People’s assessments of the state of the world often deviate systematically from the information available to them []. 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 [], has been explained as suppression of post-decisional dissonance []. 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 []. 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 []. Our results point to a recurrent interplay between decision-making and selective attention.

Results

9 Wimmer 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. 10 Nienborg H.

Cumming B.G. Decision-related activity in sensory neurons reflects more than a neuron’s causal effect. 11 Siegel M.

Buschman T.J.

Miller E.K. Cortical information flow during flexible sensorimotor decisions. 12 Maunsell J.H.R.

Treue S. Feature-based attention in visual cortex. 13 Reynolds J.H.

Heeger D.J. The normalization model of attention. 14 Herrmann K.

Heeger D.J.

Carrasco M. Feature-based attention enhances performance by increasing response gain. 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 []. 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 [].

15 Tsetsos K.

Chater N.

Usher M. Salience driven value integration explains decision biases and preference reversal. 16 Tsetsos K.

Moran R.

Moreland J.

Chater N.

Usher M.

Summerfield C. Economic irrationality is optimal during noisy decision making. 17 Drugowitsch J.

Wyart V.

Devauchelle A.-D.

Koechlin E. Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality. 18 Wyart V.

de Gardelle V.

Scholl J.

Summerfield C. Rhythmic fluctuations in evidence accumulation during decision making in the human brain. 19 Jazayeri M.

Movshon J.A. A new perceptual illusion reveals mechanisms of sensory decoding. 20 Stocker A.A.

Simoncelli E.P. A Bayesian Model of Conditioned Perception. 21 Zamboni E.

Ledgeway T.

McGraw P.V.

Schluppeck D. Do perceptual biases emerge early or late in visual processing? Decision-biases in motion perception. 22 Luu L.

Stocker A.A. Post-decision biases reveal a self-consistency principle in perceptual inference. 19 Jazayeri M.

Movshon J.A. A new perceptual illusion reveals mechanisms of sensory decoding. 20 Stocker A.A.

Simoncelli E.P. A Bayesian Model of Conditioned Perception. 22 Luu L.

Stocker A.A. Post-decision biases reveal a self-consistency principle in perceptual inference. 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 []. 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 []. 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 [] (but see []). 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.

Figure 1 Perceptual Task with Discrimination and Estimation Judgments Show full caption (A) Schematic sequence of events within a trial. A first dot motion stimulus was shown on all trials for 750 ms and then paused. On two-thirds of trials, an auditory prompt instructed a direction discrimination judgment (CW or CCW with respect to reference line, at 45° in this example trial) as shown here. A third of trials, not analyzed here, did not require a choice. After half of the discrimination judgments, feedback was given, and the trial terminated. After the other half, a second motion stimulus was presented (equal coherence as first but independent direction), and participants were asked to estimate the mean direction of both stimuli. (B) Proportion of CW choices as a function of stimulus direction, along with psychometric function fit. (C) Top: Continuous estimations as function of mean direction across both stimuli. Bottom: Distribution of mean directions across trials. Black, data; blue, predictions generated from best-fitting parameters of Choice-based Selective Gain model; data points, group mean; error bars, SEM; gray, predictions by Extended Conditioned Perception model under several levels of output noise for average subject. Stimulus directions and estimations were always expressed as the angular distance from the reference, the position of which varied from trial to trial (0° equals reference). See also STAR Methods Figure S1 , and Video S1 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 1 A; 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.

The second, complementary approach corroborated this conclusion ( Figures 2 E 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 2 E, left, and S2 B). Critically, for the actual data, ROC indices were larger for the Consistent than Inconsistent condition ( Figure 2 F). 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 2 A) and could not produce the consistency-dependent ROC effect neither for the individually fitted parameters ( Figure 2 E, middle) nor for any combination of parameters that we simulated ( Figure S2 B).

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 2 A). 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 S2 F). 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 2 E, right) as was evident in the data ( Figure S2 B). 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 S2 C and S2D) or by the disparity between first and second stimulus ( Figure S2 E). 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 2 A). Critically, the selective gain effect was larger for the parameters estimated by Choice-based Selective Gain model ( Figure 2 D). In sum, the selective modulation in sensitivity was linked to the participants’ categorical choice.

20 Stocker A.A.

Simoncelli E.P. A Bayesian Model of Conditioned Perception. 22 Luu L.

Stocker A.A. Post-decision biases reveal a self-consistency principle in perceptual inference. 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 []. 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 2 E, right, and S2 B). 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 S2 B, right). However, the later model did not account well for the relationship between overall estimations and mean stimulus direction (gray lines in Figure 1 C; for further comparison between Extended Conditioned Perception and Choice-based Selective Gain, see Figures S2 G 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.