Learning features in a complex and changing environment: A distribution-based framework for visual attention and vision in general

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

doi:10.1016/bs.pbr.2017.07.001

Cited by 23 publications

(24 citation statements)

References 116 publications

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“…In other words, we argue that the peripheral visual system constructed a representation of the distractor distribution in both experiments; however, this representation was less precise in Experiment 2, where more weight was given to the mean of the distribution in building the representation of the distribution, rather than its other features. Such an explanation is consistent with a distribution-based framework of visual attention (see Chetverikov et al [2017c] for a review).…”

Section: Ensemble Coding With Central Versus Peripheral Visionsupporting

confidence: 80%

“…These explicit judgments on statistical parameters of a feature distribution might have limited power in revealing how accurately feature distributions in an ensemble are encoded by the visual system. Recently, Chetverikov, Campana, and Kristjánsson (2016 , 2017a , 2017b , 2017c , 2020 ) used a novel method to demonstrate that observers can encode the probability density function underlying the distractor distribution in an odd-one-out visual search task for orientation (2016, 2017a) and color (2017b). Instead of using explicit judgments of distribution statistics, they measured observers’ visual search times varying target similarity to previously learned distractors, which revealed observers’ expectations of distractor feature distributions.…”

Section: Introductionmentioning

confidence: 99%

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Encoding perceptual ensembles during visual search in peripheral vision

Tanrıkulu

Chetverikov²,

Kristjánsson

2020

Journal of Vision

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Observers can learn complex statistical properties of visual ensembles, such as their probability distributions. Even though ensemble encoding is considered critical for peripheral vision, whether observers learn such distributions in the periphery has not been studied. Here, we used a visual search task to investigate how the shape of distractor distributions influences search performance and ensemble encoding in peripheral and central vision. Observers looked for an oddly oriented bar among distractors taken from either uniform or Gaussian orientation distributions with the same mean and range. The search arrays were either presented in the foveal or peripheral visual fields. The repetition and role reversal effects on search times revealed observers' internal model of distractor distributions. Our results showed that the shape of the distractor distribution influenced search times only in foveal, but not in peripheral search. However, role reversal effects revealed that the shape of the distractor distribution could be encoded peripherally depending on the interitem spacing in the search array. Our results suggest that, although peripheral vision might rely heavily on summary statistical representations of feature distributions, it can also encode information about the distributions themselves.

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Section: Ensemble Coding With Central Versus Peripheral Visionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Encoding perceptual ensembles during visual search in peripheral vision

Tanrıkulu

Chetverikov²,

Kristjánsson

2020

Journal of Vision

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“…We simulated the predictions from three models (Figure 2E-F; the simulation code is available at https://osf.io/rg2h8). For our main model of interest, the "bimodal" model, we assumed that the probabilities of different distractors can be represented by two Gaussian templates (for simplicity, we ignore the fact that the stimuli distributions might be more accurately represented by non-Gaussian templates (Chetverikov, Campana, & Kristjánsson, 2017a)) centered on the means of distractor distribution segments. We assumed that observers utilize the knowledge they obtained about distractors and targets optimally.…”

Section: Probabilistic Rejection Templates In Visual Working Memorymentioning

confidence: 99%

Probabilistic rejection templates in visual working memory

2020

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Our interactions with the visual world are guided by attention and visual working memory. Things that we look for and those we ignore are stored as templates that reflect our goals and the tasks at hand. The nature of such templates has been widely debated. A recent proposal is that these templates can be thought of as probabilistic representations of task-relevant features. Crucially, such probabilistic templates should accurately reflect feature probabilities in the environment. Here we ask whether observers can quickly form a correct internal model of a complex (bimodal) distribution of distractor features. We assessed observers' representations by measuring the slowing of visual search when target features unexpectedly match a distractor template. Distractor stimuli were heterogeneous, randomly drawn on each trial from a bimodal probability distribution. Using two targets on each trial, we tested whether observers encode the full distribution, only one peak of it, or the average of the two peaks. Search was slower when the two targets corresponded to the two modes of a previous distractor distribution than when one target was at one of the modes and another between them or outside the distribution range. Furthermore, targets on the modes were reported later than targets between the modes that, in turn, were reported later than targets outside this range. This shows that observers use a correct internal model, representing both distribution modes using templates based on the full probability distribution rather than just one peak or simple summary statistics. The findings further confirm that performance in odd-one out search with repeated distractors cannot be described by a simple decision rule. Our findings indicate that probabilistic visual working memory templates guiding attention, dynamically adapt to task requirements, accurately reflecting the probabilistic nature of the input.

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“…Not only do we seem to be able to generate strong incidental representations, but the memories we have gathered on the fly, during natural interactions, might in fact be critical for proactively guiding our behavior. Chetverikov, Campana, and Kristjánsson (2017a) have shown how repeated searching within search arrays with particular feature distributions of orientation or color (Chetverikov et al, 2017c;Tanrikulu, Chetverikov, & Kristjánsson, 2020) enables observers to learn the probabilities of feature values and build up a probabilistic template of the set for distractor rejection (Chetverikov, Campana, & Kristjánsson, 2020a). Using a repeated-search task, Võ and Wolfe (2012) demonstrated that attentional guidance by memories from previous encounters was more effective if these memories were established when looking for an item (during search), compared to looking at targets (explicit memorization and free viewing).…”

Section: Building and Using Behaviorally Optimal Long-term Representamentioning

confidence: 99%

Keeping it real: Looking beyond capacity limits in visual cognition

Kristjánsson

Draschkow

2021

Atten Percept Psychophys

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Research within visual cognition has made tremendous strides in uncovering the basic operating characteristics of the visual system by reducing the complexity of natural vision to artificial but well-controlled experimental tasks and stimuli. This reductionist approach has for example been used to assess the basic limitations of visual attention, visual working memory (VWM) capacity, and the fidelity of visual long-term memory (VLTM). The assessment of these limits is usually made in a pure sense, irrespective of goals, actions, and priors. While it is important to map out the bottlenecks our visual system faces, we focus here on selected examples of how such limitations can be overcome. Recent findings suggest that during more natural tasks, capacity may be higher than reductionist research suggests and that separable systems subserve different actions, such as reaching and looking, which might provide important insights about how pure attentional or memory limitations could be circumvented. We also review evidence suggesting that the closer we get to naturalistic behavior, the more we encounter implicit learning mechanisms that operate “for free” and “on the fly.” These mechanisms provide a surprisingly rich visual experience, which can support capacity-limited systems. We speculate whether natural tasks may yield different estimates of the limitations of VWM, VLTM, and attention, and propose that capacity measurements should also pass the real-world test within naturalistic frameworks. Our review highlights various approaches for this and suggests that our understanding of visual cognition will benefit from incorporating the complexities of real-world cognition in experimental approaches.

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Learning features in a complex and changing environment: A distribution-based framework for visual attention and vision in general

Cited by 23 publications

References 116 publications

Encoding perceptual ensembles during visual search in peripheral vision

Encoding perceptual ensembles during visual search in peripheral vision

Probabilistic rejection templates in visual working memory

Keeping it real: Looking beyond capacity limits in visual cognition

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