Eye-Tracking Causality

By Tobias Gerstenberg, Matthew Peterson, Noah D. Goodman, David A. Lagnado, Josh Tenenbaum. 2017

117 citations recorded by the source corpus.

How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants’ eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants’ looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, General Decision Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Bernhard Schölkopf
2. Tim Miller
3. Song-Chun Zhu
4. Tao Gao
5. Siyuan Huang
6. Josh Tenenbaum
7. Ying Nian Wu
8. Tomer D. Ullman

In Developmental and Educational Psychology

1. Song-Chun Zhu
2. Bernhard Schölkopf
3. Feng Gao
4. Tim Miller
5. Tao Gao
6. Josh Tenenbaum
7. Yixin Zhu
8. Tomer D. Ullman

In General Decision Sciences

1. Tim Miller
2. Bernhard Schölkopf
3. Josh Tenenbaum

Citations in the corpus

• Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “[49] tested whether people consider counterfactuals when making causal judgements in an experiment involving colliding balls.”
• A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, methodology): “[121], and counterfactual simulation is used to predict future events [64].”
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “heir counterfactual reasoning capacity to interpret visual events. In other words, interpretation is not based only on what is observed, but also on what would have happened but did not. In one study [137], participants judged whether one billiard ball caused another to go or prevented it from going”