The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging

Abstract

Processing speed is impaired in patients with psychosis, and deteriorates as a function of normal aging. These observations, in combination with other lines of research, suggest that psychosis may be a syndrome of accelerated aging. But do patients with psychosis perform poorly on tasks of processing speed for the same reasons as older adults? Fifty-one patients with psychotic illnesses and 90 controls with similar mean IQ (aged 19-69 years, all African American) completed a computerized processing-speed task, reminiscent of the classic digit-symbol coding task. The data were analyzed using the drift-diffusion model (DDM), and Bayesian inference was used to determine whether psychosis and aging had similar or divergent effects on the DDM parameters. Psychosis and aging were both associated with poor performance, but had divergent effects on the DDM parameters. Patients had lower information-processing efficiency ("drift rate") and longer nondecision time than controls, and psychosis per se did not influence response caution. By contrast, the primary effect of aging was to increase response caution, and had inconsistent effects on drift rate and nondecision time across patients and controls. The results reveal that psychosis and aging influenced performance in different ways, suggesting that the processing-speed impairment in psychosis is more than just accelerated aging. This study also demonstrates the potential utility of computational models and Bayesian inference for finely mapping the contributions of cognitive functions on simple neurocognitive tests.

Keywords: Bayesian inference; aging; computational psychiatry; digit–symbol; processing speed; psychosis.

Fig. 1.

(A) Example trial and task instructions. This is a “match” trial, so the correct response is “yes.” (B) The top part shows a schematic of the drift-diffusion model. It is valid only for match trials. The blue shaded region at the top is the reaction-time distribution for “yes” (correct) responses, and the red shaded region at the bottom is the reaction-time distribution for “no” (incorrect) responses. The blue and red traces are example diffusion patterns (ie, different trials in the experiment). Note that the corresponding schematic for nonmatch trials would be flipped, and “no” responses would be correct. The bottom part of the panel illustrates how changes in v, a, and t independently influence accuracy and response-time distributions. Smaller v causes more errors, indicated by the increasing height of the error distributions. Greater a causes fewer errors, but also causes the correct distribution to become more rightward-skewed, leading to slower responses on average. Greater t causes both the distributions to be shifted rightward, leading to slower responses. Any one or combination of these effects would lead to poorer performance on the task.

Fig. 2.

(A) Directed acyclic graph showing the probabilistic relationships between all the variables in the final model. Unfilled single-lined circles represent stochastic random variables, double-lined circles represent deterministic variables, and the shaded circle represents the observed data, consisting of reaction times and choices. (B) DIC values of the 8 candidate models.

Fig. 3.

(A) Mean number of correct responses per group (with 95% confidence intervals). (B) Number of correct responses for all subjects as a function of age. Solid green and blue lines show linear regression lines for the patient and control groups, respectively, and shaded regions are 95% confidence intervals around the regression coefficient. Yellow lines are the results of the PPC (see text for explanation). (C) Distribution of reaction times collapsed across all subjects. Reaction times for error responses are plotted as negative values. Yellow lines are kernel-density estimates of the data simulated via the PPC.

Fig. 4.

(A) Posterior densities reflecting the effects of psychosis on drift rate (top left), response caution (top right), and nondecision time (bottom left), with shaded areas representing 95% HDRs. The bottom-right schematic shows the predicted (posterior mean) values of drift rate, response caution, and nondecision time for the average 18-year-old patient and 18-year-old control. (B) Posterior densities reflecting the effects of psychosis on drift rate (top left), response caution (top right), and nondecision time (bottom left), with shaded areas representing 95% HDRs. Solid vertical lines represent a value of 0, reflecting no effect of aging. The bottom-right schematic shows the predicted (posterior mean) values of drift rate, response caution, and nondecision time for the average 20 (solid lines), 40 (dashed lines), and 60 (dotted lines) year-old patient and control.