Second-order conditioning in human predictive judgements when there is little time to think

Inhibitory Learning with Bidirectional Outcomes: Prevention Learning or Causal Learning in the Opposite Direction?

Influential models of causal learning assume that learning about generative and preventive relationships are symmetrical to each other. That is, a preventive cue directly prevents an outcome from occurring (i.e., "direct" prevention) in the same way a generative cue directly causes an outcome to occur. However, previous studies from our lab have shown that many participants do not infer a direct prevention causal structure after feature-negative discrimination (A+/AB-) with a unidirectional outcome (Lee & Lovibond, 2021). Melchers et al. (2006) suggested that the use of a bidirectional outcome that can either increase or decrease from baseline, encourages direct prevention learning. Here we test an alternative possibility that a bidirectional outcome encourages encoding of a generative relationship in the opposite direction, where B directly causes a decrease in the outcome. Thus, previous evidence of direct prevention learning using bidirectional outcomes may instead be explained by some participants inferring an "Opposite Causal" structure. In two experiments, participants did indeed report an opposite causal structure. In Experiment 1, these participants showed the lowest outcome predictions when B was combined with a novel cause in a summation test, and lowest outcome predictions when B was presented alone. In Experiment 2, B successfully blocked learning to a novel cue that was directly paired with a reduction in the outcome, and this effect was strongest among participants who endorsed an Opposite Causal structure. We conclude that previous evidence of direct prevention learning using bidirectional outcomes may be a product of excitatory rather than inhibitory learning.

Second-Order Conditioning in Humans

In contrast to the large body of work demonstrating second-order conditioning (SOC) in non-human animals, the evidence for SOC in humans is scant. In this review, I examine the existing literature and suggest theoretical and procedural explanations for why SOC has been so elusive in humans. In particular, I discuss potential interactions with conditioned inhibition, whether SOC is rational, and propose critical parameters needed to obtain the effect. I conclude that SOC is a real but difficult phenomenon to obtain in humans, and suggest directions for future research.

Can We Set Aside Previous Experience in a Familiar Causal Scenario?

Causal and predictive learning research often employs intuitive and familiar hypothetical scenarios to facilitate learning novel relationships. The allergist task, in which participants are asked to diagnose the allergies of a fictitious patient, is one example of this. In such studies, it is common practice to ask participants to ignore their existing knowledge of the scenario and make judgments based only on the relationships presented within the experiment. Causal judgments appear to be sensitive to instructions that modify assumptions about the scenario. However, the extent to which prior knowledge continues to affect competition for associative learning, even after participants are instructed to disregard it, is unknown. To answer this, we created a cue competition design that capitalized on prevailing beliefs about the allergenic properties of various foods. High and low allergenic foods were paired with foods moderately associated with allergy to create two compounds; high + moderate and low + moderate. We expected high allergenic foods to produce greater competition for associative memory than low allergenic foods. High allergenic foods may affect learning either because they generate a strong memory of allergy or because they are more salient in the context of the task. We therefore also manipulated the consistency of the high allergenic cue-outcome relationship with prior beliefs about the nature of the allergies. A high allergenic food that is paired with an inconsistent allergenic outcome should generate more prediction error and thus more competition for learning, than one that is consistent with prior beliefs. Participants were instructed to either use or ignore their knowledge of food allergies to complete the task. We found that while participants were able to set aside their prior knowledge when making causal judgments about the foods in question, associative memory was weaker for the cues paired with highly allergenic foods than cues paired with low allergenic foods regardless of instructions. The consistency manipulation had little effect on this result, suggesting that the effects in associative memory are most likely driven by selective attention to highly allergenic cues. This has implications for theories of causal learning as well as the way causal learning tasks are designed.

Individual differences in causal structures inferred during feature negative learning

Traditional associative learning theories predict that training with feature negative (A+/AB-) contingencies leads to the feature B acquiring negative associative strength and becoming a conditioned inhibitor (i.e., prevention learning). However, feature negative training can sometimes result in negative occasion setting, where B modulates the effect of A. Other studies suggest that participants learn about configurations of cues rather than their individual elements. In this study, we administered simultaneous feature negative training to participants in an allergist causal learning task and tested whether evidence for these three types of learning (prevention, modulation, configural) could be captured via self-report in the absence of any procedural manipulation. Across two experiments, we show that only a small subset of participants endorse the prevention option, suggesting that traditional associative models that predict conditioned inhibition do not completely capture how humans learn about negative contingencies. We also show that the degree of transfer in a summation test corresponds to the implied causal structure underlying conditioned inhibition, occasion-setting, and configural learning, and that participants are only partially sensitive to explicit hints about causal structure. We conclude that feature negative training is an ambiguous causal scenario that reveals individual differences in the representation of inhibitory associations, potentially explaining the modest group-level inhibitory effects often found in humans.

A dissociation between causal judgement and the ease with which a cause is categorized with its effect

The associative view of human causal learning argues that causation is attributed to the extent that the putative cause activates, via an association, a mental representation of the effect. That is, causal learning is a human analogue of animal conditioning. We tested this associative theory using a task in which a fictitious character suffered from two allergic reactions, rash (O1) and headache (O2). In a conditioned inhibition design with each of these two outcomes (A–O1/AX– and B–O2/BY–), participants were trained that one herbal remedy (X) prevented O1 and that the other (Y) prevented O2. These inhibitory properties were revealed in a causal judgement summation test. In a subsequent categorization task, X was most easily categorized with O1, and Y with O2. Thus, the categorization data indicated an excitatory X–O1 and Y–O2 association, the reverse of the inhibitory relationship observed on the causal judgement measure. A second experiment showed that this pattern of excitation and inhibition is dependent on intermixed A–O1 and AX– trials. These results are problematic for the standard application of associative activation theories to causal judgement. We argue instead that the inhibition revealed in the causal judgement task reflects inferential reasoning, which relies, in part, on the ability of the cue in question to excite a representation of the outcome, as revealed in the categorization test.

Associationism and cognition: Human contingency learning at 25

A major topic within human learning, the field of contingency judgement, began to emerge about 25 years ago following publication of an article on depressive realism by Alloy and Abramson (1979). Subsequently, associationism has been the dominant theoretical framework for understanding contingency learning but this has been challenged in recent years by an alternative cognitive or inferential approach. This article outlines the key conceptual differences between these approaches and summarizes some of the main methods that have been employed to distinguish between them.

Compound Stimulus Presentation Does Not Deepen Extinction in Human Causal Learning

Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training.

Effects of outcome and trial frequency on the inverse base-rate effect

The inverse base-rate effect is a bias in contingency learning in which participants tend to predict a rare outcome for a conflicting set of perfectly predictive cues. Although the effect is often explained by attention biases during learning, inferential strategies at test may also contribute substantially to the effect. In three experiments, we manipulated the frequencies of outcomes and trial types to determine the critical conditions for the effect, thereby providing novel tests of the reasoning processes that could contribute to it. The rare bias was substantially reduced when the outcomes were experienced at equal rates in the presence of predictive-cue frequency differences (Exp. 2), and when the predictive cues were experienced at equal rates in the presence of outcome frequency differences (Exp. 3). We also found a consistent common-outcome bias for novel cue compounds. The results indicate the importance of both cue and outcome frequencies to the inverse base-rate effect, and reveal a combination of necessary conditions that are not well captured by appealing to inferential strategies at test. Although both attention-based and inferential theories explain some aspects of these data, no existing theory fully accounts for these effects of relative novelty.

Second-order conditioning and conditioned inhibition: influences of speed versus accuracy on human causal learning

In human causal learning, excitatory and inhibitory learning effects can sometimes be found in the same paradigm by altering the learning conditions. This study aims to explore whether learning in the feature negative paradigm can be dissociated by emphasising speed over accuracy. In two causal learning experiments, participants were given a feature negative discrimination in which the outcome caused by one cue was prevented by the addition of another. Participants completed training trials either in a self-paced fashion with instructions emphasising accuracy, or under strict time constraints with instructions emphasising speed. Using summation tests in which the preventative cue was paired with another causal cue, participants in the accuracy groups correctly rated the preventative cue as if it reduced the probability of the outcome. However, participants in the speed groups rated the preventative cue as if it increased the probability of the outcome. In Experiment 1, both speed and accuracy groups later judged the same cue to be preventative in a reasoned inference task. Experiment 2 failed to find evidence of similar dissociations in retrospective revaluation (release from overshadowing vs. mediated extinction) or learning about a redundant cue (blocking vs. augmentation). However in the same experiment, the tendency for the accuracy group to show conditioned inhibition and the speed group to show second-order conditioning was consistent even across sub-sets of the speed and accuracy groups with equivalent accuracy in training, suggesting that second-order conditioning is not merely a consequence of poorer acquisition. This dissociation mirrors the trade-off between second-order conditioning and conditioned inhibition observed in animal conditioning when training is extended.

Second-order conditioning in Drosophila

Associative conditioning in Drosophila melanogaster has been well documented for several decades. However, most studies report only simple associations of conditioned stimuli (CS, e.g., odor) with unconditioned stimuli (US, e.g., electric shock) to measure learning or establish memory. Here we describe a straightforward second-order conditioning (SOC) protocol that further demonstrates the flexibility of fly behavior. In SOC, a previously conditioned stimulus (CS1) is used as reinforcement for a second conditioned stimulus (CS2) in associative learning. This higher-order context presents an opportunity for reassessing the roles of known learning and memory genes and neuronal networks in a new behavioral paradigm.

Learning without thinking

The main conclusion to draw from Mitchell et al's article is that it is difficult to disentangle cognitive and learning processes in contingency and causal experiments. More compelling evidence for human associate learning comes from research where, because of the type of events involved, participants are unable or unlikely to think about the relationships between the events.

Link-based learning theory creates more problems than it solves

In this response, we provide further clarification of the propositional approach to human associative learning. We explain why the empirical evidence favors the propositional approach over a dual-system approach and how the propositional approach is compatible with evolution and neuroscience. Finally, we point out aspects of the propositional approach that need further development and challenge proponents of dual-system models to specify the systems more clearly so that these models can be tested.

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.