Longitudinal Effects of Brain Stimulation on Decision-Making and Reward Learning

Punishment learning rates

The primary outcome are punishment learning rate estimates from a computational reinforcement learning model. Learning rates will be compared within and between stimulation conditions. Reinforcement learning is repeatedly measured with a bandit task with fluctuating reward probabilities (reward learning task). Reward learning behavior will be collected online over up to 60 runs, each including 150 trials.

Stimulation-induced mid-term changes in the backward digit span

The primary outcome is the change in the backward digit span. Changes over time will be compared within conditions and between ECT and the other conditions. The backward digit span is the maximum sequence length that could be repeated correctly backwards in the digit span task (at least one out of two correct). The task runs two sequences per sequence length, starting at two digits and increasing by one digit up to eight digits. The task is terminated if both sequences of a sequence length could not be repeated correctly backwards. Scores range from 0 to 16 and are the number of correctly repeated sequences.

Correct choices in the reward learning task

The outcome describes the percentage of correct choices (wins) collected during the reward learning task. Choices are collected in each of the 150 trials per run.

Reward sensitivity

The primary outcome is reward sensitivity estimates from a computational reinforcement learning model reflected by the inverse temperature parameters. Changes over time will be compared within and between stimulation conditions. Reinforcement learning is repeatedly measured with an bandit task with fluctuating reward probabilities (reward learning task). Reward learning behavior will be collected online over up to 60 runs, each including 150 trials.

Weighting of learned values and rewards at stake (lambda)

The primary outcome is the weighting of win probability to reward magnitude weighting (lambda) from a computational reinforcement learning model. Changes over time will be compared within and between stimulation conditions. Reinforcement learning is repeatedly measured with an bandit task with fluctuating reward probabilities (reward learning task). Reward learning behavior will be collected online over up to 60 runs, each including 150 trials.

Mood state

Ecological Momentary Assessment (EMA) using two 0-100 visual analog scales (mood state is calculated as state happiness minus state sadness), administered repeatedly across the reward learning task and lab sessions to capture mood state. We will assess changes in mood and associations with learning.

Metabolic state

Ecological Momentary Assessment (EMA) using a 0-100 visual analog scale (sated - hungry), administered repeatedly across the reward learning task and lab sessions to capture metabolic state. We will assess changes in metabolic state and associations with learning.

Motivational state

Ecological Momentary Assessment (EMA) using a 0-100 visual analog scale (unmotivated - motivated), administered repeatedly across the reward learning task and lab sessions to capture motivation. We will assess changes in motivation and associations with learning.