Active causal structure learning in continuous time

Gong, Tianwei; Mayrhofer, Ralf; Bramley, Neil R

doi:10.1016/j.cogpsych.2022.101542

Cited by 8 publications

(7 citation statements)

References 76 publications

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“…Another key question for the future work is how people make stopping decisions when actively monitoring the outcome of their own or others’ interventions or experiments. Efficient information sampling is of practical importance to cognition, since learners must balance the rewards and costs by making sensible stopping and task switching decisions (Callaway et al, 2022; Gong et al, 2023; Yu et al, 2014). This becomes even more critical in the kinds of dynamic contexts and complex causal effects that are ubiquitous in everyday life (Anvari et al, 2023; Coenen et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Evidence from the future.

Gong,

Bramley

2024

Journal of Experimental Psychology: General

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The outcome of any scientific experiment or intervention will naturally unfold over time. How then should individuals make causal inferences from measurements over time? Across three experiments, we had participants observe experimental and control groups over several days posttreatment in a fictional biological research setting. We identify competing perspectives in the literature: contingency-driven accounts predict no effect of the outcome timing while the contiguity principle suggests people will view a treatment as more harmful to the extent that bad treatment outcomes occur earlier rather than later. In contrast, inference of the functional form of a treatment effect can license extrapolation beyond the measurements and lead to different causal inferences. We find participants' causal strength and direction judgments in temporal settings vary with minimal manipulations of instruction framing. When it is implied that the observations are made over a preplanned number of days, causal judgments depend strongly on contiguity. When it is implied that the observation may be ongoing, participants extrapolate current trends into the future and adapt their causal judgments accordingly. When data are revealed sequentially, participants rely on extrapolation regardless of instruction framing. Our results demonstrate human flexibility in interpreting temporal evidence for causal reasoning and emphasize human tendency to generalize from evidence in ways that are acutely sensitive to task framing. Public Significance StatementEveryday decision making is shaped by our judgments about how the world works, such as whether a new vaccine is beneficial or harmful for our health. Since more evidence is arriving all the time, we inevitably have to make these judgments with incomplete information. Here we show for the first time that people may use the timing of what has already happened to predict what will happen next, and then incorporate this predicted future evidence into their causal judgments. For instance, we find settings in which people take a rising trend to indicate that greater problems are to come, or a falling trend to indicate that the peak of a causal influence has already passed, leading them to make dramatically different causal judgments from the same overall case statistics. These results have implications both for understanding the sophistication of individual human causal reasoning and for pinpointing the source of disagreements in public discourse around scientific evidence.

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Section: Discussionmentioning

confidence: 99%

Evidence from the future.

Gong,

Bramley

2024

Journal of Experimental Psychology: General

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“…In future studies, it would also be interesting to go beyond actual causation judgments and look at the role of causal reversibility in the induction of general causal relations, or the selection of causal interventions. A widely accepted view is that general causal relations are induced based on observable covariations (Cheng, 1997; Cheng & Novick, 1990; Griffiths & Tenenbaum, 2005; Meder et al, 2014; Novick & Cheng, 2004), and some studies have begun to look more closely at how covariation interacts with temporal information in reasoners’ causal learning (Bramley et al, 2018; Gong et al, 2023; Greville & Buehner, 2010; Hagmayer & Waldmann, 2002). These latter studies find that a crucial temporal factor is causal latency, which can be defined as the time it takes a cause to produce an effect (see also Stephan et al, 2020; Stephan & Waldmann, 2022).…”

Section: Discussionmentioning

confidence: 99%

Reasoning about actual causation in reversible and irreversible causal structures.

Stephan

2024

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…In future studies, it would also be interesting to go beyond actual causation judgments and look at the role of causal reversibility in the induction of general causal relations, or the selection of causal interventions. A widely accepted view is that general causal relations are induced based on observable covariations (Cheng, 1997;Cheng & Novick, 1990;Griffiths & Tenenbaum, 2005;Meder, Mayrhofer, & Waldmann, 2014;Novick & Cheng, 2004), and some studies have begun to look more closely at how covariation interacts with temporal information in reasoners' causal learning (Bramley, Gerstenberg, Mayrhofer, & Lagnado, 2018;Gong, Gerstenberg, Mayrhofer, & Bramley, 2023;Greville & Buehner, 2010;Hagmayer & Waldmann, 2002). These latter studies find that a crucial temporal factor is causal latency, which can be defined as the time it takes a cause to produce an effect (see also Stephan et al, 2020;Stephan & Waldmann, 2022).…”

Section: Discussionmentioning

confidence: 99%

Reasoning about Actual Causation in Reversible and Irreversible Causal Structures

Stephan

2024

Preprint

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This article investigates people’s judgments of actual causation in the context of a previously neglected property of causal structures—their reversibility, that is, whether an effect persists or returns to its original state if its causes are removed. Causal reversibility, and its potential impact on causal judgment, was recently analyzed theoretically by Ross and Woodward (2022). They hypothesized that reversibility might affect people’s evaluation of causes in late-preemption scenarios. The typical finding in preemption scenarios is that events happening earlier are considered to be actual causes, while events happening later are regarded as non-causes. The hypothesis is that this robust intuition depends on causal reversibility and that in reversible structures later events are regarded as actual causes. Across three main experiments and one supplementary study (N = 590), it is shown that reversibility has the predicted effect: later causes are perceived to make an actual causal contribution to the effect. It is also shown that Henne et al. (2023), in a first study, did not find evidence for Ross and Woodward’s hypothesis because they did not test whether people regard later causes in preemption-like sequences of reversible structures as maintainers and not as triggers of their effect. Because they used test questions that asked explicitly for triggering rather than maintaining or were at least ambiguous, their results seemed to show that people think that later events have no causal impact. Maintaining is a relevant causal concept deserving more attention in both philosophical theories and psychological studies on causal cognition.

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Active causal structure learning in continuous time

Cited by 8 publications

References 76 publications

Evidence from the future.

Evidence from the future.

Reasoning about actual causation in reversible and irreversible causal structures.

Reasoning about Actual Causation in Reversible and Irreversible Causal Structures

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