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, 107 (13), 6005-9

Overcoming Status Quo Bias in the Human Brain

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Overcoming Status Quo Bias in the Human Brain

Stephen M Fleming et al. Proc Natl Acad Sci U S A.

Abstract

Humans often accept the status quo when faced with conflicting choice alternatives. However, it is unknown how neural pathways connecting cognition with action modulate this status quo acceptance. Here we developed a visual detection task in which subjects tended to favor the default when making difficult, but not easy, decisions. This bias was suboptimal in that more errors were made when the default was accepted. A selective increase in subthalamic nucleus (STN) activity was found when the status quo was rejected in the face of heightened decision difficulty. Analysis of effective connectivity showed that inferior frontal cortex, a region more active for difficult decisions, exerted an enhanced modulatory influence on the STN during switches away from the status quo. These data suggest that the neural circuits required to initiate controlled, nondefault actions are similar to those previously shown to mediate outright response suppression. We conclude that specific prefrontal-basal ganglia dynamics are involved in rejecting the default, a mechanism that may be important in a range of difficult choice scenarios.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Task design. (A) Participants played a “tennis line-judgment” game in which the default was systematically manipulated in a balanced factorial design. At the beginning of each trial, participants were asked to depress the “default” key and fixate on the cross between the two tramlines. They then saw a ball land on the court, before being asked to make a decision on whether it was “IN” (overlapping the line) or “OUT.” This decision was indicated by continuing to depress the key to accept the default, or releasing it and switching to the opposite key to reject. Easy and difficult (low and high difficulty) trials were randomly interleaved within a block and balanced across whether the correct response was to accept or reject the default. (B) A possible theoretical account of the status quo bias in our task. We assume that the appearance of the ball gives rise to an internal state along an arbitrary decision axis sampled from separate IN (black) and OUT (gray) probability distributions. These probability distributions are nonoverlapping for low-difficulty decisions (Left) but overlap considerably for high-difficulty decisions (Right). The vertical line in each case represents the decision criterion—how the observer splices up this decision axis to report IN or OUT. The upper row shows an ideal observer's neutral criterion (black line), the lower row a criterion biased toward the accepting the default (blue line; here, reporting “IN”). A shifted criterion has more impact on stimuli drawn from overlapping probability distributions, leading to a greater status quo bias on high-difficulty trials.
Fig. 2.
Fig. 2.
Behavioral results. (A) Status quo bias was calculated as the percentage of default acceptance greater than 50% on both high- and low-difficulty trials. A bias toward accepting the default was seen on high- but not low-difficulty trials, resulting in suboptimal choice behavior. This pattern of results was replicated in an independent sample outside of the scanner (Fig. S1). Error bars reflect ±SEM. (B) Histogram of RT counts across subjects for high- and low-difficulty rejection responses, showing slower (more negatively skewed) RTs on high-difficulty trials.
Fig. 3.
Fig. 3.
Interaction of decision difficulty and default rejection. (A) T-map for the interaction contrast [(reject_highaccept_high) − (reject_lowaccept_low)], shown in coronal and axial sections (Right: P < 0.05, whole-brain corrected; Left: P < 0.05, SVC; shown at P < 0.005, uncorrected). Activity is seen bilaterally in the region of the STN (peak voxels; Left: −6, −24, −3; Right: 12, −18, 0). Insets: Overlap between the active clusters and STN ROIs (10 × 10 × 10-mm boxes centered on ±10, −15, −5). (B) Average difference in percentage signal change (rejectaccept) calculated from an unbiased average of all voxels within each STN box ROI. Events are split as a function of difficulty level. High-difficulty trials were further split into correct and incorrect (the relative rarity of an incorrect, low-difficulty response precluded the same split on low-difficulty trials). The interaction effect was driven by a greater STN response for rejecting the default on high- compared with low-difficulty trials. Post hoc paired t tests: *P < 0.05, **P < 0.005. Error bars reflect ±SEM.
Fig. 4.
Fig. 4.
Effects of decision difficulty and default rejection on connectivity. (A) Coronal sections are shown through the group T-map for positive correlations with the RT regressor (shown at P < 0.005, uncorrected). Circled are the regions that were entered into the subsequent connectivity analysis. (B) Schematic showing the winning DCM model and the pattern of significant connections. Default rejection (reject) was associated with increased influence of the rIFC on the STN. *P < 0.05, **P < 0.005.

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