Comparing set summary statistics and outlier pop out in vision

Hochstein, Shaul; Pavlovskaya, Marina; Bonneh, Yoram; Soroker, Nachum

doi:10.1167/18.13.12

Cited by 32 publications

(44 citation statements)

References 42 publications

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“…Hansmann-Roth et al (2019) found that while observers were sensitive to differences in mean color, and in variance, they were not able to visually detect any differences when the two arrays differ only in terms of the shape of the underlying colour distribution. These findings agree with other findings (Hochstein et al 2018), as well as Rahnev's view on perceptual decisions. When faced with a perceptual decision (i.e.…”

Section: Perception Versus Perceptual Decisionsupporting

confidence: 94%

“…There was remarkable correspondence in the CT-PD curves between the shape of the underlying representation of the distractor distribution and the shape of the physical distractor distribution. This correspondence is especially important given that the two different distractor distributions had the same mean and range, which are summary statistical variables crucial for outlier detection among a set of visual items (Hochstein, Pavlovskaya, Bonneh & Soroker, 2018). This indicates that the difference observed in the CT-PD curves (Figure 4) cannot be attributed to summary statistic representations.…”

Section: Resultsmentioning

confidence: 90%

See 1 more Smart Citation

What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception

Tanrıkulu¹,

Chetverikov²,

Hansmann-Roth³

et al. 2019

Preprint

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Recent accounts of perception and cognition propose that the brain represents information probabilistically. While this assumption is common, empirical support for such probabilistic representations has recently been criticized. It has been argued that due to methodological limitations of perceptual experiments, probabilistic theories only provide a model for perceptual decisions, but not perception itself (Block 2018). Moreover, results showing that the visual system represents sensory uncertainty can be explained by non-probabilistic or summary statistical accounts that rely on correlations between stimulus uncertainty and inner visual states (Rahnev, 2017; Block 2018). Here, we argue that a recently proposed psychophysical method, Feature Distribution Learning (FDL), provides promising evidence for probabilistic representations by sidestepping these recent criticisms. The method uses priming and role-reversal effects in visual search. Observers’ search times reveal the structure of perceptual representations in which the probability distribution of distractors is encoded. This method avoids the criticism mentioned and its results cannot be accounted for by non- probabilistic representations, since they suggest that the structure of inner perceptual states follows stimulus uncertainty. We explain how FDL results provide evidence for a stronger sense of the notion of representation that relies on structural isomorphism between stimulus uncertainty and perceptual representations, rather than a co-variation between the two. We also discuss further research directions in the light of the results from the FDL method. Our main goal is to demonstrate that FDL studies provide a novel perspective on these issues and provide the most promising evidence for probabilistic mental representations to date

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Section: Perception Versus Perceptual Decisionsupporting

confidence: 94%

Section: Resultsmentioning

confidence: 90%

What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception

Tanrıkulu¹,

Chetverikov²,

Hansmann-Roth³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Or is detailed information about feature distributions retained in some way? Information can be reduced to the mean and variance of a distribution and perceptual tasks like outlier detection or categorization can be performed through knowledge of the statistics (Hochstein et al, 2018;Khayat & Hochstein, 2018;Utochkin, 2015). However, optimal behaviour requires the encoding of a full feature probability distribution that is not always easy to summarize with simple statistics.…”

Section: Discussionmentioning

confidence: 99%

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Hansmann-Roth¹,

Kristjánsson²,

Whitney³

et al. 2020

Preprint

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Visual ensembles, like leaves on a tree, often share properties such as shape or color. Thisredundancy can be quantified as a feature probability distribution whose summary statistics (e.g.,mean) observers can report explicitly. Here, we show that such explicit reports underestimate therichness of ensemble perception. Participants (N=12 per condition) searched for an odd-one-outtarget among heterogeneous distractors and their memory of distractor characteristics was testedexplicitly or implicitly. Our observers could explicitly distinguish distractor sets with differentmean and variance, but not differently-shaped probability distributions. In contrast, implicitassessment revealed encoding of mean, variance, and importantly, also distribution shape.Furthermore, explicit measures had common noise sources that distinguished them from implicitmeasures. We conclude that feature distributions are encoded in rich detail and can guidebehaviour, while explicit summary judgments are based on information equivalent to a fewsamples from fully encoded distributions.

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“…My lab turned to the field of set mean perception, showing that participants perceive both set mean and range, and that these are perceived automatically, implicitly (i.e., while consciously performing only a different task that is unrelated to set mean or range), and on-the-fly trial-by-trial (Khayat & Hochstein, 2018; see also Khayat & Hochstein, 2019). We also studied the relationship between set statistics perception and feature search pop out (Hochstein et al, 2018). We used similar experimental displays for these two tasks, asking participants to compare two arrays of variously oriented bars and report either which set was oriented more clockwise on average (mean perception) or which contained an outlier (feature pop-out).…”

Section: Set Summary Mean Perceptionmentioning

confidence: 99%

“…We found that mean perception depended only on the difference in orientation means of the two arrays and not on their orientation range. In contrast, outlier detection depended only on the distance of the outlier from the set range edge (in order that it be an outlier) and again not on the range itself (Hochstein et al, 2018).…”

Section: Set Summary Mean Perceptionmentioning

confidence: 99%

The gist of Anne Treisman’s revolution

Hochstein

2019

Atten Percept Psychophys

Self Cite

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Anne Treisman investigated many aspects of perception, and in particular the roles of different forms of attention. Four aspects of her work are reviewed here, including visual search, set mean perception, perception in special populations, and binocular rivalry. The importance of the breakthrough in each case is demonstrated. Search is easy or slow depending on whether it depends on the application of global or focused attention. Mean perception depends on global attention and affords simultaneous representation of the means of at least two sets of elements, and then of comparing them. Deficits exhibited in Balint's or unilateral neglect patients identify basic sensory system mechanisms. And, the ability to integrate binocular information for stereopsis despite simultaneous binocular rivalry for color, demonstrates the division of labor underlying visual system computations. All these studies are related to an appreciation of the difference between perceiving the gist of a scene, its elements or objects, versus perceiving the details of the scene and its components. This relationship between Anne Treisman's revolutionary discoveries and the concept of gist perception is the core of the current review.

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Comparing set summary statistics and outlier pop out in vision

Cited by 32 publications

References 42 publications

What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception

What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

The gist of Anne Treisman’s revolution

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