Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making

Li, Vickie; Michael, Elizabeth; Balaguer, Jan; Castañón, Santiago Herce; Summerfield, Christopher

doi:10.1073/pnas.1805224115

Cited by 58 publications

(62 citation statements)

References 74 publications

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“…One central assumption of our model is gain control whereby the spatial average signal is constantly adjusted across temporal frames. Some previous studies also incorporated gain control in their models and successfully accounted for human tendencies 31,52 . Another central assumption is the limited sampling of N elements in estimating spatial average orientation for each frame.…”

Section: Discussionmentioning

confidence: 99%

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Yashiro

Satō

Oide

et al. 2020

Sci Rep

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A number of studies on texture and ensemble perception have shown that humans can immediately estimate the average of spatially distributed visual information. the present study characterized mechanisms involved in estimating averages for information distributed over both space and time. observers viewed a rapid sequence of texture patterns in which elements' orientation were determined by dynamic Gaussian noise with variable spatial and temporal standard deviations (SDs). We found that discrimination thresholds increased beyond a certain spatial SD if temporal SD was small, but if temporal SD was large, thresholds remained nearly constant regardless of spatial SD. these data are at odds with predictions that threshold is uniquely determined by spatiotemporal SD. Moreover, a reverse correlation analysis revealed that observers judged the spatiotemporal average orientation largely depending on the spatial average orientation over the last few frames of the texture sequence -a recency effect widely observed in studies of perceptual decision making. Results are consistent with the notion that the visual system rapidly computes spatial ensembles and adaptively accumulates information over time to make a decision on spatiotemporal average. A simple computational model based on this notion successfully replicated observed data.Humans achieve stable perception of scenes and objects at a glance in spite of the spatial complexity and uncertainty of the natural image. While such perception seems to involve highly complicated and specialized neural processing, recent research has shown that perception builds upon image statistics computed relatively easily in the early stages of visual processing 1-4 . A vast psychophysical literature has suggested that the visual system is capable of rapidly estimating the characteristics of an ensemble of complex elements (e.g., objects, faces) 5-8 as well as discriminating textures defined by simple visual features such as form, color and motion 9-16 . These studies offer clear evidence that the visual system automatically extracts a statistical representation of the spatial properties of the image. Such statistical visual representations are thought to be subserved by neural mechanisms in early visual cortex with large spatial receptive field or cortico-cortical interactions 17-23 .Visual inputs inherently contain much temporal uncertainty owing to gaze shifts and object motions, and little is known about how mechanisms extracting spatial statistics cope with such temporal uncertainty. Psychophysical studies have examined how performance for orientation discrimination or global form detection in dynamic texture patterns varies as a function of stimulus duration. Results revealed that temporal summation is relatively short (over a few hundred milliseconds) and consistent with the idea that spatial statistics are computed rapidly by low-level mechanisms 24,25 . However, experiments using stochastic motion stimuli have shown that detecting global and biological motion requires a much longe...

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Section: Discussionmentioning

confidence: 99%

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Yashiro

Satō

Oide

et al. 2020

Sci Rep

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“…This account of decoy effects suggests that it should be possible to identify a simple model that can reproduce the full decoy influence map with parameters that control this repulsive principle, as well as additional degrees of freedom that allow for asymmetric scaling of the attribute values (quality and economy). Our initial modelling focus was informed by previous simulation work suggesting that adaptive gain control might offer a unifying explanation for choice biases (21) as well as the recent proposal of related models for decoy phenomena involving logistic normalisation (20,29). Here, we describe a model variant that offers a clear explanatory framework for the human data reported here.…”

Section: Computational Modellingmentioning

confidence: 99%

“…This allowed us to explore the dimensionality of the data, with a view to asking whether a single principle can explain the ensemble of reported decoy effects. We found that a remarkably simple model, which draws on a computational framework that we have described previously (21), can capture the full decoy influence (RCS) map. Critically, the model suggests that the three canonical decoy effects are not in fact distinct phenomena but fall naturally out of previously described dynamics of attraction and repulsion of decision values towards and away from a reference value given by the mean of available options.…”

Section: Introductionmentioning

confidence: 97%

“…These three phenomena have been a major object of study in psychology and behavioural economics for several decades. This has led to a proliferation of empirical results and a plethora of computational explanations, including models that rely on loss aversion (11), pairwise normalisation (12), attentional weighting (13)(14)(15)(16), associative biases (14), power-law transformation of attribute values (14), sampling from memory (17,18), or various other forms of reference-dependent computation (19)(20)(21)(22). However, there has been a notable lack of consensus about the computational principles that give rise to decoy effects (10).…”

Section: Introductionmentioning

confidence: 99%

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A map of decoy influence in human multi-alternative choice

Dumbalska¹,

Li²,

Tsetsos³

et al. 2020

Preprint

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Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases has been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multi-attribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis revealed that the decoy influence map was highly structured even beyond the three classic biases. We identified a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps understand why the biases are typically correlated across participants and allows us to validate a new prediction about their interrelationship. This work helps to clarify the origin of three of the most widely-studied biases in human decision-making.

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“…This is particularly important when salient distractors are present and interfering with the processing of targets. The interference induced by distractors, so-called “distractibility”, has been shown to link with several key cognitive functions, such as working memory 12 , endogenous and exogenous attention 13 , perceptual and value-based decision 14 , response inhibition 15 , cognitive control 16 . Moreover, atypical distractibility has been discovered in several psychiatry disorders, including ADHD 17 , autism 18 , depression 19 .…”

Section: Introductionmentioning

confidence: 99%

Unexpected higher resilience to distraction during visual working memory in schizophrenia

Zhao

Ran

Zhang

et al. 2019

Preprint

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Visual working memory (VWM) and distractibility are two core executive functions in human cognition. It has been suggested that schizophrenia (SZ) patients exhibit worse VWM performance and lower resilience to distraction compared with healthy control (HC) subjects. Previous studies, however, have largely investigated these two functions separately. It still remains unclear what are the mechanisms of the deficits, especially the interactions between the two cognitive domains. Here we modify the standard delay-estimation task in VWM and explicitly add distractors in the task so as to examine the two domains simultaneously. We find that SZ indeed exhibit worse performance compared with HC in almost all VWM load and distraction levels, a result consistent with most prior experimental findings. But adding distractors does not selectively impose larger impacts on SZ performance. Furthermore, unlike most previous studies that only focused on behavioral performance, we use the variable precision model to disentangle the distraction effect on different computational components of VWM (resources and resources allocation variability etc.). Surprisingly, adding distractors significantly elevates resources allocation variability—a parameter describing the heterogeneity of resource allocation across different targets—in HC but not in SZ. This counterintuitive result suggests that the internal VWM process in SZ is less interfered by the distractors. However, this unexpected higher resilience to distraction might be associated with less flexible cognitive control mechanisms. In sum, our work demonstrates that multiple cognitive functions might jointly contribute to dysfunctions in SZ and their interactions might manifest differently from merely summing their independent effects.

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Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making

Cited by 58 publications

References 74 publications

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

A map of decoy influence in human multi-alternative choice

Unexpected higher resilience to distraction during visual working memory in schizophrenia

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