Human judgments of positive and negative causal chains.

Baetu, Irina; Baker, Andrew G.

doi:10.1037/a0013764

Cited by 25 publications

(47 citation statements)

References 58 publications

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“…4 Example scale used to elicit conditional probability judgments in Experiments 1a and 1b predictions for transitive inferences for the global A→C relation rely on qualitatively correct judgments of the local A→B and B→C relationships. Therefore, following previous research (Baetu & Baker, 2009), we included only participants who correctly judged both local relationships to be positive. In Experiment 1a, 25% of participants failed to meet this criterion in each of the two relations; in Experiment 1b an average of 19% failed in each of the two relations.…”

Section: Resultsmentioning

confidence: 99%

“…Previous research on causal chains has shown that people make transitive inferences from A to C when observing only relations A→B and B→C (Ahn & Dennis, 2000;Baetu & Baker, 2009). Ahn and Dennis (2000) used a sequential learning paradigm, providing participants with evidence on the covariation of events A and B (fertilizer→level of chemicals in soil) intermixed with evidence about events B and C (chemicals→blooming of flower).…”

Section: Previous Research On Transitive Inferences In Causal Chainsmentioning

confidence: 99%

“…The studies of Ahn and Dennis (2000) and Baetu and Baker (2009) indicate that in the absence of direct evidence on the relation A→C, people make transitive inferences as if they are inducing causal chains that obey the Markov condition. In these studies, no contradictory data were available, so it seems like a reasonable default assumption for learners.…”

Section: Goals and Scopementioning

confidence: 99%

“…A primacy effect when constructing the local relations in conjunction with transitive reasoning may explain this. Baetu and Baker (2009) investigated the influence of transitive reasoning with chains in more detail. In their studies, positive, negative, and zero contingencies for A→B and B→C were combined.…”

Section: Previous Research On Transitive Inferences In Causal Chainsmentioning

confidence: 99%

“…Intransitive chains provide a stronger test for the hypothesis that people integrate the links into causal models and tend to reason causally coherent, deviating from the data, as if the Markov condition holds (causal coherence hypothesis). Also, we did not use a sequential learning task (Ahn & Dennis, 2000;Baetu & Baker, 2009;cf. Hebbelmann & von Sydow, 2014; but presented all items in an overview format.…”

Section: Goals and Scopementioning

confidence: 99%

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Transitive reasoning distorts induction in causal chains

2015

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A probabilistic causal chain A→B→C may intuitively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive if the so-called Markov condition holds. In two experiments, we examined how people make probabilistic judgments about indirect relationships A→C in causal chains A→B→C that violate the Markov condition. We hypothesized that participants would make transitive inferences in accordance with the Markov condition although they were presented with counterevidence showing intransitive data. For instance, participants were successively presented with data entailing positive dependencies A→B and B→C. At the same time, the data entailed that A and C were statistically independent. The results of two experiments show that transitive reasoning via a mediating event B influenced and distorted the induction of the indirect relation between A and C. Participants' judgments were affected by an interaction of transitive, causalmodel-based inferences and the observed data. Our findings support the idea that people tend to chain individual causal relations into mental causal chains that obey the Markov condition and thus allow for transitive reasoning, even if the observed data entail that such inferences are not warranted. Transitive reasoning enables judgments about unobserved relationships based on indirect evidence. If one observes that object A is heavier than object B, and that B is heavier than C, one can infer that A is heavier than C. Not all relations, however, are transitive. If A is the mother of B, and B is the mother of C, this does not mean that A is the mother of C.We investigate whether and to what extent people reason transitively about causal relations, even when the conditions for transitive inferences do not hold true. We focus on probabilistic causal chains of the type A→B→C, where individual relations A→B and B→C can be combined to form a chain A→B→C to make probabilistic inferences from the chain's initial event A to the terminal event C. First, we specify the conditions under which transitive reasoning in causal chains is valid. We then report the findings of two experiments investigating whether people make transitive inferences even when the available data entail that such inferences are not warranted.Our research builds on the idea that people represent the world in terms of mental causal models (

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Section: Resultsmentioning

confidence: 99%

Section: Previous Research On Transitive Inferences In Causal Chainsmentioning

confidence: 99%

Section: Goals and Scopementioning

confidence: 99%

Section: Previous Research On Transitive Inferences In Causal Chainsmentioning

confidence: 99%

Section: Goals and Scopementioning

confidence: 99%

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Transitive reasoning distorts induction in causal chains

2015

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Betting on transitivity in probabilistic causal chains

Hebbelmann

Sydow

2017

Cogn Process

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Causal reasoning is crucial to people's decision making in probabilistic environments. It may rely directly on data about covariation between variables (correspondence) or on inferences based on reasonable constraints if larger causal models are constructed based on local relations (coherence). For causal chains an often assumed constraint is transitivity. For probabilistic causal relations, mismatches between such transitive inferences and direct empirical evidence may lead to distortions of empirical evidence. Previous work has shown that people may use the generative local causal relations A → B and B → C to infer a positive indirect relation between events A and C, despite data showing that these events are actually independent (von Sydow et al. in Proceedings of the thirty-first annual conference of the cognitive science society. Cognitive Science Society, Austin, 2009, Proceedings of the 32nd annual conference of the cognitive science society. Cognitive Science Society, Austin, 2010, Mem Cogn 44(3):469-487, 2016). Here we used a sequential learning scenario to investigate how transitive reasoning in intransitive situations with negatively related distal events may relate to betting behavior. In three experiments participants bet as if they were influenced by a transitivity assumption, even when the data strongly contradicted transitivity.

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Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

Jones¹,

Love²

2011

Behav Brain Sci

429

330

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The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.

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Human judgments of positive and negative causal chains.

Cited by 25 publications

References 58 publications

Transitive reasoning distorts induction in causal chains

Transitive reasoning distorts induction in causal chains

Betting on transitivity in probabilistic causal chains

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

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