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

Second-order fear conditioning involves formation of competing stimulus-danger and stimulus-safety associations

How do animals process experiences that provide contradictory information? The present study addressed this question using second-order fear conditioning in rats. In second-order conditioning, rats are conditioned to fear a stimulus, S1, through its pairings with foot-shock (stage 1); and some days later, a second stimulus, S2, through its pairings with the already-conditioned S1 (stage 2). However, as foot-shock is never presented during conditioning to S2, we hypothesized that S2 simultaneously encodes 2 contradictory associations: one that drives fear to S2 (S2-danger) and another that reflects the absence of the expected unconditioned stimulus and partially masks that fear (e.g. S2-safety). We tested this hypothesis by manipulating the substrates of danger and safety learning in the brain (using a chemogenetic approach) and assessing the consequences for second-order fear to S2. Critically, silencing activity in the basolateral amygdala (important for danger learning) reduced fear to S2, whereas silencing activity in the infralimbic cortex (important for safety learning) enhanced fear to S2. These bidirectional changes are consistent with our hypothesis that second-order fear conditioning involves the formation of competing S2-danger and S2-safety associations. More generally, they show that a single set of experiences can produce contradictory associations and that the brain resolves the contradiction by encoding these associations in distinct brain regions.

Understanding Associative Learning Through Higher-Order Conditioning

Associative learning is often considered to require the physical presence of stimuli in the environment in order for them to be linked. This, however, is not a necessary condition for learning. Indeed, associative relationships can form between events that are never directly paired. That is, associative learning can occur by integrating information across different phases of training. Higher-order conditioning provides evidence for such learning through two deceptively similar designs – sensory preconditioning and second-order conditioning. In this review, we detail the procedures and factors that influence learning in these designs, describe the associative relationships that can be acquired, and argue for the importance of this knowledge in studying brain function.

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.

Inhibitory causal structures in serial and simultaneous feature negative learning

We have previously reported that human participants trained with a simultaneous feature negative discrimination (intermixed A+/AB− trials) show only modest transfer of inhibitory properties of feature B to a separately trained excitor in a summation test. Their self-reported causal structure suggested that many participants learned that the effect of feature B was somewhat specific to the excitor it had been trained with (modulation), rather than learning that the feature prevented the outcome (prevention). This pattern is reminiscent of the distinction between negative occasion-setting and conditioned inhibition in the animal conditioning literature. However, in animals, occasion-setting is more commonly seen with a serial procedure, in which the feature (B) precedes the training excitor (A). Accordingly, we ran three experiments to compare serial with simultaneous training in an allergist causal judgement task. Transfer in a summation test was stronger to a previously modulated test excitor compared to a simple excitor after both simultaneous and serial training. There was a numerical trend towards a larger effect in the serial group, but it failed to reach significance and the Bayes Factor indicated support for the null. Serial training had no differential effect on the self-reported causal structure and did not significantly reduce overall transfer. After both simultaneous and serial training, transfer was strongest in participants who reported a prevention structure, replicating and extending our previous results to a previously modulated excitor. These results suggest that serial feature negative training does not promote a qualitatively different inhibitory causal structure compared to simultaneous training in humans.

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.

Evidential diversity increases generalisation in predictive learning

In property induction tasks, encountering a diverse range of instances (e.g., hippos and hamsters) with a given property usually increases our willingness to generalise that property to a novel instance, relative to non-diverse evidence (e.g., hippos and rhinos). Although generalisation in property induction and predictive learning tasks share conceptual similarities, it is unknown whether this diversity principle applies to generalisation of a predictive association. We tested this hypothesis in two predictive learning experiments using differential training where one category of stimuli (e.g., fruits) predicted an outcome and another category (e.g., vegetables) predicted no outcome. We compared generalisation between a Non-Diverse group who were presented with non-diverse evidence in both positive (predicted the outcome) and negative (predicted no outcome) categories, and two groups who received the same training as the Non-Diverse group but with a more diverse range of exemplars in the positive (Diverse+ group) or negative (Diverse– group) category. Diversity effects were found for both positive and negative categories, in that learning about a diverse range of exemplars increased generalisation of a predictive association to novel exemplars from that same category. The results suggest that diversity, a key principle describing how we reason inductively, also applies to generalisation in associative learning tasks.

Three Ways That Non-associative Knowledge May Affect Associative Learning Processes

Associative learning theories offer one account of the way animals and humans assess the relationship between events and adapt their behavior according to resulting expectations. They assume knowledge about event relations is represented in associative networks, which consist of mental representations of cues and outcomes and the associative links that connect them. However, in human causal and contingency learning, many researchers have found that variance in standard learning effects is controlled by "non-associative" factors that are not easily captured by associative models. This has given rise to accounts of learning based on higher-order cognitive processes, some of which reject altogether the notion that humans learn in the manner described by associative networks. Despite the renewed focus on this debate in recent years, few efforts have been made to consider how the operations of associative networks and other cognitive operations could potentially interact in the course of learning. This paper thus explores possible ways in which non-associative knowledge may affect associative learning processes: (1) via changes to stimulus representations, (2) via changes to the translation of the associative expectation into behavior (3) via a shared source of expectation of the outcome that is sensitive to both the strength of associative retrieval and evaluation from non-associative influences.

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.