Requiem for the max rule?

Ji, Wei; Shen, Suhung; Dziugaite, Gintare Karolina; Berg, Ronald van den

doi:10.1016/j.visres.2014.12.019

Cited by 26 publications

(25 citation statements)

References 41 publications

(77 reference statements)

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“…An example is the maximum-of-outputs model in visual search (Nolte & Jaarsma, 1967). However, a review of studies in which that model did well showed that the optimal model described human data as well or better (Ma, Shen, Dziugaite, & Van den Berg, 2015). Thus, when we restrict ourselves to perceptual studies in which the generative model is well characterized, observers have fully learned the generative model, and proper model comparison has been performed, strong evidence for suboptimality in human behavior seems to be absent.…”

Section: Discussionmentioning

confidence: 99%

A detailed comparison of optimality and simplicity in perceptual decision making.

Shen¹,

Ji²

2016

Psychological Review

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Two prominent ideas in the study of decision-making have been that organisms behave near-optimally, and that they use simple heuristic rules. These principles might be operating in different types of tasks, but this possibility cannot be fully investigated without a direct, rigorous comparison within a single task. Such a comparison was lacking in most previous studies, because a) the optimal decision rule was simple; b) no simple suboptimal rules were considered; c) it was unclear what was optimal, or d) a simple rule could closely approximate the optimal rule. Here, we used a perceptual decision-making task in which the optimal decision rule is well-defined and complex, and makes qualitatively distinct predictions from many simple suboptimal rules. We find that all simple rules tested fail to describe human behavior, that the optimal rule accounts well for the data, and that several complex suboptimal rules are indistinguishable from the optimal one. Moreover, we found evidence that the optimal model is close to the true model: first, the better the trial-to-trial predictions of a suboptimal model agree with those of the optimal model, the better that suboptimal model fits; second, our estimate of the Kullback-Leibler divergence between the optimal model and the true model is not significantly different from zero. When observers receive no feedback, the optimal model still describes behavior best, suggesting that sensory uncertainty is implicitly represented and taken into account. Beyond the task and models studied here, our results have implications for best practices of model comparison.

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Section: Discussionmentioning

confidence: 99%

A detailed comparison of optimality and simplicity in perceptual decision making.

Shen¹,

Ji²

2016

Psychological Review

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“…We assumed that observers utilize the knowledge they obtained about distractors and targets optimally. To find a target in a localization search task, an ideal observer , would compare the probability that a given noisy measurement of orientation x at each location L is a target versus the probability that it is a distractor (Ma, Navalpakkam, Beck, van den Berg, & Pouget, 2011;Ma, Shen, Dziugaite, & van den Berg, 2015):…”

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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“…If the absolute difference between the measured distractor orientation closest to the target orientation, and the target orientation, is below some threshold ρ, they report "target present", otherwise they report "target absent". Models of this kind have been used extensively in visual search research (Ma, Shen, Dziugaite & van den Berg, 2015). Note that, because the observer applies a criterion to a noisy variable to determine their response, this heuristic observer model is also a SDT model Palmer et al, 1993).…”

Section: Alternative Modelsmentioning

confidence: 99%

Explaining the effects of distractor statistics in visual search

Calder-Travis

2020

Preprint

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Visual search, the task of detecting or locating target items amongst 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 behaviour based on the full set of stimuli in a trial. 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. claim that Treisman and Gelade (1980) only explored part of the stimulus space spanned by targetdistractor similarity and distractor variability; the appearance of a dichotomy would then stem from the use of stimuli drawn from two distinct clusters in this space.While Duncan and Humphreys (1989) made a valuable contribution in suggesting that performance likely lies on a continuum, their exploration of this claim was necessarily limited by the stimuli that they used. Using letters and joined lines, they could not parametrically vary properties of these stimuli along an easily quantified dimension. Therefore, it could be that Duncan and Humphreys (1989), while describing performance in a larger area of stimulus space than Treisman and Gelade (1980), missed areas of the space, along with distinctive qualitative effects.Other visual search researchers have used stimuli that can easily be parametrically varied (Palmer,

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Requiem for the max rule?

Cited by 26 publications

References 41 publications

A detailed comparison of optimality and simplicity in perceptual decision making.

A detailed comparison of optimality and simplicity in perceptual decision making.

Probabilistic rejection templates in visual working memory

Explaining the effects of distractor statistics in visual search

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