Drift-diffusion modeling of attention shifting during frustration: Associations with state and trait irritability

Irritability is a prevalent symptom across many mood and anxiety disorders that significantly contribute to interpersonal difficulties. Irritability is conceptualized as aberrant behavioral and emotional responses to frustrative nonreward, i.e., the emotional state induced by the failure to receive an expected reward. Past research investigating irritability have used a cued-attention task with rigged feedback, the Affective Posner Task (AP), to assess attentional shifting following frustrative nonreward. Previous studies have not been successful in linking differences in selfreported irritability to traditional AP metrics (i.e., reaction time, accuracy). Computational modeling, via the estimation of nuanced parameters reflecting latent cognitive processes, may improve our understanding of the cognitive mechanisms of irritability. This study aimed to apply
the drift-diffusion model (DDM) to the AP to determine if DDM parameters are useful in
discovering individual differences in irritability. A sample of 152 young adults (Mage = 20.93±1.98) completed the AP and self-reported state (i.e., during the task) and trait (i.e., over the past two weeks) irritability. Regression models were used to evaluate whether DDM parameters better predict state and trait irritability over traditional AP metrics. Higher state irritability was predicted by lower decision threshold during the frustration block, and larger decrease in the decision threshold parameter between non-frustration and frustration blocks, over traditional AP metrics. This indicates that higher state irritability was associated with an impulsive decision-making style (preferring speed over accuracy) during frustration. These findings suggest that decision threshold may serve as a cognitive marker for mechanism-based interventions aiming at reducing irritability symptoms.