A ballistic model of choice response time

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Mathematical Psychology

Background:

• Response time (RT) models commonly employ stochastic accumulation processes.
• Accounting for benchmark RT phenomena necessitates including between-trial variability in starting point or accumulation rate.
• Existing linear and nonlinear models address this variability, but often with complexity.

Purpose of the Study:

• To demonstrate that a ballistic (within-trial deterministic) model can account for benchmark RT phenomena.
• To simplify nonlinear accumulation processes by integrating between-trial variability in accumulation rate and starting point.
• To validate the model's efficacy by fitting it to existing empirical data.

Main Methods:

• Developed a simplified ballistic model based on Usher and McClelland's nonlinear accumulation process.
• Incorporated between-trial variability in both the accumulation rate and the starting point.
• Fitted the proposed model to response time data exhibiting benchmark phenomena.

Main Results:

• The ballistic model successfully accounted for benchmark response time phenomena.
• The model provided a good fit to the empirical data of Ratcliff and Rouder (1998).
• The findings suggest a deterministic within-trial process can explain complex RT patterns.

Conclusions:

• A simplified ballistic model with between-trial variability offers a viable alternative to complex stochastic accumulation models for explaining RT phenomena.
• The study highlights the potential of deterministic processes in cognitive modeling.
• The model's success in fitting empirical data supports its explanatory power for decision-making processes.