Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals
Fig 2
Task validation and model comparison.
(A) The choices of participants in both groups (i, ii), between option one and option two, were guided by the learnt reward and effort differences between the options (estimated from a Bayesian model). They were more likely to choose the option with higher reward and lower effort magnitudes. (B) Regression analysis (bGLM1) predicting whether participants selected the same option again as on the last trial (“stay”) or selected the alternative option (“switch”). Participants took all relevant features of the task into account: they were more likely to choose options that had a higher displayed probability, higher learnt reward, and lower effort magnitudes (all p < 10−8; no group differences, all p > 0.2; omnibus ANOVA including regression weights for probability, learnt reward and effort also revealed no group difference: F(1,27) = 2.3, p = 0.14). Participants were also more likely to choose an option again if they had received a real reward on the last trial (t(28) = 3.04, p = 0.005). There was no difference between the groups in the overall amount of money earned. (C) Model comparison using summed Bayesian Information Criterion (BIC) values revealed that models in which choice utility was computed as a linear sum (i.e., reward + probability − effort, “Add”) provided a far better fit to the data than models computing choice utility multiplicatively (i.e., reward x probability—effort, “Mult”). Of these models, a Bayesian model (no free parameters for learning rate, reward/effort predictions are instead derived using Bayes’ rule) provided the best fit to the data (“Bayesian—Add”: BIC = 4375), closely followed by a model in which there was one free and shared parameter for the reward and effort learning rate (“Shared learning rate—Add”: BIC = 4378). The regressors for learnt reward and effort magnitudes used in the behavioral and neural analyses derived from “Bayesian—Add” were highly correlated with regressors derived from “Shared learning rate − Add” (r > 0.99). Error bars are standard error of the mean. Data for individual participants can be found in S1 Data.