Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals
Fig 5
Citalopram protects reward learning from interference.
(A) A regression analysis (bGLM2) assessed interference in reward learning (impact of relative RPEs) by effort learning (relative EPEs) and reward type (receiving a real or only a hypothetical reward). Larger regression weights indicate larger interference. While the placebo group’s learning was affected by interfering factors (ANOVA, testing the average size of the two interference factors against zero in the placebo group: F(1,13) = 5.39, p = 0.033), this was remedied by citalopram (ANOVA, testing the average size of the two interference factors against zero in the citalopram group: F(1,14) = 1.04, p = 0.32; ANOVA, testing whether the two groups differed in the average size of the two interference factors: F(1,27) = 7.00, p = 0.013). This effect can be illustrated more directly by comparing how much participants could take RPEs into account for making decisions on the next trial when there was interference or when there was not (analyses bGLM3a and b). (B) When reward was real, the two groups did not differ in how well they could use RPEs (t(27) = −0.47, p = 0.64). However, when reward was only hypothetical, the citalopram group was better at using RPEs (t(27) = −2.21, p = 0.036). (C) Similarly, when EPEs were favorable, the two groups did not differ in how well they could use RPEs (t(27) = −0.32, p = 0.75), but when EPEs were unfavorable, the citalopram group was better at using RPEs (t(27) = −2.69, p = 0.012). Error bars show standard error of the mean, *p < 0.05. Data for individual participants can be found in S5 Data.