Experiment paradigm.

<p>In Task1 (stop-sign), subjects were instructed to drive the car to the stop-sign as quickly as possible and stop as close as possible to the sign without crossing the white line. In Task 2 (wall), subjects received the same instructions as in task 1, with the addition that they could not crash into the wall (instead of not crossing the stop-sign). Both tasks have a fixed time window of 6 seconds.</p

The influence of depressed mood on stopping distance in stop-sign and wall condition.

<p>a. Group comparison. P-values are Bonferroni corrected for multiple comparisons. Black line is the target (stop-sign or wall). Blue bars represent the stopping distance of the 3 groups in stop-sign condition and red bars represent that in wall condition, respectively. b. Stopping distance as a function of BDI. Each data point represents each individual’s stopping position relative to the target.</p

Demographic and BDI comparison across the three depression severity groups.

<p>Demographic and BDI comparison across the three depression severity groups.</p

Joystick action as a function of distance to target: a (stop-sign), b (wall).

<p>Black solid line: the position of the target (stop-sign or the wall). Green solid line: Non Dep. Red solid line: Mod-Sev Dep. For each condition, joystick action as a function of car distance to target is plotted on the left, and zoom-in in the last 2 seconds is plotted on the right. Positive action (>0) leads to acceleration of the car’s speed, while negative action (<0) leads to deceleration of the car’s speed.</p

Car distance as a function of time within a trial (6-seconds): a (stop-sign), b (wall).

<p>Black solid line: the position of the target (stop-sign or wall). Green solid line: Non Dep. Blue solid line: Mild Dep. Red solid line: Mod-Sev Dep. For each condition, car distance to task target as a function of time is plotted on the left, and zoom-in in the last 2 seconds is plotted on the right.</p

Anhedonia and anxiety underlying depressive symptomatology have distinct effects on reward-based decision-making - Fig 1

<p>A. 2-arm bandit task trial timeline. Participants completed 30 games, each with 16 trials. On each trial of each game, participants had to assign one token (stacked horizontally at the top of the screen) to one of the two lottery arms. After placing each token, they either earned 1 point if the token turned green or zero points if the token turned red. Each trial lasted about 2s, including participants’ trial reaction time to assign a token and a 500ms outcome phase shoeing the token color once assigned. At the end of each 16-trials game, participants saw a brief screen (4s) with their total points earned in the game, and the next game followed. At the beginning of the task, participants were instructed to try earning as many points as possible in the task. They were further told upfront they would be paid in proportion to their total points earned in the game (actual paid amounts ranged from $5 to$10). Each trial decision and the arms reward rates were recorded. B. DBM illustration and the generative equations. The reward rate of each arm are assumed to be independently drawn at the start of a game from a Beta distribution <i>q</i>_{0 =} Beta (<i>α</i>_<i>0</i>, <i>β</i>_<i>0</i>), fixed throughout the game, and with mean <i>r = (α</i>_<i>0</i>)/(<i>α</i>_<i>0</i> + <i>β</i>_<i>0</i>)_. DBM assumes that subjects believe that the reward rate <i>θ</i> for any arm can reset on any trial with probability 1-<i>γ</i>, otherwise it is the same value as the last trial.</p

Correlation between state anxiety and model evidence ratio for WSLS relative to Softmax, by model usage group.

<p>WSLS = Win-Stay/Lose-Shift.</p