Causal imprinting in causal structure learning

Taylor, Eric; Ahn, Woo‐kyoung

doi:10.1016/j.cogpsych.2012.07.001

Cited by 16 publications

(15 citation statements)

References 70 publications

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“…To model this, Fernbach and Sloman introduced a Local Computations (LC) model, which posits that people focus on "evidence for individual causal relations rather than evidence for fully specified causal structures" (Fernbach and Sloman, 2009, p. 680). By ignoring the possible influences of other causes, their model captures a strong empirical tendency for human learners to exhibit order effects and overconnect their causal hypotheses (also see Taylor and Ahn, 2012). Bramley et al (2017a) extended this finding, finding evidence suggesting that people consider local changes that modify their previously favored hypothesis.…”

Section: Learningmentioning

confidence: 89%

Causal Structure Learning in Continuous Systems

2020

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Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e., those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss-Markov ("Ornstein-Uhlenbeck") processes and show how such OU networks can express complex dynamic phenomena, such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world.

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Section: Learningmentioning

confidence: 89%

Causal Structure Learning in Continuous Systems

2020

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“…This effect obtains not only for beliefs participants have prior to entering the experiment but also those acquired as part of the experiment (e.g., Garcia-Retamero, Müller, Catena, & Maldonado, 2009, Exp. 1;Marsh & Ahn, 2006;Taylor & Ahn, 2012). In particular, for simple causal relations, a primacy effect is observed in the interpretation of contingency evidence that favors participants' initial hypotheses (Marsh & Ahn, 2006;Taylor & Ahn, 2012).…”

Section: Implausible Causesmentioning

confidence: 94%

Differences in the weighting and choice of evidence for plausible versus implausible causes.

Goedert¹,

Ellefson

Rehder

2014

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Individuals have difficulty changing their causal beliefs in light of contradictory evidence. We hypothesized that this difficulty arises because people facing implausible causes give greater consideration to causal alternatives, which, because of their use of a positive test strategy, leads to differential weighting of contingency evidence. Across 4 experiments, participants learned about plausible or implausible causes of outcomes. Additionally, we assessed the effects of participants' ability to think of alternative causes of the outcomes. Participants either saw complete frequency information (Experiments 1 and 2) or chose what information to see (Experiments 3 and 4). Consistent with the positive test account, participants given implausible causes were more likely to inquire about the occurrence of the outcome in the absence of the cause (Experiments 3 and 4) than those given plausible causes. Furthermore, they gave less weight to Cells A and B in a 2 × 2 contingency table and gave either equal or less weight to Cells C and D (Experiments 1 and 2). These effects were inconsistently modified by participants' ability to consider alternative causes of the outcome. The total of the observed effects are not predicted by either dominant models of normative causal inference or by the particular positive test account proposed here, but they may be commensurate with a more broadly construed positive test account.

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“…Participants received $0.10 for their participation, a small reward consistent with past mTurk studies (Buhrmester, Kwang & Gosling, 2011;Brandt & Wetherell, 2012). Similar to studies by Gardner, Brown, and Boice (2012) and Taylor and Ahn (2012), only participants who had 85% or more of their previous mTurk assignments completed, as shown by the website statistics, were allowed to participate. Previous studies have shown that mTurk participants are highly motivated to complete the tasks, even when they were offered only a few cents for several minutes of work (Buhrmester et al, 2011;Mason & Suri, 2012).…”

Section: Methodsmentioning

confidence: 99%

Predicting the brighter and darker sides of interpersonal relationships: Does psychological need thwarting matter?

2014

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Recent studies have indicated that assessments of need thwarting better predict diminished functioning and ill-being compared to low need satisfaction, which better predict optimal functioning and well-being. In this study we aimed to further explore the important theoretical distinction between psychological need thwarting and need satisfaction in the domain of interpersonal relationships. We examined whether the distinction between need satisfaction and thwarting is due to method effects resulting from positive and negative item wording, however, multi-trait multi-method analyses indicated no substantial method effects.Further, we showed that a lack of need satisfaction (i.e., need dissatisfaction) is not equivalent to experiences of need thwarting. In fact, need thwarting better predicted compromised relational functioning compared to need dissatisfaction. Need satisfaction was a stronger predictor of interpersonal competence compared to need thwarting and need dissatisfaction. The current findings underline the importance of assessing need thwarting when examining compromised functioning in interpersonal relationships.

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Causal imprinting in causal structure learning

Cited by 16 publications

References 70 publications

Causal Structure Learning in Continuous Systems

Causal Structure Learning in Continuous Systems

Differences in the weighting and choice of evidence for plausible versus implausible causes.

Predicting the brighter and darker sides of interpersonal relationships: Does psychological need thwarting matter?

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