The neural basis of the speed–accuracy tradeoff

Bogacz, Rafał; Wagenmakers, Eric–Jan; Forstmann, Birte U.; Nieuwenhuis, Sander

doi:10.1016/j.tins.2009.09.002

Cited by 666 publications

(688 citation statements)

References 51 publications

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“…This shift to striatum-mediated learning has also been found when participants performed the weather prediction task under cognitive load (Foerde et al, 2006). In studies of the speed-accuracy tradeoff, increased activation was found in the striatum during trials with speed emphasis compared to trials with accuracy emphasis (Forstmann et al, 2008;van Veen et al, 2008), suggesting that enhanced striatal activity may be critical in reducing inhibitory control and facilitating speeded responses (Bogacz et al, 2010). Here, we significantly expand these findings by directly demonstrating that increased time pressure decreases the involvement of frontal and parietal regions and shifts probabilistic multi-cue decision-making to the striatum, even after the completion of initial cue learning.…”

Section: Discussionmentioning

confidence: 64%

“…Specifically, learning the weather prediction task under stress induced by the cold pressor test has been associated with increased use of implicit, striatum-mediated strategies (Schwabe and Wolf, 2012). Similarly, time pressure on perceptual decision-making has been associated with a deterioration in information processing in early sensory areas (Ho et al, 2012) and increased activity in the striatum (Bogacz et al, 2010;Forstmann et al, 2008), indicating that the striatum may promote faster but possibly premature or sub-optimal decisions.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

et al. 2017

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A B S T R A C TReal-life decision-making often involves combining multiple probabilistic sources of information under finite time and cognitive resources. To mitigate these pressures, people "satisfice", foregoing a full evaluation of all available evidence to focus on a subset of cues that allow for fast and "good-enough" decisions. Although this form of decision-making likely mediates many of our everyday choices, very little is known about the way in which the neural encoding of cue information changes when we satisfice under time pressure. Here, we combined human functional magnetic resonance imaging (fMRI) with a probabilistic classification task to characterize neural substrates of multi-cue decision-making under low (1500 ms) and high (500 ms) time pressure. Using variational Bayesian inference, we analyzed participants' choices to track and quantify cue usage under each experimental condition, which was then applied to model the fMRI data. Under low time pressure, participants performed nearoptimally, appropriately integrating all available cues to guide choices. Both cortical (prefrontal and parietal cortex) and subcortical (hippocampal and striatal) regions encoded individual cue weights, and activity linearly tracked trial-by-trial variations in the amount of evidence and decision uncertainty. Under increased time pressure, participants adaptively shifted to using a satisficing strategy by discounting the least informative cue in their decision process. This strategic change in decision-making was associated with an increased involvement of the dopaminergic midbrain, striatum, thalamus, and cerebellum in representing and integrating cue values. We conclude that satisficing the probabilistic inference process under time pressure leads to a cortical-to-subcortical shift in the neural drivers of decisions.

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Section: Discussionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

et al. 2017

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“…The advantage of model-based analysis of neural data is strongly enhanced by using a dynamic model. Thus far, such analyses have been limited to models that, although they may have a dynamic component, only specify the internal state of the participant at the level of a whole trial (Bogacz, Wagenmakers, Forstmann, & Nieuwenhuis, 2010;Turner et al, 2013). A dynamic model like the one we have presented here makes predictions about the entire trajectory of internal states through which a participant is expected to pass on the way toward making a final recognition decision.…”

Section: Prospects For Neurosciencementioning

confidence: 99%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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“…This speed-accuracy tradeoff can be straightforwardly implemented in sequential sampling models, which not only account for performance in a wide range of perceptual tasks (Brown & Heathcote, 2005;Ratcliff & McKoon, 2008) but can also be plausibly linked to neurophysiological correlates of decision making (Bogacz, Wagenmakers, Forstmann, & Nieuwenhuis, 2010;Forstmann et al, 2008Forstmann et al, , 2010Forstmann et al, , 2011Ho et al, 2012;Ivanoff, Branning, & Marois, 2008;Philiastides, Ratcliff, & Sajda, 2006;van Veen, Krug, & Carter, 2008). 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).…”

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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The neural basis of the speed–accuracy tradeoff

Cited by 666 publications

References 51 publications

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

A Dynamic Approach to Recognition Memory

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

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