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Qualitative speed-accuracy tradeoff effects can be explained by a diffusion/fast-guess mixture model.

Roger Ratcliff1, Inhan Kang2

  • 1The Ohio State University, 1835 Neil Avenue, Columbus, OH, 43210, USA. ratcliff.22@osu.edu.

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A new mixture model accurately explains orientation discrimination data, including fast guesses, which the standard diffusion model could not. This addresses limitations in prior research on speed-accuracy trade-offs.

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience

Background:

  • The standard diffusion model is widely used to explain decision-making.
  • Previous research suggested this model fails to account for data under speed-accuracy stress.
  • Fast guesses (RTs < 300 ms) were observed in high-stress conditions.

Purpose of the Study:

  • To develop and validate a model that can account for decision-making data, including fast guesses.
  • To re-evaluate the applicability of the diffusion model under varying speed-accuracy stress and stimulus contrast.
  • To investigate the selective influence of experimental manipulations on cognitive processes.

Main Methods:

  • A mixture model was developed, combining a normal distribution for fast guesses and the standard diffusion process for other responses.
  • The model's fit was assessed against accuracy and response time (RT) data from an orientation discrimination task.
  • Experimental conditions included manipulated speed-accuracy stress and stimulus contrast.

Main Results:

  • The mixture model successfully fit the entire pattern of accuracy and RTs across all conditions.
  • The model captured complex RT distributions, including bimodal shapes.
  • Speed-accuracy stress and stimulus contrast selectively influenced different parameters within the model.

Conclusions:

  • The standard diffusion model's failure in prior studies was likely due to the experimental design encouraging fast guesses.
  • The developed mixture model provides a more comprehensive account of decision-making under speed-accuracy trade-offs.
  • This work refines our understanding of how cognitive processes are modulated by task demands and stimulus properties.