Risky choice with heuristics: reply to Birnbaum (2008), Johnson, Schulte-Mecklenbeck, and Willemsen (2008), and Rieger and Wang (2008)

E. Brandst?tter, G. Gigerenzer, and R. Hertwig (2006) showed that the priority heuristic matches or outperforms modifications of expected utility theory in predicting choice in 4 diverse problem sets. M. H. Birnbaum (2008) argued that sets exist in which the opposite is true. The authors agree--but stress that all choice strategies have regions of good and bad performance. The accuracy of various strategies systematically depends on choice difficulty, which the authors consider a triggering variable underlying strategy selection. Agreeing with E. J. Johnson, M. Schulte-Mecklenbeck, and M. C. Willemsen (2008) that process (not "as-if") models need to be formulated, the authors show how quantitative predictions can be derived and test them. Finally, they demonstrate that many of Birnbaum's and M. O. Rieger and M. Wang's (2008) case studies championing their preferred models involved biased tests in which the priority heuristic predicted data, whereas the parameterized models were fitted to the same data. The authors propose an adaptive toolbox approach of risky choice, according to which people first seek a no-conflict solution before resorting to conflict-resolving strategies such as the priority heuristic.     

Process models deserve process data: comment on Brandstätter, Gigerenzer, and Hertwig (2006)

Comments on the article by E. Brandst?tter, G. Gigerenzer, and R. Hertwig. Resolution of debates in cognition usually comes from the introduction of constraints in the form of new data about either the process or representation. Decision research, in contrast, has relied predominantly on testing models by examining their fit to choices. The authors examine a recently proposed choice strategy, the priority heuristic, which provides a novel account of how people make risky choices. The authors identify a number of properties that the priority heuristic should have as a process model and illustrate how they may be tested. The results, along with prior research, suggest that although the priority heuristic captures some variability in the attention paid to outcomes, it fails to account for major characteristics of the data, particularly the frequent transitions between outcomes and their probabilities. The article concludes with a discussion of the properties that should be captured by process models of risky choice and the role of process data in theory development.     

What is behind the priority heuristic? A mathematical analysis and comment on Brandstätter, Gigerenzer, and Hertwig (2006)

Comments on the article by E. Brandst?tter, G. Gigerenzer, and R. Hertwig. The authors discuss the priority heuristic, a recent model for decisions under risk. They reanalyze the experimental validity of this approach and discuss how these results compare with cumulative prospect theory, the currently most established model in behavioral economics. They also discuss how general models for decisions under risk based on a heuristic approach can be understood mathematically to gain some insight in their limitations. They finally consider whether the priority heuristic model can lead to some understanding of the decision process of individuals or whether it is better seen as an as-if model.     

Producing biased diagnoses with unambiguous stimuli: The importance of feature instantiations

In this article, the authors demonstrate a laboratory analogue of medical diagnostic biasing (V. R. LeBlanc, G. R. Norman, & L. R. Brooks, 2001) in 2 experiments and explore the basis of this effect. Before categorizing novel exemplars, participants first evaluated the likelihood that the item was a member of the category suggested on that trial: either the correct category or a plausible alternative category. This was sufficient to produce a substantial bias toward the suggested category despite the use of unambiguous stimuli, explicit rules, and unhurried conditions--each of which would be likely to limit diagnostic bias. The authors argue that the production of this effect requires distinguishing between particular feature instantiations and more abstract representations of those features as well as allowing people to adopt a particular decision strategy mediating the use of instantiated features: a feature-recognition heuristic.     

From recognition to decisions: Extending and testing recognition-based models for multialternative inference

The recognition heuristic is a noncompensatory strategy for inferring which of two alternatives, one recognized and the other not, scores higher on a criterion. According to it, such inferences are based solely on recognition. We generalize this heuristic to tasks with multiple alternatives, proposing a model of how people identify the consideration sets from which they make their final decisions. In doing so, we address concerns about the heuristic’s adequacy as a model of behavior: Past experiments have led several authors to conclude that there is no evidence for a noncompensatory use of recognition but clear evidence that recognition is integrated with other information. Surprisingly, however, in no study was this competing hypothesis—the compensatory integration of recognition—formally specified as a computational model. In four studies, we specify five competing models, conducting eight model comparisons. In these model comparisons, the recognition heuristic emerges as the best predictor of people’s inferences.     

An experimental test of two mathematical models applied to the size-weight illusion.

Two quantitative models, which make different quantitative predictions for the amount of the size-weight illusion, were tested according to the psychophysical methods employed by the respective authors (magnitude estimation versus category ratings). Both models with their corresponding method were supported. This causes uncertainty over Anderson's chaim that the validity of both a model and the applied scale used is sufficiently test by the socalled joint testing procedure.     

SWIFT: a dynamical model of saccade generation during reading

Mathematical models have become an important tool for understanding the control of eye movements during reading. Main goals of the development of the SWIFT model (R. Engbert, A. Longtin, & R. Kliegl, 2002) were to investigate the possibility of spatially distributed processing and to implement a general mechanism for all types of eye movements observed in reading experiments. The authors present an advanced version of SWIFT that integrates properties of the oculomotor system and effects of word recognition to explain many of the experimental phenomena faced in reading research. They propose new procedures for the estimation of model parameters and for the test of the model's performance. They also present a mathematical analysis of the dynamics of the SWIFT model. Finally, within this framework, they present an analysis of the transition from parallel to serial processing.     

Exemplars in the mist: the cognitive substrate of the representativeness heuristic

The idea that people often make probability judgments by a heuristic short-cut, the representativeness heuristic, has been widely influential, but also criticized for being vague. The empirical trademark of the heuristic is characteristic deviations between normative probabilities and judgments (e.g., the conjunction fallacy, base-rate neglect). In this article the authors contrast two hypotheses concerning the cognitive substrate of the representativeness heuristic, the prototype hypothesis (Kahneman & Frederick, 2002) and the exemplar hypothesis (Juslin & Persson, 2002), in a task especially designed to elicit representativeness effects. Computational modelling and an experiment reveal that representativeness effects are evident early in training and persist longer in a more complex task environment and that the data are best accounted for by a model implementing the exemplar hypothesis.     

Inhibitory mechanism of the matching heuristic in syllogistic reasoning

A number of heuristic-based hypotheses have been proposed to explain how people solve syllogisms with automatic processes. In particular, the matching heuristic employs the congruency of the quantifiers in a syllogism — by matching the quantifier of the conclusion with those of the two premises. When the heuristic leads to an invalid conclusion, successful solving of these conflict problems requires the inhibition of automatic heuristic processing. Accordingly, if the automatic processing were based on processing the set of quantifiers, no semantic contents would be inhibited. The mental model theory, however, suggests that people reason using mental models, which always involves semantic processing. Therefore, whatever inhibition occurs in the processing implies the inhibition of the semantic contents.     

The integration of continuous and discrete latent variable models: potential problems and promising opportunities

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model, nonnormal continuous measures, and nonlinear relationships among observed and/or latent variables. When the objective of a SEMM analysis is the identification of latent classes, these conditions should be considered as alternative hypotheses and results should be interpreted cautiously. However, armed with greater knowledge about the estimation of SEMMs in practice, researchers can exploit the flexibility of the model to gain a fuller understanding of the phenomenon under study.     

On Adaptation in Analogy: Tests of Pragmatic Importance and Adaptability in Analogical Problem Solving

When people use analogies to solve problems, they form an analogical mapping between two domains of knowledge. This mapping may support inferences by analogy that suggest a novel solution to a problem. Several factors have been proposed to be important in selecting this mapping from among several alternative mappings: structural factors (systematicity and structural consistency) and pragmatic factors (the exploitation of higher-order planning categories). We suggest another set of factors plays a role in selecting mappings: adaptability. Specifically, if a mapped solution can be adapted easily to a problem, then it will be preferred over an alternative mapping that is less adaptable. Two experiments are reported which test the effects of pragmatic and adaptation factors, using a novel technique in which the story analogue has two alternative plans, either of which can be used to solve an insight problem. In Experiment 1, these plans were varied in terms of their pragmatic importance (success or failure) and their adaptability. In Experiment 2, the relative adaptability of plans was manipulated. The results suggest that there is little evidence for these specific pragmatic factors, but that adaptability plays a definite role in selecting an analogous plan. The findings suggest that most models need to be extended to include adaptation constraints.     

Probabilistic Subset Conjunction

The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only main effects or only interaction effects. This results in a confounding of the main and interaction effects when there is little or no response error. With greater response error, a logistic regression, even if it gives a good fit to data, can produce parameter estimates that do not reflect the underlying decision process. The authors propose a model in which the binary classification of alternatives into acceptable/unacceptable categories is based on a probabilistic implementation of a subset-conjunctive process. The satisfaction of decision criteria biases the odds toward one outcome or the other. The authors then describe a two-stage choice model in which a (possibly large) set of alternatives is first reduced using a subset-conjunctive rule, after which an alternative is selected from this reduced set of items. They describe methods for estimating the unobserved consideration probabilities from classification and choice data, and illustrate the use of the models for cancer diagnosis and consumer choice. They report the results of simulations investigating estimation accuracy, incidence of local optima, and model fit. The authors thank the Editor, the Associate Editor, and three anonymous reviewers for their constructive suggestions, and also thank Asim Ansari and Raghuram Iyengar for their helpful comments. They also thank Sawtooth Software, McKinsey and Company, and Intelliquest for providing the PC choice data, and the University of Wisconsin for making the breast-cancer data available at the machine learning archives.     

热手谬误和赌徒谬误心理机制研究述评

在知觉随机序列时,人们倾向于把序列中实际上各自独立的相邻结果看作是存在正相关或负相关的,而出现热手谬误或赌徒谬误,又称为随机序列中的正负近因效应。有关这两谬误最早的心理机制的探索中,Kahneman 和Tversky采用局部代表性启发法对此进行了解释。但是这种解释也存在诸多不足,因此近期的一些模型（因果模型、Urn模型、记忆模型、适应模型和两阶段格式塔模型）分别从不同角度对两种谬误提出了新的解释。最后文章指出研究方法、神经机制、个体差异以及生活中的应用是该领域研究的几个未来发展方向     

Social outcomes in childhood brain disorder: a heuristic integration of social neuroscience and developmental psychology

The authors propose a heuristic model of the social outcomes of childhood brain disorder that draws on models and methods from both the emerging field of social cognitive neuroscience and the study of social competence in developmental psychology/psychopathology. The heuristic model characterizes the relationships between social adjustment, peer interactions and relationships, social problem solving and communication, social-affective and cognitive-executive processes, and their neural substrates. The model is illustrated by research on a specific form of childhood brain disorder, traumatic brain injury. The heuristic model may promote research regarding the neural and cognitive-affective substrates of children's social development. It also may engender more precise methods of measuring impairments and disabilities in children with brain disorder and suggest ways to promote their social adaptation.     

Cue abstraction and exemplar memory in categorization

In this article, the authors compare 3 generic models of the cognitive processes in a categorization task. The cue abstraction model implies abstraction in training of explicit cue-criterion relations that are mentally integrated to form a judgment, the lexicographic heuristic uses only the most valid cue, and the exemplar-based model relies on retrieval of exemplars. The results from 2 experiments showed that, in lieu of the lexicographic heuristic, most participants spontaneously integrate cues. In contrast to single-system views, exemplar memory appeared to dominate when the feedback was poor, but when the feedback was rich enough to allow the participants to discern the task structure, it was exploited for abstraction of explicit cue-criterion relations.     

Exploring Message Framing Outcomes When Systematic,Heuristic, or Both Types of Processing Occur

Mixed findings have emerged in message framing studies, even when such studies employ the same general type of framing, such as goal framing. This article attempts to show that by extending the heuristic–systematic model‐based explanation of message framing effects to incorporate conditions that may prompt both systematic and heuristic processing, this theory may accommodate some of the aberrant findings. The research reported shows that by varying a message issue's risky implications and its personal relevance, 2 factors that potentially influence the type of processing people employ, systematic, heuristic, or concurrently both types of processing were evoked and influenced people's judgments, causing alternative patterns of message framing effects to occur. The results offer insight into how each of these types of processing can affect message framing outcomes, and they imply that certain seemingly aberrant findings in the literature can be reconciled with this extended theory.     

An integrated model of choices and response times in absolute identification

Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a model (SAMBA: selective attention, mapping, and ballistic accumulation) that integrates shorter and longer term memory processes and accounts for both the choices made and the associated response time distributions, including sequential effects in each. The model's predictions arise as a consequence of its architecture and require estimation of only a few parameters with values that are consistent across numerous data sets. The authors show that SAMBA provides a quantitative account of benchmark choice phenomena in classical absolute identification experiments and in contemporary data involving both choice and response time.