Reconsidering “evidence” for fast-and-frugal heuristics

Hilbig, Benjamin E.

doi:10.3758/pbr.17.6.923

Cited by 64 publications

(56 citation statements)

References 73 publications

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“…An important point of contention is that toolbox models as a whole are difficult to falsify and it is not always clear how they can be tested against alternative models of cognition (e.g., Bröder, 2000;Dougherty, Franco-Watkins, & Thomas, 2008;Hilbig, 2010;Newell, 2005;Todd & Gigerenzer, 2001).…”

Section: Difficulties When Testing Toolbox Modelsmentioning

confidence: 99%

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Scheibehenne

Rieskamp

Wagenmakers

2013

Psychological Review

103

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Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra-and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

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Section: Difficulties When Testing Toolbox Modelsmentioning

confidence: 99%

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Scheibehenne

Rieskamp

Wagenmakers

2013

Psychological Review

103

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“…Over the past decade, the RH has attracted significant attention and has generated much controversy (Hilbig, 2010;Marewski, Pohl, & Vitouch, 2010, 2011a, 2011bPachur, Bröder, & Marewski, 2008;Pachur et al, 2011). From a processing perspective, three issues have been particularly pertinent.…”

Section: Ffh Framework: the Recognition Heuristic (Rh)mentioning

confidence: 99%

“…From a processing perspective, three issues have been particularly pertinent. First, the idea that any knowledge beyond recognition is ignored (at the PMM stage) has been very controversial from the outset, with one camp claiming to provide evidence in support of noncompensatory processing (e.g., Goldstein & Gigerenzer, 2002;Marewski, Gaissmaier, et al, 2010;Pachur & Hertwig, 2006;Volz et al, 2006) and an opposing camp claiming to provide evidence against it (e.g., Bröder & Eichler, 2006;Glöckner & Bröder, 2011;Hilbig, 2010;Hilbig, Erdfelder, & Pohl, 2010;Hilbig, Pohl, & Bröder, 2009;Newell & Fernandez, 2006;Newell & Shanks, 2004;Oppenheimer, 2003;Pohl, 2006;Richter & Späth, 2006). Second, the RH is assumed to operate on the strictly binary output of a recognition judgment; however, the link between the recognition-based inference and the underlying recognition process has remained unclear (e.g., Dougherty, Franco-Watkins, & Thomas, 2008;Erdfelder, Küpper-Tetzel, & Mattern, 2011;Pleskac, 2007;Rosburg, Mecklinger, & Frings, 2011;Schooler & Hertwig, 2005).…”

Section: Ffh Framework: the Recognition Heuristic (Rh)mentioning

confidence: 99%

Magnitude comparison extended: How lack of knowledge informs comparative judgments under uncertainty.

Schweickart¹,

Brown²

2014

Journal of Experimental Psychology: General

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How do people compare quantitative attributes of real-world objects? (e.g., Which country has the higher per capita GDP, Mauritania or Nepal?). The research literature on this question is divided: Although researchers in the 1970s and 1980s assumed that a 2-stage magnitude comparison process underlies these types of judgments (Banks, 1977), more recent approaches emphasize the role of probabilistic cues and simple heuristics (Gigerenzer, Todd, & The ABC Research Group, 1999). In this article, we review the magnitude comparison literature and propose a framework for magnitude comparison under uncertainty (MaC). Predictions from this framework were tested in a choice context involving one recognized and one unrecognized object, and were contrasted with those based on the recognition heuristic (Goldstein & Gigerenzer, 2002). This was done in 2 paired-comparison studies. In both, participants were timed as they decided which of 2 countries had the higher per capita gross domestic product (GDP). Consistent with the MaC account, we found that response times (RTs) displayed a classic symbolic distance effect: RTs were inversely related to the difference between the subjective per capita GDPs of the compared countries. Furthermore, choice of the recognized country became more frequent as subjective difference increased. These results indicate that the magnitude comparison process extends to choice contexts that have previously been associated only with cue-based strategies. We end by discussing how several findings reported in the recent heuristics literature relate to the MaC framework.

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“…Table 1), it is ecologically rational to rely on the recognition heuristic . Recognition validity a a = R/(R + W), "where R is the number of correct (right) inferences the recognition heuristic would achieve, computed across all pairs in which one object is recognized and the other is not, and W is the number of incorrect (wrong) inferences under the same circumstances" (Goldstein & Gigerenzer, 2002, p. 78 Over the last 10 years of research on the recognition heuristic, evidence has been provided that people indeed adopt this heuristic in environments in which it yields accurate inferences (for a review, see ; for an overview of current studies, see special issues edited by Marewski, Pohl, & Vitouch, 2010, 2011a; for a critical position, see Hilbig, 2010;and Pohl, 2011), namely, people appear to select their strategies in an adaptive way by relying on recognition information when recognition is systematically related to a criterion but discounting it when it is not related (e.g., Pachur & Hertwig, 2006;Pohl, 2006). Hence, there is a positive correlation of r = .64 between the recognition validity and the proportion of judgments consistent with the recognition heuristic across 11 studies (Pachur, Todd, Gigerenzer, Schooler, & Goldstein, 2011).…”

Section: The Recognition Heuristic In Individualsmentioning

confidence: 99%

“…Although we cannot expect that verbalized reasoning exactly mirrors the strategies actually used (as those might not be accessible to people; e.g., Nisbett & Wilson, 1977), the process may still reveal participants' subjective, important reasons and provide a source for new insights for researchers (see Keller, Gummerum, Canz, Gigerenzer, & Takezawa, 2013; for a similar claim). This may help to address the question of whether people really use the recognition heuristic (Hilbig, 2010), that is, whether they rely on their recognition when the recognition heuristic models their choices best. Translated to the group level, the question is whether the recognition cue is a behavioral correlate of the RBM and thus more often used during discussion by groups that predominantly use the RBM than by groups that predominantly use the KBM.…”

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confidence: 99%

The Adaptive Use of Recognition in Group Decision Making

2014

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Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task-relevant features. The authors focused on the recognition heuristic, so the task-relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty-three threemember groups performed an inference task in which they had to infer which of two German companies had the higher market capitalization. Results based on the choice data support the hypothesis that groups adaptively apply the strategy that leads to the highest theoretically achievable performance. Time constraints had no effect on strategy use but did have an effect on the proportions of different types of arguments. Possible mechanisms underlying the adaptive use of recognition in group decision making are discussed.

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Reconsidering “evidence” for fast-and-frugal heuristics

Abstract: In several recent reviews, authors have argued for the pervasive use of fast-and-frugal

Cited by 64 publications

References 73 publications

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Magnitude comparison extended: How lack of knowledge informs comparative judgments under uncertainty.

The Adaptive Use of Recognition in Group Decision Making

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