Past experience influences the processing of stimulus compounds in human Pavlovian conditioning

How do humans want causes to combine their effects? The role of analytically-defined causal invariance for generalizable causal knowledge

The present paper reports two experiments (N = 232, 254) addressing the question: How do reasoners reconcile the desire to have useable (i.e., invariant) causal knowledge - knowledge that holds true when applied in new circumstances/contexts - with the reality that causes often interact with other causes present in the context? The experiments test two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners' ability to learn rules (functions) describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function-learning view predicts that participants would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one's current representation may not yield useable causal knowledge. This view posits that the human causal-induction process incorporates invariant knowledge as an aspiration, entailing that observed deviation from causal invariance when the knowledge is applied serves as a signal for a need to revise causal knowledge: Only invariance across contexts with potentially new causal factors justifies generalization across them. The invariance view therefore predicts that reasoners would revise their representation so that they have whole causes - potentially consisting of interacting components - that do not interact with each other, even when in their relevant experience all (pre-defined) causes interact. Across both experiments, our results favor the causal-invariance view: Participants generalize their empirically learned function (which may involve interactions) to new stimuli, but switch to the analytic causal-invariance function for both old and new stimuli at the level of the whole cause, indicating that how humans want causes to combine their effects shapes the knowledge they induce.

Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre-schoolers

The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal-invariance decomposition functions is essential for this process. Such knowledge specifies the decomposition of an observed outcome into contributions from constituent causes under the default assumption that the empirical knowledge acquired is invariant across contextual/background causes. The paper reports two psychological experiments (and replication studies) with pre-school-age children on generalization across contexts involving binary cause and effect variables. The critical role of causal invariance for constructing useable causal knowledge predicts that even young children should (tacitly) use the causal-invariance decomposition function for such variables rather than a non-causal-invariance decomposition function common in statistical practice in research involving binary outcomes. The findings support the rational shaping of empirical causal knowledge by the causal-invariance constraint, ruling out alternative explanations in terms of non-causal-invariance decomposition functions, heuristics, and biases. For the same causal structure involving candidate causes and outcomes that are binary variables with a "present" value and an "absent" value, the paper argues against the possibility of multiple rational characterizations of the "sameness of causal influence" that justifies generalization across contexts.

Causal invariance as a tacit aspiration: Analytic knowledge of invariance functions

For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candidate cause requires an assumption regarding the decomposition of the observed outcome into the unobservable influences from the candidate and from background causes. Acquiring stable, useable causal knowledge is challenging when the search space of candidate causes is large, such that the reasoner's current set of candidates may fail to include a cause that generalizes well to an application context. We have hypothesized that an indispensable navigation device that shapes our causal representations toward useable knowledge involves the concept of causal invariance - the sameness of how a cause operates to produce an effect across contexts. Here, we tested our causal invariance hypothesis by making use of the distinct mathematical functions expressing causal invariance for two outcome-variable types: continuous and binary. Our hypothesis predicts that, given identical prior domain knowledge, intuitive causal judgments should vary in accord with the causal-invariance function for a reasoner's perceived outcome-variable type. The judgments are made as if the reasoner aspires to formulate causally invariant knowledge. Our experiments involved two cue-competition paradigms: blocking and overexpectation. Results show that adult humans tacitly use the appropriate causal-invariance functions for decomposition. Our analysis offers an explanation for the apparent elusiveness of the blocking effect and the adaptiveness of intuitive causal inference to the representation-dependent reality in the mind.

Learning: from association to cognition

Since the very earliest experimental investigations of learning, tension has existed between association-based and cognitive theories. Associationism accounts for the phenomena of both conditioning and "higher" forms of learning via concepts such as excitation, inhibition, and reinforcement, whereas cognitive theories assume that learning depends on hypothesis testing, cognitive models, and propositional reasoning. Cognitive theories have received considerable impetus in regard to both human and animal learning from recent research suggesting that the key illustration of cue selection in learning, blocking, often arises from inferential reasoning. At the same time, a dichotomous view that separates noncognitive, unconscious (implicit) learning from cognitive, conscious (explicit) learning has gained favor. This review selectively describes key findings from this research, evaluates evidence for and against associative and cognitive explanatory constructs, and critically examines both the dichotomous view of learning as well as the claim that learning can occur unconsciously.

Concurrent excitors limit the extinction of conditioned fear in humans

In a human fear conditioning experiment, with on-line expectancy ratings and electrodermal responding as indices of fear, two neutral stimuli (pictures of geometric shapes) were first established as reliable predictors of an electric shock. In the subsequent extinction phase, the two stimuli were repeatedly presented in compound, without the shock. The final test phase consisted of individual stimulus presentations again, which resulted in a strong return of the conditioned responses. This effect was not observed in non-conditioned control stimuli. Hence, behavioral effects of extinction seem highly specific to the stimulus constellation that has gone through the extinction procedure. We argue that pharmacological, behavioral and/or cognitive manipulations that could prevent configural processing of stimulus constellations have direct clinical potential.

Generalization decrement in human contingency learning

An association between a cue and an outcome will generalize to a similar novel cue to some extent, but not completely. Learning theorists refer to the discrepancy between responding elicited by the original cue and the novel cue as a generalization decrement. Two experiments used a contingency learning task with human participants to compare the size of a generalization decrement between configurations of cues that were altered by adding or subtracting compositional elements. The results suggest that adding elements to a configuration can produce a generalization decrement, but removing elements produces a more robust generalization decrement. Furthermore, the generalization decrement caused by adding elements was not likely to be caused by competing orienting responses. The results are used to contrast Pearce's (1987, 1994) and Wagner's (2003) models of stimulus generalization.

The experimental task influences cue competition in human causal learning

Participants were shown A+ and C- trials followed by AB+ and CD+ trials. These trials were embedded in a causal learning task in which participants had to learn either the relationship between different foods and allergic reactions or the relationship between different stocks and an increase in the stock market index. The authors orthogonally varied the manner in which the different cues were presented to participants during training. Cue competition was related to the causal learning scenario but not to the manner in which the different cues were presented. These results question claims of a human bias toward configural processing that were based on difficulties in finding cue competition in some previous causal learning experiments.