Fig 1.
(A) In both tasks, four fractal cues indicated the combination of action (go/no-go) and valence at the outcome (win/loss). (B) In each trial, a fractal cue was presented, followed by a variable delay. After the delay, actions were required in response to a circle, and participants had to decide whether to press a button. After an additional brief delay, the probabilistic outcome was presented, indicating monetary reward (green upward arrow on a ₩1000 bill) or monetary punishment (red downward arrow on a ₩1000 bill). A yellow horizontal bar indicated no win or loss. In the WMGNG task, the original GNG task was followed by a 2-back response and 2-back outcome phases. (C) The participants were asked to indicate whether the cue in the current trial was identical to the cue in the two preceding trials. Here, because the cue in trial 3 differed from the cue in trial 1, “DIFF” was the correct response. Similarly, because the cue in trial 4 was identical to the cue in trial 3, “SAME” was the correct response. The lines mark two cues for comparison: the purple line indicates that the cues differ, while the pink line indicates that the cues are identical.
Fig 2.
(A) Task accuracies (mean percentages of correct responses) in the GNG and WMGNG tasks show that participants performed better in the GNG task than in the WMGNG task. (B) Accuracy in each of the four trial types between the two tasks demonstrated that participants performed better in “go to win” and “no-go to avoid losing” trials (Pavlovian-congruent, blue) than in “no-go to win” and “go to avoid losing” trials (Pavlovian-incongruent, red). (C) The learning curve (i.e., the increase in accuracy across trials) was shallower in the WMGNG task than in the GNG task. Note that moving average smoothing was applied with filter size 5 to remove the fine variation between time steps. Lines indicate group means and ribbons indicate means ± standard errors of the means. (D) Pavlovian bias was calculated by subtracting accuracy in Pavlovian-incongruent conditions (“no-go to win” + “go to avoid losing”) from accuracy in Pavlovian-congruent conditions (“go to win” + “no-go to avoid losing”). No significant difference in Pavlovian bias was observed between the GNG and WMGNG tasks. (A)-(B), (D) Black dots indicate group means and error bars indicate means ± standard errors of the means. Gray dots indicate individual accuracies; lines connect a single participant’s performances. Asterisks indicate the results of pairwise t-tests. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05.
Fig 3.
Model comparison results and posterior distribution of the group-level parameters of the best-fitting model (N = 49).
(A) Relative LOOIC difference indicates the difference in LOOIC between the best-fitting model and each of the other models. The best-fitting model was the full model, which assumed separate Pavlovian bias, learning rate, and irreducible noise in GNG and WMGNG tasks. Lower LOOIC indicates better model fit. (B) Posterior distributions of group-level parameters from the best-fitting model. Learning rate and irreducible noise estimates were credibly different in the GNG and WMGNG tasks, while Pavlovian bias estimates were not. Dots indicate medians and bars indicate 95% HDIs. Asterisks indicate that the 95% HDIs of the two parameters’ posterior distributions do not overlap (i.e., differences are credible).
Fig 4.
(A) Mean percentage of go choices for different quantiles of action weight differences (Wgo—Wnogo) between “go” and “no-go” choices, where higher quantiles indicate higher decision values for “go” choices. Under WM load, the increase in go ratio according to quantile was less steep. (B) Mean accuracies for different quantiles of absolute value differences (|Wgo—Wnogo|), where higher quantiles indicate larger value differences between two options or easier choices. Under WM load, the increase in accuracy according to quantile was less steep. (A)-(B) Dots are group means, and error bars are means ± standard errors of the means. Asterisks show the results of pairwise t-tests. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05.
Fig 5.
(A) In the predefined ROI-based analysis, RPE signaling in the striatum was stronger in the WMGNG task than in the GNG task. The left figure shows significant regions at p < 0.05 (SVC) in yellow. The right figure shows an increased mean beta estimate in the striatum under WM load. (B) The left figure shows that functional connectivity between the striatum (seed region, top) and prefrontal regions, including vmPFC (bottom left) and dlPFC (bottom right), was weaker in the WMGNG task than in the GNG task when computing reward expectation (p < 0.05, whole-brain cluster-level FWE). The right figures show decreased mean beta estimates in vmPFC and dlPFC under WM load. In all figures, error bars are means ± standard errors of the means. Overlays are shown with a threshold of p < 0.001 (uncorrected). Color scale indicates t-values.
Table 1.
Free parameters of all models.