Similarities between Pavlovian conditioning in nonhumans and causal judgment by humans suggest that similar processes operate in these situations. Notably absent among the similarities is backward blocking (i.e., retrospective devaluation of a signal due to increased valuation of another signal that was present during training), which has been observed in causal judgment by humans but not in Pavlovian responding by animals. The authors used rats to determine if this difference arises from the target cue being biologically significant in the Pavlovian case but not in causal judgment. They used a sensory preconditioning procedure in Experiments 1 and 2, in which the target cue retained low biological significance during the treatment, and obtained backward blocking. The authors found in Experiment 3 that forward blocking also requires the target cue to be of low biological significance. Thus, low biological significance is a necessary condition for a stimulus to be vulnerable to blocking. In recent years, numerous researchers have remarked on the similarity of the conditions that encourage the acquisition of causal relationships in humans and those that foster

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.

Matute and Pineño (1998a) showed evidence of interference between elementally trained cues and suggested that this effect occurs when the interfering association is more strongly activated than the target association at the time of testing. The present experiments tested directly the role of the relative activation of the associations in the effect of interference between elementally trained cues. In three human experiments we manipulated the relative activation of the interfering and target associations in three different ways: (a) introducing a retention interval between training of the interfering association and the test trial (Experiment 1); (b) training the target and the interfering associations in a single phase, instead of training them in separate phases (Experiment 2); and (c) introducing, just before testing, a novel cue which, like the retention interval used in Experiment 1, had the purpose of separating the interfering trials from the test trial (Experiment 3). All three manipulations led to an enhancement of responding to the target association at testing, suggesting that they were effective in preventing the interfering association from being the most strongly activated one at the time of testing. Taken together, these results add further

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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

The present study aimed to replicate an associative learning effect, overshadowing, both in the traditional laboratory conditions and over the internet. The experimental task required participants to predict an outcome based on the presence of several cues. When a cue that was always trained together with a second cue was presented on isolation at test, the expectancy of the outcome was impaired, which revealed overshadowing. This experimental task was performed by undergraduate students (N = 106) in the laboratory and by a different set of anonymous participants over the internet (N = 91). Similar levels of overshadowing were obtained in both locations. These similarities show that web-delivered experiments can be used as a complement of traditional experiments. © 2006 Elsevier B.V. All rights reserved.

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

Most theoretical accounts of backward blocking place heavy stress on the necessity of the target cue having been trained in compound with the competing cue to produce a decrement in responding. Yet, other evidence suggests that a similar reduction in responding to the target cue can be observed when the outcome is later paired with a novel cue never trained in compound with the target cue (interference between cues trained apart). The present experiment shows that pairing another nonassociated cue with the same outcome may be sufficient to produce a decremental effect on the target cue, but the presence of a within-compound association between the target and the competing cue adds to this effect. Thus, both interference between cues trained apart and within-compound associations independently contribute to backward blocking.

Standard connectionist models of pattern completion like an auto-associator, typically fill in the activation of a missing feature with internal input from nodes that are connected to it. However, associative studies on competition between alternative causes, demonstrate that people do not always complete the activation of a missing feature, but rather actively encode it as missing whenever its presence was highly expected. Dickinson and Burke's revaluation hypothesis [4] predicts that there is always forward competition of a novel cause, but that backward competition of a known cause depends on a consistent (positive) relation with the alternative cause. This hypothesis was confirmed in several experiments. These effects cannot be explained by standard auto-associative networks, but can be accounted for by a modified auto-associative network that is able to recognize absent information as missing and provides it with negative, rather than positive activation from related nodes. 1.