Tests of two theories of decision in an "expanded judgment" situation.

Irwin, Francis W.; Smith, Walter; Mayfield, Jane F.

doi:10.1037/h0041911

Cited by 85 publications

(72 citation statements)

References 5 publications

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“…This relationship is seen when comparing confidence across different conditions manipulating decision time (e.g., different stopping points in an interrogation paradigm or between speed and accuracy conditions in optional stopping tasks). Irwin et al (1956); Vickers & Packer (1982); 6. Slow errors For difficult conditions, particularly when accuracy is emphasized, mean decision times for incorrect choices are slower than mean decision times for correct choices.…”

Section: Dynamic Signal Detection Theorymentioning

confidence: 99%

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Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Pleskac¹,

Busemeyer²

2010

Psychological Review

679

947

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The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

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Section: Dynamic Signal Detection Theorymentioning

confidence: 99%

“…Irwin, Smith, and Mayfield (1956) used an expanded judgment task to manipulate the amount of evidence collected before making a choice and confidence judgment. An expanded judgment task externalizes the sequential sampling process by asking people to physically sample observations from a distribution and then make a choice.…”

Section: Pierce's Model Of Confidencementioning

confidence: 99%

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Pleskac¹,

Busemeyer²

2010

Psychological Review

679

947

View full text Add to dashboard Cite

show abstract

“…Humans are more trusting of data they believe is more representative (Kahneman & Tversky, 1972). Numerous studies highlight the importance of the law of large numbers in establishing representativeness: Humans are intuitively aware that larger samples typically provide more reliable information (e.g., Evans & Dusior, 1977;Evans & Pollard, 1985;Irwin, Smith & Mayfield, 1956;Jacobs & Narloch, 2001;Masnick & Morris, 2008;Nisbett, Krantz, Jepson & Kunda, 1983;Obrecht, Chapman & Gelman, 2007;Peterson & Beach, 1967;Sedlmeier & Gigerenzer, 1997;Sedlmeier & Gigerenzer, 2000). However, laypeople do not use sample size in what researchers consider to be a normative fashion.…”

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confidence: 99%

Statistical judgments are influenced by the implied likelihood that samples represent the same population

Chesney

Obrecht

2011

Mem Cogn

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When sample information is combined, it is generally considered normative to weight information based on larger samples more heavily than information based on smaller samples. However, if samples appear likely to have been drawn from different subpopulations, it is reasonable to combine estimates of these subpopulation means (typically, the sample means) without weighting these estimates by sample size. This study investigated whether laypeople are influenced by the likelihood of samples coming from the same population when determining how to combine information. In two experiments we show that (1) implied binomial variability affected participants' judgments of the likelihood that a sample was drawn from a given population, (2) participants' judgments were more affected by sample size when samples were implied to be drawn randomly from a general population, compared to when they were implied to be drawn from different subpopulations, and (3) people higher in numeracy gave more normative responses. We conclude that when determining how to weight and combine samples, laypeople use not only the provided data, but also information about likelihood and sampling processes that these data imply.

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“…This stands in contrast with psychophysical paradigms for studying evidence-based perceptual choice where the flow of sensory evidence can be fully controlled by the experimenter (14,15). To obtain more precise control on the decision input, we introduce an experimental paradigm, which we call "value psychophysics," at the interface of psychophysics and motivation-based decisions, similar to the expanded judgment task developed in a different context (16,17). Participants simultaneously view two or three rapidly varying sequences of numerical values, described as stock market values or slot machines' past payouts.…”

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confidence: 99%

Salience driven value integration explains decision biases and preference reversal

Tsetsos

Chater

Usher

2012

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

220

264

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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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Tests of two theories of decision in an "expanded judgment" situation.

Cited by 85 publications

References 5 publications

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Statistical judgments are influenced by the implied likelihood that samples represent the same population

Salience driven value integration explains decision biases and preference reversal

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