Probabilistic rejection templates in visual working memory

Abstract

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.

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

“…www.nature.com/scientificreports/ Implicit assessment. Overall, observers had a remarkably detailed representation of the distractor distribution, well above the mean and variance only, in stark contrast with typical findings on ensemble perception, but importantly corroborating previous findings of implicit encoding of distractor distributions 20,[24][25][26][27] . Figure 4A plots the RT curves for the implicit condition and as previously found, the RTs for the current target as a function of the previous distractor distribution (CT-PD) reflect the Gaussian distribution shape.…”

“…Search times on these test trials are analyzed as a function of the distance in feature space between the current target (that is searched for) and the previously learned distractor distribution mean (hereafter named CT-PD, current target-previous distractors distance). Previous results 20,[24][25][26][27] have shown that the RT curve should follow the shape of the encoded distribution, providing evidence that detailed distribution characteristics are encoded.…”

“…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.…”

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

“…An ideal observer utilizing the knowledge of previous distractors would 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 (Chetverikov et al, 2016(Chetverikov et al, , 2017a(Chetverikov et al, , 2020. We refer to the test target position in the feature space as the current-target to previous distractor-distance (CT-PD).…”

Encoding perceptual ensembles during visual search in peripheral vision

“…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.…”