Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics

Abstract

Background and Hypothesis Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals—potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis. Study Design People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control. Study Results PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits. Conclusions Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

Cognitive Mechanisms of Aberrant Self-Referential Social Perception in Psychosis and Bipolar Disorder: Insights from Computational Modeling

“…This aligns with prior data [33][34][35][36]39,[80][81][82] to suggest that patients' performance alterations across a range of tasks reflect a "general inefficiency" -potentially due to general cognitive deficits 39 in the ability to extract information and accumulate it as evidence to make a decision. Crucially, our results show that these deficits also impact key social cognitive processes like gaze perception.…”

“…Two additional findings warrant further discussion. First, we did not find group differences in NDT, which is frequently slowed in SZ 33,34,36,81 . Here, we used a relatively simple detection task, whereas prior studies have used more complex tasks.…”

“…Three additional findings warrant further discussion. First, we did not find differences in NDT, which is frequently slowed in SZ 38,39,41,96 . This may be due to our relatively simple task: prior studies used more complex/demanding ones and NDT increases with task complexity 41 .…”

“…For SZ, heightened response caution functioned as a protective factor, offsetting their lower drift rates which in turn preserved their response accuracy at the expense of slower RTs. Past studies that modeled SZ decision processes report mixed findings with greater response caution in SZ in some cases 34,35 (including one interpretation of 80 ), but not others 33,36,81,93 . This suggests that response caution is highly task-dependent, which is consistent with the cognitive literature that treats response caution as under the control of the participant as a means for controlling how they trade speed for accuracy.…”