Effects of context on the rate of conjunctive responses in the probabilistic truth table task

Jubin, Jonathan; Barrouillet, Pierre

doi:10.1080/13546783.2018.1477689

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…The probability verification task is similar to versions of the probabilistic truth table task (Over et al, 2007). This task has been criticized as perhaps not revealing people's probabilistic interpretations of the conditional (Jubin & Barrouillet, 2019). The precise reasons do not matter, but an immediate response is that (a) these tasks (especially our task which involves filling in 9 cells of the JPD) creates a lot of room for error, and (b) the subjective Bayesian approach rejects the frequentist method and the ratio formula for calculating conditional probabilities.…”

Section: Probabilitiesmentioning

confidence: 99%

“…Testing these predictions requires manipulating probabilities. Reasoning researchers have manipulated probabilities in many ways, using pretested content (Oaksford, Chater, & Grainger, 1999; Oaksford et al, 2000), frequency formats (Gigerenzer & Hoffrage, 1995) combined with concrete visualizations (stacks of cards; Oaksford, Chater, Grainger, & Larkin, 1997, 1999), contingency tables, or “probabilistic truth tables” (Evans, Handley, & Over, 2003; Oberauer & Wilhelm, 2003), as in causal judgment (Ward & Jenkins, 1965), a procedure that has also been reversed so participants construct the contingency table given a conditional (Oaksford & Moussakowski, 2004; Oaksford & Wakefield, 2003; Oberauer, 2006; Over, Hadjichristidis, Evans, Handley, & Sloman, 2007), and sequential tasks where trial frequency reflects the probabilities (Fugard, Pfeifer, Mayerhofer, & Kleiter, 2011; Oaksford & Moussakowski, 2004; Oaksford & Wakefield, 2003), and where learning effects are observed (for critiques, see Jubin & Barrouillet, 2019; Oberauer, Weidenfeld, & Hörnig, 2004). In these experiments, we used experiential learning of probabilities, which leads to improved performance in judgment and decision making, and which has not used before in reasoning research.…”

Section: Experience: Manipulating Probabilitiesmentioning

confidence: 99%

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Explaining the implicit negations effect in conditional inference: Experience, probabilities, and contrast sets.

Vance¹,

Oaksford²

2021

Journal of Experimental Psychology: General

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Psychologists are beginning to uncover the rational basis for many of the biases revealed over the last 50 years in deductive and causal reasoning, judgement and decision-making. In this paper, it is argued that a manipulation, experiential learning, shown to be effective in judgement 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 modelled 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, judgement and decision-making may be common to logical reasoning, which may require no special purpose machinery or module.

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Section: Probabilitiesmentioning

confidence: 99%