Heuristic thinking and human intelligence: a commentary on Marewski,Gaissmaier and Gigerenzer

Marewski, Gaissmaier and Gigerenzer (2009) present a review of research on fast and frugal heuristics, arguing that complex problems are best solved by simple heuristics, rather than the application of knowledge and logical reasoning. We argue that the case for such heuristics is overrated. First, we point out that heuristics can often lead to biases as well as effective responding. Second, we show that the application of logical reasoning can be both necessary and relatively simple. Finally, we argue that the evidence for a logical reasoning system that co-exists with simpler heuristic forms of thinking is overwhelming. Not only is it implausible a priori that we would have evolved such a system that is of no use to us, but extensive evidence from the literature on dual processing in reasoning and judgement shows that many problems can only be solved when this form of reasoning is used to inhibit and override heuristic thinking.     

Identifying the relevant aspects of a problem text

Forty-nine subjects judged the relevancy of sentence parts of a word problem (the Allsports problem). Patterns of subjects' judgments suggest three problem-solving heuristics: a SETS heuristic, a TIME heuristic, and a QUESTION heuristic. Presentation of the question before the problem tends to suppress SETS and TIME heuristics. A computer program (ATTEND) is presented to simulate subjects' behavior on the Allsports problem. The program is context-sensitive in that it can change a relevance judgment upon the acquisition of further information. Averaged subject judgments and ATTEND judgments agree for 87% of the items.     

How (far) can rationality be naturalized?

The paper shows why and how an empirical study of fast-and-frugal heuristics can provide norms of good reasoning, and thus how (and how far) rationality can be naturalized. We explain the heuristics that humans often rely on in solving problems, for example, choosing investment strategies or apartments, placing bets in sports, or making library searches. We then show that heuristics can lead to judgments that are as accurate as or even more accurate than strategies that use more information and computation, including optimization methods. A standard way to defend the use of heuristics is by reference to accuracy-effort trade-offs. We take a different route, emphasizing ecological rationality (the relationship between cognitive heuristics and environment), and argue that in uncertain environments, more information and computation are not always better (the ??less-can-be-more?? doctrine). The resulting naturalism about rationality is thus normative because it not only describes what heuristics people use, but also in which specific environments one should rely on a heuristic in order to make better inferences. While we desist from claiming that the scope of ecological rationality is unlimited, we think it is of wide practical use.     

Understanding collision dynamics

In two experiments we investigated people's ability to judge the relative mass of two objects involved in a collision. It was found that judgments of relative mass were made on the basis of two heuristics. Roughly stated, these heuristics were (a) an object that ricochets backward upon impact is less massive than the object that it hit, and (b) faster moving objects are less massive. A heuristic model of judgment is proposed that postulates that different sources of information in any event may have different levels of salience for observers and that heuristic access is controlled by the rank ordering of salience. It was found that observers ranked dissimilarity in mass on the basis of the relative salience of angle and velocity information and not proportionally to the distal mass ratio. This heuristic model was contrasted with the notion that people can veridically extract dynamic properties of motion events when the kinematic data are sufficient for their specification.     

The generation and resemblance heuristics in face recognition: cooperation and competition

Like all probabilistic decisions, recognition memory judgments are based on inferences about the strength and quality of stimulus familiarity. In recent articles, B. W. A. Whittlesea and J. Leboe (2000; J. Leboe & B. W. A. Whittlesea, 2002) proposed that such memory decisions entail various heuristics, similar to well-known heuristics in overt decision making. Using verbal stimulus materials, Whittlesea and Leboe illustrated 3 separate memory heuristics: fluency, generation, and resemblance. In the present investigation, the authors examined the generation and resemblance heuristics in face recognition. In 12 experiments, people memorized faces and later performed exclusion (source memory) tasks. Every experiment contained natural groups of facial photographs (e.g., Caucasian vs. Asian faces), but such groups were not always valid source-memory predictors. Instead, across experiments, the potential utility of generation and resemblance strategies was systematically varied. People were quite sensitive to such variations, changing from one heuristic to another as needed. However, they also combined heuristics, both improving and damaging performance across conditions. The relevance of recognition decision heuristics to eyewitness memory is considered.     

判断与决策中的情感启发式

情感启发式是指在判断与决策的过程中,个体会有意识或无意识的利用自己对任务选项的主观情感反应来做出决策。具体来说,个体头脑中物体和事件的表征会激起不同水平的情感体验,这种不同的体验会对所有的表征做出积极或消极的标记,人们根据这些被标记的体验来做出判断和决策。情感启发式是个体在决策中常用的策略。目前,关于情感启发式心理机制的解释主要有情感启发式模型和双加工理论。此外,情感启发式的影响因素主要包括经验、时间压力、可评估性和计数能力等。未来的研究应主要集中在探究情感启发式的产生根源和进一步扩展情感启发式应用领域的研究。     

Models of ecological rationality: the recognition heuristic

One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counterintuitive less-is-more effect in which less knowledge is better than more for making accurate inferences.     

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.     

When the brain decides: a familiarity-based approach to the recognition heuristic as evidenced by event-related brain potentials

Humans can make fast and highly efficient decisions by using simple heuristics that are assumed to exploit basic cognitive functions. In the study reported here, we used event-related potentials (ERPs) to disclose the psychological mechanisms underlying one of the most frugal decision rules, namely, the recognition heuristic. According to this heuristic, whenever two objects have to be ranked by a specific criterion and only one object is recognized, the recognized object is ranked higher than the unrecognized object. Using a standard recognition-heuristic paradigm, we predicted participants' decisions by analyzing an ERP correlate of familiarity-based recognition occurring 300 to 450 ms after stimulus onset. The measure remained a significant predictor even when later ERP correlates were taken into account. These findings are evidence for the thesis that simple heuristics exploit basic cognitive processes. Specifically, the findings show that familiarity--that is, recognition in the absence of recollection--contributes to decisions made on the basis of such heuristics.     

The heuristic-analytic theory of reasoning: Extension and evaluation

An extensively revised heuristic-analytic theory of reasoning is presented incorporating three principles of hypothetical thinking. The theory assumes that reasoning and judgment are facilitated by the formation of epistemic mental models that are generated one at a time (singularity principle) by preconscious heuristic processes that contextualize problems in such a way as to maximize relevance to current goals (relevance principle). Analytic processes evaluate these models but tend to accept them unless there is good reason to reject them (satisficing principle). At a minimum, analytic processing of models is required so as to generate inferences or judgments relevant to the task instructions, but more active intervention may result in modification or replacement of default models generated by the heuristic system. Evidence for this theory is provided by a review of a wide range of literature on thinking and reasoning.     

Thinking straight while seeing red: the influence of anger on information processing

Because angry people apparently rely on heuristic cues when making judgments, anger has been claimed to trigger superficial, nonanalytic information processing. In three studies, the authors found that induced anger promoted analytic processing. Experiment 1 showed that angry participants were more likely to discriminate between weak and strong arguments than participants in neutral moods. Experiment 2 demonstrated that anger overrode dispositional preferences not to process, causing even those low in need for cognition to process analytically. Experiment 3 reconciled these findings with previous work by showing that angry people used accessible, valid, and relevant heuristics but otherwise processed analytically, as indicated by attitude change and elaboration data. Together, these experiments showed that angry people can have both the capacity and motivation to process and that their selective use of heuristics reflects the cue's perceived validity and not the failure to process analytically.     

The similarity heuristic

Decision makers often make snap judgments using fast‐and‐frugal decision rules called cognitive heuristics. Research into cognitive heuristics has been divided into two camps. One camp has emphasized the limitations and biases produced by the heuristics; another has focused on the accuracy of heuristics and their ecological validity. In this paper we investigate a heuristic proposed by the first camp, using the methods of the second. We investigate a subset of the representativeness heuristic we call the “similarity” heuristic, whereby decision makers who use it judge the likelihood that an instance is a member of one category rather than another by the degree to which it is similar to others in that category. We provide a mathematical model of the heuristic and test it experimentally in a trinomial environment. In this environment, the similarity heuristic turns out to be a reliable and accurate choice rule and both choice and response time data suggest it is also how choices are made. We conclude with a theoretical discussion of how our work fits in the broader “fast‐and‐frugal” heuristics program, and of the boundary conditions for the similarity heuristic. Copyright © 2009 John Wiley & Sons, Ltd.     

A matter of time: Antecedents of one-reason decision making based on recognition

The notion of adaptive decision making implies that strategy selection in both inferences and preferences is driven by a trade-off between accuracy and effort. A strategy for probabilistic inferences which is particularly attractive from this point of view is the recognition heuristic (RH). It proposes that judgments rely on recognition in isolation-ignoring any further information that might be available-and thereby allows for substantial effort-reduction. Consequently, it is herein hypothesized that and tested whether increased necessity of effort-reduction-as implemented via time pressure-fosters reliance on the RH. Two experiments corroborated that this was the case, even with relatively mild time pressure. In addition, this result held even when non-compliance with the response deadline did not yield negative monetary consequences. The current investigations are among the first to tackle the largely open question of whether effort-related factors influence the reliance on heuristics in memory-based decisions.     

Intuition and The Junctures of Judgment in Decision Procedures for Clinical Ethics

Moral decision procedures such as principlism or casuistry require intuition at certain junctures, as when a principle seems indeterminate, or principles conflict, or we wonder which paradigm case is most relevantly similar to the instant case. However, intuitions are widely thought to lack epistemic justification, and many ethicists urge that such decision procedures dispense with intuition in favor of forms of reasoning that provide discursive justification. I argue that discursive justification does not eliminate or minimize the need for intuition, or constrain our intuitions. However, this is not a problem, for intuitions can be justified in easy or obvious cases, and decision procedures should be understood as heuristic devices for reaching judgments about harder cases that approximate the justified intuitions we would have about cases under ideal conditions, where hard cases become easy. Similarly, the forms of reasoning which provide discursive justification help decision procedures perform this heuristic function not by avoiding intuition, but by making such heuristics more accurate. Nonetheless, it is possible to demand too much justification; many clinical ethicists lack the time and philosophical training to reach the more elaborate levels of discursive justification. We should keep moral decision procedures simple and user-friendly so that they will provide what justification can be achieved under clinical conditions, rather than trying to maximize our epistemic justification out of an overstated concern about intuition.     

On the binary quality of recognition and the inconsequentiality of further knowledge: two critical tests of the recognition heuristic

The recognition heuristic is claimed to be distinguished from notions of availability and fluency through its categorical or “binary” treatment of information and the “inconsequentiality” of further knowledge to inferences based on recognition. Using the city‐size task of Goldstein and Gigerenzer ( 2002 ) we demonstrate that: (1) increasing the validity of other information in the environment decreases the reliance on recognition; (2) cities that are both recognized and have other information known about them (e.g. they have a soccer team) are chosen more often than those which are simply recognized; and (3) there is a negative correlation between the time taken to identify a city and the proportion of times it is selected as the larger of a pair. None of these results is predicted by the process model of the recognition heuristic. The implication of the results for the distinction between the recognition, availability and fluency heuristics is discussed. Copyright © 2006 John Wiley & Sons, Ltd.     

The probability heuristics model of syllogistic reasoning

A probability heuristic model (PHM) for syllogistic reasoning is proposed. An informational ordering over quantified statements suggests simple probability based heuristics for syllogistic reasoning. The most important is the "min-heuristic": choose the type of the least informative premise as the type of the conclusion. The rationality of this heuristic is confirmed by an analysis of the probabilistic validity of syllogistic reasoning which treats logical inference as a limiting case of probabilistic inference. A meta-analysis of past experiments reveals close fits with PHM. PHM also compares favorably with alternative accounts, including mental logics, mental models, and deduction as verbal reasoning. Crucially, PHM extends naturally to generalized quantifiers, such as Most and Few, which have not been characterized logically and are, consequently, beyond the scope of current mental logic and mental model theories. Two experiments confirm the novel predictions of PHM when generalized quantifiers are used in syllogistic arguments. PHM suggests that syllogistic reasoning performance may be determined by simple but rational informational strategies justified by probability theory rather than by logic.     

It just felt right: The neural correlates of the fluency heuristic

Simple heuristics exploit basic human abilities, such as recognition memory, to make decisions based on sparse information. Based on the relative speed of recognizing two objects, the fluency heuristic infers that the one recognized more quickly has the higher value with respect to the criterion of interest. Behavioral data show that reliance on retrieval fluency enables quick inferences. Our goal with the present functional magnetic resonance imaging study was to isolate fluency-heuristic-based judgments to map the use of fluency onto specific brain areas that might give a better understanding of the heuristic’s underlying processes. Activation within the claustrum for fluency heuristic decisions was found. Given that claustrum activation is thought to reflect the integration of perceptual and memory elements into a conscious gestalt, we suggest this activation correlates with the experience of fluency.     

归纳推理和演绎推理的关系理论及其模型

推理是人类高级认知过程的一种,相关的心理学研究一般将其分为归纳推理和演绎推理两个方面。归纳推理是从特殊到一般的推理过程,与之相对的演绎推理则是从一般到特殊的过程。归纳推理和演绎推理的关系问题是当前心理推理研究领域的一个重点问题。这一问题主要有两种理论解释：一种是单过程理论,该理论认为归纳推理和演绎推理本质上是同一个认知过程,以单过程理论为基础构建的推理模型称为单维模型;另一种是双过程理论,认为归纳推理和演绎推理是两个不同的认知过程,并不同程度地受到启发和分析过程的影响。未来研究可多关注推理的时间进程,以及采用不同的研究方法对各自理论提供数据支持。     

Can cognitive psychological research on reasoning enhance the discussion around moral judgments?

In this article we will demonstrate how cognitive psychological research on reasoning and decision making could enhance discussions and theories of moral judgments. In the first part, we will present recent dual-process models of moral judgments and describe selected studies which support these approaches. However, we will also present data that contradict the model predictions, suggesting that approaches to moral judgment might be more complex. In the second part, we will show how cognitive psychological research on reasoning might be helpful in understanding moral judgments. Specifically, we will highlight approaches addressing the interaction between intuition and reflection. Our data suggest that a sequential model of engaging in deliberation might have to be revised. Therefore, we will present an approach based on Signal Detection Theory and on intuitive conflict detection. We predict that individuals arrive at the moral decisions by comparing potential action outcomes (e.g., harm caused and utilitarian gain) simultaneously. The response criterion can be influenced by intuitive processes, such as heuristic moral value processing, or considerations of harm caused.     

Depressive realism and clinical depression

Depressive realism suggests that depressed individuals make more accurate judgments of control than their nondepressed counterparts. However, most studies demonstrating this phenomenon were conducted in nonclinical samples. In this study, psychiatric patients who met criteria for major depressive disorder underestimated control in a contingent situation and were consistently more negative in their judgments than were nondepressed controls. Depressed patients were less likely than their nondepressed counterparts to overestimate control in a noncontingent situation, but largely because they perceived receiving less reinforcement. Depressed patients were no more likely to use the appropriate logical heuristic to generate their judgments of control than their nondepressed counterparts and each appeared to rely on different primitive heuristics. Depressed patients were consistently more negative than their nondepressed counterparts and when they did appear to be more “accurate” in their judgments of control (as in the noncontingent situation) it was largely because they applied the wrong heuristic to less accurate information. These findings do not support the notion of depressive realism and suggest that depressed patients distort their judgments in a characteristically negative fashion.