Thorsten Pachur

On the psychology of the recognition heuristic : retrieval primacy as a key determinant of its use

The recognition heuristic is a prime example of a boundedly rational mind tool that rests on an evolved capacity, recognition, and exploits environmental structures. When originally proposed, it was conjectured that no other probabilistic cue reverses the recognition-based inference (D. G. Goldstein & G. Gigerenzer, 2002). More recent studies challenged this view and gave rise to the argument that recognition enters inferences just like any other probabilistic cue. By linking research on the heuristic with research on recognition memory, the authors argue that the retrieval of recognition information is not tantamount to the retrieval of other probabilistic cues. Specifically, the retrieval of subjective recognition precedes that of an objective probabilistic cue and occurs at little to no cognitive cost. This retrieval primacy gives rise to 2 predictions, both of which have been empirically supported: Inferences in line with the recognition heuristic (a) are made faster than inferences inconsistent with it and (b) are more prevalent under time pressure. Suspension of the heuristic, in contrast, requires additional time, and direct knowledge of the criterion variable, if available, can trigger such suspension.

Judgments of risk frequencies : Tests of possible cognitive mechanisms

How do people judge which of 2 risks claims more lives per year? The authors specified 4 candidate mechanisms and tested them against peoples judgments in 3 risk environments. Two mechanisms, availability by recall and regressed frequency, conformed best to peoples choices. The same mechanisms also accounted well for the mapping accuracy of estimates of absolute risk frequencies. Their nearly indistinguishable level of performance is remarkable given their different assumptions about the underlying cognitive processes and the fact that they give rise to different expectations regarding the accuracy of peoples inferences. The authors discuss this seeming paradox, the lack of impact of financial incentives on judgmental accuracy, and the dominant interpretation of inaccurate inferences in terms of biased information processing.

Cognitive models of risky choice: Parameter stability and predictive accuracy of prospect theory ☆

In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are individual differences as measured by model parameters stable enough to improve the ability to predict behavior as compared to modeling without adjustable parameters? We examined this issue in cumulative prospect theory (CPT), arguably the most widely used framework to model decisions under risk. Specifically, we examined (a) the temporal stability of CPTs parameters; and (b) how well different implementations of CPT, varying in the number of adjustable parameters, predict individual choice relative to models with no adjustable parameters (such as CPT with fixed parameters, expected value theory, and various heuristics). We presented participants with risky choice problems and fitted CPT to each individuals choices in two separate sessions (which were 1 week apart). All parameters were correlated across time, in particular when using a simple implementation of CPT. CPT allowing for individual variability in parameter values predicted individual choice better than CPT with fixed parameters, expected value theory, and the heuristics. CPTs parameters thus seem to pick up stable individual differences that need to be considered when predicting risky choice.

How Do People Judge Risks: Availability Heuristic, Affect Heuristic, or Both?

How does the public reckon which risks to be concerned about? The availability heuristic and the affect heuristic are key accounts of how laypeople judge risks. Yet, these two accounts have never been systematically tested against each other, nor have their predictive powers been examined across different measures of the publics risk perception. In two studies, we gauged risk perception in student samples by employing three measures (frequency, value of a statistical life, and perceived risk) and by using a homogeneous (cancer) and a classic set of heterogeneous causes of death. Based on these judgments of risk, we tested precise models of the availability heuristic and the affect heuristic and different definitions of availability and affect. Overall, availability-by-recall, a heuristic that exploits peoples direct experience of occurrences of risks in their social network, conformed to peoples responses best. We also found direct experience to carry a high degree of ecological validity (and one that clearly surpasses that of affective information). However, the relative impact of affective information (as compared to availability) proved more pronounced in value-of-a-statistical-life and perceived-risk judgments than in risk-frequency judgments. Encounters with risks in the media, in contrast, played a negligible role in peoples judgments. Going beyond the assumption of exclusive reliance on either availability or affect, we also found evidence for mechanisms that combine both, either sequentially or in a composite fashion. We conclude with a discussion of policy implications of our results, including how to foster peoples risk calibration and the success of education campaigns.

Type of learning task impacts performance and strategy selection in decision making.

In order to be adaptive, cognition requires knowledge about the statistical structure of the environment. We show that decision performance and the selection between cue-based and exemplar-based inference mechanisms can depend critically on how this knowledge is acquired. Two types of learning tasks are distinguished: learning by comparison, by which the decision maker learns which of two objects has a higher criterion value, and direct criterion learning, by which the decision maker learns an objects criterion value directly. In three experiments, participants were trained either with learning by comparison or with direct criterion learning and subsequently tested with paired-comparison, classification, and estimation tasks. Experiments 1 and 2 showed that although providing less information, learning by comparison led to better generalization (at test), both when generalizing to new objects and when the task format at test differed from the task format during training. Moreover, learning by comparison enabled participants to provide rather accurate continuous estimates. Computational modeling suggests that the advantage of learning by comparison is due to differences in strategy selection: whereas direct criterion learning fosters the reliance on exemplar processing, learning by comparison fosters cue-based mechanisms. The pattern in decision performance reversed when the task environment was changed from a linear (Experiments 1 and 2) to a nonlinear structure (Experiment 3), where direct criterion learning led to better decisions. Our results demonstrate the critical impact of learning conditions for the subsequent selection of decision strategies and highlight the key role of comparison processes in cognition.

The recognition heuristic: A review of theory and tests

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

Memory-based decision-making with heuristics: Evidence for a controlled activation of memory representations

Many of our daily decisions are memory based, that is, the attribute information about the decision alternatives has to be recalled. Behavioral studies suggest that for such decisions we often use simple strategies (heuristics) that rely on controlled and limited information search. It is assumed that these heuristics simplify decision-making by activating long-term memory representations of only those attributes that are necessary for the decision. However, from behavioral studies alone, it is unclear whether using heuristics is indeed associated with limited memory search. The present study tested this assumption by monitoring the activation of specific long-term-memory representations with fMRI while participants made memory-based decisions using the “take-the-best” heuristic. For different decision trials, different numbers and types of information had to be retrieved and processed. The attributes consisted of visual information known to be represented in different parts of the posterior cortex. We found that the amount of information required for a decision was mirrored by a parametric activation of the dorsolateral PFC. Such a parametric pattern was also observed in all posterior areas, suggesting that activation was not limited to those attributes required for a decision. However, the posterior increases were systematically modulated by the relative importance of the information for making a decision. These findings suggest that memory-based decision-making is mediated by the dorsolateral PFC, which selectively controls posterior storage areas. In addition, the systematic modulations of the posterior activations indicate a selective boosting of activation of decision-relevant attributes.

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers’ estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choice—cumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test–retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.

Testing process predictions of models of risky choice: a quantitative model comparison approach

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.

Ecological Rationality: A Framework for Understanding and Aiding the Aging Decision Maker

The notion of ecological rationality sees human rationality as the result of the adaptive fit between the human mind and the environment. Ecological rationality focuses the study of decision making on two key questions: First, what are the environmental regularities to which people’s decision strategies are matched, and how frequently do these regularities occur in natural environments? Second, how well can people adapt their use of specific strategies to particular environmental regularities? Research on aging suggests a number of changes in cognitive function, for instance, deficits in learning and memory that may impact decision-making skills. However, it has been shown that simple strategies can work well in many natural environments, which suggests that age-related deficits in strategy use may not necessarily translate into reduced decision quality. Consequently, we argue that predictions about the impact of aging on decision performance depend not only on how aging affects decision-relevant capacities but also on the decision environment in which decisions are made. In sum, we propose that the concept of the ecological rationality is crucial to understanding and aiding the aging decision maker.