Associative and statistical theories of causal and predictive learning make opposite predictions for situations in which the most recent information contradicts the information provided by older trials (e.g., acquisition followed by extinction). Associative theories predict that people will rely on the most recent information to best adapt their behavior to the changing environment. Statistical theories predict that people will integrate what they have learned in the two phases. The results of this study showed one or the other effect as a function of response mode (trial by trial vs. global), type of question (contiguity, causality, or predictiveness), and postacquisition instructions. That is, participants are able to give either an integrative judgment, or a judgment that relies on recent information as a function of test demands. The authors concluded that any model must allow for flexible use of information once it has been acquired. Learning to predict the events in our environment is critical for survival. Both humans and other animals are known to learn predictive and causal relations between the events in their environment, and the question of how they do it has preoccupied philosophers and psychologists for many years.

Associative learning theories assume that cue interaction and, specifically, retrospective revaluation occur only when the target cue is previously trained in compound with the to-be-revalued cue. However, there are recent demonstrations of retrospective revaluation in the absence of compound training (e.g., Matute & Pineño, 1998a, 1998b). Nevertheless, it seems reasonable to assume that cue interaction should be stronger when the cues are trained together than when they are trained apart. In two experiments with humans, we directly compared compound and elemental training of cues. The results showed that retrospective revaluation in the elemental condition can be as strong as and, sometimes, stronger than that in the compound condition. This suggests that within-compound associations are not necessary for retrospective revaluation to occur and that these effects can possibly be best understood in the framework of general interference theory. In the literature of animal conditioning and human associative learning, it is well known that if a cue, X, is consistently followed by an outcome, O (i.e., X–O), X is generally learned as a predictor of the occurrence of the outcome. It is also well known that responding to X in a subsequent test phase becomes altered if another cue, A, is trained in compound with X as a predictor of the same outcome. Some classic instances of these cue interaction effects in the animal learning literature are overshadowing (Pavlov, 1927), blocking (Kamin, 1968), conditioned inhibition (Pavlov, 1927), and the relative stimulus validity

this paper. Correspondence should be addressed to M. J. Dennis, Madsen Center, Augustana College, 2001 S. Summit Ave., Sioux Falls, SD 57197 (e-mail: dennis@inst.augie . edu)

Several studies have shown that predictive and causal judgments vary depending on whether the question used to assess the relationship between events is presented after each piece of information or only after all the available information has been observed. This effect could be understood by assuming that in the two cases people perceive that the test question requires that different sets of evidence be taken into account. This hypothesis is tested in the present experiments through contextual manipulations that take place at the time of training and at the time of test. Our results show that people use this contextual information to infer which set of events should be considered when making their subjective assessments. The results are at odds with current theoretical approaches, but it is possible to develop mechanisms that would allow these models to account for the observed evidence. Learning to predict future events from present events is one of the most powerful adaptive tools, since it allows an organism to find the necessary resources for survival and to avoid dangerous situations. Given its importance, this kind of predictive learning was the central focus of animal behavior research throughout the twentieth century. During the last decades, predictive learning has also become important in the area of human cognition, where it has given rise to a great amount of empirical and theoretical research. The vast amount of evidence provided by this research has sometimes turned out to be quite difficult to explain by the available theoretical approaches. Many variables usually neglected by theoretical models influence the process of human learning of predictive relations among events or the way in which humans use the acquired information. Among other things, it has been shown that the probe question used to assess participants ’ judgment (Matute,

The authors previously described a procedure that permits rapid, multiple within-participant assessments of the contingency between a cue and an outcome (the “streamed-trial ” procedure, Crump, Hannah, Allan, & Hord, 2007). In the present experiments, the authors modified this procedure to investigate cue-interaction effects, replicating conventional findings in both the one- and two-phase blocking paradigms. The authors show that the streamed-trial procedure is not restricted to the geometric forms used as cues and outcomes by Crump et al., and that it can incorporate the conventional allergy stimuli, where food is the cue and an allergic reaction is the outcome. The authors discuss the value of the streamed-trial procedure as a method for advancing our theoretical understanding of cue-interaction effects.

Within-compound associations in retrospective revaluation and in direct learning: A challenge for comparator theory

Information about the structure of a causal system can come in the form of observational data--- random samples of the system's autonomous behavior---or interventional data---samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision-making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.

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The role of cue information in the outcome-density effect: Evidence from neural network simulations and a causal learning experiment a * b * c b

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