Understanding visual processing of motion: completing the picture using experimentally driven computational models of MT.

Human perception of visual motion is thought to involve two stages--estimation of local motion (i.e., of small features) and global motion (i.e., of larger objects)--identified with cortical areas V1 and MT, respectively. We asked if poor discrimination of oblique compared to cardinal directions (the oblique effect for motion; OEM) reflects a deficit in local or in global motion processing. We used an equivalent noise (EN) paradigm--where one measures direction discrimination thresholds in the presence of directional variability--to quantify local and global limits. We report that the OEM diminishes with increasing directional variability, indicating that global motion processing (the number of local motion signals pooled) is equal across all directions and that the OEM is attributable to anisotropies in local motion processing. To investigate the origin of this effect, we measured local motion statistics from natural movies (filmed from the point of view of a walking observer). This analysis reveals that the distribution of local directional energy on the oblique directions tends to be broader, and frequently more asymmetric, than on the cardinal directions. If motion detectors are optimized to deal with our visual world then such anisotropies likely explain the local nature of the OEM.

This paper presents an algorithm for multiple-object tracking without using object detection. We concentrate on creating long-term trajectories for unknown moving objects by using a model-free tracking algorithm. Each individual object is tracked by modeling the temporal relationship between sequentially occurring local motion patterns. The algorithm is based on shape and motion descriptors of moving objects, obtained at two hierarchical levels from an event understanding system. By considering both local and global motion patterns, two sets of initial tracks, called linklets, are obtained. Then, a set of sparse tracks, referred to in the literature as tracklets, is produced by grouping linklets demonstrating similar motion patterns. This produces two sets of independent tracklets, referred to as the lowand high-level tracklets. We adopt Markov Chain Monte Carlo Data Association (MCMCDA) to estimate a varying number of trajectories given a set of tracklets as input. To this end, we formulate tracklet association as a Maximum A Posteriori (MAP) problem to create a chain of tracklets. The final output of the data association algorithm is a partition of the set of tracklets such that those belong to individual objects have been grouped. This yields individual tracks for each object in a video.

Extremely crowded scenes present unique challenges to video analysis that cannot be addressed with conventional approaches. We present a novel statistical framework for modeling the local spatio-temporal motion pattern behavior of extremely crowded scenes. Our key insight is to exploit the dense activity of the crowded scene by modeling the rich motion patterns in local areas, effectively capturing the underlying intrinsic structure they form in the video. In other words, we model the motion variation of local space-time volumes and their spatial-temporal statistical behaviors to characterize the overall behavior of the scene. We demonstrate that by capturing the steady-state motion behavior with these spatio-temporal motion pattern models, we can naturally detect unusual activity as statistical deviations. Our experiments show that local spatio-temporal motion pattern modeling offers promising results in real-world scenes with complex activities that are hard for even human observers to analyze.

Perceiving biological motion (BM) is crucial for human survival and social interaction. Many studies have reported impaired BM perception in autism spectrum disorder, which is characterised by deficits in social interaction. Children with attention deficit hyperactivity disorder (ADHD) often exhibit similar difficulties in social interaction. However, few studies have investigated BM perception in children with ADHD. Here, we compared differences in the ability to process local kinematic and global configurational cues, two fundamental abilities of BM perception, between typically developing and ADHD children. We further investigated the relationship between BM perception and social interaction skills measured using the Social Responsiveness Scale and examined the contributions of latent factors (e.g., sex, age, attention, and intelligence) to BM perception. The results revealed that children with ADHD exhibited atypical BM perception. Local and global BM processing showed distinct features. Local BM processing ability was related to social interaction skills, whereas global BM processing ability significantly improved with age. Critically, general BM perception (i.e., both local and global BM processing) may be affected by sustained attentional ability in children with ADHD. This relationship was primarily mediated by reasoning intelligence. These findings elucidate atypical BM perception in ADHD and the latent factors related to BM perception. Moreover, this study provides new evidence that BM perception is a hallmark of social cognition and advances our understanding of the potential roles of local and global processing in BM perception and social cognitive disorders.

Computational models are fundamentally important for testing the feasibility of theories of the visual processing of body movements and for deriving well-defined theoretical predictions that can be tested experimentally. A computational model is proposed for the recognition of transitive and nontransitive hand actions from real videos that reproduces several key neurophysiological properties of the action perception system. Limitations of the proposed model, along with novel predictions and areas for future research, are discussed.

Local and global motion processing in premature children with cerebral visual impairment (CVI)

Effects of spatial attention and salience cues on chromatic and achromatic motion processing

Anatomical and neurophysiological studies have established that Area X, a songbird nucleus essential for vocal learning, is a basal ganglia structure, with mammalian striatal properties. However, Area X also sends a gamma-aminobutyric acid (GABA)ergic projection to the medial portion of the dorsolateral thalamus (DLM), a projection characteristic of the pallidum. These findings suggested that Area X contains both striatal and pallidal neurons. To test this hypothesis further, we investigated the neurochemistry and connectivity of Area X and its projections by using neurotransmitter antibodies, in combination with tracing studies. Like the mammalian striatum, Area X contains small enkephalin- and substance P-immunopositive neurons. Choline acetyltransferase-positive cells of Area X do not retrogradely label from DLM and are probably cholinergic interneurons similar to those in mammals. Like pallidal cells, large GABAergic cells project from Area X to the thalamus, but they also contain enkephalin, a characteristic of striatal neurons projecting to indirect pathway pallidal neurons. Moreover, many Area X cells are labeled with the pallidal marker Nkx2.1, but these do not include any thalamus-projecting neurons, suggesting that the projection cells are not of pallidal embryonic origin. Thus, although Area X combines both striatal and pallidal features, it is not a simple recapitulation of the mammalian circuit or of the avian lateral striatopallidal pathway: some individual Area X neurons may function as pallidal-like projection neurons but have striatal characteristics as well. Such heterogeneity of basal ganglia circuitry, both within and across species, may be facilitated by the developmental history of basal ganglia, which involves extensive migration and cellular intermixing.

Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an ‘abstract’ discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in a theory. This paper discusses the relation between behavioural studies, theories, and computational models of spoken-word recognition. We explain how computational models can be assessed in terms of the goodness of fit with the behavioural data and the cognitive plausibility of the algorithms. An in-depth analysis of several models provides insights into how computational modelling has led to improved theories and to a better understanding of the human spoken-word recognition process.

Chromatic input to motion processing in the absence of attention

Variation of muscle moment arms with elbow and forearm position

The core ideas of the proposed framework for empirical aesthetics are interpreted as focusing on processes, interaction, and phenomenological experience. This commentary first touches on some methodological impediments to developing theories of processing and interaction, and emphasizes the necessity of computational cognitive modeling using robots to test the empirical adequacy of such theories. Further, the importance of developing and integrating phenomenological methods into current experimental research is stressed, using experimental phenomenology as reference. Situated cognition, affective computing, human-robot interaction research, computational cognitive modeling and social and cultural neuroscience are noted as providing relevant insight into the empirical adequacy of current theories of cognitive and emotional processing. In the near future these fields will have a stimulating impact on empirical aesthetics and research on music and the mind.

There are significant local-migrant and urban-rural dichotomies that stand in the way of China’s modernization, which can ascribe to the enormous inter- regional economic and institutional disparities, as well as the longstanding hukou system. With a focus on migrants’ home region’s and host region’s economic and institutional differences, this research investigates how push and pull forces and local integration influence internal migrant entrepreneurship in China. Drawing on an integrated conceptual model which incorporates social integration theory into the push-pull framework, we investigate a national sample of 128,539 internal migrants across the country. The results suggest that push and pull forces and local integration significantly influence migrants’ choice of entrepreneurial entry. We also find that social integration plays a mediating role in the relationship between push-pull forces and entrepreneurial entry. Furthermore, we notice that migrants’ local integration is also largely driven by push and pull forces. The study advances theory by incorporating the integration theory with the push-pull theory and argues that migrants’ entrepreneurial entry decisions are not only determined by the economic and institutional push-pull factors but also significantly affected by their integration in the host society. It also identifies the mediating effects of local integration exist in the relationship between push-pull factors and entrepreneurial entry. We propose practical implications that may assist policy-makers in further facilitating the internal movement of people and entrepreneurship in China and other emerging economies.

We measured neural responses to local and global aspects of form and motion stimuli using frequency-tagged, steady-state visual evoked potentials (SSVEPs) combined with magnetic resonance imaging (MRI) data. Random dot stimuli were used to portray either dynamic Glass patterns (Glass, 1969) or coherent motion displays. SSVEPs were used to estimate neural activity in a set of fMRI-defined visual areas in each subject. To compare activity associated with local versus global processing, we analyzed two frequency components of the SSVEP in each visual area: the high temporal frequency at which the local dots were updated (30 Hz) and the much lower frequency corresponding to updates in the global structure (0.83 Hz). Local and global responses were evaluated in the context of two different behavioral tasks--subjects had to either direct their attention toward or away from the global coherence of the stimuli. The data show that the effect of attention on global and local responses is both stimulus and visual area dependent. When attention was directed away from stimulus coherence, both local and global responses were higher in the coherent motion than Glass pattern condition. Directing attention to coherence in Glass patterns enhanced global activity in areas LOC, hMT+, V4, V3a, and V1, while attention to global motion modulated responses by a smaller amount in a smaller set of areas: V4, hMT+, and LOC. In contrast, directing attention towards stimulus coherence weakly increased local responses to both coherent motion and Glass patterns. These results suggest that visual attention differentially modulates the activity of early visual areas at both local and global levels of structural encoding.

A moving observer needs to be able to estimate the trajectory of other objects moving in the scene. Without the ability to do so, it would be difficult to avoid obstacles or catch a ball. We hypothesized that neural mechanisms sensitive to the patterns of motion generated on the retina during self-movement (optic flow) play a key role in this process, "parsing" motion due to self-movement from that due to object movement. We investigated this "flow parsing" hypothesis by measuring the perceived trajectory of a moving probe placed within a flow field that was consistent with movement of the observer. In the first experiment, the flow field was consistent with an eye rotation; in the second experiment, it was consistent with a lateral translation of the eyes. We manipulated the distance of the probe in both experiments and assessed the consequences. As predicted by the flow parsing hypothesis, manipulating the distance of the probe had differing effects on the perceived trajectory of the probe in the two experiments. The results were consistent with the scene geometry and the type of simulated self-movement. In a third experiment, we explored the contribution of local and global motion processing to the results of the first two experiments. The data suggest that the parsing process involves global motion processing, not just local motion contrast. The findings of this study support a role for optic flow processing in the perception of object movement during self-movement.