Eye-tracking causality

Peterson, Matthew; Goodman, Noah D.; Lagnado, David A.; Tenenbaum, Josh

doi:10.31234/osf.io/jr73v

Cited by 45 publications

(75 citation statements)

References 30 publications

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“…The CSM is a concrete implementation of this idea. We assume that people use their mental model of the situation not only to predict what will happen, but also to simulate what would have happened in different counterfactual contingencies (Chater & Oaksford, 2013;Gerstenberg, Peterson, Goodman, Lagnado, & Tenenbaum, 2017;Kahneman & Tversky, 1982;Roese, 1997;Waskan, 2003). A detailed generative model of the situation allows us to capture both whether a candidate cause made a difference to whether the outcome occurred as well as to how it came about (Lewis, 2000;Woodward, 2011a).…”

Section: Shmentioning

confidence: 99%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

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How do people make causal judgments? We propose the counterfactual simulation model (CSM) of causal judgment which predicts causal judgments by comparing what actually happened with counterfactual simulations of what would have happened in relevant contingencies. It postulates different aspects of causation that capture whether the cause made a difference to whether and how the outcome occurred, and if the cause was sufficient and robust. We test the CSM in three experiments that ask participants to make judgments about dynamic collision events. Experiment 1 reveals a very close quantitative mapping between causal judgments, and participants’ belief that the outcome would have been different without the cause. Experiment 2 demonstrates that counterfactual contrasts are necessary for explaining causal judgments. Participants’ judgments differ dramatically between pairs of situations in which what actually happened was identical, but what would have happened differed. Experiment 3 features two candidate causes and shows that participants’ judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants’ judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, deal with causation by omission, and be extended beyond the physical domain.

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Section: Shmentioning

confidence: 99%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

Self Cite

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“…Necessity captures our intuition that causal variables ought to make a difference to outcomes: We judge dropping an ice-cube on the ground as the cause of it shattering, since, in the absence of the first event, the second would not have occurred. Indeed, a wealth of evidence shows such evaluations are central in our causal judgments (e.g., Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2014;Gerstenberg, Peterson, Goodman, Lagnado, & Tenenbaum, 2017;Icard, Kominsky, & Knobe, 2017;Morris et al, 2018;Wells & Gavanski, 1989).…”

Section: The Search For Invariance (Si) Hypothesismentioning

confidence: 99%

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Lapidow

Walker

2020

Language and Concept Acquisition From Infancy Through Childhood

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To appear in Childers, J.B., Graham, S.A. & Namy, L. (Eds). (2019). Learning Language and Concepts from Multiple Examples in Infancy and Childhood What is invariant does not emerge unequivocally except with a flux. The essentials become evident in the context of changing nonessentials.-James Gibson, 1979 Human learners are intuitively exploratory: We acquire new knowledge from the outcomes of our actions. However, in order for exploration to support learning, at least some of these actions must serve to evaluate our existing knowledge. Despite this need for informative 'hypothesis testing' in everyday learning, decades of research examining self-directed experimentation suggests that learners rarely choose informative tests. That is, instead of selecting actions to test whether their current hypothesis is correct, both children and adults tend to prefer 'positive tests': actions that will produce an effect assuming their current hypothesis is correct (see Klayman, 1995; Zimmerman, 2007). To illustrate, suppose you drop an ice cube on the floor and it shatters. As a learner, you might form an initial hypothesis that 'impact with an unyielding surface causes ice to shatter.' This hypothesis is also a causal explanation for your observation: indicating how one variable (X) makes a difference to the state of another variable (Y). According to traditional interpretations of Popper's (1959) falsificationist approach, testing this hypothesis would require disconfirming its alternatives. That is, assessing whether 'X is the cause of Y,' requires 'negative tests,' or actions to determine whether Y occurs in the absence of X. Here, since Y is 'shattering' and X is 'impacting an unyielding surface,' you should drop an ice cube on a yielding surface (not-X), like rubber or cotton, to determine whether it will shatter. However, learners rarely choose this kind of disconfirming action during their exploration. Instead, they are much more likely to repeat the initial observation: e.g., to pick up another ice-cube and drop it on the same surface, or a similar one. This tendency to generate multiple positive examples is a puzzling characteristic of self-directed learning, since it does not initially appear to be informative. After all, these repeated demonstrations often produce the same evidence, and do not distinguish between the current hypothesis (i.e., impacting an unyielding surface) and potential alternatives (e.g., impacting any surface at a particular speed), since they are consistent with both. Why then, do self-directed learners consistently and repeatedly conduct positive tests? In this chapter, we propose a novel answer to this question: the Search for Invarinace (SI) hypothesis, which suggests that observing muliple, positive examples may facilitate learning by allowing us to assess the invariance of our causal theories. That is, by repeatedly activating a hypothesized cause and checking if its anticipated effect occurs, positive tests generate information about the degree to which this relationship holds across tim...

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“…People often make surprising errors in simple intuitive physics judgments such as drawing future trajectories of an object that has rolled off a cliff, has been dropped from a moving airplane, or released from a circular ramp (Caramazza, McCloskey, & Green, 1981;McCloskey, Caramazza, & Green, 1980;McCloskey & Kohl, 1983;Proffitt & Gilden, 1989;Ranney, 1994), but when people predict trajectories of billiard balls, estimate properties of colliding objects, determine how fluids will pour, or judge the stability of towers, their physical reasoning is often very accurate and consistent with the principles of Newtonian mechanics (Bates, Battaglia, Yildirim, & Tenenbaum, 2015;Battaglia, Hamrick, & Tenenbaum, 2013;Gerstenberg, Peterson, Goodman, Lagnado, & Tenenbaum, 2017;Kubricht et al, 2016;Sanborn, Mansinghka, & Griffiths, 2013;. Prior literature has attempted to explain this discrepancy by suggesting that some human knowledge of physical principles is accurate, while other knowledge is erroneous (e.g., people can estimate the stability of stacked objects, but have erroneous conceptions of ballistic motion; Marcus & Davis, 2013).…”

Section: Introductionmentioning

confidence: 99%

Different physical intuitions exist between tasks, not domains

Battaglia¹,

Vul²

2018

Preprint

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Does human behavior exploit deep and accurate knowledge about how the world works, or does it rely on shallow and often inaccurate heuristics? This fundamental question is rooted in a classic dichotomy in psychology: human intuitions about even simple scenarios can be poor, yet their behaviors can exceed the capabilities of even the most advanced machines. One domain where such a dichotomy has classically been demonstrated is intuitive physics. Here we demonstrate that this dichotomy is rooted in how physical knowledge is measured: extrapolation of ballistic motion is idiosyncratic and erroneous when people draw the trajectories, but consistent with accurate physical inferences under uncertainty when people use the same trajectories to catch a ball or release it to hit a target. Our results suggest that the contrast between rich and calibrated, versus poor and inaccurate patterns of physical reasoning exist as a result of using different systems of knowledge across tasks, rather than as a universal system of knowledge that is inconsistent across physical principles.

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Eye-tracking causality

Cited by 45 publications

References 30 publications

A counterfactual simulation model of causal judgments for physical events

A counterfactual simulation model of causal judgments for physical events

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Different physical intuitions exist between tasks, not domains

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