Abstract
When evaluating causal explanations, simpler explanations are widely regarded as better explanations. However, little is known about how people assess simplicity in causal explanations or what the consequences of such a preference are. We contrast 2 candidate metrics for simplicity in causal explanations: node simplicity (the number of causes invoked in an explanation) and root simplicity (the number of unexplained causes invoked in an explanation). Across 4 experiments, we find that explanatory preferences track root simplicity, not node simplicity; that a preference for root simplicity is tempered (but not eliminated) by probabilistic evidence favoring a more complex explanation; that committing to a less likely but simpler explanation distorts memory for past observations; and that a preference for root simplicity is greater when the root cause is strongly linked to its effects. We suggest that a preference for root-simpler explanations follows from the role of explanations in highlighting and efficiently representing and communicating information that supports future predictions and interventions.
Original language | English (US) |
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Pages (from-to) | 1761-1780 |
Number of pages | 20 |
Journal | Journal of Experimental Psychology: General |
Volume | 146 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2017 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Experimental and Cognitive Psychology
- Developmental Neuroscience
- General Psychology
Keywords
- Causal inference
- Explanation
- Inference to the best explanation
- Parsimony
- Simplicity