Modeling the effects of payoff on response bias in a perceptual discrimination task: Bound-change, drift-rate-change, or two-stage-processing hypothesis

Diederich, Adele; Busemeyer, Jerome R.

doi:10.3758/bf03193669

Cited by 124 publications

(173 citation statements)

References 32 publications

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“…These effects are difficult to account for in many theories of choice; it is thus natural to ask whether they can be accounted within the rankdependent framework proposed here. One plausible way by which people may process multiattribute choice problems, suggested in Tversky's early work (8) and then extended in the decision-field theory model (11,12), is by sequentially switching attention from one choice aspect to another. Assuming that both attributes are sampled independently and with equal probability, the rank-weighted additive value of an option will be determined by its attribute values, the corresponding (attribute-wise) ranks and how often each value/rank combination occurs.…”

Section: Resultsmentioning

confidence: 99%

“…Preference formation arises from the integration of multiple values that are actively sampled either from the environment (11,12) or from memory (28). Here, using a psychophysical task where numerical values are sequentially presented, we controlled the sampling process and probed the micromechanism of value integration.…”

Section: Discussionmentioning

confidence: 99%

“…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)(12)(13). A detailed understanding of these computations might explain the systematic anomalies observed in motivation-based decisions.…”

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confidence: 99%

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Salience driven value integration explains decision biases and preference reversal

Tsetsos

Chater

Usher

2012

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

206

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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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Salience driven value integration explains decision biases and preference reversal

Tsetsos

Chater

Usher

2012

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

206

264

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“…In general, decision making in sequential sampling models is based on the accumulation of evidence over time until a boundary (or criterion) is reached and an associated response is initiated (Brown & Heathcote, 2005Busemeyer & Townsend, 1993;Diederich & Busemeyer, 2006;Hübner, Steinhauser, & Lehle, 2010;Ratcliff & Smith, 2004;Ratcliff, 1978;Usher & McClelland, 2001;White, Ratcliff, & Starns, 2011). The negative relation between decision speed and accuracy results from the lowering of decision boundaries when time pressure increases.…”

Section: Introductionmentioning

confidence: 99%

Time pressure affects the efficiency of perceptual processing in decisions under conflict

Dambacher

Hübner

2014

Psychological Research

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The negative correlation between speed and accuracy in perceptual decision making is often explained as a tradeoff, where lowered decision boundaries under time pressure result in faster but more error-prone responses. Corresponding implementations in sequential sampling models confirmed the success of this account, which has led to the prevalent assumption that a second component of decision making, the efficiency of perceptual processing, is largely independent from temporal demands. To test the generality of this claim, we examined time pressure effects on decisions under conflict. Data from a flanker task were fit with a sequential sampling model that

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“…This parameter is usually manipulated via payoff or proportion manipulations (Edwards, 1965;Wagenmakers et al, 2008; but see Diederich & Busemeyer, 2006). Often, z is reported as a proportion of boundary separation a and referred to as bias B.…”

Section: Mean Starting Point (Z) Starting Point Reflects the A Priorimentioning

confidence: 99%

Testing Theories of Post-Error Slowing

Dutilh¹,

Vandekerckhove²,

Forstmann³

et al. 2011

PsycEXTRA Dataset

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People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here we used the drift diffusion model to isolate and identify the psychological processes responsible for post-error slowing. In a lexical decision data set comprised of 1,094,886 responses we found that post-error slowing was associated with an increase in response caution and-to a lesser extent-a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response monitoring account of post-error slowing.

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Modeling the effects of payoff on response bias in a perceptual discrimination task: Bound-change, drift-rate-change, or two-stage-processing hypothesis

Cited by 124 publications

References 32 publications

Salience driven value integration explains decision biases and preference reversal

Salience driven value integration explains decision biases and preference reversal

Time pressure affects the efficiency of perceptual processing in decisions under conflict

Testing Theories of Post-Error Slowing

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