Résultats de recherche pour « Causal Induction »

Most research into causal learning has focused on atemporal contingency data settings while fewer studies have examined learning and reasoning about systems exhibiting events that unfold in continuous time. Of these, none have yet explored learning about preventative causal influences. How do people use temporal information to infer which components of a causal system are generating or preventing activity of other components? In what ways do generative and preventative causes interact in shaping the behavior of causal mechanisms and their learnability? Across three experiments, we explore human causal structure learning within a space of hypotheses that combine generative and preventative causal relationships. Participants observe the behavior of causal devices as they are perturbed by fixed interventions and subject to either regular or irregular spontaneous activations. We find that participants are capable learners in this setting, successfully identifying the large majority of generative, preventative and non-causal relationships but making certain attribution errors when neighboring causal connections interact. We lay out a computational-level framework for normative inference in this setting and propose a family of more cognitively plausible algorithmic approximations. We find that participants’ judgment patterns can be quantitatively captured by a model that approximates normative inference via a simulation and summary statistics scheme.

A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this paper, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson--Gamma distribution family. We derive the qualitative signatures of causal influence, and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in reanalysis of seven experimental datasets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings.

A longstanding focus in the causal learning literature has been on inferring causal relations from contingency data, which abstracts away from time by collating independent instances or aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow without distinct experimental trials. Consequently, the process of learning causal relationships in naturalistic environments remains comparatively less understood. In this paper, we develop a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, linking causal influence with dependence between events in continuous time via stochastic processes from the Poisson--Gamma distribution family. We demonstrate the qualitative differences in temporal patterns when two variables are related vs. unrelated, and the quantitative steps needed to infer causal structure from temporal patterns. We show that our rational account parsimoniously explains the human preference for causal explanations that involve shorter, more reliable and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across seven experimental datasets from the causal learning literature.

A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this paper, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson--Gamma distribution family. We derive the qualitative signatures of causal influence, and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in reanalysis of seven experimental datasets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings.