Spontaneous Causal Learning While Controlling A Dynamic System

Abstract

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 makers' 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 system's 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.

Observing and Intervening: Rational and Heuristic Models of Causal Decision Making

“…We discussed these cases in the context of alternative methods of modeling interventions. However, so far little is known about how people reason about different kinds of interventions, and future research needs to examine these issues in more detail (but see [35][36][37]). …”

“…However, a recent set of studies has examined causal reasoning in situations with uncertain and unreliable interventions [35; see also 36,37]. In these studies participants were confronted with causal scenarios in which the causal effects of the available interventions and the structure of the causal system acted upon were unknown prior to learning.…”

“…One important question concerns the empirical validity of the heuristic, both in the laboratory and in real-world situations. Previous research has provided strong evidence that people take into account causal knowledge when making probabilistic inferences and when engaging in decision making [35][36][37]45], but the particular decision problem examined in the present paper has not been examined empirically yet. One important line of research will be to investigate how people exploit different cues to causality in order to establish a skeletal causal model, which provides the representational basis for the subsequent steps of the Intervention-Finder heuristic.…”

Dynamic Decision Making

“…Furthermore, subjective policies tend to evolve over trial blocks toward the optimal policy (Jagacinski and Miller, 1978;Jagacinski and Hah, 1988;You, 1989). Therefore, learning processes are important for explaining much of the variance in human performance on dynamic decision tasks (cf., Hagmayer et al, 2010;Hogarth, 1981). Three different frameworks for modeling human learning processes in dynamic decision tasks have been proposed (Osman, 2010).…”

Hierarchical Bayesian models as formal models of causal reasoning

“…Research on the control of complex systems shows that participants often gain very limited knowledge about the underlying causal structure despite extensive learning experience (Osman 2010). HBMs can explain this finding as the available evidence in these studies was often compatible with numerous causal hypotheses, and participants normally lacked domain specific knowledge (see, Hagmayer, Meder, Osman, Mangold, and Lagnado 2010, for a more detailed discussion).…”