Abductive, Causal, and Counterfactual Conditionals Under Incomplete Probabilistic Knowledge

TL;DR

This study investigates how people interpret abductive, causal, and counterfactual conditionals under incomplete probabilistic knowledge, revealing predominant interpretation patterns and reasoning deviations.

Contribution

It introduces a probabilistic truth table task framework for analyzing conditionals under incomplete knowledge and discusses implications for probability logic and reasoning paradigms.

Findings

Conditional event interpretations are most common among responses.

Conjunction interpretations are the second most frequent.

Some participants neglect imprecision in premises when estimating probabilities.

Abstract

We study abductive, causal, and non-causal conditionals in indicative and counterfactual formulations using probabilistic truth table tasks under incomplete probabilistic knowledge (N = 80). We frame the task as a probability-logical inference problem. The most frequently observed response type across all conditions was a class of conditional event interpretations of conditionals; it was followed by conjunction interpretations. An interesting minority of participants neglected some of the relevant imprecision involved in the premises when inferring lower or upper probability bounds on the target conditional/counterfactual ("halfway responses"). We discuss the results in the light of coherence-based probability logic and the new paradigm psychology of reasoning.