Explaining the Implicit Negations Effect in Conditional Inference: Experience, Probabilities, and Contrast Sets

doi:10.1037/xge0000954

Explaining the Implicit Negations Effect in Conditional Inference: Experience, Probabilities, and Contrast Sets

2021-02-01

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DOI：10.1037/xge0000954

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Psychologists arc beginning to uncover the rational basis for many of the biases revealed over the last 50 years in deductive and causal reasoning, judgment, and decision making. In this article, it is argued that a manipulation. experiential learning, shown to be effective in judgment and decision making, may elucidate the rational underpinning of the implicit negation effect in conditional inference. In three experiments, this effect was created and removed by using probabilistically structured contrast sets acquired during a brief learning phase. No other theory of the implicit negations effect predicts these results, which can be modeled using Bayes nets as in causal approaches to category structure. It is also shown how these results relate to a recent development in the psychology of reasoning called inferentialism. It is concluded that many of the same cognitive mechanisms that underpin causal reasoning, judgment and decision making may be common to logical reasoning, which may require no special purpose machinery or module.

Keywords:

causal Bayes nets

experiential learning

inferentialism

negations

new paradigm

Explaining the Implicit Negations Effect in Conditional Inference: Experience, Probabilities, and Contrast Sets

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