Speed Accuracy Trade-off under Response Deadlines

Karşılar, Hakan; Simen, Patrick; Papadakis, Samantha; Balcı, Fuat

doi:10.1016/j.sbspro.2014.02.371

Cited by 14 publications

(17 citation statements)

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“…Simen et al (2009) found that the participants used higher than optimal decision thresholds. Also, the results of Karlar et al (2014) showed that in experiments with deadline, where the optimal decision threshold is time-decreasing, the participants did not decrease their decision threshold within a trial. One reason for this sub-optimality could be the lack of enough practice.…”

Section: Optimal Decision Thresholdmentioning

confidence: 97%

“…Previous research on rewarded perceptual decision making experiments have mainly focused on the "optimal speed-accuracy trade-off", specifically, quantifying the optimal behavior and examining if the participants can learn this optimal behavior Bogacz et al (2006) ;Frazier & Yu (2008); Simen et al (2009); Balci et al (2011); Karlar et al (2014); Khodadadi et al (2014). However, little is known about how the participants adjust their decision threshold in these experiments.…”

Section: Computational Models Of Threshold Adjustmentmentioning

confidence: 99%

“…In the previous studies that investigated the optimal speed-accuracy trade-off, all trials in each block were of the same type Simen et al (2009);Balci et al (2011); Karlar et al (2014). Our experimental design can be considered as an extension to these paradigms in that here the amount of time a participant should allocate to one type of decision to achieve the optimal performance, depends on the task parameters for all types of decisions.…”

Section: Allocation Of Limited Time To Decisions With Different Outcomementioning

confidence: 99%

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Learning to allocate limited time to decisions with different expected outcomes

Khodadadi

Fakhari

Busemeyer

2017

Cognitive Psychology

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The goal of this article is to investigate how human participants allocate their limited time to decisions with different properties. We report the results of two behavioral experiments. In each trial of the experiments, the participant must accumulate noisy information to make a decision. The participants received positive and negative rewards for their correct and incorrect decisions, respectively. The stimulus was designed such that decisions based on more accumulated information were more accurate but took longer. Therefore, the total outcome that a participant could achieve during the limited experiments' time depended on her "decision threshold", the amount of information she needed to make a decision. In the first experiment, two types of trials were intermixed randomly: hard and easy. Crucially, the hard trials were associated with smaller positive and negative rewards than the easy trials. A cue presented at the beginning of each trial would indicate the type of the upcoming trial. The optimal strategy was to adopt a small decision threshold for hard trials. The results showed that several of the participants did not learn this simple strategy. We then investigated how the participants adjusted their decision threshold based on the feedback they received in each trial. To this end, we developed and compared 10 computational models for adjusting the decision threshold. The models differ in their assumptions on the shape of the decision thresholds and the way the feedback is used to adjust the decision thresholds. The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model. In the second experiment, the cues were not presented. We showed that the optimal strategy is to use a single time-decreasing decision threshold for all trials. The results of the computational modeling showed that the participants did not use this optimal strategy. Instead, they attempted to detect the difficulty of the trial first and then set their decision threshold accordingly.

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Section: Optimal Decision Thresholdmentioning

confidence: 97%

Section: Computational Models Of Threshold Adjustmentmentioning

confidence: 99%

Section: Allocation Of Limited Time To Decisions With Different Outcomementioning

confidence: 99%

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Learning to allocate limited time to decisions with different expected outcomes

Khodadadi

Fakhari

Busemeyer

2017

Cognitive Psychology

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“…A model with boundaries that collapse over time has also been argued to be optimal if there is a cost associated with time spent on a decision (Busemeyer & Rapoport, 1988; Drugowitsch et al, 2012; Rapoport & Burkheimer, 1971). It has also been argued that collapsing boundary models are optimal in response-deadline tasks in which subjects are trying to find a balance between being accurate and still making a response before a deadline (Frazier & Yu, 2008), although in practice, subjects do not appear to behave optimally in these kinds of tasks (Balci et al, 2011; Karsilar, Simen, Papadakis, & Balci, 2014), and this type of data can be accounted for with a forced decision at the deadline (Ratcliff, 1988, 2006). Karsilar et al (2014) examined accuracy as a function of response time in a response deadline task and found that subjects do show a decrease in accuracy before a response deadline, but the size of the decrease is more consistent with the predictions of a fixed boundary diffusion model than with the predictions of a collapsing boundary model with boundaries selected to optimize reward rate.…”

Section: Introductionmentioning

confidence: 99%

Comparing fixed and collapsing boundary versions of the diffusion model

Voskuilen

Ratcliff

Smith

2016

Journal of Mathematical Psychology

124

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Optimality studies and studies of decision-making in monkeys have been used to support a model in which the decision boundaries used to evaluate evidence collapse over time. This article investigates whether a diffusion model with collapsing boundaries provides a better account of human data than a model with fixed boundaries. We compared the models using data from four new numerosity discrimination experiments and two previously published motion discrimination experiments. When model selection was based on BIC values, the fixed boundary model was preferred over the collapsing boundary model for all of the experiments. When model selection was carried out using a parametric bootstrap cross-fitting method (PBCM), which takes into account the flexibility of the alternative models and the ability of one model to account for data from another model, data from 5 of 6 experiments favored either fixed boundaries or boundaries with only negligible collapse. We found that the collapsing boundary model produces response times distributions with the same shape as those produced by the fixed boundary model and that its parameters were not well-identified and were difficult to recover from data. Furthermore, the estimated boundaries of the best-fitting collapsing boundary model were relatively flat and very similar to those of the fixed-boundary model. Overall, a diffusion model with decision boundaries that converge over time does not provide an improvement over the standard diffusion model for our tasks with human data.

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“…An ongoing debate has centered on whether slow errors are, in general, better explained by a collapsing decision bound 62 (equivalent to additive, evidenceindependent urgency 8,9,11 ) or by drift rate variability 2 (e.g. see Hawkins et al 63 and Ratcliff et al 3 ).…”

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confidence: 99%

Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

Steinemann

O’Connell

Kelly

2017

Preprint

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When decision makers prioritize speed over accuracy, neural activity is elevated in brain circuits involved in preparing actions. Such "urgency" signal components, defined by their independence from sensory evidence, are observed even before evidence is presented and can grow dynamically during decision formation. Is urgency applied globally, or are there adjustments of a distinct nature applied at different processing levels? Using a novel multi-level recording paradigm, we show that dynamic urgency impacting cortical action-preparation signals is echoed downstream in electromyographic indices of muscle activation, but does not directly influence upstream cortical levels. A motor-independent representation of cumulative evidence reached lower pre-response levels under conditions of greater motor-level urgency, paralleling a decline in choice accuracy. At the sensory level itself, we find a boost in differential evidence, which is correlated with changes in pupil size and acts to alleviate, rather than contribute to, the overall accuracy cost under speed pressure.. CC-BY-NC 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/203141 doi: bioRxiv preprint first posted online Oct. 14, 2017; 3 When situations call for it, animals can prioritize speed over accuracy in their sensoryguided actions. Prominent computational models suggest that sensorimotor decisions are made by drawing sequential samples from noisy evidence representations and integrating them up to an action-triggering threshold 1,2 . In this framework speed can be emphasized at the expense of accuracy by lowering this threshold, which in the models may be constant or collapsing (i.e., narrowing) over the timeframe of the decision 3,4 .Neural circuits involved in preparing decision-reporting actions have been found to implement such adjustments in the form of "urgency" signal components, which nonselectively elevate activity towards action thresholds. A "static" component of urgency has been widely observed in raised baseline activity before evidence presentation 5-8 , and recent work has further revealed a "dynamic" component that grows over the course of a decision, effectively implementing a collapsing bound [8][9][10][11] . A key defining property of urgency is that it is generated purely from knowledge of time constraints and/or elapsed time itself, and it contributes to neural buildup activity alongside, but strictly independent of, the influence of sensory evidence 12 . This means that any speed benefits of urgency necessarily incur a cost to choice accuracy. Thus far, urgency components have been identified only in neural circuits involved in preparing actions. Recent work has implicated diffusely-projecting neuromodulatory systems in the generation of urgency 11,13 , suggesting that it may, in fact, act globally, i.e., at all levels of the sensor...

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Speed Accuracy Trade-off under Response Deadlines

Cited by 14 publications

References 0 publications

Learning to allocate limited time to decisions with different expected outcomes

Learning to allocate limited time to decisions with different expected outcomes

Comparing fixed and collapsing boundary versions of the diffusion model

Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

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