Belief digitization: Do we treat uncertainty as probabilities or as bits?

Area of Science:

• Cognitive Psychology
• Decision Science
• Philosophy of Mind

Background:

• Humans are traditionally viewed as Bayesian reasoners, using probabilities to represent degrees of belief.
• The assumption that probabilities directly reflect subjective beliefs is a cornerstone of Bayesian models of cognition.

Purpose of the Study:

• To challenge the assumption that probabilities represent degrees of belief in human reasoning.
• To investigate whether people employ a digital (true/false) approach to uncertain information during inference.

Main Methods:

• Eight experimental studies were conducted.
• Participants learned about two hypotheses with varying plausibility.
• Reasoning processes were analyzed across different manipulations of plausibility and task variations.

Main Results:

• Participants treated uncertain information digitally, categorizing it as strictly true or false for predictive purposes.
• Even when explicitly assigned a positive probability, less-plausible hypotheses were ignored in predictions.
• This digital reasoning pattern persisted across various plausibility cues (simplicity, evidence fit, explicit probabilities).

Conclusions:

• Human reasoning in prediction tasks exhibits digitization, not a continuous probabilistic approach.
• Digitization likely serves to bypass cognitive processing limitations in simulating hypothetical scenarios.
• Findings impact theories in the philosophy of science and cognitive architecture.