Spontaneous causal learning while controlling a dynamic system

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After the control task, we modified the causal structure of the environment and assessed decision makersʼ sensitivity to this manipulation. While purely instrumental knowledge does not support inferences given the new modified structure, causal knowledge does. The results showed that most participants learned the structure of the underlying causal system. However, participants acquired surprisingly little knowledge of the systemʼs parameters when the causal processes that governed the system were not perceptually separated (Experiment 1). Knowledge improved considerably once processes were separated and feedback was made more transparent (Experiment 2). These findings indicate that even without instruction, causal learning is a favored strategy for interacting with and controlling a dynamic causal system. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Accession Number: 2011-18863-001. Partial author list: First Author & Affiliation: Hagmayer, York; Department of Psychology, University of Gottingen, Gottingen, Germany. Release Date: 20111010. Publication Type: Journal (0100), Peer Reviewed Journal (0110). Format Covered: Electronic. Document Type: Journal Article. Language: English. Major Descriptor: Causality; Cognitive Ability; Decision Making; Learning. Minor Descriptor: Attribution; Knowledge Level. Classification: Cognitive Processes (2340). Population: Human (10); Male (30); Female (40). Location: Germany. Age Group: Adulthood (18 yrs & older) (300). Methodology: Empirical Study; Quantitative Study. References Available: Y. ArtID: 145-162. Issue Publication Date: 2010. Publication History: Accepted Date: Jan 7, 2010; Revised Date: Dec 15, 2009; First Submitted Date: Aug 30, 2009. Copyright Statement: Hagmayer et al.