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Gain Control Explains the Effect of Distraction in Human Perceptual, Cognitive, and Economic Decision Making

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Gain Control Explains the Effect of Distraction in Human Perceptual, Cognitive, and Economic Decision Making Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making Vickie Lia,1, Elizabeth Michaelb, Jan Balaguera, Santiago Herce Castañóna,c, and Christopher Summerfielda aDepartment of Experimental Psychology, University of Oxford, OX2 6GG Oxford, United Kingdom; bDepartment of Psychology, University of Cambridge, CB2 3EB Cambridge, United Kingdom; and cDepartment of Psychology and Educational Sciences, University of Geneva, 1202 Geneva, Switzerland Edited by Randolph Blake, Vanderbilt University, Nashville, TN, and approved July 3, 2018 (received for review March 26, 2018) When making decisions, humans are often distracted by irrelevant economists have charted the irrational influence that a decoy al- information. Distraction has a different impact on perceptual, ternative of value Z has on choices between two choice-relevant cognitive, and value-guided choices, giving rise to well-described prospects X and Y,whereX > Y (10–13). A common finding is behavioral phenomena such as the tilt illusion, conflict adaptation, that rational choices (i.e., for X > Y) initially decline as Z increases or economic decoy effects. However, a single, unified model that in value but then increase sharply as Z comes to approximately can account for all these phenomena has yet to emerge. Here, we match the other two items in value (Fig. 1C); other stereotypical “ ” X offer one such account, based on adaptive gain control, and decoy effects are observed when alternatives are character- additionally show that it successfully predicts a range of counter- ized by more than one attribute (discussed below). intuitive new behavioral phenomena on variants of a classic In the fields of psychology, economics, and neuroscience, diverse cognitive paradigm, the Eriksen flanker task. We also report that theoretical proposals have been offered to explain the cost that blood oxygen level-dependent signals in a dorsal network prom- distracters incur during decision making. These include models that inently including the anterior cingulate cortex index a gain- describe how control systems detect and resolve conflict among in- puts (14, 15), accounts that emphasize inhibitory interactions among modulated decision variable predicted by the model. This work competing sensory neurons or favor a normalization of stimulus unifies the study of distraction across perceptual, cognitive, and values by a local average or range (10, 16–18), and Bayesian accounts economic domains. that model spatial uncertainty among targets and distracters (19–21) or that assume a nonuniform prior on the compatibility of decision cognitive control | tilt illusion | decoy effects | gain control | information (21). These accounts disagree about the computational anterior cingulate cortex mechanisms involved, the neural processing stages at which the cost of distraction arises, and the brain structures that are recruited to ecisions about sensory signals, cognitive propositions, or protect decisions against irrelevant information. For example, di- Deconomic prospects are often made in the context of com- visive normalization mechanisms may occur in sensory neurons in peting or distracting information. Consider the following everyday visual cortex (16), or among value representations in the orbito- situations: You are judging whether a painting hangs straight on frontal cortex (22), whereas the control systems that detect and re- the wall, but the nearby pictures are hung askew; you are waiting solve conflict have been attributed to medial and lateral prefrontal structures (14). As such, the field currently lacks a single, unified COGNITIVE SCIENCES at a red stop signal, but the car in front decides to jump the light; PSYCHOLOGICAL AND you are contemplating the purchase of a new watch, but it is theory that can account for the effect of distraction on human de- displayed next to a range of more elegant but unaffordable cisions, or an integrated neural account of its implementation across models. In each of these situations, the best decisions will be made perceptual, cognitive, and economic domains. The goal of the current paper is to offer such an account. We by ignoring the distracting sensory signals (the competing picture begin with a simple computational model that is motivated by past frames, vehicles, or watches) and focusing exclusively on the work and shows that contextual signals determine the gain of pro- choice-relevant information. This normative contention can be cessing of consistent (or “expected”) features during decision-making formalized in a variety of ways, for example via the notion that NEUROSCIENCE rational choices should be independent of irrelevant alternatives Significance (1, 2) or that sensory signals should be weighted lawfully by their reliability and relevance to the choice at hand (3–6). Nevertheless, empirical observations suggest that human deci- Information in the world can sometimes be irrelevant for our sions are unduly influenced by distracting information. Consider a decisions. A good decision maker should take into account the generic problem in which a target stimulus Xi and distracters X j relevant information and ignore the distracting information. occur at fixed spatial locations i and j. In this general formulation, However, empirical observation showed that human decisions decision values X may be perceptual features (such as the tilt of a are unduly influenced by distracting information. Diverse the- grating) or economic attributes (such as the quality of a consumer ories have been proposed to explain the cost that distracters product) that are to be evaluated or categorized. Humans show incur during decision making across perceptual, cognitive, and systematic biases that reflect the influence of the distracters on economics domains. Here, we propose a single, unified model decisions about the target. For example, vision scientists have long that is based on adaptive gain control to explain the influence studied the “tilt illusion,” in which the reported orientation of X i of distraction across domains. (e.g., a central grating) is repulsed away from the mean tilt of X j (surrounding gratings with similar but nonidentical tilt; Fig. 1A) Author contributions: V.L., E.M., and C.S. designed research; V.L., J.B., and S.H.C. per- (7). In cognitive psychology, the influence of distracter items is formed research; V.L., J.B., and C.S. contributed new reagents/analytic tools; V.L. and usually studied with a view to understanding the attentional or C.S. analyzed data; and V.L. and C.S. wrote the paper. control mechanisms that allow information to be selected in the The authors declare no conflict of interest. face of conflict. For example, in the classic Eriksen flanker task, This article is a PNAS Direct Submission. observers classify a target stimulus (e.g., a central arrow) that is This open access article is distributed under Creative Commons Attribution-NonCommercial- flanked by distracters (e.g., arrows pointing in compatible or in- NoDerivatives License 4.0 (CC BY-NC-ND). compatible directions) (8, 9). It is ubiquitously observed that in- 1To whom correspondence should be addressed. Email: [email protected]. compatible flankers incur a cost, and compatible flankers confer a This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. benefit, relative to a neutral condition, as measured in response 1073/pnas.1805224115/-/DCSupplemental. times (RTs) and accuracy (Fig. 1B). Finally, behavioral and neural Published online August 30, 2018. www.pnas.org/cgi/doi/10.1073/pnas.1805224115 PNAS | vol. 115 | no. 38 | E8825–E8834 Downloaded by guest on September 27, 2021 simulations show that the model predicts a range of striking, A “ ” i) ii) iii) 4 counterintuitive behavioral findings, including reverse compati- 6 3 bility effects (where fully visible, compatible flankers actually 4 2 7 11 hinder, rather than help, behavioral performance). Over four be- 2 15 havioral experiments involving human participants, we validate 0 0 these predictions, using visual stimuli defined by both tilt and color. bias(deg) -2 bias (deg) -2 -4 Finally, we use functional brain imaging to show that the modula- tory influence on decision signals predicted by the model correlates -6 -4 -90 -45 0 45 90 -054 54 with blood oxygen level-dependent (BOLD) signals in the dor- flank tilt (deg) mean flank tilt (deg) sal anterior cingulate cortex (dACC) and interconnected struc- B tures, where neural signals have variously been implicated in the i) ii) iii) context-sensitive encoding of action values (26), and the expec- 540 ted value of cognitive control (27). We show how our framework, which is not wholly inconsistent with either account, can bring HHXHH together diverse views concerning the function of this controver- sial brain region (28). RT (msec) RT simulated RT 420 0 Results CO SI RI CO SI RI Our adaptive gain model is based on a framework that was C Choice trial Y previously developed to understand how humans performing i) ii) 3 iii) spatial and temporal averaging tasks adapt to the context pro- 3 vided by proximal decision information (23, 29). Inputs arrive at 2 a population of n decision neurons each characterized by a 2 Gaussian tuning curve centered on its preferred feature value θk. 1 i 1 Each neuron k responds to the target stimulus X with rate i Logistic slope Rk = f X θk σk f X θ σ choice set normalised difference ð j , Þ, where ð j , Þ denotes the probability density 0 2 02.0 064. 08. .0 1 0 042. 06. 08. .0 1 function of the normal distribution with mean θ and variance σ . Distractor value (norm) normalised The estimated output of the neural population is then linearly Distractor value (Z) c decoded into a subjective percept or value estimate X i by weighting R θ Fig. 1. The effect of distraction across perceptual, cognitive, and economic the population activity by the corresponding feature values : domains. (A, i) Participants were asked to discriminate the tilt (relative to Xn horizontal) of a central Gabor surrounded by tilted distracters.
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