Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the model response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

Individual differences in reasoning: Implications for the rationality debate?-Open Peer Commentary-Differences, games, and pluralism

In three experiments, we show that people respond differently when they make predictions as opposed to when they are asked to estimate the causal or the predictive value of cues: Their response to each of those three questions is based on different sets of information. More specifically, we show that prediction judgments depend on the probability of the outcome given the cue, whereas causal and predictivevalue judgments depend on thecue-outcome contingency. Although these results might seem problematic for most associative models in their present form, they can be explained by explicitly assuming the existence of postacquisition processes that modulate participants’ responses in a flexible way.

Causal and predictive-value judgments, but not predictions, are based on cue-outcome contingency

In some product categories, low-priced brands are consistently of low quality, but high-priced brands can be anything from terrible to excellent. In other product categories, high-priced brands are consistently of high quality, but quality of low-priced brands varies widely. Three experiments demonstrate that such heteroscedasticity leads to more extreme price-based quality predictions. This finding suggests that quality inferences do not only stem from what consumers have learned about the average level of quality at different price points through exemplar memory or rule abstraction. Instead, quality predictions are also based on learning about the covariation between price and quality. That is, consumers inappropriately conflate the conditional mean of quality with the predictability of quality. We discuss implications for theories of quantitative cue learning and selective information processing, for pricing strategies and luxury branding, and for our understanding of the emergence and persistence of erroneous beliefs and stereotypes beyond the consumer realm.

https://www.jstor.org/stable/10.1086/678035

Fooled by Heteroscedastic Randomness: Local Consistency Breeds Extremity in Price-Based Quality Inferences

In laboratory contingency learning tasks, people usually give accurate estimates of the degree of contingency between a cue and an outcome. However, if they are asked to estimate the probability of the outcome in the presence of the cue, they tend to be biased by the probability of the outcome in the absence of the cue. This bias is often attributed to an automatic contingency detection mechanism, which is said to act via an excitatory associative link to activate the outcome representation at the time of testing. We conducted 3 experiments to test alternative accounts of contingency bias. Participants were exposed to the same outcome probability in the presence of the cue, but different outcome probabilities in the absence of the cue. Phrasing the test question in terms of frequency rather than probability and clarifying the test instructions reduced but did not eliminate contingency bias. However, removal of ambiguity regarding the presence of additional causes during the test phase did eliminate contingency bias. We conclude that contingency bias may be due to ambiguity in the test question, and therefore it does not require postulation of a separate associative link-based mechanism.

Pranjal More

Papers

Contingency bias in probability judgement may arise from ambiguity regarding additional causes.