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In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

The illusory causation effect describes the tendency to judge an unrelated cue and outcome to be causally related. The standard procedure for assessing the illusion is based on the implicit assumptions that participants start as naïve observers with no prior beliefs about the likely relationship between the cue and outcome, and that learning can be adequately captured as a point-estimate causal rating after null contingency training. Here, we use a novel distributional measure to assess participants’ beliefs over a range of causal relationships prior to, as well as after, exposure to non-contingent cues and outcomes. Across two experiments with different causal scenarios and 50% cue and outcome density, we show that participants have an initial bias towards expecting a causal relationship between the cue and outcome, and that this bias is mostly corrected after exposure to the null contingency. We conclude that distributional measures of causal beliefs can offer novel insights in understanding the illusory causation effect.

In a causal illusion task, participants rate a cue that has an objectively null contingency with an outcome as causal. Trials are usually organized according to a 2x2 table representing the presence/absence of a binary cue and a binary outcome. Cell A outcomes (cue, outcome) can be attributed to the cue. But how do participants interpret trials from cell C (no cue, outcome), where the cause of the outcome is unspecified? In two experiments we asked participants to provide causal explanations for cell A and C trials in a medicine-recovery causal illusion task. Participants who reported that the cause of cell C outcomes (e.g., strong immunity, spontaneous recovery) did not also apply to cell A outcomes showed the strongest causal illusion. Such a causal reasoning process undermines the logic behind the delta P metric typically used to define a contingency, and thereby provides a potential normative account of causal “illusions”.

In the field of stimulus generalization, an old yet unresolved discussion pertains to what extent stimulus misidentifications contribute to the pattern of conditioned responding. In this article, we perform cluster analysis on six datasets (four published datasets and two unpublished datasets, included N = 950) to examine the relationship between interindividual differences in (a) stimulus identification, (b) patterns of generalized responding, and (c) verbalized generalization rules. The datasets were obtained from online predictive learning tasks where participants learned associations between colored cues and the presence or absence of a hypothetical outcome. In these datasets, stimulus identification and expectancy ratings were assessed in separate phases to a range of colors varying between blue-green. Using cluster analyses on performance during stimulus identification, we identified different subgroups of participants (good vs. bad identifiers). In all six datasets, we found a close relationship between the pattern of stimulus identification and the shape of the expectancy gradient across the test dimension between the identified subgroups. Furthermore, participants classified as good identifiers were more likely to report a similarity generalization rule than a relational or linear rule, suggesting that individual differences in stimulus identification are related to individual differences in generalization rules. These findings suggest that greater consideration should be given to interindividual variability in stimulus identification, inductive rules, and their relationship in explaining patterns of generalized responses.

Evidence from a variety of learning tasks suggests that cuesthat are more predictive of an outcome attract greater attentionand are learned about more effectively in subsequent tasks. Wetested whether this learned predictiveness effect is due to theobjective strength of the cue-outcome association (cue-outcome correlation), or the degree to which the cue isinformative for making the correct choice on each trial (choicerelevance), by manipulating the possible outcome choicesavailable on each trial. Experiment 1 compared two sets of cuesthat were equally (and imperfectly) correlated with outcomesand showed learning biases in favor of the set of cues that hadinitially been more relevant for choices made on each trial.Experiment 2 used a more conventional learned predictivenessdesign in which the cue-outcome correlation was stronger forone set of cues (perfect predictors) than the other set (imperfectpredictors). However, here we manipulated whether or not theimperfect predictors could be used to make a correct choice,and thus whether the imperfect predictors possessed choicerelevance that was equal to or less than the perfect predictors.In this case, we found no evidence that the relevancemanipulation made any difference; learning biases towards theperfect predictor were evident regardless. The results suggestthat both cue-outcome correlation and choice relevance canlead to changes in associability and learning biases; both wereindividually sufficient but neither were necessary.

How is new information organized in memory? According to latent state theories, this is determined by the level of surprise, or prediction error, generated by the new information: a small prediction error leads to the updating of existing memory, large prediction error leads to encoding of a new memory. We tested this idea using a protocol in which rats were first conditioned to fear a stimulus paired with shock. The stimulus was then gradually extinguished by progressively reducing the shock intensity until the stimulus was presented alone. Consistent with latent state theories, this gradual extinction protocol (small prediction errors) was better than standard extinction (large prediction errors) in producing long-term suppression of fear responses, and the benefit of gradual extinction was due to updating of the conditioning memory with information about extinction. Thus, prediction error determines how new information is organized in memory, and latent state theories adequately describe the ways in which this occurs. [ABSTRACT FROM AUTHOR]

People often rely on the covariation between events to infer causality. However, covariation between cues and outcomes may change over time. In the associative learning literature, extinction provides a model to study updating of causal beliefs when a previously established relationship no longer holds. Prediction error theories can explain both extinction and protection from extinction when an inhibitory (preventive) cue is present during extinction. In three experiments using the allergist causal learning task, we found that protection could also be achieved by a hidden cause that was inferred but not physically present, so long as that cause was a plausible preventer of the outcome. We additionally showed complete protection by a physically presented cue that was neutral rather than inhibitory at the outset of extinction. Both findings are difficult to reconcile with dominant prediction error theories. However, they are compatible with the idea of theory protection, where the learner attributes the absence of the outcome to the added cue (when present) or to a hidden cause, and therefore does not need to revise their causal beliefs. Our results suggest that prediction error encourages changes in causal beliefs, but the nature of the change is determined by reasoning processes that incorporate existing knowledge of causal mechanisms and may be biased toward preservation of existing beliefs. [ABSTRACT FROM AUTHOR]

Generalization of learning can arise from 2 distinct sources: failure to discriminate a novel test stimulus from the trained stimulus and active extrapolation from the trained stimulus to the test stimulus despite them being discriminable. We investigated these 2 processes in a predictive learning task by testing stimulus discriminability (identification of the trained stimulus) as well as generalization of learning (outcome expectancy). Generalization gradients were broader for expectancy than for identification, in both single cue and differential (discrimination) designs, implying a substantial extrapolation component for the most dissimilar stimuli. The shapes of the expectancy gradients were strongly determined by the training design (single cue vs. differential) and by the rules inferred by participants (similarity vs. linear). By contrast, the identification gradients were unaffected by the training design or inferred rules and were equivalent for predictive and nonpredictive stimuli. These results indicate that perceptual discriminability plays a substantial role in generalization, but it is largely unaffected by associative learning. Instead, learning appears to impact on generalization via an independent extrapolation component which involves cognitive processes such as inductive reasoning.

Two experiments tested whether a peak-shifted generalization gradient could be explained by the averaging of distinct gradients displayed in subgroups reporting different generalization rules. Across experiments using a causal judgment task (Experiment 1) and a fear conditioning paradigm (Experiment 2), we found a close concordance between self-reported rules and generalization gradients using a continuous stimulus dimension (hue). Both experiments also showed an overall peak-shifted gradient after differential conditioning, but not after single cue conditioning. Importantly, the peak shift could be decomposed into linear and peaked gradients when participants were divided into rule subgroups. Our results highlight the need to consider individual differences in the rules that participants derive in human generalization studies and suggest that in some situations, peak shift may be a consequence of averaging across diverse rule subgroups.

Illusory causation is a phenomenon in which people mistakenly perceive a causal relationship between a cue and outcome even though the contingency between them is actually zero. Illusory causation studies typically use a unidirectional causal rating scale, where one endpoint refers to no relationship and the other to a strongly positive causal relationship. This procedure may bias mean causal ratings in a positive direction, either by censoring negative ratings or by discouraging participants from giving the normative rating of zero which is at the bottom extreme of the scale. To test this possibility, we ran two experiments that directly compared the magnitude of causal illusions when assessed with a unidirectional (zero—positive) versus a bidirectional (negative—zero—positive) rating scale. Experiment 1 used high cue and outcome densities (both 75%), whereas Experiment 2 used neutral cue and outcome densities (both 50%). Across both experiments, we observed a larger illusory causation effect in the unidirectional group compared with the bidirectional group, despite both groups experiencing the same training trials. The causal illusions in Experiment 2 were observed despite participants accurately learning the conditional probabilities of the outcome occurring in both the presence and absence of the cue, suggesting that the illusion is driven by the inability to accurately integrate conditional probabilities to infer causal relationships. Our results indicate that although illusory causation is a genuine phenomenon that is observable with either a undirectional or a bidirectional rating scale, its magnitude may be overestimated when unidirectional rating scales are used. [ABSTRACT FROM AUTHOR]

Single-cue delay eyeblink conditioning is presented as a prototypical example of automatic, nonsymbolic learning that is carried out by subcortical circuits. However, it has been difficult to assess the role of cognition in single-cue conditioning because participants become aware of the simple stimulus contingency so quickly. In this experiment ("n" = 166), we masked the contingency to reduce awareness. We observed a strong relationship between contingency awareness and conditioned responding, with both trace and delay procedures. This finding suggests that explicit associative knowledge and anticipatory behavior are regulated by a coordinated system rather than by functionally and neurally distinct systems.