Abstract
Plausibility has been implicated as playing a critical role in many cognitive phenomena from comprehension to problem solving. Yet, across cognitive science, plausibility is usually treated as an operationalized variable or metric rather than being explained or studied in itself. This article describes a new cognitive model of plausibility, the Plausibility Analysis Model (PAM), which is aimed at modeling human plausibility judgment. This model uses commonsense knowledge of concept-coherence to determine the degree of plausibility of a target scenario. In essence, a highly plausible scenario is one that fits prior knowledge well: with many different sources of corroboration, without complexity of explanation, and with minimal conjecture. A detailed simulation of empirical plausibility findings is reported, which shows a close correspondence between the model and human judgments. In addition, a sensitivity analysis demonstrates that PAM is robust in its operations. Copyright © 2006 Cognitive Science Society, Inc. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 95-120 |
| Number of pages | 25 |
| Journal | Cognitive Science |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2006 |
Keywords
- Cognition
- Computer simulation
- Plausibility
- Psychology
- Reasoning
- Symbolic computational modeling