Biologically Inspired Dynamic Textures for Probing Motion Perception

Perception is often described as a predictive process based on an optimal inference with respect to a generative model. We study here the principled construction of a generative model specifically crafted to probe motion perception. In that context, we first provide an axiomatic, biologically-driven derivation of the model. This model synthesizes random dynamic textures which are defined by stationary Gaussian distributions obtained by the random aggregation of warped patterns. Importantly, we show that this model can equivalently be described as a stochastic partial differential equation. Using this characterization of motion in images, it allows us to recast motion-energy models into a principled Bayesian inference framework. Finally, we apply these textures in order to psychophysically probe speed perception in humans. In this framework, while the likelihood is derived from the generative model, the prior is estimated from the observed results and accounts for the perceptual bias in a principled fashion.Comment: Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), Dec 2015, Montreal, Canad

How Good Are These UML Diagrams? An Empirical Test of the Wand and Weber Good Decomposition Model

In 1989, Wand and Weber proposed a formal model of systems decomposition based on ontology. Chidamber and Kemerer (1994) soon applied this model to develop complexity metrics for object-oriented design (OOD). Chidamber and Kemererís OOD metrics suite continues to receive interest in software engineering (Bansiya and Davis 2002; Basili et al. 1996). To date, however, Wand and Weberís good decomposition model has received almost no application in information systems (IS). For three reasons, we believe the theory might assist IS researchers. First, object-oriented analysis (OOA) has not been as successful in practice as OOD or OO programming (Chuang and Yadav 2000). The good decomposition model may help IS researchers investigate improvements to OOA. Second, Johnson (2002) recently lamented how few OOA studies employ any theory. Wand and Weberís theory may, therefore, be a useful approach. Third, many believe OOA is a revolutionary step away from traditional approaches (Sircar et al. 2001). Practicing analysts could benefit from theory-based principles to guide their use of this ìrevolutionaryî technique. In this study, we report an experiment to determine the utility of the good decomposition model in OOA. We operationalized each condition of Wand and Weberís model in a set of UML diagrams and tested participantsí understanding of the diagrams across three levels. Our results lend support to Wand and Weberís theory, but only across dependent variables that tested participantsí actual understanding. The impact on participantsí perceptions of their understanding remained equivocal

The Effects of Decomposition Quality and Multiple Forms of Information on Novices’ Understanding of a Domain from a Conceptual Model

Individuals can often use conceptual models to learn about the business domain to be supported by an information system. We investigate the extent to which such models can help novices (i.e., individuals who lack knowledge in the business domain and in conceptual modeling) to obtain an understanding of the domain codified in the model. We focus on two factors that we predict will influence novices’ understanding: (1) decomposition quality: whether the conceptual model manifests a good decomposition of the domain, and (2) multiple forms of information: whether the conceptual model is accompanied by information in another form (e.g., a textual narrative). We hypothesize that both factors will have positive effects on understanding and that these effects depend on whether the individual seeks a surface or deep understanding. Our results are largely in line with our predictions. Moreover, our results suggest that while novices are generally aware that having multiple forms of information affects their understanding, they are unaware that decomposition quality affects their understanding. Based on these results, we recommend that practitioners include complementary forms of information (such as a textual narrative) along with conceptual models and be careful to ensure that their conceptual models manifest a good decomposition of the domain

Numerosity and density judgments: Biases for area but not for volume

International audienceHuman observers can rapidly judge the number of items in a scene. This ability is underpinned by specific mechanisms encoding number or density. We investigated whether judgments of number and density are biased by a change in volume, as they are by a change in area. Stimuli were constructed using nonoverlapping black and white luminance-defined dots. An eight-mirror Wheatstone stereoscope was used to present the dots as though in a volume. Using a temporal two-alternative forced-choice (2AFC) task and the Method of Constant Stimuli (MOCS), we measured the precision and bias (PSE shift) of numerosity and density judgments, separately, for stimuli differing in area or volume. For two-dimensional (2-D) stimuli, consistent with previous literature, perceived density was biased as area increased. However, perceived number was not. For three-dimensional (3-D) stimuli, despite a vivid impression of the dots filling a cylindrical volume, there was no bias in perceived density or number as volume increased. A control experiment showed that all of our observers could easily perceive disparity in our stimuli. Our findings reveal that number and density judgments that are biased by area are not similarly biased by volume changes

Cognitive-perceptual traits associated with autism and schizotypy influence use of physics during predictive visual tracking

Schizophrenia and autism spectrum disorder (ASD) can disrupt cognition and consequently behaviour. Traits of ASD and the subclinical manifestation of schizophrenia called schizotypy have been studied in healthy populations with overlap found in trait profiles linking ASD social deficits to negative schizotypy and ASD attention to detail to positive schizotypy. Here, we probed the relationship between subtrait profiles, cognition and behaviour, using a predictive tracking task to measure individuals' eye movements under three gravity conditions. A total of 48 healthy participants tracked an on-screen projected ball under familiar gravity, inverted upward acceleration (against gravity) and horizontal gravity control conditions while eye movements were recorded and dynamic performance quantified. Participants completed ASD and schizotypy inventories generating highly correlated scores, r = 0.73. All tracked best under the gravity condition, producing anticipatory downward responses from stimulus onset which were delayed under upward inverted gravity. Tracking performance was not associated with overall ASD or schizotypy trait levels. Combining measures using principal components analysis (PCA), we decomposed the inventories into subtraits unveiling interesting patterns. Positive schizotypy was associated with ASD dimensions of rigidity, odd behaviour and face processing, which all linked to anticipatory tracking responses under inverted gravity. In contrast, negative schizotypy was associated with ASD dimensions of social interactions and rigidity and to early stimulus-driven tracking under gravity. There was also substantial nonspecific overlap between ASD and schizotypy dissociated from tracking. Our work links positive-odd traits with anticipatory tracking when physics rules are violated and negative-social traits with exploitation of physics laws of motion

Understanding the impact of recurrent interactions on population tuning: Application to MT cells characterization

International audienceA ring network model under neural fields formalism with a structured input is studied. Bifurcation analysis is applied to understand the behaviour of the network model under different connectivity regimes and input conditions. The parameter regimes over which the localised input bumps could be preserved, combined or selected are used to identify the potential network regimes under which direction selective cells in MT area exhibiting analogous behaviour could be operating. The parameter regimes are further explored to identify possible transitions in the tuning behaviour with respect to change of driving stimuli as observed in experimental recordings

Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probed human speed representation using ocular tracking of stimuli of different statistics. First, we compared ocular responses to single drifting gratings (DGs) with a given set of spatiotemporal frequencies to broadband motion clouds (MCs) of matched mean frequencies. Motion energy distributions of gratings and clouds are point-like, and ellipses oriented along the constant speed axis, respectively. Sampling frequency space, MCs elicited stronger, less variable, and speed-tuned responses. DGs yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supralinearly integrated along an iso-velocity line and sublinearly integrated away from it. A dynamical probabilistic model characterizes these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal “scale” axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organized along speed and scale axes

The relative contribution of noise and adaptation to competition during tri-stable motion perception

International audienceAnimals exploit antagonistic interactions for sensory processing and these can cause oscillations between competing states. Ambiguous sensory inputs yield such perceptual multi-stability. Despite numerous empirical studies using binocular rivalry or plaid pattern motion, the driving mechanisms behind the spontaneous transitions between alternatives remain unclear. In the current work, we used a tri-stable barberpole motion stimulus combining empirical and modelling approaches to elucidate the contributions of noise and adaptation to underlying competition. We first robustly characterised the coupling between perceptual reports of transitions and continuously recorded eye direction, identifying a critical window of 480ms before button presses within which both measures were most strongly correlated. Second, we identified a novel non monotonic relationship between stimulus contrast and average perceptual switching rate with an initially rising rate before a gentle reduction at higher contrasts. A neural fields model of the underlying dynamics introduced in previous theoretical work and incorporating noise and adaptation mechanisms was adapted, extended and empirically validated. Noise and adaptation contributions were confirmed to dominate at the lower, and higher, contrasts respectively. Model simulations with two free parameters, controlling adaptation dynamics and direction thresholds, captured the measured mean transition rates for participants. We verified the shift from noise dominated towards adaptation-driven in both the eye direction distributions and inter-transition duration statistics. This work combines modelling and empirical evidence to demonstrate the signal strength dependent interplay between noise and adaptation during tri- stability. We propose that the findings generalise beyond the barberpole stimulus case to ambiguous perception in continuous feature spaces

Evidence of inverted-gravity driven variation in predictive sensorimotor function.

We move our eyes to place the fovea into the part of a viewed scene currently of interest. Recent evidence suggests that each human has signature patterns of eye movements like handwriting which depend on their sensitivity, allocation of attention and experience. Use of implicit knowledge of how earth's gravity influences object motion has been shown to aid dynamic perception. We used a projected ball tracking task with a plain background offering no context cues to probe the effect of acquired experience about physical laws of gravitation on performance differences of 44 participants under a simulated gravity and an atypical (upward) antigravity condition. Performance measured by the unsigned difference between instantaneous eye and stimulus positions (RMSE) was consistently worse in the antigravity condition. In the vertical RMSE, participants took about 200ms longer to improve to the best performance for antigravity compared to gravity trials. The antigravity condition produced a divergence of individual performance which was correlated with levels of questionnaire based quantified traits of schizotypy but not control traits. Grouping participants by high or low traits revealed a negative relationship between schizotypy traits level and both initiation and maintenance of tracking, a result consistent with trait related impoverished sensory prediction. The findings confirm for the first time that where cues enabling exact estimation of acceleration are unavailable, knowledge of gravity contributes to dynamic prediction improving motion processing. With acceleration expectations violated, we demonstrate that antigravity tracking could act as a multivariate diagnostic window into predictive brain function

Scene regularity interacts with individual biases to modulate perceptual stability

Sensory input is inherently ambiguous but our brains achieve remarkable perceptual stability. Prior experience and knowledge of the statistical properties of the world are thought to play a key role in the stabilization process. Individual differences in responses to ambiguous input and biases towards one or the other interpretation could modulate the decision mechanism for perception. However, the role of perceptual bias and its interaction with stimulus spatial properties such as regularity and element density remain to be understood. To this end, we developed novel bi-stable moving visual stimuli in which perception could be parametrically manipulated between two possible mutually exclusive interpretations: transparently or coherently moving. We probed perceptual stability across three composite stimulus element density levels with normal or degraded regularity using a factorial design. We found that increased density led to the amplification of individual biases and consequently to a stabilization of one interpretation over the alternative. This effect was reduced for degraded regularity, demonstrating an interaction between density and regularity. To understand how prior knowledge could be used by the brain in this task, we compared the data with simulations coming from four different hierarchical models of causal inference. These models made different assumptions about the use of prior information by including conditional priors that either facilitated or inhibited motion direction integration. An architecture that included a prior inhibiting motion direction integration consistently outperformed the others. Our results support the hypothesis that direction integration based on sensory likelihoods maybe the default processing mode with conditional priors inhibiting integration employed in order to help motion segmentation and transparency perception