A dissociation between causal judgment and outcome recall

Hearing hooves, thinking zebras: A review of the inverse base-rate effect

People often fail to use base-rate information appropriately in decision-making. This is evident in the inverse base-rate effect, a phenomenon in which people tend to predict a rare outcome for a new and ambiguous combination of cues. While the effect was first reported in 1988, it has recently seen a renewed interest from researchers concerned with learning, attention and decision-making. However, some researchers have raised concerns that the effect arises in specific circumstances and is unlikely to provide insight into general learning and decision-making processes. In this review, we critically evaluate the evidence for and against the main explanations that have been proposed to explain the effect, and identify where this evidence is currently weak. We argue that concerns about the effect are not well supported by the data. Instead, the evidence supports the conclusion that the effect is a result of general mechanisms that provides a useful opportunity to understand the processes involved in learning and decision making. We discuss gaps in our knowledge and some promising avenues for future research, including the relevance of the effect to models of attentional change in learning, an area where the phenomenon promises to contribute new insights.

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.

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.

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.