Causal Imprinting in Causal Structure Learning

Taylor, Eric; Ahn, Woo‐kyoung

doi:10.1037/e520602012-203

Cited by 6 publications

(7 citation statements)

References 34 publications

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“…Nevertheless, if one looks in the right way, one can find belief perseverance in many causal learning paradigms . For instance, subjects in Taylor and Ahn () were tasked with learning the causal connections between fictitious diseases. In the first 20 trials, subjects were introduced to two fictitious diseases B(urlosis) and C(aprix).…”

Section: Problems For the Bayesianmentioning

confidence: 99%

Troubles with Bayesianism: An introduction to the psychological immune system

Mandelbaum

2018

Mind & Language

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A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well‐suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.

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Section: Problems For the Bayesianmentioning

confidence: 99%

Troubles with Bayesianism: An introduction to the psychological immune system

Mandelbaum

2018

Mind & Language

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“…Tenenbaum, Wagenmakers, & Blum 2003;Taylor & Ahn, 2012;White, 2006). Many of these studies have focused on two types of information: statistical information, usually provided as a result of intervening on variables in the system, and temporal order information, which results from observing the temporal pattern of event occurrence.…”

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confidence: 99%

Temporal and statistical information in causal structure learning.

McCormack¹,

Frosch²,

Patrick³

et al. 2015

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Three experiments examined children’s and adults’ abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical system that operated probabilistically. Participants of all ages preferentially relied on the temporal pattern of events in their inferences, even if this conflicted with statistical information. In Experiments 2 and 3, participants observed a series of interventions on the system, which in these experiments operated deterministically. In Experiment 2, participants found it easier to use temporal pattern information than statistical information provided as a result of interventions. In Experiment 3, in which no temporal pattern information was provided, children from 6- to 7-years-old, but not younger children, were able to use intervention information to make causal chain judgments, although they had difficulty when the structure was a common cause. The findings suggest that participants, and children in particular, may find it more difficult to use statistical information than temporal pattern information because of its demands on information processing resources. However, there may also be an inherent preference for temporal information.

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“…As a consequence, a causal system will benefit from the ability to implicitly or explicitly learn abstract knowledge of the environment over a temporal interval, coupled with the ability to transfer this acquired knowledge to guide causal inferences about different cues that occur close to, but outside of, the initial time period. Ahn and her colleagues (Luhmann & Ahn, 2011;Taylor & Ahn, 2012) have provided evidence supporting this view, showing that humans develop expectations during causal learning, and that these expectations affect the interpretations of the causal beliefs derived from subsequently encountered covariation information.…”

Section: Discussionmentioning

confidence: 94%

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Rojas

Beckers

et al. 2015

Cognitive Science

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Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.

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Causal Imprinting in Causal Structure Learning

Cited by 6 publications

References 34 publications

Troubles with Bayesianism: An introduction to the psychological immune system

Troubles with Bayesianism: An introduction to the psychological immune system

Temporal and statistical information in causal structure learning.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

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