal segmentation is also essential to build hierarchical models for action synthesis in computer animation. Ideally, such segmentations should be computed automatically in an unsupervised manner. We present an unsupervised segmentation algorithm that is based on Bayesian Binning (BB) and compare it to human segmentations derived from psychophysical data. BB has the advantage that the observation model can be easily exchanged. Moreover, being an exact Bayesian method, BB allows for the automatic determination of the number and positions of segmentation points. We applied this method to motion capture sequences from martial arts and compared the results to segmentations provided by humans from movies that showed characters that were animated with the motion capture data. Human segmentation was then assessed by an interactive adjustment paradigm, where participants had to indicate segmentation points by selection of the relevant frames. Results show a good agreement between automatically generated segmentations and human performance when the trajectory segments between the transition points were modeled by polynomials of at least third order. This result is consistent with theories about differential invariants of human movements.
“…Comparing the position of perceived and expected endpoints, subjects had a mean hit rate of 0.69 (standard error 0.05) within an accuracy window of less than 500 ms. These results are in accordance with previous findings about the agreement of human raters on boundary placing in movement sequences [Dickman 1963;Newtson and Engquist 1976;Zacks et al 2009]. …”
Section: Segmentation Results Of Human Observerssupporting
Natural body movements arise in the form of temporal sequences of individual actions. During visual action analysis, the human visual system must accomplish a temporal segmentation of the action stream into individual actions. Such temporal segmentation is also essential to build hierarchical models for action synthesis in computer animation. Ideally, such segmentations should be computed automatically in an unsupervised manner. We present an unsupervised segmentation algorithm that is based on Bayesian Binning (BB) and compare it to human segmentations derived from psychophysical data. BB has the advantage that the observation model can be easily exchanged. Moreover, being an exact Bayesian method, BB allows for the automatic determination of the number and positions of segmentation points. We applied this method to motion capture sequences from martial arts and compared the results to segmentations provided by humans from movies that showed characters that were animated with the motion capture data. Human segmentation was then assessed by an interactive adjustment paradigm, where participants had to indicate segmentation points by selection of the relevant frames. Results show a good agreement between automatically generated segmentations and human performance when the trajectory segments between the transition points were modeled by polynomials of at least third order. This result is consistent with theories about differential invariants of human movements.
“…For instance, there are data consistent with the view that, among children and adults, the interpretation and production of linguistic and nonlinguistic activity with overall goals is based partly on subgoaldirected aspects of the activity (Barker & Wright, 1971;Dickman, 1963;Goldman, 1982;Omanson et al, 1978;Rumelhart, 1976). It is reasonable to begin to address the role that pragmatic conversational structure might play in comprehension models.…”
“…As observers view videos of ongoing activities, they press a key to indicate the moments when one action unit ends and another begins, termed breakpoints. Although activities could be segmented at inflnite temporal locations, people are remarkably consistent, both with one another and with themselves across viewings, in marking breakpoints (Dickman, 1963;Hard, Tversky, & Lang, 2006;Newtson & Engquist, 1976;Zacks, Tversky, & Iyer, 2001). The action units bookended by these breakpoints are described or recalled with expressions like rinsed the dish or smoothed the sheet, indicating that they correspond to actions on objects or accomplished goals (e.g., Baldwin & Baird, 1999;Kurby & Zacks, 2008;Newtson, 1973;Zacks, Speer, Swallow, Braver, & Reynolds, 2007;Zacks, Tversky, & Iyer, 2001).…”
How do people understand the everyday, yet intricate, behaviors that unfold around them? In the present research, we explored this by presenting viewers with self-paced slideshows of everyday activities and recording looking times, subjective segmentation (breakpoints) into action units, and slide-to-slide physical change. A detailed comparison of the joint time courses of these variables showed that looking time and physical change were locally maximal at breakpoints and greater for higher level action units than for lower level units. Even when slideshows were scrambled, breakpoints were regarded longer and were more physically different from ordinary moments, showing that breakpoints are distinct even out of context. Breakpoints are bridges: from one action to another, from one level to another, and from perception to conception.
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