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• W2891654265 abstract "•People’s interpretation of new evidence is often biased by their previous choices•Talluri, Urai et al. developed a new task for probing the underlying mechanisms•Evidence consistent with an observer’s initial choice is processed more efficiently•This “choice-induced gain change” affects both perceptual and numerical decisions People’s assessments of the state of the world often deviate systematically from the information available to them [1Tversky A. Kahneman D. Judgment under Uncertainty: Heuristics and Biases.Science. 1974; 185: 1124-1131Crossref PubMed Scopus (19190) Google Scholar]. Such biases can originate from people’s own decisions: committing to a categorical proposition, or a course of action, biases subsequent judgment and decision-making. This phenomenon, called confirmation bias [2Nickerson R.S. Confirmation bias: A ubiquitous phenomenon in many guises.Rev. Gen. Psychol. 1998; 2: 175-220Crossref Scopus (3702) Google Scholar], has been explained as suppression of post-decisional dissonance [3Festinger L. A theory of cognitive dissonance. Stanford University Press, Stanford1957Crossref Google Scholar, 4Brehm J.W. Postdecision changes in the desirability of alternatives.J. Abnorm. Psychol. 1956; 52: 384-389Crossref PubMed Scopus (704) Google Scholar]. Here, we provide insights into the underlying mechanism. It is commonly held that decisions result from the accumulation of samples of evidence informing about the state of the world [5Gold J.I. Shadlen M.N. The neural basis of decision making.Annu. Rev. Neurosci. 2007; 30: 535-574Crossref PubMed Scopus (2264) Google Scholar, 6Bogacz R. Brown E. Moehlis J. Holmes P. Cohen J.D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.Psychol. Rev. 2006; 113: 700-765Crossref PubMed Scopus (1146) Google Scholar, 7Liu F. Wang X.-J. A common cortical circuit mechanism for perceptual categorical discrimination and veridical judgment.PLoS Comput. Biol. 2008; 4: e1000253Crossref PubMed Scopus (23) Google Scholar, 8Wang X.-J. Decision making in recurrent neuronal circuits.Neuron. 2008; 60: 215-234Abstract Full Text Full Text PDF PubMed Scopus (424) Google Scholar]. We hypothesized that choices bias the accumulation process by selectively altering the weighting (gain) of subsequent evidence, akin to selective attention. We developed a novel psychophysical task to test this idea. Participants viewed two successive random dot motion stimuli and made two motion-direction judgments: a categorical discrimination after the first stimulus and a continuous estimation of the overall direction across both stimuli after the second stimulus. Participants’ sensitivity for the second stimulus was selectively enhanced when that stimulus was consistent with the initial choice (compared to both, first stimuli and choice-inconsistent second stimuli). A model entailing choice-dependent selective gain modulation explained this effect better than several alternative mechanisms. Choice-dependent gain modulation was also established in another task entailing averaging of numerical values instead of motion directions. We conclude that intermittent choices direct selective attention during the evaluation of subsequent evidence, possibly due to decision-related feedback in the brain [9Wimmer K. Compte A. Roxin A. Peixoto D. Renart A. de la Rocha J. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.Nat. Commun. 2015; 6: 6177Crossref PubMed Scopus (88) Google Scholar]. Our results point to a recurrent interplay between decision-making and selective attention. People’s assessments of the state of the world often deviate systematically from the information available to them [1Tversky A. Kahneman D. Judgment under Uncertainty: Heuristics and Biases.Science. 1974; 185: 1124-1131Crossref PubMed Scopus (19190) Google Scholar]. Such biases can originate from people’s own decisions: committing to a categorical proposition, or a course of action, biases subsequent judgment and decision-making. This phenomenon, called confirmation bias [2Nickerson R.S. Confirmation bias: A ubiquitous phenomenon in many guises.Rev. Gen. Psychol. 1998; 2: 175-220Crossref Scopus (3702) Google Scholar], has been explained as suppression of post-decisional dissonance [3Festinger L. A theory of cognitive dissonance. Stanford University Press, Stanford1957Crossref Google Scholar, 4Brehm J.W. Postdecision changes in the desirability of alternatives.J. Abnorm. Psychol. 1956; 52: 384-389Crossref PubMed Scopus (704) Google Scholar]. Here, we provide insights into the underlying mechanism. It is commonly held that decisions result from the accumulation of samples of evidence informing about the state of the world [5Gold J.I. Shadlen M.N. The neural basis of decision making.Annu. Rev. Neurosci. 2007; 30: 535-574Crossref PubMed Scopus (2264) Google Scholar, 6Bogacz R. Brown E. Moehlis J. Holmes P. Cohen J.D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.Psychol. Rev. 2006; 113: 700-765Crossref PubMed Scopus (1146) Google Scholar, 7Liu F. Wang X.-J. A common cortical circuit mechanism for perceptual categorical discrimination and veridical judgment.PLoS Comput. Biol. 2008; 4: e1000253Crossref PubMed Scopus (23) Google Scholar, 8Wang X.-J. Decision making in recurrent neuronal circuits.Neuron. 2008; 60: 215-234Abstract Full Text Full Text PDF PubMed Scopus (424) Google Scholar]. We hypothesized that choices bias the accumulation process by selectively altering the weighting (gain) of subsequent evidence, akin to selective attention. We developed a novel psychophysical task to test this idea. Participants viewed two successive random dot motion stimuli and made two motion-direction judgments: a categorical discrimination after the first stimulus and a continuous estimation of the overall direction across both stimuli after the second stimulus. Participants’ sensitivity for the second stimulus was selectively enhanced when that stimulus was consistent with the initial choice (compared to both, first stimuli and choice-inconsistent second stimuli). A model entailing choice-dependent selective gain modulation explained this effect better than several alternative mechanisms. Choice-dependent gain modulation was also established in another task entailing averaging of numerical values instead of motion directions. We conclude that intermittent choices direct selective attention during the evaluation of subsequent evidence, possibly due to decision-related feedback in the brain [9Wimmer K. Compte A. Roxin A. Peixoto D. Renart A. de la Rocha J. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.Nat. Commun. 2015; 6: 6177Crossref PubMed Scopus (88) Google Scholar]. Our results point to a recurrent interplay between decision-making and selective attention. Brain regions implicated in evidence accumulation, decision-making, and attentional control maintain their activity states over long timescales and send feedback to regions encoding the incoming evidence [9Wimmer K. Compte A. Roxin A. Peixoto D. Renart A. de la Rocha J. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.Nat. Commun. 2015; 6: 6177Crossref PubMed Scopus (88) Google Scholar, 10Nienborg H. Cumming B.G. Decision-related activity in sensory neurons reflects more than a neuron’s causal effect.Nature. 2009; 459: 89-92Crossref PubMed Scopus (222) Google Scholar, 11Siegel M. Buschman T.J. Miller E.K. Cortical information flow during flexible sensorimotor decisions.Science. 2015; 348: 1352-1355Crossref PubMed Scopus (224) Google Scholar]. We thus reasoned that the consistency of new evidence with a previous choice might affect the decision-maker’s sensitivity to the new evidence. Specifically, we hypothesized that a categorical choice induces a multiplicative gain modulation of new evidence, selectively boosting the sensitivity to consistent evidence. Such a selective gain modulation is commonly observed when explicit cues direct feature-based attention [12Maunsell J.H.R. Treue S. Feature-based attention in visual cortex.Trends Neurosci. 2006; 29: 317-322Abstract Full Text Full Text PDF PubMed Scopus (627) Google Scholar, 13Reynolds J.H. Heeger D.J. The normalization model of attention.Neuron. 2009; 61: 168-185Abstract Full Text Full Text PDF PubMed Scopus (872) Google Scholar, 14Herrmann K. Heeger D.J. Carrasco M. Feature-based attention enhances performance by increasing response gain.Vision Res. 2012; 74: 10-20Crossref PubMed Scopus (53) Google Scholar]. Previous studies have identified gain modulations in evidence accumulation by presenting multiple samples of evidence in succession and asking participants to report a binary choice based on the mean evidence at the end of the sequence [15Tsetsos K. Chater N. Usher M. Salience driven value integration explains decision biases and preference reversal.Proc. Natl. Acad. Sci. USA. 2012; 109: 9659-9664Crossref PubMed Scopus (143) Google Scholar, 16Tsetsos K. Moran R. Moreland J. Chater N. Usher M. Summerfield C. Economic irrationality is optimal during noisy decision making.Proc. Natl. Acad. Sci. USA. 2016; 113: 3102-3107Crossref PubMed Scopus (70) Google Scholar, 17Drugowitsch J. Wyart V. Devauchelle A.-D. Koechlin E. Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality.Neuron. 2016; 92: 1398-1411Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar, 18Wyart V. de Gardelle V. Scholl J. Summerfield C. Rhythmic fluctuations in evidence accumulation during decision making in the human brain.Neuron. 2012; 76: 847-858Abstract Full Text Full Text PDF PubMed Scopus (155) Google Scholar]. Those studies did not assess the effect of intermittent choices in biasing the accumulation process. Other work has probed the interaction between categorical choices and continuous estimations by combining discrimination and estimation judgments based on the same evidence presented before [19Jazayeri M. Movshon J.A. A new perceptual illusion reveals mechanisms of sensory decoding.Nature. 2007; 446: 912-915Crossref PubMed Scopus (125) Google Scholar, 20Stocker A.A. Simoncelli E.P. A Bayesian Model of Conditioned Perception.Adv. Neural Inf. Process. Syst. 2007; 2007: 1409-1416PubMed Google Scholar, 21Zamboni E. Ledgeway T. McGraw P.V. Schluppeck D. Do perceptual biases emerge early or late in visual processing? Decision-biases in motion perception.Proc. Biol. Sci. 2016; 283: 20160263Crossref PubMed Scopus (21) Google Scholar, 22Luu L. Stocker A.A. Post-decision biases reveal a self-consistency principle in perceptual inference.eLife. 2018; 7: e33334Crossref PubMed Scopus (32) Google Scholar]. Here, choice-related estimation biases may be a by-product of the bottom-up sensory decoding (i.e., weighting of sensory neurons) being tailored to the discrimination judgment [19Jazayeri M. Movshon J.A. A new perceptual illusion reveals mechanisms of sensory decoding.Nature. 2007; 446: 912-915Crossref PubMed Scopus (125) Google Scholar] (but see [20Stocker A.A. Simoncelli E.P. A Bayesian Model of Conditioned Perception.Adv. Neural Inf. Process. Syst. 2007; 2007: 1409-1416PubMed Google Scholar, 22Luu L. Stocker A.A. Post-decision biases reveal a self-consistency principle in perceptual inference.eLife. 2018; 7: e33334Crossref PubMed Scopus (32) Google Scholar]). Whether a categorical choice occurring during a protracted stream of decision-relevant evidence selectively modulates the gain of evidence subsequent to that choice has remained unknown. We addressed this question by combining the above two approaches. Our task required participants to report a continuous estimate of the overall motion direction across two successively presented random dot motion stimuli. In the majority of trials, participants were also prompted to report a binary categorical judgment after the first stimulus (see Figure 1A; STAR Methods): discriminating whether its direction was clockwise (CW) or counter-clockwise (CCW) with respect to a reference line. Importantly, the stimulus following the intermittent choice contributed only to the final estimation but not to the discrimination judgment. This psychophysical protocol enabled us to isolate the impact of an intermittent categorical choice on decision-makers’ sensitivity to subsequent evidence for continuous estimation. Participants made use of the stimulus information for both judgments: the fraction of CW choices increased as a function of the direction of the first stimulus from the reference (Figures 1B and S1A), and continuous estimations scaled with the mean stimulus direction across both intervals (Figures 1C, top, S1A, and S1B). The estimations were generally attracted toward the reference (Figure 1C, top, compare black and gray dashed line), in line with the non-uniform distribution of the mean stimulus directions (Figure 1C, bottom; STAR Methods). Post-decisional selective gain modulation predicts that evidence subsequent to a choice produces larger (smaller) deviations in the overall estimations when these new directions are consistent (inconsistent) with that choice. We used two complementary approaches to test this prediction. The first approach modeled the overall estimations as a noisy weighted average of the directional evidence in both stimulus intervals (see STAR Methods). The weight for each stimulus quantified its gain in the estimation process. Trials with second stimulus directions consistent or inconsistent with the choice were modeled separately. This model, referred to as the Choice-based Selective Gain model in the following (STAR Methods), provided a good account of observers’ estimation reports (Figures 1C and 2). Smaller values of Bayes information criterion (BIC) within the majority of individual participants indicated that Choice-based Selective Gain explained the data better than a Baseline model without choice-dependent change in evidence weighting (Figure 2A; STAR Methods). Further, choice-consistent second stimuli received larger weight than choice-inconsistent second stimuli (Figure 2B; see Figure S2A for noise estimates). This weight difference was not evident for the first stimuli (Figure 2C). Indeed, weights were increased compared to the first stimulus for choice-consistent second stimuli and reduced for choice-inconsistent stimuli (Figure 2C). In sum, observers prioritized choice-consistent evidence after the categorical choice, in a way resembling feature-based attention. The second, complementary approach corroborated this conclusion (Figures 2E and 2F). We developed a model-free measure based on the receiver-operating characteristic (ROC) that quantified the sensitivity to the second stimulus. ROC indices measured the extent to which single-trial estimations separated between second stimuli of nearby directions (i.e., 10° versus 20°, or −10° versus −20°; see STAR Methods for details). Simulations confirmed that the difference between these ROC indices, computed separately for choice-consistent and choice-inconsistent stimuli, captured the choice-dependent gain modulation described by the Choice-based Selective Gain model (Figures 2E, left, and S2B). Critically, for the actual data, ROC indices were larger for the Consistent than Inconsistent condition (Figure 2F). In sum, the model-free analysis also revealed a selective modulation of sensitivity to additional evidence, in line with feature-based attention. This consistency-dependent change in sensitivity for subsequent evidence, as quantified by the ROC indices, could not be explained by other mechanisms lacking multiplicative gain modulation. In a first alternative model, biases shared among choice and subsequent estimations resulted from slow fluctuations in noise corrupting both judgments, without any genuine effect of the choice. This so-called Correlated Noise model (STAR Methods) provided a worse account of estimation reports (in 9 out of 10) than Choice-based Selective Gain (Figure 2A) and could not produce the consistency-dependent ROC effect neither for the individually fitted parameters (Figure 2E, middle) nor for any combination of parameters that we simulated (Figure S2B). In a second alternative model, the initial choice shifted the internal representation of the evidence toward the chosen category in an additive fashion. This Shift model (STAR Methods) also produced systematic estimation biases and accounted well for the overall estimation behavior (Figure 2A). The shift parameter was larger than zero (p = 0.038, two-sided permutation test), indicating that participants may have shifted their decision variable in the direction of the chosen category. The shift parameter was even significant (p = 0.05, two-sided permutation test) for an Extended Choice-based Selective Gain model, which contained an extra free parameter for the shift (all other parameters constrained from the Choice-based Selective Gain model fits, STAR Methods; Figure S2F). But critically, the Shift model also could not capture the specific behavioral feature that was diagnostic of selective gain modulation: the consistency-dependent sensitivity change (Figure 2E, right) as was evident in the data (Figure S2B). It is possible that an additive shift and multiplicative gain modulation jointly governed choice-induced biases in the overall estimation behavior (see Discussion). Taken together, the analyses presented so far indicate that consistency-dependent gain modulation was necessary to account for certain features of participants’ behavior. Further analyses indicated that this gain modulation was, in fact, induced by the intermittent choice (i.e., participants’ categorization of the first stimulus) rather than by the first stimulus itself (Figures S2C and S2D) or by the disparity between first and second stimulus (Figure S2E). We fitted a variant of the Selective Gain model, in which the consistency of the second stimulus was defined based on the first physical stimulus direction, rather the participants’ choice (STAR Methods). This so-called Stimulus-based Selective Gain model provided a worse account of the data than the Choice-based Selective Gain (Figure 2A). Critically, the selective gain effect was larger for the parameters estimated by Choice-based Selective Gain model (Figure 2D). In sum, the selective modulation in sensitivity was linked to the participants’ categorical choice. A recent Bayesian account of post-decision biases has proposed that perceptual inference is “conditioned” on choice in order to ensure consistency between binary discrimination and continuous estimation judgments of the same stimulus [20Stocker A.A. Simoncelli E.P. A Bayesian Model of Conditioned Perception.Adv. Neural Inf. Process. Syst. 2007; 2007: 1409-1416PubMed Google Scholar, 22Luu L. Stocker A.A. Post-decision biases reveal a self-consistency principle in perceptual inference.eLife. 2018; 7: e33334Crossref PubMed Scopus (32) Google Scholar]. This account is framed at a different level of description (Bayesian inference), but the notion of a choice-dependent prior for estimation is similar to our idea of a choice-induced top-down modulation. Could choice-based conditioning of internal representations explain the present results? Our task and analyses isolated the impact of binary choice on the processing of subsequent evidence for continuous estimation, requiring additional assumptions about the conditioning operation. If only the representation of the first stimulus was conditioned, this would yield an offset of the representation of the second stimulus—equivalent to the Shift model considered above, which did not account consistency-effect on ROC indices observed in the data (Figures 2E, right, and S2B). If also the representation of the second stimulus was conditioned on the choice (referred to as Extended Conditioned Perception, see STAR Methods), this reproduced the ROC-effect (Figure S2B, right). However, the later model did not account well for the relationship between overall estimations and mean stimulus direction (gray lines in Figure 1C; for further comparison between Extended Conditioned Perception and Choice-based Selective Gain, see Figures S2G and S2H). Future work should develop biologically plausible and dynamic approximations of choice-based conditioning operation in order to unravel possible links to choice-dependent gain modulation. The post-decisional biasing effect in the visual perceptual task resembled well-documented effects in reasoning [2Nickerson R.S. Confirmation bias: A ubiquitous phenomenon in many guises.Rev. Gen. Psychol. 1998; 2: 175-220Crossref Scopus (3702) Google Scholar] and preference reports [4Brehm J.W. Postdecision changes in the desirability of alternatives.J. Abnorm. Psychol. 1956; 52: 384-389Crossref PubMed Scopus (704) Google Scholar, 23Chen M.K. Risen J.L. How choice affects and reflects preferences: revisiting the free-choice paradigm.J. Pers. Soc. Psychol. 2010; 99: 573-594Crossref PubMed Scopus (122) Google Scholar]. It is unknown, however, whether the latter high-level post-decision biases are mediated by selective gain modulations akin to attention. To test for this, we re-analyzed and modeled previously published [24Bronfman Z.Z. Brezis N. Moran R. Tsetsos K. Donner T. Usher M. Decisions reduce sensitivity to subsequent information.Proc. Biol. Sci. 2015; 282: 20150228Crossref PubMed Scopus (35) Google Scholar] data from a numerical averaging task that also required the combination of evidence presented before and after a choice into an overall estimation (Figure 3A; see STAR Methods for task and analysis details). Again, the weights were larger on Consistent than Inconsistent conditions, specifically for evidence after choice (Figure 3B) again with an interaction between interval and consistency (Figure S3D). Likewise, the ROC indices were also larger for Consistent than Inconsistent conditions (Figure 3C). In sum, the choice-induced biasing mechanism we uncovered for perceptual decision-making, including the selective gain modulation, also accounts for post-decision biases in higher-level decisions based on numerical evidence. Decision-makers are often systematically influenced by their own choices: committing to a categorical hypothesis or choosing a course of action biases the subsequent evaluation of the decision-relevant evidence [2Nickerson R.S. Confirmation bias: A ubiquitous phenomenon in many guises.Rev. Gen. Psychol. 1998; 2: 175-220Crossref Scopus (3702) Google Scholar, 4Brehm J.W. Postdecision changes in the desirability of alternatives.J. Abnorm. Psychol. 1956; 52: 384-389Crossref PubMed Scopus (704) Google Scholar]. The mechanisms underlying such post-decisional confirmation biases have so far remained unknown. Here, we have shown that choices selectively increased the gain of subsequent evidence that was consistent with that choice, for perceptual as well as numerical decisions. A selective modulation of the gain of sensory responses is commonly observed during attention to certain stimulus features [12Maunsell J.H.R. Treue S. Feature-based attention in visual cortex.Trends Neurosci. 2006; 29: 317-322Abstract Full Text Full Text PDF PubMed Scopus (627) Google Scholar, 13Reynolds J.H. Heeger D.J. The normalization model of attention.Neuron. 2009; 61: 168-185Abstract Full Text Full Text PDF PubMed Scopus (872) Google Scholar, 14Herrmann K. Heeger D.J. Carrasco M. Feature-based attention enhances performance by increasing response gain.Vision Res. 2012; 74: 10-20Crossref PubMed Scopus (53) Google Scholar]. In sum, our results illuminate the linkage between decision-making and attention—two capacities commonly studied in isolation but interacting in real-life behavior. Our findings indicate that an agent’s decision acts like a cue for selective attention, biasing subsequent decision processing. Evidence inconsistent with an initial choice may induce post-decisional dissonance, possibly related to conflict between competing cognitive states or motor responses [3Festinger L. A theory of cognitive dissonance. Stanford University Press, Stanford1957Crossref Google Scholar, 25van Veen V. Krug M.K. Schooler J.W. Carter C.S. Neural activity predicts attitude change in cognitive dissonance.Nat. Neurosci. 2009; 12: 1469-1474Crossref PubMed Scopus (168) Google Scholar]. Previous work has shown that such conflict boosts top-down control, increasing task performance and response caution on subsequent trials [26Botvinick M.M. Braver T.S. Barch D.M. Carter C.S. Cohen J.D. Conflict monitoring and cognitive control.Psychol. Rev. 2001; 108: 624-652Crossref PubMed Scopus (5139) Google Scholar, 27Miller E.K. Cohen J.D. An integrative theory of prefrontal cortex function.Annu. Rev. Neurosci. 2001; 24: 167-202Crossref PubMed Scopus (8428) Google Scholar]. But this line of work has not associated conflict with subsequent decision biases. In particular, it has not shown that conflict induces selective modulations of new information that is consistent with respect to a previous choice. We have recently established that sensitivity for new information is generally reduced after an overt choice, compared to no overt choice [24Bronfman Z.Z. Brezis N. Moran R. Tsetsos K. Donner T. Usher M. Decisions reduce sensitivity to subsequent information.Proc. Biol. Sci. 2015; 282: 20150228Crossref PubMed Scopus (35) Google Scholar]. To this end, we assessed a non-selective reduction in sensitivity for any post-decision evidence. Our current work goes beyond this by uncovering a selective mechanism of confirmation bias: preferentially sampling the evidence that confirms one’s prior belief. This effect indicates a more refined mechanism than the non-selective reduction in overall sensitivity due to an overt choice. Identifying this effect was afforded by an improved modeling approach (see STAR Methods) combined with a model-free behavioral readout of selective gain modulation (ROC analysis), both yielding consistent results (Figure 3D). It is conceivable that a non-selective gain reduction due to overt choice (possibly reflecting reduced arousal and/or cortical attractor dynamics [24Bronfman Z.Z. Brezis N. Moran R. Tsetsos K. Donner T. Usher M. Decisions reduce sensitivity to subsequent information.Proc. Biol. Sci. 2015; 282: 20150228Crossref PubMed Scopus (35) Google Scholar]) and selective attention toward choice-consistent evidence conspire to shape overall estimation behavior. Our analysis of the perceptual task also revealed, in some of the participants, an additive shift in the direction of the chosen category, on top of the gain modulation. This additive shift may reflect previously identified choice-induced biases [19Jazayeri M. Movshon J.A. A new perceptual illusion reveals mechanisms of sensory decoding.Nature. 2007; 446: 912-915Crossref PubMed Scopus (125) Google Scholar]. This additive shift could not, however, account for the consistency-dependent change in sensitivity (Figure 2E), which we found in the data (Figure 2F). The co-existence of additive and multiplicative effects may relate to the observation that common manipulations of selective attention produce effects on both sensitivity and decision criteria, which are dissociable at behavioral and neural levels [28Luo T.Z. Maunsell J.H.R. Neuronal Modulations in Visual Cortex Are Associated with Only One of Multiple Components of Attention.Neuron. 2015; 86: 1182-1188Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar, 29Luo T.Z. Maunsell J.H.R. Attentional Changes in Either Criterion or Sensitivity Are Associated with Robust Modulations in Lateral Prefrontal Cortex.Neuron. 2018; 97: 1382-1393.e7Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar]. 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• W2891654265 title "Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence" @default.
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