Do People Take Stimulus Correlations into Account in Visual Search?

Explaining the effects of distractor statistics in visual search

Visual search, the task of detecting or locating target items among distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behavior based on the stimulus presented. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.

Imperfect Bayesian inference in visual perception

Optimal Bayesian models have been highly successful in describing human performance on perceptual decision-making tasks, such as cue combination and visual search. However, recent studies have argued that these models are often overly flexible and therefore lack explanatory power. Moreover, there are indications that neural computation is inherently imprecise, which makes it implausible that humans would perform optimally on any non-trivial task. Here, we reconsider human performance on a visual-search task by using an approach that constrains model flexibility and tests for computational imperfections. Subjects performed a target detection task in which targets and distractors were tilted ellipses with orientations drawn from Gaussian distributions with different means. We varied the amount of overlap between these distributions to create multiple levels of external uncertainty. We also varied the level of sensory noise, by testing subjects under both short and unlimited display times. On average, empirical performance-measured as d'-fell 18.1% short of optimal performance. We found no evidence that the magnitude of this suboptimality was affected by the level of internal or external uncertainty. The data were well accounted for by a Bayesian model with imperfections in its computations. This "imperfect Bayesian" model convincingly outperformed the "flawless Bayesian" model as well as all ten heuristic models that we tested. These results suggest that perception is founded on Bayesian principles, but with suboptimalities in the implementation of these principles. The view of perception as imperfect Bayesian inference can provide a middle ground between traditional Bayesian and anti-Bayesian views.

Variable precision in visual perception

Given the same sensory stimuli in the same task, human observers do not always make the same response. Well-known sources of behavioral variability are sensory noise and guessing. Visual short-term memory (STM) studies have suggested that the precision of the sensory noise is itself variable. However, it is unknown whether precision is also variable in perceptual tasks without a memory component. We searched for evidence for variable precision in 11 visual perception tasks with a single relevant feature, orientation. We specifically examined the effect of distractor stimuli: distractors were absent, homogeneous and fixed across trials, homogeneous and variable, or heterogeneous and variable. We first considered 4 models: with and without guessing, and with and without variability in precision. We quantified the importance of both factors using 6 metrics: factor knock-in difference, factor knock-out difference, and log factor posterior ratio, each based on AIC or BIC. According to all 6 metrics, we found strong evidence for variable precision in 5 experiments. Next, we extended our model space to include potential confounding factors: the oblique effect and decision noise. This left strong evidence for variable precision in only 1 experiment, in which distractors were homogeneous but variable. Finally, when we considered suboptimal decision rules, the evidence also disappeared in this experiment. Our results provide little evidence for variable precision overall and only a hint when distractors are variable. Methodologically, the results underline the importance of including multiple factors in factorial model comparison: Testing for only 2 factors would have yielded an incorrect conclusion. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Visual Decisions in the Presence of Measurement and Stimulus Correlations

Humans and other animals base their decisions on noisy sensory input. Much work has been devoted to understanding the computations that underlie such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, how the statistical structure of stimuli, along with perceptual measurement noise, affects perceptual judgments is not well understood. Here we examine how correlations between the components of a stimulus-stimulus correlations-together with correlations in sensory noise, affect decision making. As an example, we consider the task of detecting the presence of a single or multiple targets among distractors. We assume that both the distractors and the observer's measurements of the stimuli are correlated. The computations of an optimal observer in this task are nontrivial yet can be analyzed and understood intuitively. We find that when distractors are strongly correlated, measurement correlations can have a strong impact on performance. When distractor correlations are weak, measurement correlations have little impact unless the number of stimuli is large. Correlations in neural responses to structured stimuli can therefore have a strong impact on perceptual judgments.