Explaining the implicit negations effect in conditional inference: Experience, probabilities, and contrast sets

Area of Science:

• Cognitive Psychology
• Decision Science
• Logic and Reasoning

Background:

• Psychological research has identified biases in deductive and causal reasoning, judgment, and decision-making over the past 50 years.
• The rational basis for these biases is increasingly being uncovered.
• The implicit negation effect in conditional inference is one such bias whose underlying rationale is being explored.

Purpose of the Study:

• To investigate the rational underpinning of the implicit negation effect in conditional inference.
• To demonstrate that experiential learning can elucidate this rational basis.
• To test predictions derived from a probabilistic contrast set learning model.

Main Methods:

• Three experiments were conducted involving a brief learning phase to acquire probabilistically structured contrast sets.
• Experiential learning was used as a manipulation to create and remove the implicit negation effect.
• Bayes nets were employed to model the observed results, drawing parallels with causal approaches to category structure.

Main Results:

• The implicit negation effect was successfully created and subsequently removed through the manipulation of contrast sets during the learning phase.
• The experimental results were accurately predicted by the Bayes net model, aligning with causal models of category structure.
• The findings were shown to be consistent with the principles of "inferentialism," a recent development in the psychology of reasoning.

Conclusions:

• Experiential learning provides a rational explanation for the implicit negation effect in conditional inference.
• Cognitive mechanisms involved in causal reasoning, judgment, and decision-making appear to be shared with logical reasoning.
• Logical reasoning may not necessitate specialized cognitive machinery or modules, but rather relies on general cognitive processes.