Learning Causal Schemata
نویسندگان
چکیده
Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple causal schemata given only raw data as input. Given a set of objects and observations of causal events involving some of these objects, our framework simultaneously discovers the causal type of each object, the causal powers of these types, the characteristic features of these types, and the characteristic interactions between these types. Previous behavioral studies confirm that humans are able to discover causal schemata, and we show that our framework accounts for data collected by Lien and Cheng and Shanks and Darby.
منابع مشابه
Running head: ABSTRACT CAUSAL KNOWLEDGE The acquisition and use of abstract causal knowledge
Real-world causal learning is often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that helps to explain how these schemata are acquired and how they guide inferences about the causal powers of new, sparsely observed objects. Given a set of objects and observations of causal events involving some of these objects, our framewor...
متن کاملCausal learning about tolerance and sensitization.
We introduce two abstract, causal schemata used during causal learning. (1) Tolerance is when an effect diminishes over time, as an entity is repeatedly exposed to the cause (e.g., a person becoming tolerant to caffeine). (2) Sensitization is when an effect intensifies over time, as an entity is repeatedly exposed to the cause (e.g., an antidepressant becoming more effective through repeated us...
متن کاملMemory-based hypothesis Error! Unknown switch argument. Memory-Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text
We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory which consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limite...
متن کاملMemory-Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text
We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory that consists of events, event schemata, episodes, causal heuristics, and cousol hypotheses. The learning algorithms are based on applying causal heuristicsto precedents of new information. The heuristics are derived from principles of causation, and, to a limited ...
متن کاملShrieking Sirens Schemata, Scripts, and Social Norms: How Change Occurs
This paper investigates the causal relationships among scripts, schemata, and social norms. The authors examine how social norms are triggered by particular schemata and are grounded in scripts. Just as schemata are embedded in a network, so too are social norms, and they can be primed through spreading activation. Moreover, the expectations that allow a social norm‘s existence are inherently g...
متن کامل