Encoding perceptual ensembles during visual search in peripheral vision

Abstract

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.

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

“…Many studies have shown that observers respond slower when a test target is similar to previous distractors with the degree of slowing monotonically related to the shape of distractor distributions. This is indeed what we found in our previous studies 24 – 28 , 43 . We refer to the test target position in the feature space as the current-target to previous distractor-distance (CT-PD).…”

“…Our previous experiments indicated that the result from Feature distribution learning cannot be explained by sampling mechanisms where observers encode only a few items from the distribution 25 , 26 . Furthermore, observers’ performance with more complex (bimodal) distributions matched predictions of ideal observer models that accurately represent the distribution shape rather than computing only summary statistics 27 and distribution learning occurred even in the visual periphery 28 . This clearly demonstrated that the visual system encodes feature distribution shape, using it to guide behavior.…”

“…All experiments were approved by the ethics committee of the National Bioethics committee in Iceland (Vísindasiðanefnd, http://vsn.is) and performed in accordance with their requirements and guidelines and the Declaration of Helsinki. Sample size and trial number were based on the results of previous studies [24][25][26][27][28]43 . In addition, we used model comparison and Bayes Factor analysis to estimate the amount of evidence in favor of our hypotheses rather than more traditional null-hypothesis frequentist testing.…”

You see what you look for: Targets and distractors in visual search can cause opposing serial dependencies

“…In a recent paper, we studied the effect of distractors upon perceptual decisions about the attended items (targets) during visual search for an oddly oriented line among distractors ( Rafiei, Hansmann-Roth, Whitney, Kristjánsson, & Chetverikov, 2021 ). In visual search, observers can surprisingly quickly learn the probability distributions of distractor sets ( Chetverikov, Campana & Kristjánsson, 2016 ; Chetverikov, Campana & Kristjánsson, 2017a ; Chetverikov, Campana & Kristjánsson, 2017b ; Chetverikov, Campana & Kristjánsson, 2017c ; Chetverikov, Campana & Kristjánsson, 2017d ; Chetverikov, Campana & Kristjánsson, 2020a ; Hansmann-Roth, Chetverikov, & Kristjánsson, 2019 ; Hansmann-Roth, Kristjánsson, & Chetverikov, 2020a ; Hansmann-Roth, Kristjánsson, Whitney, & Chetverikov, 2021 ; Tanrıkulu, Chetverikov & Kristjánsson, 2020 ). They can learn which distractor features are more probable than others in surprising detail, and, importantly, unlike the items typically used in serial dependence studies, observers learn to ignore them.…”

Keeping it real: Looking beyond capacity limits in visual cognition

“…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).…”