Sense-making under ignorance.

Area of Science:

• Cognitive Psychology
• Decision Science
• Artificial Intelligence

Background:

• Human cognition frequently relies on explanatory inferences to understand the world, linking observable evidence to unobservable causes or categories.
• These inferences are often made under conditions of incomplete information, where relevant facts or diagnostic features are unknown.

Purpose of the Study:

• To investigate how individuals make explanatory inferences when faced with uncertainty and missing evidence.
• To test whether people infer the presence or absence of diagnostic evidence using irrelevant cues and then base their explanations on this inferred evidence.

Main Methods:

• Seven experiments were conducted involving diagnostic causal reasoning and categorization tasks.
• Participants' reasoning processes were analyzed to identify strategies used when evidence was incomplete.
• Evidence-seeking behavior and beliefs about evidence presence were also examined.

Main Results:

• Participants demonstrated a tendency to infer missing evidence, even from normatively irrelevant cues like base rates.
• This strategy was employed in both causal reasoning and categorization tasks, leading to illusory inferences.
• Behavioral data confirmed predictions regarding evidence-seeking and beliefs about evidence presence.

Conclusions:

• The findings highlight a common inferential mechanism underlying different forms of diagnostic reasoning, including causal inference and classification.
• People may employ a unified cognitive strategy to handle uncertainty in explanatory tasks.
• This research sheds light on the nature of human reasoning and potential biases in decision-making under uncertainty.