Reasoning about causal relationships: Inferences on causal networks.

Rottman, Benjamin M.; Hastie, Reid

doi:10.1037/a0031903

Cited by 138 publications

(169 citation statements)

References 124 publications

(284 reference statements)

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“…Moreover, recently Fernbach and Erb (2013) have proposed a CBN model of modus ponens in causal conditional reasoning where disablers are represented explicitly as in Figure 1. Moreover, a similar CBN representation has been proposed by Rottman and Hastie (2013) as a general approach to conditional inference. So there is nothing inherent to the CBN approach that precludes the explicit representation of disablers.…”

Section: Figure 1 About Herementioning

confidence: 99%

“…4 This view commits one to more than just probability theory, e.g., CBNs assume the acyclicity of dependencies, directedness, faithfulness, and the parental Markov property. All these assumptions are about making inference more tractable but some of these assumptions have been questioned (for a review, see Rottman & Hastie, 2013). However, the potential of CBNs to model conditional reasoning has not been fully explored and so it would be premature to dismiss them solely on these grounds (Oaksford & Chater, 2013;Rottman & Hastie, 2013).…”

Section: The Probabilistic Approachmentioning

confidence: 99%

“…All these assumptions are about making inference more tractable but some of these assumptions have been questioned (for a review, see Rottman & Hastie, 2013). However, the potential of CBNs to model conditional reasoning has not been fully explored and so it would be premature to dismiss them solely on these grounds (Oaksford & Chater, 2013;Rottman & Hastie, 2013). The idea is that conditionals build the appropriate dependency structures in System 2, i.e., in WM.…”

Section: The Probabilistic Approachmentioning

confidence: 99%

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Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

Oaksford

Chater

2014

Thinking & Reasoning

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In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data.LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the nonmonotonicity of human reasoning. Dual process theories (Evans 2003(Evans , 2007Evans & Stanovich, 2013;Kahneman, 2011;Sloman, 1996;Stanovich, 2011; Stanovich & West, 2000;Wason & Evans, 1975) invoke two separate cognitive systems to explain performance on a variety of cognitive tasks. These are labelled System 1 and System 2. System 1 is rapid, parallel, automatic, do not require the resources of working memory (WM), and only their final product is posted in consciousness.In contrast, System 2 is slow sequential, and analytic and makes use of the central working memory system. In particular, System 2 ''permits abstract hypothetical thinking that cannot be achieved by System 1' ' (Evans 2003, p. 454). Recently, Oaksford and Chater (2012) argued that accounting for non-monotonic or defeasible reasoning in dual process theory required that both System 2 WM representations and System 1 long term memory (LTM) representations need to be interpreted probabilistically. This position is consistent with Evans and Over (2004; see also Over, Evans, & Elqayam, 2010) adoption of probability logic (Adams, 1998) as underpinning analytic processes in System 2. But it contrasts with accounts which treat analytic processes in System 2 as underpinned by standard binary truth functional logic (Heit & Rotello, 2010;Klauer et al., 2010;Rips, 2001Rips, , 2002 Stanovich & West, 2000;Stanovich, 2011). Oaksford and Chater (2012) labelled the former approach, the single function dual process (SFDP) approach and the latter the dual function dual process (DFDP) approach. Our goal in this paper is to confront some further problems and challenges for the probabilistic SFDP approach but first we rehearse Oaksford and Chater's (2012) argument in detail. Probabilistic Single Function Dual Process TheoryBoth the dual process theory (Evans 2002) and the probabilistic approach (Oaksford and Chater 1991, 1998 developed out of a critique of the classical logicist approach to cognitive architecture (Fodor 1975;Pylyshyn 1984), which is a logical single 4 function dual process theory. The store of world knowledge in LTM consists of a consistent set of logical formulae i...

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Section: Figure 1 About Herementioning

confidence: 99%

Section: The Probabilistic Approachmentioning

confidence: 99%

Section: The Probabilistic Approachmentioning

confidence: 99%

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Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

Oaksford

Chater

2014

Thinking & Reasoning

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“…A full representation of these causal networks would also include parameters to represent the probability of each node being in a particular state given the states of the nodes that directly cause it (see Rottman & Hastie, 2013, for an introduction to causal networks). But here we focus on intuitions involving the causal graphs alone.…”

Section: Causal Networkmentioning

confidence: 99%

Children Use Temporal Cues to Learn Causal Directionality

2013

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The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real-world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.

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“…According to Bayes nets, causal chains and common-cause structures are BMarkov equivalent.^This suggests identical inferential distortion effects for both structures. Empirically, however, the evidence on the direct psychological validity of the Markov condition for common-cause structures is mixed (Rehder & Burnett, 2005; see also Jarecki et al, 2013;Mayrhofer, Goodman, Waldmann, & Tenenbaum, 2008;Mayrhofer & Waldmann, 2015;Rottman & Hastie, 2013;von Sydow, 2011von Sydow, , 2013. Future research should compare reasoning with different causal structures when the data violate the Markov assumption .…”

Section: Contingency Conditionmentioning

confidence: 99%

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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Reasoning about causal relationships: Inferences on causal networks.

Cited by 138 publications

References 124 publications

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

Children Use Temporal Cues to Learn Causal Directionality

Transitive reasoning distorts induction in causal chains

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