The spatial resolutions of stereo and motion perception and their neural basis

PhD ThesisDepth perception requires finding matching features between the two eye’s images to estimate binocular disparity. This process has been successfully modelled using local cross-correlation. The model is based on the known physiology of primary visual cortex (V1) and has explained many aspects of stereo vision including why spatial stereoresolution is low compared to the resolution for luminance patterns, suggesting that the limit on spatial stereoresolution is set in V1. We predicted that this model would perform better at detecting square-wave disparity gratings, consisting of regions of locally constant disparity, than sine-waves which are slanted almost everywhere. We confirmed this through computational modelling and performed psychophysical experiments to test whether human performance followed the predictions of the model. We found that humans perform equally well with both waveforms. This contradicted the model’s predictions raising the question of whether spatial stereoresolution may not be limited in V1 after all or whether changing the model to include more of the known physiology may make it consistent with human performance. We incorporated the known size-disparity correlation into the model, giving disparity detectors with larger preferred disparities larger correlation windows, and found that this modified model explained the new human results. This provides further evidence that spatial stereoresolution is limited in V1. Based on previous evidence that MT neurons respond well to transparent motion in different depth planes we predicted that the spatial resolution of joint motion/disparity perception would be limited by the significantly larger MT receptive field sizes and therefore be much lower than the resolution for pure disparity. We tested this using a new joint motion/disparity grating, designed to require the detection of conjunctions between motion and disparity. We found little difference between the resolutions for disparity and joint gratings, contradicting our predictions and suggesting that a different area than MT was used

The spatial resolutions of stereo and motion perception and their neural basis

Depth perception requires finding matching features between the two eye’s images to estimate binocular disparity. This process has been successfully modelled using local cross-correlation. The model is based on the known physiology of primary visual cortex (V1) and has explained many aspects of stereo vision including why spatial stereoresolution is low compared to the resolution for luminance patterns, suggesting that the limit on spatial stereoresolution is set in V1. We predicted that this model would perform better at detecting square-wave disparity gratings, consisting of regions of locally constant disparity, than sine-waves which are slanted almost everywhere. We confirmed this through computational modelling and performed psychophysical experiments to test whether human performance followed the predictions of the model. We found that humans perform equally well with both waveforms. This contradicted the model’s predictions raising the question of whether spatial stereoresolution may not be limited in V1 after all or whether changing the model to include more of the known physiology may make it consistent with human performance. We incorporated the known size-disparity correlation into the model, giving disparity detectors with larger preferred disparities larger correlation windows, and found that this modified model explained the new human results. This provides further evidence that spatial stereoresolution is limited in V1. Based on previous evidence that MT neurons respond well to transparent motion in different depth planes we predicted that the spatial resolution of joint motion/disparity perception would be limited by the significantly larger MT receptive field sizes and therefore be much lower than the resolution for pure disparity. We tested this using a new joint motion/disparity grating, designed to require the detection of conjunctions between motion and disparity. We found little difference between the resolutions for disparity and joint gratings, contradicting our predictions and suggesting that a different area than MT was used.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Variation in the “coefficient of variation”

The coefficient of variation (CV), also known as relative standard deviation, has been used to measure the constancy of the Weber fraction, a key signature of efficient neural coding in time perception. It has long been debated whether or not duration judgments follow Weber's law, with arguments based on examinations of the CV. However, what has been largely ignored in this debate is that the observed CVs may be modulated by temporal context and decision uncertainty, thus questioning conclusions based on this measure. Here, we used a temporal reproduction paradigm to examine the variation of the CV with two types of temporal context: full-range mixed vs. sub-range blocked intervals, separately for intervals presented in the visual and auditory modalities. We found a strong contextual modulation of both interval-duration reproductions and the observed CVs. We then applied a two-stage Bayesian model to predict those variations. Without assuming a violation of the constancy of the Weber fraction, our model successfully predicted the central-tendency effect and the variation in the CV. Our findings and modeling results indicate that both the accuracy and precision of our timing behavior are highly dependent on the temporal context and decision uncertainty. And, critically, they advise caution with using variations of the CV to reject the constancy of the Weber fraction of duration estimation

Learning to suppress likely distractor locations in visual search is driven by the local distractor frequency

Salient but task-irrelevant distractors interfere less with visual search when they appear in a display region where distractors have appeared more frequently in the past (‘distractor-location probability cueing’). This effect could reflect the (re-)distribution of a global, limited attentional ‘inhibition resource’. Accordingly, changing the frequency of distractor appearance in one display region should also affect the magnitude of interference generated by distractors in a different region. Alternatively, distractor-location learning may reflect a local response (e.g., ‘habituation’) to distractors occurring at a particular location. In this case, the local distractor frequency in one display region should not affect distractor interference in a different region. To decide between these alternatives, we conducted three experiments in which participants searched for an orientation-defined target while ignoring a more salient orientation distractor that occurred more often in one vs. another display region. Experiment 1 varied the ratio of distractors appearing in the frequent vs. rare regions (60/40–90/10), with a fixed global distractor frequency. The results revealed the cueing effect to increase with increasing probability ratio. In Experiments 2 and 3, one (‘test’) region was assigned the same local distractor frequency as in one of the conditions of Experiment 1, but a different frequency in the other region – dissociating local from global distractor frequency. Together, the three experiments showed that distractor interference in the test region was not significantly influenced by the frequency in the other region, consistent with purely local learning. We discuss the implications for theories of statistical distractor-location learning

Duration reproduction under memory pressure: Modeling the roles of visual memory load in duration encoding and reproduction

Duration estimates are often biased by the sampled statistical context, yielding the classical central-tendency effect, i.e., short durations are over- and long duration underestimated. Most studies of the central-tendency bias have primarily focused on the integration of the sensory measure and the prior information, without considering any cognitive limits. Here, we investigated the impact of cognitive (visual working-memory) load on duration estimation in the duration encoding and reproduction stages. In four experiments, observers had to perform a dual, attention-sharing task: reproducing a given duration (primary) and memorizing a variable set of color patches (secondary). We found an increase in memory load (i.e., set size) during the duration-encoding stage to increase the central-tendency bias, while shortening the reproduced duration in general; in contrast, increasing the load during the reproduction stage prolonged the reproduced duration, without influencing the central tendency. By integrating an attentional-sharing account into a hierarchical Bayesian model, we were able to predict both the general over- and underestimation and the central-tendency effects observed in all four experiments. The model suggests that memory pressure during the encoding stage increases the sensory noise, which elevates the central-tendency effect. In contrast, memory pressure during the reproduction stage only influences the monitoring of elapsed time, leading to a general duration over-reproduction without impacting the central tendency.Competing Interest StatementThe authors have declared no competing interest

Long‐term (statistically learnt) and short‐term (inter‐trial) distractor‐location effects arise at different pre‐ and post‐selective processing stages

A salient distractor interferes less with visual search if it appears at a location where it is likely to occur, referred to as distractor-location probability cueing. Conversely, if the current target appears at the same location as a distractor on the preceding trial, search is impeded. While these two location-specific “suppression” effects reflect long-term, statistically learnt and short-term, inter-trial adaptations of the system to distractors, it is unclear at what stage(s) of processing they arise. Here, we adopted the additional-singleton paradigm and examined lateralized event-related potentials (L-ERPs) and lateralized alpha (8–12 Hz) power to track the temporal dynamics of these effects. Behaviorally, we confirmed both effects: reaction times (RTs) interference was reduced for distractors at frequent versus rare (distractor) locations, and RTs were delayed for targets that appeared at previous distractor versus non-distractor locations. Electrophysiologically, the statistical-learning effect was not associated with lateralized alpha power during the pre-stimulus period. Rather, it was seen in an early N1pc referenced to the frequent distractor location (whether or not a distractor or a target occurred there), indicative of a learnt top-down prioritization of this location. This early top-down influence was systematically modulated by (competing) target- and distractor-generated bottom-up saliency signals in the display. In contrast, the inter-trial effect was reflected in an enhanced SPCN when the target was preceded by a distractor at its location. This suggests that establishing that an attentionally selected item is a task-relevant target, rather than an irrelevant distractor, is more demanding at a previously “rejected” distractor location

Predictive coding in ASD: inflexible weighting of prediction errors when switching from stable to volatile environments

Individuals with autism spectrum disorder (ASD) have been widely reported to show atypicalities in predictive coding, though there remains a controversy regarding what causes such atypical processing. Suggestions range from overestimation of volatility to rigidity in the reaction to environmental changes. Here, we tested two accounts directly using duration reproduction of volatile and non-volatile interval sequences. Critically, both sequences had the same set of intervals but differed in their stimulus presentation orders. Comparing individuals with ASD vs. their matched controls, we found both groups to respond to the volatility in a similar manner, albeit with a generally reduced prior in the ASD group. Interestingly, though, relative to the control group, the ASD group exhibited a markedly reduced trust in the prior in the volatile trial session when this was performed after the non-volatile session, while both groups performed comparably in the reverse session order. Our findings suggest that it is not the learning of environmental volatility that is compromised in ASD. Rather, it is their response to a change of the volatility regimen from stable to volatile, which causes a highly inflexible weighting of prediction errors.Competing Interest StatementThe authors have declared no competing interest

Acquisition and Use of 'Priors' in Autism: Typical in Deciding Where to Look, Atypical in Deciding What Is There

Individuals with Autism Spectrum Disorder (ASD) are thought to under-rely on prior knowledge in perceptual decision-making. This study examined whether this applies to decisions of attention allocation, of relevance for 'predictive-coding' accounts of ASD. In a visual search task, a salient but task-irrelevant distractor appeared with higher probability in one display half. Individuals with ASD learned to avoid 'attentional capture' by distractors in the probable region as effectively as control participants-indicating typical priors for deploying attention. However, capture by a 'surprising' distractor at an unlikely location led to greatly slowed identification of a subsequent target at that location-indicating that individuals with ASD attempt to control surprise (unexpected attentional capture) by over-regulating parameters in post-selective decision-making

Spatial Stereoresolution for Depth Corrugations May Be Set in Primary Visual Cortex

Stereo “3D” depth perception requires the visual system to extract binocular disparities between the two eyes' images. Several current models of this process, based on the known physiology of primary visual cortex (V1), do this by computing a piecewise-frontoparallel local cross-correlation between the left and right eye's images. The size of the “window” within which detectors examine the local cross-correlation corresponds to the receptive field size of V1 neurons. This basic model has successfully captured many aspects of human depth perception. In particular, it accounts for the low human stereoresolution for sinusoidal depth corrugations, suggesting that the limit on stereoresolution may be set in primary visual cortex. An important feature of the model, reflecting a key property of V1 neurons, is that the initial disparity encoding is performed by detectors tuned to locally uniform patches of disparity. Such detectors respond better to square-wave depth corrugations, since these are locally flat, than to sinusoidal corrugations which are slanted almost everywhere. Consequently, for any given window size, current models predict better performance for square-wave disparity corrugations than for sine-wave corrugations at high amplitudes. We have recently shown that this prediction is not borne out: humans perform no better with square-wave than with sine-wave corrugations, even at high amplitudes. The failure of this prediction raised the question of whether stereoresolution may actually be set at later stages of cortical processing, perhaps involving neurons tuned to disparity slant or curvature. Here we extend the local cross-correlation model to include existing physiological and psychophysical evidence indicating that larger disparities are detected by neurons with larger receptive fields (a size/disparity correlation). We show that this simple modification succeeds in reconciling the model with human results, confirming that stereoresolution for disparity gratings may indeed be limited by the size of receptive fields in primary visual cortex

Logarithmic encoding of ensemble time intervals

Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, there is evidence that logarithmic timing provides a better fit to behavioral data. A major challenge for investigating the nature of the internal scale is that the retrieval process required for time judgments may involve a remapping of the subjective time back to the objective scale, complicating any direct interpretation of behavioral findings. Here, we used a novel approach, requiring rapid intuitive ‘ensemble’ averaging of a whole set of time intervals, to probe the subjective timeline. Specifically, observers’ task was to average a series of successively presented, auditory or visual, intervals in the time range 300–1300 ms. Importantly, the intervals were taken from three sets of durations, which were distributed such that the arithmetic mean (from the linear scale) and the geometric mean (from the logarithmic scale) were clearly distinguishable. Consistently across the three sets and the two presentation modalities, our results revealed subjective averaging to be close to the geometric mean, indicative of a logarithmic timeline underlying time perception