Academic journal article Canadian Journal of Experimental Psychology

Dissociations among Judgments Do Not Reflect Cognitive Priority: An Associative Explanation of Memory for Frequency Information in Contingency Learning

Academic journal article Canadian Journal of Experimental Psychology

Dissociations among Judgments Do Not Reflect Cognitive Priority: An Associative Explanation of Memory for Frequency Information in Contingency Learning

Article excerpt

Previous research on causal learning has usually made strong claims about the relative complexity and temporal priority of some processes over others based on evidence about dissociations between several types of judgments. In particular, it has been argued that the dissociation between causal judgments and trial-type frequency information is incompatible with the general cognitive architecture proposed by associative models. In contrast with this view, we conduct an associative analysis of this process showing that this need not be the case. We conclude that any attempt to gain a better insight on the cognitive architecture involved in contingency learning cannot rely solely on data about these dissociations.

Keywords: contingency learning, probability learning, statistical models, associative models

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Inferring the causal structure of the environment is an invaluable skill for survival in an ever changing environment. As first noted by Hume (1739/1964), we are unable to directly perceive the link connecting causes and effects, which means that causal relations can only be inferred on the basis of indirect evidence. Unfortunately, it is not easy to determine when a given event is a real cause of an effect because some events can co-occur regularly without any causal link between them. Psychological models of causal learning try to explain when and how humans can learn new causal links.

Following the advice of Marr (1982), some authors have focused on developing computational models of causal learning (e.g., Allan, 1980; Cheng, 1997; Cheng & Novick, 1992; Holyoak & Cheng, 2011; Pearl, 2000). According to the popular levels-ofprocessing framework, computational models do not aim at specifying every step that the cognitive system has to give to solve a problem. Instead, these models should clarify, for a given task, what is the function that maps the input of the cognitive system with its output, while being agnostic about the algorithm involved in that computation. Following this general perspective, several statistical models of causal learning have been proposed to describe what input allows us to determine that two events are causally related. These models usually consist of simple mathematical equations that provide a numerical index of the strength of the relationship between a candidate cause and an effect: Values different from zero usually indicate that a causal relation exists. Note that, in principle, computational models are not concerned about how humans actually acquire this causal knowledge: Computational models only establish when a causal link must be inferred (if the system works well).

However, some authors have gone one step further suggesting that the statistical calculations proposed by these computational models could also be appropriate theories about how humans actually learn new causal associations. According to these authors, the mathematical formulas of statistical models of causal learning could also be considered algorithm-level theories of causal learning that specify the real steps that people give when they solve a causal induction problem. From this algorithm-level viewpoint, people act as intuitive statisticians who first gather information about the joint occurrence of two events and then combine this information following certain rules to decide whether there is an statistical connection between those two events. For instance, when faced with a sequence of trials in which a cause, C, and an effect, E, appear together or in isolation, people are assumed to first encode this information in a mental representation to some extent isomorphic to the 2 X 2 contingency table depicted in Table 1 (Beyth-Marom, 1982; Busemeyer, 1991; Shaklee & Mims, 1982). This contingency table would summarise the evidence experienced by the participant regarding the joint occurrence or absence of the target cause and effect, including the number of occasions in which both the cause and the effect have appeared together (a), the number of occasions in which only the effect or only the cause has appeared (b or c, respectively), and the number of occasions in which both the cause and the effect have been simultaneously absent (

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