Recency effects (REs) have been well established in memory and probability learning paradigms but have received little attention in category learning research. Extant categorization models predict REs to be unaffected by learning, whereas a functional interpretation of REs, suggested by results in other domains, predicts that people are able to learn sequential dependencies and incorporate this information into their responses. These contrasting predictions were tested in 2 experiments involving a classification task in which outcome sequences were autocorrelated. Experiment 1 showed that reliance on recent outcomes adapts to the structure of the task, in contrast to models ’ predictions. Experiment 2 provided constraints on how sequential information is learned and suggested possible extensions to current models to account for this learning. Recency effects (REs) are a robust phenomenon in cognitive psychology. REs are said to occur whenever more recent experiences are better remembered or are more influential in judgments about present situations. For example, in research on verbal working memory, REs are arguably among the most fundamental established phenomena, most commonly seen as increased performance

In this article we describe some of the experimental software we have developed for the study of associative human learning and memory. All these programs have the appearance of very simple video games. Some of them use the participants ’ behavioral responses to certain stimuli during the game as a dependent variable for measuring their learning of the target cue-outcome associations. Some others explicitly ask participants to rate the degree of relationship they perceive between the cues and the outcomes. These programs are implemented in Web pages using JavaScript, which allows their use both in traditional laboratory experiments as well as in Internet-based experiments. The psychology of learning is a research area that has usually been investigated with nonhuman animals and in which, traditionally, there existed too many procedural and ethical problems to conduct experiments with humans. However, human learning is today a flourishing research area in which many interesting effects are being reported around the world (see, e.g., De Houwer &

models provided the best account of data generated in tasks that require human observers to judge the relationship between binary events. In the intervening years, new data have been reported that provide evidence for higherorder processes. Some have argued that these new data pose a serious threat to the viability of the associative account. The purpose of the present paper is to review this evidence and to assess the severity of this threat. In 1978, Brooks described the interaction between analytic and nonanalytic processes, and argued that “there are many factors that push a person’s strategy toward one end of the scale or another – that is, toward learning individuals by codings that are designed to retain the item’s individuality, or toward tracking the validity of characteristics of the stimulus

Three experiments examined human processing of stimuli as predictors and causes. In Experiments 1A and 1B, two serial events that both preceded a third were assessed as predictors and as causes of the third event. Instructions successfully provided scenarios in which one of the serial (target) stimuli was viewed as a strong predictor but as a weak cause of the third event. In Experiment 2, participants ’ preexperimental knowledge was drawn upon in such a way that two simultaneous antecedent events were processed as predictors or causes, which strongly influenced the occurrence of overshadowing between the antecedent events. Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal. Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition in causal than predictive attribution. This paper examines differences between predictive and causal learning in humans. Events often occur in our environment according to a consistent temporal distribution. Some events occur simultaneously (e.g., the sound and sight of water running out of the tap), whereas other events occur sequentially (e.g., hunger dissipates after the intake of food). When the events repeatedly take place following a sequential distribution in time, the first event (i.e., the antecedent event) can become a signal for the occurrence of the second event (i.e., the subsequent event). Learning to predict the occurrence of an event on O.P. was supported by a postdoctoral fellowship from the Spanish

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,

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