How does the visual system combine information from different depth cues to estimate three-dimensional scene parameters? We tested a maximum-likelihood estimation (MLE) model of cue combination for perspective (texture) and binocular disparity cues to surface slant. By factoring the reliability of each cue into the combination process, MLE provides more reliable estimates of slant than would be available from either cue alone. We measured the reliability of each cue in isolation across a range of slants and distances using a slant-discrimination task. The reliability of the texture cue increases as |slant | increases and does not change with distance. The reliability of the disparity cue decreases as distance increases and varies with slant in a way that also depends on viewing distance. The trends in the single-cue data can be understood in terms of the information available in the retinal images and issues related to solving the binocular correspondence problem. To test the MLE model, we measured perceived slant of two-cue stimuli when disparity and texture were in conflict and the reliability of slant estimation when both cues were available. Results from the two-cue study indicate, consistent with the MLE model, that observers weight each cue according to its relative reliability: Disparity weight decreased as distance and |slant | increased. We also observed the expected improvement in slant estimation when both cues were available. With few discrepancies, our data indicate that observers combine cues in a statistically optimal fashion and thereby reduce the variance of slant estimates below that which could be achieved from either cue alone. These results are consistent with other studies that quantitatively examined the MLE model of cue combination.

Perception is an “inverse problem, ” in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (Helmholtz, 1856; Marr, 1982) and corrupted by noise (Green & Swets, 1989), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (Berger, 1985; Cox, 1961). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (Knill & Pouget, 2004). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.

Abstract not found

Abstract – Visual-haptic perception of an object’s compliance demands integration of haptic position and force information as well as visual position information. In this investigation the role of active exploration on visual perception as well as the influence of visualhaptic information was addressed. Participants were instructed to directly match a compliant stimulus displayed either by vision (static passive or active), haptics, or both. Active testing of the visually displayed cube resulted in no difference in visual thresholds, suggesting that exploration method did not influence visual position discrimination. However, the threshold of visual matching of the cube’s indentation was smaller than for haptic compliance matching, not only unimodally but bimodally, in which case the alternate modality was present and adds noise resulting in an increase in bimodal thresholds.

The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer’s behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location— ‘‘model selection.’ ’ A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of

Visual-haptic compliance perception demands processing of haptic position and force information as well as visual position information. In this study it was analyzed how different compliances are perceived and how single modality estimates are combined to a visual-haptic percept of compliance. Participants estimated the compliance of a virtual cube displayed by a human system interface. Thereby, psychometric functions were recorded and statistically evaluated. Results indicated that human’s ability to discriminate differences relative to a standard compliance decreased as the standard compliance increased. Furthermore, in the conflicting case participants’ bimodal percept was close to the modality that captured the less compliant information.

We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and nonattended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.

Abstract: We provide quantitative measurements of night vision device (NVD) halos formed by light sources as a function of intensity and distance, describe a method to simulate their effects in the lab and present results from psychophysical experiments designed to analyze haloinduced errors in slope estimation. The effective halo generating potential of a point source is presumed to be a function of ambient illumination, source intensity, spectral content and distance. We designed a study to compare perceptual and objective measures of NVD halo size directly using identical laboratory conditions. NVD halo size is effectively invariant of light source intensity and distance when the halo is perceived. Source intensity and distance affect the likelihood that a primary and secondary halo will be perceived and the vividness of the halo but have little effect on halo image size when halo is present. The fact that primary halos do not change angular size with distance of the generating sources however lends them interesting perceptual properties. A given halo should appear to shrink as one approaches the light source and an isolated bright halo should appear nearer than a dim one even if further away. We have verified these predictions in the lab. The effects of halo on judgments of slope were studied during simulated helicopter approach and landing. Three-dimensional computer graphic simulations of flight over modeled terrain were

This paper tests the hypothesis that language comprehenders update their beliefs about the statistics of their language throughout the lifespan, and that this belief update allows comprehenders to combine probabilistic linguistic cues according to their reliability. We conduct a multi-day sentence comprehension study in which the reliability of a probabilistic cue to syntactic structure is manipulated between subjects. We find that as the reliability of one cue to syntactic structure decreases, comprehenders come to rely more on a second cue to syntactic structure. The results are consonant with rational models of cue integration in speech perception and in nonlinguistic domains, thus suggesting a unifying computational principle governing the way humans use information across both perceptual and higher-level cognitive tasks.

Abstract not found