Contributions of anterior cingulate cortex and basolateral amygdala to decision confidence and learning under uncertainty

Abstract

The subjective sense of certainty, or confidence, in ambiguous sensory cues can alter the interpretation of reward feedback and facilitate learning. We trained rats to report the orientation of ambiguous visual stimuli according to a spatial stimulus-response rule that must be learned. Following choice, rats could wait a self-timed delay for reward or initiate a new trial. Waiting times increase with discrimination accuracy, demonstrating that this measure can be used as a proxy for confidence. Chemogenetic silencing of BLA shortens waiting times overall whereas ACC inhibition renders waiting times insensitive to confidence-modulating attributes of visual stimuli, suggesting contribution of ACC but not BLA to confidence computations. Subsequent reversal learning is enhanced by confidence. Both ACC and BLA inhibition block this enhancement but via differential adjustments in learning strategies and consistent use of learned rules. Altogether, we demonstrate dissociable roles for ACC and BLA in transmitting confidence and learning under uncertainty.

Fig. 1

Schematic of the experimental paradigm, visual stimuli, and stimulus–response rule. a Timeline of each trial in the learning under perceptual uncertainty task. The rat first initiated a trial by nosepoking a white square in the center of the screen. The initiation stimulus then disappeared, and the rat was briefly presented (1 s) with a single horizontal (H) or vertical (V) Gabor patch masked by noise. Rats were required to report the dominant orientation (H or V) via nosepoke based on a complementary stimulus–response rule; e.g., H → left and V → right. Correct choices were rewarded probabilistically (on 70% of randomly selected trials), following variable delay times. After stimulus discrimination, rats could wait a self-timed delay in anticipation of reward or initiate a new trial. The initiation stimulus appeared on the touchscreen 2 s after a rat indicated its choice. b Examples of visual stimuli and one of the two stimulus–response rules. We refer to their discriminability as an SNR value reflecting the strength of visual signal (4, most discriminable; 3, moderately discriminable; 2, least discriminable). After discrimination of the visual stimulus, the rat makes a response (using the touchscreen) according to the rule H → Left and V → right

Fig. 2

Waiting time serves as a proxy for confidence that is more sensitive than reaction time. a Waiting time before reinitiation increases as SNR increases. Plotted are the distributions of waiting times for each SNR for all sessions, and probability correct (P(c)) as a second axis: 2 (blue), 3 (cyan), and 4 (magenta), following vehicle administration. Solid lines show the median of each distribution. b Reaction time decreases as SNR increases. Plotted are the distributions of reaction time for all sessions, and probability correct (P(c)) as a second axis. Conventions are the same as in panel a. c Waiting time before reinitiation of a new trial is negatively correlated with reaction time to make a choice. Waiting time is plotted as a function of the reaction time for all trials and all rats. Each data point is a trial in a session following vehicle administration. d Waiting time is larger for correct compared to incorrect responses for any SNR. Plotted is waiting time for all trials (black), correct trials (green), and incorrect trials (purple) for different SNR. The inset shows the relative difference in waiting time between correct and incorrect responses for different SNR. Error bars show the S.E.M. over sessions (typically smaller than the symbol). e Reaction time only weakly reflects the accuracy of the response. Plotted is the reaction time for all trials and separately for correct and incorrect responses. Conventions are the same as in panel d. The inset shows the relative difference in reaction time between correct and incorrect responses for different SNR. Overall, response accuracy is reflected in waiting time an order of magnitude better than in reaction time. Source data are provided as a Source Data file

Fig. 3

Expression of inhibitory DREADDs and null virus EGFP in anterior cingulate cortex and basolateral amygdala. a Top, representative expression of hM4Di-mCherry DREADDs under CaMKIIa is shown for ACC (left) and BLA (right). Bottom, reconstructions of maximum viral spread for all rats. Numerals depict + anterior–posterior (AP) level relative to Bregma. Scale bars 1000with 100 μm (inset). b Top, representative expression of eGFP (null virus) under CaMKIIa is shown for ACC (left) and BLA (right). Bottom, reconstructions of maximum viral spread for all rats. Numerals depict + anterior–posterior (AP) level relative to Bregma. Scale bars 1000 with 100 μm (inset). Source data are provided as a Source Data file

Fig. 4

ACC and BLA inhibition result in opposing changes in waiting time, but no change in reaction times. a Waiting time increases following ACC inhibition. Plotted are the distributions of the waiting time separately for each SNR, following ACC inhibition. Solid lines show the median of each distribution. Dashed lines show the median of the same condition but following vehicle administration as is shown in Fig. 2a. b Waiting time decreases following BLA inhibition. Same as in panel A but for sessions following inhibition of BLA. c Inhibition of ACC renders waiting times insensitive to the strength of sensory signal, whereas BLA inhibition shift waiting times. Plotted is the waiting time for all trials as a function of SNR following vehicle administration (yellow), inhibition of BLA (orange), and inhibition of ACC (blue). d, e Same as in panel (c) but only on trials in which a correct (d) or incorrect (e) response was made. Error bars show the S.E.M. over sessions (typically smaller than the symbols). f Reaction time is unaffected by inhibition of either ACC or BLA. Plotted is the reaction time for all trials as a function of SNR following vehicle administration (yellow), inhibition of BLA (orange), and inhibition of ACC (blue). g, h Same as in panel (f) but only on trials in which a correct (g) or incorrect (h) response was made. Source data are provided as a Source Data file

Fig. 5

Discrimination performance is intact following inhibition of either ACC or BLA, but metacognitive efficiency is decreased following inhibition of ACC only. a Plotted is the probability of correct responses for the sessions following vehicle administration (yellow), sessions following BLA inhibition (orange), and sessions following inhibition of ACC (blue). Error bars show S.E.M. b Plotted is discrimination performance, d′ in the three conditions defined in panel (a). c Plotted is the metacognitive efficiency (meta—d′/d′) in the three conditions defined in panel (a). (*) indicates p < 0.05 in one-way ANOVA test. Source data are provided as a Source Data file

Fig. 6

Influence of decision confidence on subsequent reversal learning. a Learning curves showing performance (probability of correct response) following reversal for rats prepared with DREADDs after vehicle administration. Plots shows the performance across all rats in high-confidence (HC; black) and low-confidence (LC; gray) conditions averaged over a sliding window of 100 trials. The inset shows the average performance in early (first half of the trials) and late (last quarter of the trials) trials, demonstrating that higher confidence improved the rate of learning but did not change the steady state. (*) indicates a significant difference in median between the two conditions (chi-square test of ratio, p < 0.05). Error bars show the S.E.M. over sessions. b The influence of confidence on learning strategies and perseveration. Plotted are the difference in the proportions of Win–Stay, Lose–Switch following correct but unrewarded responses, Lose–Switch after incorrect responses, and rule-based repetition index between HC and LC conditions in the first half of trials after the reversal. Source data are provided as a Source Data file

Fig. 7

ACC and BLA inhibition differentially modulate the effects of perceptual uncertainty on learning strategies and perseveration. a Learning curves (probability of correct response) after a reversal for rats prepared with ACC DREADDs following CNO (blue) or vehicle (Veh; black) administration. Plot shows the performance across all rats averaged over a sliding window of 100 trials for high-confidence (HC) and low-confidence (LC) conditions. The inset shows the average performance in early (first half of the trials) and late (last quarter of the trials) trials, demonstrating that either perceptual uncertainty or ACC inhibition decrease the rate of learning. Following ACC inhibition, rats eventually reach a similar performance level compared to the control condition (vehicle administration). (*) indicates a significant difference in median between the two conditions (chi-square test of ratio, p < 0.05). Error bars show the S.E.M. over sessions. b Win–Stay, Lose–Switch following correct response, Lose–Switch after incorrect response, and rule-based repetition index for the HC and LC conditions in ACC DREADDs during the first half of trials after the reversal. ACC inhibition only removes the benefit of confidence on Win–Stay but weakens the effect of confidence on learning from negative feedback or consistency in rule selection. (*) indicates a median significantly different from zero or a significant difference in median between the two conditions (Permutation test, Bonferroni corrected, p < 0.01). Magenta squares indicate a significance difference between ACC and BLA inhibition (Permutation test, p < 0.05). c Learning curves (probability of correct response) after a reversal for rats prepared with BLA DREADDs following CNO (red) or vehicle (black) administration. BLA inhibition decreases the rate of learning but eventually rats reach a similar performance level compared to the vehicle administration condition. Error bars show the S.E.M. over sessions. d The same as in panel B but for BLA DREADDs. Unlike ACC inhibition, BLA inhibition reverses the benefits of confidence on all learning strategies and consistency in rule selection. Source data are provided as a Source Data file