Many imitative behaviors entail complex sequences of component actions that must be recalled and performed in the proper order. It is well known that imitation of complex actions tends to improve with repeated opportunities to observe and execute the target behavior. But what actually makes this practice-based improvement possible? To address this question, we had subjects view and then reproduce sequences of connected, randomly directed motions of a disc. Even a single repetition of a motion sequence substantially reduced errors in reproduction. Improvement seemed to follow a power law, with accuracy in reproducing each motion segment improving by an amount proportional to the current error for that segment. Analysis of the pauses separating a reproduction’s segments suggests that with learning, multiple segments in memory are grouped into more compact representations. To test overt performance’s contribution to repetition-based improvement, we compared subjects ’ performance when they reproduced the stimulus trajectory after each repetition to when they did so only once, after the final repetition. Performance was similar following the final repetition in both conditions, indicating that seeing the model, without actual imitation, was sufficient for learningVeven in the absence of an explicit error signal. In another experiment, subjects viewed three presentations of each model, with the second presentation given in forward (start to end) or backward (end to start) order. Performance was significantly better when all three presentations were in the same, consistent order, suggesting that repetition reinforced some temporal aspects of a trajectory as it was being learned, and not merely a better representation of the static shape traced by the motion of the

When observers are asked to remember the final location of an object undergoing apparent or implied motion, a forward displacement is observed. The magnitude of this form of motion extrapolation is known to depend on various factors including stimulus attributes, action plans, and other cognitive cues. Here we present a modelling approach that aims at bridging different existing theories of displacement within a single theoretical framework. A network model consisting of interacting excitatory and inhibitory cell populations coding for stimulus attributes like position or orientation is used to study the response to motion displays. The intrinsic network dynamics can be modulated by additional information sources representing action plans directed at the moving target or cognitive cues such as prior knowledge about the trajectory. These factors decide the extent to which the dynamic representation overshoots the final position. The model predictions are quantitatively compared with the experimental findings. The results are discussed in relation to theoretical ideas about processing principles underlying motion extrapolation and a comparison with neurophysiological findings linked to

Can human adults perform arithmetic operations with large approximate numbers, and what effect, if any, does an internal spatial–numerical representation of numerical magnitude have on their responses? We conducted a psychophysical study in which subjects viewed several hundred short videos of sets of objects being added or subtracted from one another and judged whether the final numerosity was correct or incorrect. Over a wide range of possible outcomes, the subjects ’ responses peaked at the approximate location of the true numerical outcome and gradually tapered off as a function of the ratio of the true and proposed outcomes (Weber’s law). Furthermore, an operational momentum effect was observed, whereby addition problems were overestimated and subtraction problems were underestimated. The results show that approximate arithmetic operates according to precise quantitative rules, perhaps analogous to those characterizing movement on an internal continuum. Human adults possess an ability to estimate and manipulate approximate numerical magnitudes, which has been termed number sense (Dehaene, 1997). This ability appears to be largely independent of language and other symbol systems, since it is present in both infants (Xu & Spelke, 2000) and other animal species (Brannon & Roitman, 2003;

implicit knowledge about motion

Information visualization is a powerful method for understanding and working with data. However, we still have an incomplete understanding of how people use visualization to think about information. We propose that people use visualization to support comprehension and reasoning by viewing abstract visual representations as physical scenes with a set of implied dynamics between objects. Inferences based on these implied dynamics are metaphorically extended to form inferences about the represented information. This view predicts that even seemingly meaningless properties of a visualization, including such minor design elements as borders, background areas, and the connectedness of parts, may affect how people perceive semantic aspects of data by suggesting different potential dynamics between data points. We present a study that supports this claim and discuss the design implications of this theory of information visualization.

v Declaration .................................... ix Acknowledgements................................ xi 1Prelude 1 TheVeryIdeaofRepresentation......................... 2 TypesofSimilarity ................................ 8 IsSimilarityIndeterminate? ........................... 11 TheRoleofSimilarityinCognition....................... 11 Summary&GeneralDiscussion......................... 14 2 Theories of Similarity 17 SimilarityDataSets................................ 17 SpatialRepresentation .............................. 21 FeaturalRepresentation.............................. 31 TreeRepresentation................................ 40 NetworkRepresentation ............................. 47 Alignment-BasedSimilarityModels....................... 48 TransformationalSimilarityModels ....................... 50 Summary&GeneralDiscussion......................... 54 i 3 On Representational Complexity 55 ApproachestoModelSelection ......................... 57 ChoosinganAdditiveClusteringRepresentation ................ 67 ChoosinganAdditiveTreeRepresentation ................... 82 ChoosingaSpatialRepresentation........................ 94 Summary&GeneralDiscussion......................... 95 4 Featural Representation 97 AMenagerieofFeaturalModels......................... 98 ClusteringModels.................................104 GeometricComplexityCriteria..........................106 AlgorithmsforFittingFeaturalModels .....................107 MonteCarloStudyI:DotheAlgorithmsWork? ................109 RepresentationsofKinshipTerms ........................117 MonteCarloStudyII:Complexity........................122 ExperimentI:Faces................................125 ExperimentII:Countries .............................1...

Abstract not found

This study investigated the representations that readers construct for narratives describing a sequence of events. Participants read narratives describing 4 successive events in chronological order (Event 1, Event

The problem of representation and processing of motion information is addressed from an integrated perspective covering the range from early visual processing to higher-level cognitive aspects. A spatio-temporal memory is presented as indispensible representational prerequisite for the recognition of spatiotemporal gestalt. We assume that this structure is replicated on different processing-levels in the visual system mirroring its hierarchical structure. Thus, each level requires a different representation for spatio-temporal information. As a first step, we present a two-layered architecture for the qualitative representation of motion trajectories: The vectorial layer is quite accurate and allows switches between deictic and intrinsic frame of reference. The propositional layer is more abstract and reveals similarities and regularities of motion paths which will be useful for motion prediction. First psychophysical experiments indicate that information about direction...

INTRODUCTION Young disciplines often experience moments of doubt: "Are we doing the right thing?" or "Is this approach viable?" [1]. Nowhere is this better exemplified than in the study of computer vision [2]. While progress has been made, the goal of general vision, on the order of human visual perception, remains elusive. Recently, this has led * PLease address all correspondence to Michael J. Tarr, P.O. Box 208205, New Haven, CT 06520-8205, E-mail address: tart@cs. yaie.edu. to the suggestion that the entire endeavor is flawed, that we should discard the dominant paradigm, and that it should be replaced with a new, more practical alternative. t While this position may not qualify as a "paradigm shift" [3], it certainly advocates a substantial change in direction. To justify this radical deviation, proponents of the new, so-called purposive approach muster three lines of support: first, that machines fall far short of the visual capabilities of humans; second, that current computer