An adaptive approach to human decision making: Learning theory, decision theory, and human performance.

Busemeyer, Jerome R.; Myung, In Jae

doi:10.1037/0096-3445.121.2.177

Cited by 183 publications

(272 citation statements)

References 48 publications

(57 reference statements)

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“…1 D and E (see SI Models for parameters). This model roughly calculates a weighted sum of the samples, with the weights decreasing exponentially from the last to the first item (21). Thus, the longer the sequences, the less the impact of the early items (Fig.…”

Section: Resultsmentioning

confidence: 99%

Salience driven value integration explains decision biases and preference reversal

Tsetsos

Chater

Usher

2012

Proc. Natl. Acad. Sci. U.S.A.

204

263

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Human choice behavior exhibits many paradoxical and challenging patterns. Traditional explanations focus on how values are represented, but little is known about how values are integrated. Here we outline a psychophysical task for value integration that can be used as a window on high-level, multiattribute decisions. Participants choose between alternative rapidly presented streams of numerical values. By controlling the temporal distribution of the values, we demonstrate that this process underlies many puzzling choice paradoxes, such as temporal, risk, and framing biases, as well as preference reversals. These phenomena can be explained by a simple mechanism based on the integration of values, weighted by their salience. The salience of a sampled value depends on its temporal order and momentary rank in the decision context, whereas the direction of the weighting is determined by the task framing. We show that many known choice anomalies may arise from the microstructure of the value integration process. decision making | decoy effects | value psychophysics | expanded judgement R ecent research on the psychology and neuroscience of simple, evidence-based choices (e.g., integrating perceptual or reward information) has made impressive progress, leading to the conclusion that the brain is optimized to make the fastest decision for a specified accuracy (1-5). Accordingly, the observer is assumed to infer the most probable cause of a perceived experience by sequentially accumulating samples of noisy evidence until a response criterion is reached. The idea that simple, evidence-based decision making is optimal contrasts with findings in more complex, motivation-based decisions, focused on multiple goals with tradeoffs (e.g., choices among cars or flats). Here, a number of paradoxical and puzzling choice behaviors (6-8) have been revealed, posing a serious challenge to the development of a unified theory of choice.Can a common theoretical framework between evidence-based and motivation-based decisions be established? A natural starting point is to propose that, in the latter, the cognitive system integrates subjective values (rather than, say, pieces of perceptual evidence), that depend on how each alternative matches the decision maker's goals (9). In particular, when alternatives are characterized by different attributes (e.g., product price and quality), preference is shaped through shifting attention across these attributes (8, 10), assessing an item's subjective value on each attribute, integrating these values across time, and finally making a choice when some threshold is reached (11-13). A detailed understanding of these computations might explain the systematic anomalies observed in motivation-based decisions.This line of research has been difficult to pursue, however, because classical laboratory preference tasks provide little control of the moment-by-moment processes of value sampling and integration. This stands in contrast with psychophysical paradigms for studying evidence-based perceptual choice wher...

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Section: Resultsmentioning

confidence: 99%

Salience driven value integration explains decision biases and preference reversal

Tsetsos

Chater

Usher

2012

Proc. Natl. Acad. Sci. U.S.A.

204

263

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“…In the social domains, researchers have argued that people rely on different strategies for social interactions (Erev & Roth, 2001;Fiske, 1992;Milinski & Wedekind, 1998), mating choices (Buss & Schmitt, 1993), and predicting other people's behavior (Costa-Gomes & Crawford, 2006). Furthermore, researchers have argued that people use different strategies for categorization (Busemeyer & Myung, 1992;Patalano, Smith, Jonides, & Koeppe, 2001;Sewell & Lewandowsky, 2011), resource allocation (Ball, Langholtz, Auble, & Sopchak, 1998), estimation and frequency judgments (Brown, 1995;Brown, Cui, & Gordon, 2002;von Helversen & Rieskamp, 2008), skill acquisition (Anderson & Lebiere, 1998;Lovett, 1988), function learning (Lewandowsky, Kalish, & Ngang, 2002), and learning processes (Erev & Barron, 2005;Gigerenzer & Gaissmaier, 2011). The idea that processes such as information search or choice are guided by qualitatively different strategies also figures prominently in research on judgment and decision making.…”

Section: Toolbox Models Are Widely Usedmentioning

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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“…Estimate Updating Rule The revised estimate is a weighted average of the past estimate and the new observation (Busemeyer & Myung, 1992;Denrell, 2005). When Individual i selects Alt.…”

Section: Model Specificationmentioning

confidence: 99%

Information Sampling, Belief Synchronization, and Collective Illusions

Denrell

Mens

2017

Management Science

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We demonstrate that a sampling-based mechanism can offer an alternative explanation for belief synchronization in social groups and the persistence of collective illusions. Our model assumes that people are more likely to sample popular alternatives than unpopular alternatives.We show that this mechanism is sufficient to explain belief synchronization: a strong majority of opinions will likely emerge in favor of one alternative. The reason is that the group is unlikely to move away from a state in which one alternative is very unpopular. If by chance most people come to dislike alternative A, they are all unlikely to sample it again and their opinions of A remain the same. When A is in fact the best alternative, a collective illusion has emerged because people mistakenly believe that a suboptimal alternative is the best. Our model implies that such a collective illusion is persistent. The model thus offers an existence proof that a collective illusion can occur even in settings where people do not infer that popular alternatives are better. The model also casts new light on the effect of online recommendation systems on attitude homogenization and the effect of majority voting on beliefs and attitudes.

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An adaptive approach to human decision making: Learning theory, decision theory, and human performance.

Cited by 183 publications

References 48 publications

Salience driven value integration explains decision biases and preference reversal

Salience driven value integration explains decision biases and preference reversal

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Information Sampling, Belief Synchronization, and Collective Illusions

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