The propensity interpretation of probability and diagnostic split in explaining away

Tesic, Marko; Liefgreen, Alice; Lagnado, David A.

doi:10.1016/j.cogpsych.2020.101293

Cited by 7 publications

(10 citation statements)

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“…People have been found to show explaining away and augmenting in intercausal reasoning tasks, though not always reliably. In particular the size of the effects has sometimes been smaller than predicted (Morris and Larrick, 1995;Ali et al, 2011;Rottman and Hastie, 2014;Liefgreen et al, 2018;Tešić et al, 2020). The present paper aims to investigate further the conditions under which these inferences are drawn.…”

Section: Introductionmentioning

confidence: 76%

“…Extant explanations for under-explaining away have pointed to possible differences in the interpretation of probability (Tešić et al, 2020), prior knowledge that changes the causal structure reasoned about, for instance by adding links between causes or additional intervening variables that must be active to allow an effect to occur (Mayrhofer et al, 2010;Rehder, 2014;Rottman and Hastie, 2014), and by positing that a subset of participants may represent the relations between variables as associative, and thus bidirectional, rather than as causal and unidirectional (Rehder and Waldmann, 2017). The latter has been referred to as the "rich get richer" principle because it implies that when one variable is present, this will increase the probability that variables connected to it will also be present, and vice-versa when a variable is absent (c.f.…”

Section: Discussionmentioning

confidence: 99%

“…Associative links differ from causal links by being bidirectional rather than unidirectional. However, further research is needed to explore alternative interpretations of these findings (see Tešić et al, 2020). Earlier studies on people's sensitivity to the impact of interactions between causes when these are made explicit in the instructions (Morris and Larrick, 1995) suggest that people's intuitions do capture the direction of the changes in probability that follow from such interactions.…”

Section: Condition 4: Uncertain Negative Evidencementioning

confidence: 99%

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Explaining Away, Augmentation, and the Assumption of Independence

et al. 2020

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In reasoning about situations in which several causes lead to a common effect, a much studied and yet still not well-understood inference is that of explaining away. Assuming that the causes contribute independently to the effect, if we learn that the effect is present, then this increases the probability that one or more of the causes are present. But if we then learn that a particular cause is present, this cause "explains" the presence of the effect, and the probabilities of the other causes decrease again. People tend to show this explaining away effect in their probability judgments, but to a lesser extent than predicted by the causal structure of the situation. We investigated further the conditions under which explaining away is observed. Participants estimated the probability of a cause, given the presence or the absence of another cause, for situations in which the effect was either present or absent, and the evidence about the effect was either certain or uncertain. Responses were compared to predictions obtained using Bayesian network modeling as well as a sensitivity analysis of the size of normative changes in probability under different information conditions. One of the conditions investigated: when there is certainty that the effect is absent, is special because under the assumption of causal independence, the probabilities of the causes remain invariant, that is, there is no normative explaining away or augmentation. This condition is therefore especially diagnostic of people's reasoning about common-effect structures. The findings suggest that, alongside earlier explanations brought forward in the literature, explaining away may occur less often when the causes are assumed to interact in their contribution to the effect, and when the normative size of the probability change is not large enough to be subjectively meaningful. Further, people struggled when given evidence against negative evidence, resembling a double negation effect.

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Section: Introductionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Condition 4: Uncertain Negative Evidencementioning

confidence: 99%

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Explaining Away, Augmentation, and the Assumption of Independence

et al. 2020

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“…Prior knowledge and expectations, underlying causal models may have contributed to setting higher priors than suggested by base rates. Previous research about explaining away inferences is not conclusive (Morris and Larrick, 1995;Oppenheimer and Monin, 2009;Rehder, 2014;Rottman andHastie, 2014, 2016;Tesic et al, 2020) and offers different views on challenges associated with explaining away inferences. Our experiments highlighted that people are able to navigate the explaining away type of scenarios and make accurate judgments about competing causes that fit with qualitative Bayesian predictions.…”

Section: Discussionmentioning

confidence: 99%

“…In explaining away situations people struggle with both qualitative and quantitative aspects of judgments (Rehder, 2014;Rottman andHastie, 2014, 2016; but also see Liefgreen et al, 2018;Tesic et al, 2020). From the qualitative point of view, the direction of inference is sometimes inaccurate and from the quantitative perspective, updating is too conservative, leading to the underweighting of evidence.…”

Section: Interpreting Competing Causes: Explaining Away and Zero-summentioning

confidence: 99%

Are Jurors Intuitive Statisticians? Bayesian Causal Reasoning in Legal Contexts

Shengelia

Lagnado

2021

Front. Psychol.

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In criminal trials, evidence often involves a degree of uncertainty and decision-making includes moving from the initial presumption of innocence to inference about guilt based on that evidence. The jurors’ ability to combine evidence and make accurate intuitive probabilistic judgments underpins this process. Previous research has shown that errors in probabilistic reasoning can be explained by a misalignment of the evidence presented with the intuitive causal models that people construct. This has been explored in abstract and context-free situations. However, less is known about how people interpret evidence in context-rich situations such as legal cases. The present study examined participants’ intuitive probabilistic reasoning in legal contexts and assessed how people’s causal models underlie the process of belief updating in the light of new evidence. The study assessed whether participants update beliefs in line with Bayesian norms and if errors in belief updating can be explained by the causal structures underpinning the evidence integration process. The study was based on a recent case in England where a couple was accused of intentionally harming their baby but was eventually exonerated because the child’s symptoms were found to be caused by a rare blood disorder. Participants were presented with a range of evidence, one piece at a time, including physical evidence and reports from experts. Participants made probability judgments about the abuse and disorder as causes of the child’s symptoms. Subjective probability judgments were compared against Bayesian norms. The causal models constructed by participants were also elicited. Results showed that overall participants revised their beliefs appropriately in the right direction based on evidence. However, this revision was done without exact Bayesian computation and errors were observed in estimating the weight of evidence. Errors in probabilistic judgments were partly accounted for, by differences in the causal models representing the evidence. Our findings suggest that understanding causal models that guide people’s judgments may help shed light on errors made in evidence integration and potentially identify ways to address accuracy in judgment.

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Models of Variability in Probabilistic Causal Judgments

Kolvoort,

Davis,

Rehder

et al. 2024

Comput Brain Behav

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Most theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler has the best fit to the data and is able to account for a number of unusual features of the response distributions (e.g., bi-modality), supporting the view that the stochastic sampling mechanism it posits reflects the cognitive processes via which people draw causal inferences. Additionally, our findings suggest that incorporating ‘non-reasoning’ processes, such as rounding and guessing, can improve the ability of models of causal reasoning to account for the observed response distributions. Overall, the study highlights the potential of computational modeling of full response distributions to shed light on the underlying mechanisms of human causal reasoning and identifies promising directions for future research.

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The propensity interpretation of probability and diagnostic split in explaining away

Cited by 7 publications

References 40 publications

Explaining Away, Augmentation, and the Assumption of Independence

Explaining Away, Augmentation, and the Assumption of Independence

Are Jurors Intuitive Statisticians? Bayesian Causal Reasoning in Legal Contexts

Models of Variability in Probabilistic Causal Judgments

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