Complex natural systems from brains to bee swarms have evolved to make adaptive multifactorial decisions. Recent theoretical and empirical work suggests that many evolved systems may take advantage of common motifs across multiple domains. We are particularly interested in value sensitivity (i.e., sensitivity to the magnitude or intensity of the stimuli or reward under consideration) as a mechanism to resolve deadlocks adaptively. This mechanism favours long-term reward maximization over accuracy in a simple manner, because it avoids costly delays associated with ambivalence between similar options; speed-value trade-offs have been proposed to be evolutionarily advantageous for many kinds of decision. A key prediction of the value-sensitivity hypothesis is that choices between equally-valued options will proceed faster when the options have a high value than when they have a low value. However, value-sensitivity is not part of idealised choice models such as diffusion to bound. Here we examine two different choice behaviours in two different species, perceptual decisions in humans and economic choices in rhesus monkeys, to test this hypothesis. We observe the same value sensitivity in both human perceptual decisions and monkey value-based decisions. These results endorse the idea that neural decision systems make use of the same basic principle of value-sensitivity in order to resolve costly deadlocks and thus improve long-term reward intake.

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Objective: Two-alternative forced-choice tasks are widely used to gain insight into specific areas of enhancement or impairment in individuals with autism spectrum disorder (ASD).Data arising from these tasks have been used to support myriad theories regarding the integrity, or otherwise, of particular brain areas or cognitive processes in ASD. The drift diffusion model (DDM) provides an account of the underlying processes which give rise to accuracy and reaction time distributions, and parameterises these processes in terms which have direct psychological interpretation. Importantly, the DDM provides further insight into the origin of potential group differences in task performance. Here, for the first time, we used the DDM to investigate perceptual decision making in ASD.Method: Adults with (N = 25) and without ASD (N = 32) performed an orientation discrimination task. A drift diffusion model was applied to the full RT distributions. Results:Participants with ASD responded more slowly than controls, the groups did not differ in accuracy. Modelled parameters indicated that: (i) participants with ASD were more cautious than controls (wider boundary separation); (ii) non-decision time was increased in ASD; and (iii) the quality of evidence extracted from the stimulus (drift rate) did not vary between groups.Conclusions: Taking the behavioural data in isolation would suggest reduced perceptual sensitivity in ASD. However, DDM results indicated that despite response slowing, there was no evidence of differential perceptual sensitivity between participants with and without ASD.Future use of the DDM in investigations of perception and cognition in ASD is highly recommended.

Response time and accuracy are fundamental measures of behavioral science, but discerning participants' underlying abilities can be masked by speed-accuracy trade-offs (SATOs). SATOs are often inadequately addressed in experiment analyses which focus on a single variable or which involve a suboptimal analytic correction. Models of decision-making, such as the drift diffusion model (DDM), provide a principled account of the decision-making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioral variables. For plausible parameters of a typical between-groups experiment, we simulate experimental data, for both real and null group differences in participants' ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time, and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modeling, but to use established decision models to demonstrate and quantify the benefits of decision modeling for experimentalists. We show, in terms of reduction of required sample size, how decision modeling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed-accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment.

Children with and without ASD performed an orientation discrimination task, in which the difficulty of the discrimination was equated across individuals. Behavioural results showed that subjects with ASD were slower in making a decision. A computational decomposition of data was performed and modelled parameters indicated that: (i) participants with ASD adopted a more conservative response criterion and (ii) motor response did not differ between groups. Our results confirm that differences in reaction times (RTs) and/or accuracy between participants with and without ASD in orientation discrimination may be related to differences in response conservativeness rather than in stimulus discriminability, in line with data previously reported from adults (Pirrone, Dickinson, Gomez, Stafford & Milne, 2017). This result has important implications for studies that have claimed impairments/enhancements in ASD on the basis of differences in RTs and/or accuracy alone.

BackgroundPrevious research has reported or predicted, on the basis of theoretical and computational work, magnitude sensitive reaction times. Magnitude sensitivity can arise (1) as a function of single-trial dynamics and/or (2) as recent computational work has suggested, while single-trial dynamics may be magnitude insensitive, magnitude sensitivity could arise as a function of overall reward received which in turn affects the speed at which decision boundaries collapse, allowing faster responses as the overall reward received increases.ResultsHere, we review previous theoretical and empirical results and we present new evidence for magnitude sensitivity arising as a function of single-trial dynamics.ConclusionsThe result of magnitude sensitive reaction times reported is not compatible with single-trial magnitude insensitive models, such as the statistically optimal drift diffusion model.

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Response time and accuracy are fundamental measures of behavioural science, but discerning participants’ underlying abilities can be masked by speed-accuracy trade-offs (SATOs). Although a well-known possibility, SATOs are often inadequately addressed in experiment analyses which focus on a single variable (e.g. psychophysics paradigms analysing accuracy alone), or which involve a suboptimal analytic correction (e.g. dividing accuracy by response time). Models of decision making, such as the drift diffusion model (DDM), provide a principled account of the decision making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioural variables. For plausible parameters of a typical between-groups experiment we simulate experimental data, for both real and null group differences in participants’ ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modelling, but to use established decision models to demonstrate and quantify the benefits of decision modelling for experimentalists. We show, in terms of reduction of required sample size, how decision modelling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed-accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment. Our results are supported by an online interactive data explorer.