A theory of decision-making using diffusion-to-bound models: choice, reaction-time and confidence

Schustek, Philipp; Moreno‐Bote, Rubén

doi:10.1186/1471-2202-15-s1-p88

Cited by 2 publications

(2 citation statements)

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“…These can be readily extended with a computation of confidence. Perhaps the most intuitive extension is within the race model framework, where the difference between decision variables for the winning and losing races provides an estimate of confidence (Vickers, 1979; Kepecs et al, 2008; Moreno-Bote, 2010; Pleskac and Busemeyer, 2010; Zylberberg et al, 2012; Drugowitsch et al, 2014; Schustek and Moreno-Bote, 2014). Mechanistically, neural network models based on attractor dynamics have also been used to study how confidence can be computed by neural circuits (Insabato et al, 2010; Rolls et al, 2010; Wei and Wang, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Framework for Statistical Decision Confidence

2016

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Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Framework for Statistical Decision Confidence

2016

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“…Sequential sampling models have been used to understand how decisions are reached based on noisy evidence across time (Bogacz et al, 2006). These can be readily extended with a computation of confidence (Drugowitsch et al, 2014;Pleskac and Busemeyer, 2010;Schustek and Moreno-Bote, 2014;Vickers, 1979). Perhaps the most intuitive extension is within the race model framework, where the difference between decision variables for the winning and losing races provides an estimate of confidence (Kepecs et al, 2008;Merkle and Van Zandt, 2006;Moreno-Bote, 2010;Vickers, 1979;Zylberberg et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A mathematical framework for statistical decision confidence

Hangya

Sanders

Kepecs

2015

Preprint

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Decision confidence is a forecast about the probability that a decision will be correct.Confidence can be framed as an objective mathematical quantity the Bayesian posterior probability, providing a formal definition of statistical decision confidence. Here we use this definition as a starting point to develop a normative statistical framework for decision confidence. We analytically prove interrelations between statistical decision confidence and other observable decision measures. Among these is a counterintuitive property of confidence that the lowest average confidence occurs when classifiers err in the presence of the strongest evidence. These results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human behavior to neural representations.

show abstract

A theory of decision-making using diffusion-to-bound models: choice, reaction-time and confidence

Cited by 2 publications

References 4 publications

A Mathematical Framework for Statistical Decision Confidence

A Mathematical Framework for Statistical Decision Confidence

A mathematical framework for statistical decision confidence

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