Comparing averaging limits for social cues over space and time

Florey, Joseph; Dakin, Steven C.; Mareschal, Isabelle

doi:10.1167/17.9.17

Cited by 12 publications

(12 citation statements)

References 36 publications

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“…An alternative hypothesis inspired by findings about perceptual decision making is that the visual system could compute spatial average promptly for each temporal frame and then estimate overall average across all temporal frames. This idea is consistent with psychophysical data that humans are capable of extracting ensembles www.nature.com/scientificreports www.nature.com/scientificreports/ with surprising speed and accuracy 13,42 even if they are composed of higher-order visual features including facial expressions 7 and head direction 37 . As for spatial average-orientation, Dakin (2001) used spatially distributed visual stimuli with durations of only 100 ms and obtained average-orientation discrimination thresholds similar to our results 13 .…”

Section: Discussionsupporting

confidence: 84%

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Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Yashiro

Satō

Oide

et al. 2020

Sci Rep

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A number of studies on texture and ensemble perception have shown that humans can immediately estimate the average of spatially distributed visual information. the present study characterized mechanisms involved in estimating averages for information distributed over both space and time. observers viewed a rapid sequence of texture patterns in which elements' orientation were determined by dynamic Gaussian noise with variable spatial and temporal standard deviations (SDs). We found that discrimination thresholds increased beyond a certain spatial SD if temporal SD was small, but if temporal SD was large, thresholds remained nearly constant regardless of spatial SD. these data are at odds with predictions that threshold is uniquely determined by spatiotemporal SD. Moreover, a reverse correlation analysis revealed that observers judged the spatiotemporal average orientation largely depending on the spatial average orientation over the last few frames of the texture sequence -a recency effect widely observed in studies of perceptual decision making. Results are consistent with the notion that the visual system rapidly computes spatial ensembles and adaptively accumulates information over time to make a decision on spatiotemporal average. A simple computational model based on this notion successfully replicated observed data.Humans achieve stable perception of scenes and objects at a glance in spite of the spatial complexity and uncertainty of the natural image. While such perception seems to involve highly complicated and specialized neural processing, recent research has shown that perception builds upon image statistics computed relatively easily in the early stages of visual processing 1-4 . A vast psychophysical literature has suggested that the visual system is capable of rapidly estimating the characteristics of an ensemble of complex elements (e.g., objects, faces) 5-8 as well as discriminating textures defined by simple visual features such as form, color and motion 9-16 . These studies offer clear evidence that the visual system automatically extracts a statistical representation of the spatial properties of the image. Such statistical visual representations are thought to be subserved by neural mechanisms in early visual cortex with large spatial receptive field or cortico-cortical interactions 17-23 .Visual inputs inherently contain much temporal uncertainty owing to gaze shifts and object motions, and little is known about how mechanisms extracting spatial statistics cope with such temporal uncertainty. Psychophysical studies have examined how performance for orientation discrimination or global form detection in dynamic texture patterns varies as a function of stimulus duration. Results revealed that temporal summation is relatively short (over a few hundred milliseconds) and consistent with the idea that spatial statistics are computed rapidly by low-level mechanisms 24,25 . However, experiments using stochastic motion stimuli have shown that detecting global and biological motion requires a much longe...

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Section: Discussionsupporting

confidence: 84%

“…Figure 2b shows a replot of the data as a function of temporal SD. We found that if temporal SD is small, discrimination thresholds increase as a function of spatial SD as has often been reported in previous studies: Threshold-vs-Noise (TvN) function 13,37 . If temporal SD is large, however, thresholds remain nearly constant.…”

Section: Proceduresupporting

confidence: 84%

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Yashiro

Satō

Oide

et al. 2020

Sci Rep

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“…Temporal integration of visual features into ensembles has been observed in many studies (Chong & Treisman 2003, Haberman, Harp & Whitney, 2009Albert & Scholl, 2010;Whiting & Oriet, 2011, Hubert-Wallander & Boynton 2015Oriet & Hozempa, 2016). Moreover, integration efficiency of temporally presented visual items can be even higher than, or at least equal to, integration efficiency of spatially presented items (Florey, Dakin & Mareschal, 2017;Gorea, Belkoura & Solomon, 2014). Our results with the FDL method are in seeming contrast with these studies.…”

Section: Discussioncontrasting

confidence: 79%

Testing temporal integration of feature probability distributions using role-reversal effects in visual search

Tanrıkulu

Chetverikov²,

Kristjánsson

2021

Vision Research

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The visual system is sensitive to statistical properties of complex scenes and can encode feature probability distributions in detail. This encoding could reflect a passive process due to the visual system's sensitivity to temporal perturbations in the input or a more active process of building probabilistic representations. To investigate this, we examined how observers temporally integrate two different orientation distributions from sequentially presented visual search trials. If the encoded probabilistic information is used in a Bayesian optimal way, observers should weigh more reliable information more strongly, such as feature distributions with low variance. We therefore manipulated the variance of the two feature distributions. Participants performed sequential odd-one-out visual search for an oddly oriented line among distractors. During successive learning trials, the distractor orientations were sampled from two different Gaussian distributions on alternating trials. Then, observers performed a 'test trial' where the orientations of the target and distractors were switched, allowing to assess observer's internal representation of distractor distributions based on changes in response times. In three experiments we observed that observer's search times on test trials depended mainly on the very last learning trial, indicating little temporal integration. Since temporal integration has been previously observed with this method, we conclude that when the input is unreliable, the visual system relies on the most recent stimulus instead of integrating it with previous ones. This indicates that the visual system prefers to utilize sensory history when the statistical properties of the environment are relatively stable.

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“…Previous studies of ensemble perception involve integrating features over time (Albrecht & Scholl, 2010;Joo, Shin, Chong, & Blake, 2009;Yamanashi Leib, Fischer, Liu, Qiu, Robertson, & Whitney, 2014), as well as that over space (Ariely, 2001;Chong & Treisman, 2003). According to two recent studies (Florey, Dakin, & Mareschal, 2017;Gorea, Belkoura, & Solomon, 2014), these two types of integration are similar in efficiency and may share common sampling mechanisms.…”

Section: Introductionmentioning

confidence: 99%

The virtual loss function in the summary perception of motion and its limited adjustability

Teng

Zhang

2021

Journal of Vision

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Humans can grasp the "average" feature of a visual ensemble quickly and effortlessly. However, it is largely unknown what is the exact form of the summary statistic humans perceive and it is even less known whether this form can be changed by feedback. Here we borrow the concept of loss function to characterize how the summary perception is related to the distribution of feature values in the ensemble, assuming that the summary statistic minimizes a virtual expected loss associated with its deviation from individual feature values. In two experiments, we investigated a random-dot motion estimation task to infer the virtual loss function implicit in ensemble perception and see whether it can be changed by feedback. On each trial, participants reported the average moving direction of an ensemble of moving dots whose distribution of moving directions was skewed. In Experiment 1, where no feedback was available, participants' estimates fell between the mean and the mode of the distribution and were closer to the mean. In particular, the deviation from the mean and toward the mode increased almost linearly with the mode-to-mean distance. The pattern was best modeled by an inverse Gaussian loss function, which punishes large errors less heavily than the quadratic loss function does. In Experiment 2, we tested whether this virtual loss function can be altered by feedback. Two groups of participants either received the mode or the mean as the correct answer. After extensive training up to five days, both groups' estimates moved slightly towards the mode. That is, feedback had no specific influence on participants' virtual loss function. To conclude, the virtual loss function in the summary perception of motion is close to inverse Gaussian, and it can hardly be changed by feedback.

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Comparing averaging limits for social cues over space and time

Cited by 12 publications

References 36 publications

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Testing temporal integration of feature probability distributions using role-reversal effects in visual search

The virtual loss function in the summary perception of motion and its limited adjustability

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