Single- and Dual-Process Models of Biased Contingency Detection

Area of Science:

• Cognitive Psychology
• Learning and Memory

Background:

• Causal and contingency learning research demonstrates probability-based biases in estimating event relationships.
• Simple single-process models traditionally explain these biases.
• Recent findings suggest dissociations across dependent variables, leading to dual-process model proposals.

Purpose of the Study:

• To review evidence supporting dual-process models in contingency learning.
• To identify shortcomings and limitations in the existing literature on these dissociations.

Main Methods:

• Literature review of studies investigating dissociations in contingency learning biases.
• Critical analysis of the replicability, generalizability, and methodological artifacts of reported dissociations.

Main Results:

• Some reported dissociations supporting dual-process models are difficult to replicate or generalize.
• Other dissociations may be attributable to methodological artifacts rather than distinct cognitive processes.

Conclusions:

• The evidence for dual-process models explaining biased contingency detection is currently scarce and inconclusive.
• Further rigorous research is needed to validate or refute the dual-process account.