What you didn't see: Prevention and generation in continuous time causal induction

Tianwei Gong, Neil R. Bramley

Research output: Contribution to conferencePaperpeer-review

Abstract

How do people use temporal information to make causal judgments? A number of studies have investigated the role of time in inferring generative causal structure, while few have examined prevention. Here, we focus on a challenging task in which participants learn the structure of several causal “devices” by watching the devices' patterns of activation over time. Each device potentially includes both generative (producing an activation of its effect) and preventative (blocking any effect activations within a short time window) causal relationships. We examine judgment patterns through the lens of a normative model which incorporates actual causation with considerations of prevention. We contrast this with a more computationally tractable feature-based approximation. Participants' performance was substantially above chance in all conditions. The majority of participants' causal judgments were best fit by the feature-based approximation based on delay and count heuristic cues.
Original languageEnglish
Pages2908-2914
Number of pages7
Publication statusPublished - 1 Aug 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: 29 Jul 20201 Aug 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period29/07/201/08/20

Keywords

  • Bayesian modelling
  • causal learning
  • prevention
  • structure induction
  • time

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