Dissecting the impact of depression on decision-making

Abstract

Background: Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression.

Methods: Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters.

Results: RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced-and decision thresholds were wider-in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did.

Conclusions: Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making-not slowed perception or response execution-caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Keywords: Computational modeling; decision-making; depression; drift diffusion model; reward.

Figure 1.

(A) The Hierarchical Drift Diffusion Model (HDDM). The HDDM represents decisions as a process of evidence accumulation towards response boundaries separated by a decision threshold. The speed of evidence accumulation is referred to as the drift rate. The drift process moves left to right over time, from a starting point that can be midway between the boundaries or shifted towards one to an extent captured by the starting bias. The time needed for perception and response execution is captured by the non-decision time. In applying the model to PRT data, we mapped the upper and lower boundaries to “rich” and “lean”, responses, respectively. The HDDM is a Bayesian extension of the original DDM (Ratcliff & McKoon, 2008) that provides enhanced parameter estimation for studies with between-group designs. (B) The probabilistic reward task (PRT). On each trial, participants must indicate whether a short (11.5 mm) or long (13.0 mm) mouth was shown. Correct identifications of one length (the “rich” stimulus) are rewarded three times more frequently than correct identifications of the other length (the “lean” stimulus).

Figure 2.. Quantile-probability plots: Study 1.

Percent correct (right) and incorrect (left) for rich (circles) and lean (crosses) stimuli as a function of RT quantiles, for adults with MDD (left column) and healthy controls (right column). The six quantiles are marked on the controls’ data; they are shifted upwards on the y-axis for the MDD group, with the magnitude of the shift increasing with longer response latency. The effect of stimulus type on accuracy (rich > lean) is restricted to the 0.1 and 0.3 quantiles, indicating that response bias is carried by fast RTs.

Figure 3.. HDDM results: Study 1.

Plots of posterior probabilities for HDDM parameters. Relative to results in the controls (dashed lines), drift rate was reduced and threshold width was increased in the MDD group (solid lines). There were no group differences in starting point bias or non-decision time. *q < 0.005

Figure 4.. Quantile-probability plots: Study 2 (Pizzagalli et al., 2008).

Percent correct (right) and incorrect (left) for rich (circles) and lean (crosses) stimuli are shown as a function of RT quantiles, for adults with MDD (left column) and healthy controls (right column). The six quantiles are marked on the controls’ data; they are shifted upwards on the y-axis for the MDD group, with the magnitude of the shift increasing with longer response latency. The effect of stimulus type on accuracy (rich > lean) is apparent in the 0.1 and 0.3 quantiles in the MDD group, but it is evident at every quantile for the controls.

Figure 5.. HDDM results: Study 2 (Pizzagalli et al., 2008).

Plots of posterior probabilities for HDDM parameters. Relative to results in the controls (dashed lines), drift rate was reduced and threshold width was increased in the MDD group (solid lines). There were no group differences in starting point bias or non-decision time. *q < 0.038