Article Figures & Data
Figures
- Figure 1.
A, Instructed probabilistic selection task. Example stimulus pairs, which minimize explicit verbal encoding. Correct choices are determined probabilistically, with percentage positive/negative feedback shown in parentheses for each stimulus. Before training, subjects were shown one randomly selected stimulus and told it “will have the highest probability of being correct.” Instructions on stimuli in the left column are accurate in training pairs. Instructions for those on the right are inaccurate. After training, subjects completed a test phase in which all stimulus combinations were presented. Instructional control is assessed by choices of the instructed stimulus i on test choose-i trials when it is the statistically superior option and avoid-i trials when it is statistically inferior. The figure shows test trials for a subject instructed to choose “F.” B, Diagram depicting neural network accounts of instructional control over learning. Dashed lines indicate anatomical projections with differing computational roles. Instruction representations either directly bias the striatal valuation, selection, and learning (bias model), or simply override the otherwise accurate striatal learning of probabilities via competition at motor cortex (override model). The dotted line indicates the time course in the evaluative loop, not an anatomical projection.
- Figure 2.
Effects of instruction on accuracy, plotted as relative accuracy: the difference in accuracy on instructed stimulus pairs relative to accuracy on uninstructed pairs of the same type (e.g., performance on instructed pair CD was compared with that of subjects in CD who had not been instructed on this pair). Error bars here and throughout reflect SE. A, Accuracy relative to control is shifted in the direction of the instructions for all stimuli (p values <0.0001, uncorrected; all remain significant after correction) except “B,” the worst stimulus statistically (p = 0.16). B, By the last block of training, “D” and “F” instruction continue to affect choice (p values <0.03, uncorrected, only “D” survives correction). Effect of “E” is marginal (p = 0.07). C, Instructional control at test. Choice of instructed stimuli increases accuracy on choose-i trials (where the instructed stimulus is statistically better than its paired alternative) and decreases accuracy on avoid-i trials (where it is statistically worse).
- Figure 3.
Gene effects on behavior. A, Gene-dose effect of DARPP-32 T alleles on choose-A (the most positive stimulus) relative to avoid-B accuracy. B, Gene-dose effect of DRD2 T alleles on avoiding the most negative stimulus (avoid-B). C, Effect of inaccurate instructions on test choices is modulated by striatal genotype. When reinforcement statistics conflicted with prior instructions, efficacy of both striatal (DARPP-32 and DRD2) and prefrontal (COMT) genotypes modulated proportion of choices in accordance with prior instructions. Accuracy is plotted in terms of avoid-i when the instructed stimulus is suboptimal in the test phase.
- Figure 4.
Model predictions and empirical results supporting bias model. A, Neural network model predictions for BG efficacy on inaccurately instructed stimuli (Doll et al., 2009). “Go-NoGo” striatal activation indexes the extent to which the striatum assigns a relatively more positive than negative value to the action associated with selecting the instructed stimulus. “Go-NoGo” evaluations are initially neutral and are learned through training experience. The bias model predicts that increased BG function should produce greater distortion of striatal action values while the override model predicts greater striatal learning of the objective contingencies. B, Algorithmic RL model showing final learned Q values after training that best explain test phase choices for instructed and uninstructed stimuli. Each unfilled bar reflects the learned Q value for the subset of participants who had been instructed on the given stimulus. Uninstructed (filled) bars reflect learned values in subjects not instructed on that stimulus. (Any individual subject contributes to one unfilled bar and five filled bars.) Q values are uniformly increased by instruction across stimulus probabilities. C, Supporting the bias model, increased striatal efficacy was associated with increased confirmation bias parameter estimates leading to these skewed Q values. DARPP-32 genotype modulated the amplification of instruction-consistent positive outcomes (αIA), whereas DRD2 genotype modulated the discounting of inconsistent negative outcomes (αID). D, Striatal genotypes differentially predict αIA and αID. Subjects fit by relatively greater than average differences in αIA than αID (in z-scores), had relatively greater than average differences in DARPP-32 than DRD2 efficacy as indexed by z-score difference in number of T alleles (DARPP-32 − DRD2).
- Figure 5.
COMT hypothesis testing effects in training phase. A, When instructions were inaccurate, COMT Met carriers showed greater persistence in instruction following than Val/Val homozygotes, who more rapidly abandoned inaccurate instructions. Some subjects completed additional training blocks to reach accuracy criteria before the test phase. Because the sample size decreases in these later blocks as subjects advance to the testing phase, only the first, second, and last blocks were assessed. Learning curve shading reflects SE. Data are smoothed over a 10-trial window. B, Schematic of choice rule in Bayesian hypothesis testing model. For this example distribution, the mean μ (best guess) of the instructed Q-value is <0.5, but the belief distribution extends well above chance. Assuming a minimal temperature parameter (ζ) for illustrative purposes, subjects abandoning instructed stimulus selection at this point would be best described by a model with ϕ <0.83, given that the mean is below chance by 83% of a standard deviation. The red dashed vertical line indicates chance, and the purple dotted horizontal line indicates variability of distribution. C, COMT Met carriers had greater ϕ parameter estimates than Val/Val homozygotes in the hypothesis testing model, indicating that they required more evidence on the inaccuracy of instructions before abandoning them.
Tables
Gene Alleles αIA (SE) αID (SE) αIA_train (SE) αID_train (SE) ϕ (SE) DARPP-32 T/T 5.79 (0.7)* 7.35 (0.7) 3.15 (0.5) 4.24 (0.8) 3.09 (1.3) C/C, C/T 3.99 (0.5) 6.52 (0.6) 3.15 (0.4) 4.13 (0.6) 1.30 (0.5) DRD2 T/T, T/C 5.03 (0.5) 7.56 (0.5)* 3.32 (0.4) 4.86 (0.6)* 2.45 (0.8) C/C 3.67 (0.8) 5.25 (0.9) 2.96 (0.6) 2.84 (0.7) 1.00 (0.4) COMT Met/Met, Val/Met 4.43 (0.7) 7.02 (0.6) 3.73 (0.4)* 4.99 (0.6)* 2.74 (0.9)* Val/Val 4.78 (0.5) 6.50 (0.8) 2.19 (0.3) 2.83 (0.6) 0.66 (0.3) ↵*Difference in parameter estimate between allele groups (p < 0.05).
Model Params AIC_Tst AIC_Tst_Wt P(Exceed_Tst) AIC_Trn AIC_Trn_ Wt P(Exceed_Trn) Uninstructed 3 185.22 0.065 0.15 74.4 0.608 1 Instructed 5 177.59 0.915 0.83 77.71 0.136 0 Strong prior 4 182.88 0.02 0.02 76.13 0.256 0 Model comparisons of fits to all subject data. Params, Number of parameters. AIC_Tst, Akaike information criterion values for test phase fits (smaller values indicate better fit). AIC_Tst_Wt, AIC weights for test phase fits of candidate models. P(Exceed_Tst), Exceedance probability of test phase candidates. AIC_Trn, AIC_Trn_Wt, P(Exceed_Trn), Same measures for training phase fits.
- Table 3.
Training phase model comparisons for iteration 1 inaccurately instructed subjects
Model Params AIC_Trn AIC_Trn_Wt P(Exceed_Trn) Uninstructed 3 71.72 0.6008 1 Instructed 5 74.66 0.1386 0 Hypothesis testing 3 74.31 0.1648 0 Strong prior 4 75.4 0.0957 0 Abbreviations are as in Table 2.
