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A comparison of single and multiple diffusion process models of continuous-outcome decision making
Philip L Smith1, Paul M Garrett1, Daniel Feuerriegel1
1Melbourne School of Psychological Sciences, University of Melbourne.
Two diffusion models accurately explain decision-making on continuous scales. Diffusive evidence accumulation, not model specifics, drives performance in speeded decision tasks.
Area of Science:
- Cognitive psychology
- Computational neuroscience
- Decision science
Background:
- Continuous-outcome decision tasks are vital for understanding speeded decision-making.
- These tasks provide rich data on response error distributions, unlike simple error proportions.
Purpose of the Study:
- To compare the efficacy of two novel diffusion process models for continuous-outcome decisions.
- To evaluate model performance across perceptual tasks with varied evidence manipulation.
Main Methods:
- Applied the circular diffusion model and the spatially continuous diffusion model.
- Assessed model fits to error and response time distributions from three perceptual tasks.
Main Results:
- Both models generally provided good accounts of performance across tasks.
- Qualitatively different patterns of response times and errors were observed.
Conclusions:
- Diffusive evidence accumulation processes are key to model success, more so than specific model architecture.
- Findings highlight the theoretical unity of diffusion models across decision-making domains.
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