Individual differences in emotion processing: how similar are diffusion model parameters across tasks?

Abstract

The goal of this study was to replicate findings of diffusion model parameters capturing emotion effects in a lexical decision task and investigating whether these findings extend to other tasks of implicit emotion processing. Additionally, we were interested in the stability of diffusion model parameters across emotional stimuli and tasks for individual subjects. Responses to words in a lexical decision task were compared with responses to faces in a gender categorization task for stimuli of the emotion categories: happy, neutral and fear. Main effects of emotion as well as stability of emerging response style patterns as evident in diffusion model parameters across these tasks were analyzed. Based on earlier findings, drift rates were assumed to be more similar in response to stimuli of the same emotion category compared to stimuli of a different emotion category. Results showed that emotion effects of the tasks differed with a processing advantage for happy followed by neutral and fear-related words in the lexical decision task and a processing advantage for neutral followed by happy and fearful faces in the gender categorization task. Both emotion effects were captured in estimated drift rate parameters-and in case of the lexical decision task also in the non-decision time parameters. A principal component analysis showed that contrary to our hypothesis drift rates were more similar within a specific task context than within a specific emotion category. Individual response patterns of subjects across tasks were evident in significant correlations regarding diffusion model parameters including response styles, non-decision times and information accumulation.

Mid-Frontal Theta Modulates Response Inhibition and Decision Making Processes in Emotional Contexts

“…A recent report looked at individual differences in emotion processing using HDDM. They concluded that “emotion effects of the tasks differed with a processing advantage for happy followed by neutral words in the lexical decision task and a processing advantage for neutral followed by happy and fearful faces in the gender categorization task” [79]. These results are similar to what we found in our ESSP using HDDM.…”

Identifying relationships between cognitive processes across tasks, contexts, and time

“…The drift rate parameters (v (e) and v (c) ) are quite strongly correlated between sessions, with the exception of the error drift rate in-scanner paired with correct drift rate out of scanner. The average correlation between drift rates between sessions (r = .42) was almost double that reported by Mueller et al (2019), which makes sense given that Forstmann et al's experiment was identical between sessions-only the context changed. Non-decision time (τ ) was uncorrelated between sessions.…”

“…In that investigation, parameters of the diffusion model related to processing speed correlated across tasks, but the other model parameters did not. Mueller et al (2019) also used the diffusion model, and analyzed data from an experiment in which one group of participants completed two tasks related to emotion perception: one task used word-based stimuli, the other used faces. Mueller et al (2019) found that parameters of the diffusion model related to response style and non-decision time were more strongly correlated across tasks than drift-related parameters, on average.…”

“…Mueller et al (2019) also used the diffusion model, and analyzed data from an experiment in which one group of participants completed two tasks related to emotion perception: one task used word-based stimuli, the other used faces. Mueller et al (2019) found that parameters of the diffusion model related to response style and non-decision time were more strongly correlated across tasks than drift-related parameters, on average. Similarly, Hedge et al (2019) found moderate-to-good correlations between response caution parameters of the diffusion model across flanker, Stroop, and random dot motion tasks.…”

Identifying relationships between cognitive processes across tasks, contexts, and time

“…In that investigation, parameters of the diffusion model related to processing speed correlated across tasks, but the other model parameters did not. Mueller, White, and Kuchinke (2019) also used the diffusion model, and analysed data from an experiment in which one group of participants completed two tasks related to emotion perception: one task used word-based stimuli, the other used faces. Mueller et al found that parameters of the diffusion model related to response style and non-decision time were more strongly correlated across tasks than drift-related parameters, on average.…”