We favor formal models of heuristics rather than lists of loose dichotomies: a reply to Evans and Over

In their comment on Marewski et al. (good judgments do not require complex cognition, 2009) Evans and Over (heuristic thinking and human intelligence: a commentary on Marewski, Gaissmaier and Gigerenzer, 2009) conjectured that heuristics can often lead to biases and are not error free. This is a most surprising critique. The computational models of heuristics we have tested allow for quantitative predictions of how many errors a given heuristic will make, and we and others have measured the amount of error by analysis, computer simulation, and experiment. This is clear progress over simply giving heuristics labels, such as availability, that do not allow for quantitative comparisons of errors. Evans and Over argue that the reason people rely on heuristics is the accuracy-effort trade-off. However, the comparison between heuristics and more effortful strategies, such as multiple regression, has shown that there are many situations in which a heuristic is more accurate with less effort. Finally, we do not see how the fast and frugal heuristics program could benefit from a dual-process framework unless the dual-process framework is made more precise. Instead, the dual-process framework could benefit if its two “black boxes” (Type 1 and Type 2 processes) were substituted by computational models of both heuristics and other processes.     

研究人类使用启发式之五原则(英文)

快速节俭启发式框架假定人们使用一套简捷的决策策略——启发式——进行推理、选择、评价及其他决策。这些启发式策略能顺应任务情境结构中的规律, 利用人类的基本认知能力。正基于此, 启发式成就了适应性行为。本文拟对启发式框架进行回顾, 并简要陈述引导研究者研究人类适应性工具箱的五条原则。我们强调, 启发式模型应(ⅰ)精确界定(ⅱ)对照检验(ⅲ)与策略选择理论相符(ⅳ)能评估其对新资料的预测力(ⅴ)能既在实验室又在现实世界中得以检验。     

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.     

Good judgments do not require complex cognition

What cognitive capabilities allow Homo sapiens to successfully bet on the stock market, to catch balls in baseball games, to accurately predict the outcomes of political elections, or to correctly decide whether a patient needs to be allocated to the coronary care unit? It is a widespread belief in psychology and beyond that complex judgment tasks require complex solutions. Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true: Humans do not need complex cognitive strategies to make good inferences, estimations, and other judgments; rather, it is the very simplicity and robustness of our cognitive repertoire that makes Homo sapiens a capable decision maker.     

Cognitive niches: an ecological model of strategy selection

How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts people's familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless people's decisions will be.     

The recognition heuristic in memory‐based inference: is recognition a non‐compensatory cue?

The recognition heuristic makes the strong claim that probabilistic inferences in which a recognized object is compared to an unrecognized one are made solely on the basis of whether the objects are recognized or not, ignoring all other available cues. This claim has been seriously challenged by a number of studies that have shown a clear effect of additional cue knowledge. In most of these studies, either recognition knowledge was acquired during the experiment, and/or additional cues were provided to participants. However, the recognition heuristic is more likely to be a tool for exploiting natural (rather than induced) recognition when inferences have to be made from memory. In our study on natural recognition and inferences from memory, around 85% of the inferences followed recognition information even when participants had learned three cues that contradicted recognition and when some of the contradictory cues were deemed more valid than recognition. Nevertheless, there were strong individual differences in the use of recognition. Whereas about half of the participants chose the recognized object regardless of the number of conflicting cues—suggestive of the hypothesized noncompensatory processing of recognition—the remaining participants were influenced by the additional knowledge. The former group of participants also tended to give higher estimates of recognition's validity. In addition, we found that the use of recognition for an inference may be affected by whether additional cue knowledge has been learned outside or within the experimental setting. Copyright © 2007 John Wiley & Sons, Ltd.