Extending systems factorial technology to errored responses.

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Psychometrics

Background:

• Systems Factorial Technology (SFT) traditionally uses error-free response times (RTs) to infer mental processing architectures.
• Existing SFT methods are limited in their ability to analyze the full spectrum of cognitive task performance.

Purpose of the Study:

• To extend the survivor interaction contrast (SIC) measure within SFT to include incorrect response times.
• To provide formal proofs and demonstrations for the extended SIC measure.
• To differentiate between cognitive processing models based on RT data, including errors.

Main Methods:

• Formal mathematical proofs were developed to extend the SIC measure to RTs conditioned on response correctness.
• The extended SIC was applied to evaluate the validity of the Wiener diffusion model and Poisson counter models.
• Stimulus conditions with varying difficulty were used to establish an ordering relation for analysis.

Main Results:

• The extended SIC measure reliably predicts canonical SFT results when an ordering relation is preserved, indicating exhaustive processing.
• The Wiener diffusion model maintains the required ordering relation for both correct and incorrect RTs.
• Certain classes of the Poisson counter model violate this ordering relation, offering a potential experimental distinction.

Conclusions:

• The extended SIC measure provides a more robust tool for inferring mental architecture by incorporating error data.
• This methodology allows for stronger differentiation between competing cognitive models, such as the Wiener diffusion and Poisson counter models.
• The findings underscore the significance of analyzing errors in perceptual and cognitive tasks for a comprehensive understanding of processing.