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Neurocomputational mechanisms of prior-informed perceptual decision-making in humans

Abstract

To interact successfully with diverse sensory environments, we must adapt our decision processes to account for time constraints and prior probabilities. The full set of decision-process parameters that undergo such flexible adaptation has proven to be difficult to establish using simplified models that are based on behaviour alone. Here, we utilize well-characterized human neurophysiological signatures of decision formation to construct and constrain a build-to-threshold decision model with multiple build-up (evidence accumulation and urgency) and delay components (pre- and post-decisional). The model indicates that all of these components were adapted in distinct ways and, in several instances, fundamentally differ from the conclusions of conventional diffusion modelling. The neurally informed model outcomes were corroborated by independent neural decision signal observations that were not used in the model’s construction. These findings highlight the breadth of decision-process parameters that are amenable to strategic adjustment and the value in leveraging neurophysiological measurements to quantify these adjustments.

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Fig. 1: Prior-cued motion discrimination task and behavioural results.
Fig. 2: Neurally informed (NI) model constrained by motor-related neural signal amplitudes and latencies.
Fig. 3: Comparison of real versus model-simulated behaviour.
Fig. 4: Parameter estimates showing regime effects of the DDM and NI models.
Fig. 5: Real and simulated average decision signal dynamics and amplitudes.
Fig. 6: Cue-evoked ERP waveforms.
Fig. 7: Relationship between drift rate biases and CPP amplitude modulations.

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Data availability

Consent was not obtained to publicly share individual participant data. However, grand average behavioural data used for modelling are available at https://osf.io/53gmw/ and other data are available from the corresponding author on request.

Code availability

All code is available publicly at https://osf.io/53gmw/.

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Acknowledgements

We thank S. Tamang and G. Price for data collection. This work was supported by a research grant to S.P.K. and R.G.O. from the U.S. National Science Foundation (grant no. BCS-1358955), a research grant from Science Foundation Ireland to S.P.K. (grant no. 15/CDA/3591) and a European Research Council (ERC) Starting Grant to R.G.O. under the European Union’s Horizon 2020 research and innovation programme (grant no. 638289). E.A.C. was supported by a Government of Ireland Postdoctoral Fellowship from the Irish Research Council (grant no. GOIPD/2017/1261) and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 842143. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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S.P.K. and R.G.O. conceived the research. S.P.K. collected and analysed the data. S.P.K. and E.A.C. developed and fit the computational models. S.P.K., E.A.C. and R.G.O. wrote the paper.

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Correspondence to Simon P. Kelly.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Consistency of regime effects across independent fits.

All 25 fits, even though each was started from an independent starting parameter vector originally and are of varying quality, agree on the qualitative trends across regimes for each parameter effect within each model. Bluer traces indicate lower G2 values (better fits), whereas redder indicate higher G2 (worse fits).

Extended Data Fig. 2 Parameter adjustment effects across Bootstrap samples.

To assess the reliability of adaptation effects indicated by the neurally-informed (NI) and drift diffusion (DDM) models, we fit the models to 500 bootstrap samples generated by sampling subjects with replacement. Scatterplots are shown of parameter effects (relative to the Easy regime or to zero as appropriate) against fit quality (G2) along the x-axis so that reliability of the NI model’s fit superiority can also be assessed. (a) Estimated accumulation onset parameters relative to the Easy regime in the NI model (left), and relative non-decision time for the DDM (right). (b) Estimated drift rate normalised to its value for the Easy regime (represented by a value of 1) to enable comparison between the DDM and NI model. (c) Estimated urgency rate expressed relative to the Easy regime for the NI model. (d) Estimated drift rate bias (positive means towards more probable alternative), normalised relative to the drift rate estimated for the Easy regime to facilitate comparison.

Extended Data Fig. 3 Fit quality and key Speed pressure effects estimated by the DDM and neurally-informed (NI) models for simulated data.

Six simulated datasets ranged from being fully DDM-compatible (no pre-evidence accumulation and zero dynamic urgency) to having the same degrees of pre-evidence accumulation and dynamic urgency as indicated in the fits to the real data (see methods). The DDM fits just as well as the Neurally-informed (NI) model for the most DDM-compatible data, but the superiority of the NI model - in terms of overall AIC (a) and in the accuracy of the relative drift rate (b) and non-decision time (c) estimated across regimes - systematically increases with the introduction of pre-evidence accumulation and dynamic urgency into the generated data. True effects computed from the parameters from which the data were generated are shown as horizontal dashed lines in (b) and (c). Drift rate (b) was normalised by the value in the Easy regime so that a value of 1 represents no effect. Total non-decision time for the NI model (c) was computed by summing the post-decision motor time and the post-stimulus accumulation onset, for comparison with the estimates of the DDM.

Extended Data Fig. 4 Fit quality for simulated datasets manipulating the presence and size of each adjustment effect.

For each of the 4 principal adjustment effects, a) non-decision time differences across regimes, b) drift rate enhancement under Deadline, c) Urgency rate differences across regimes, and d) the presence of Prior Probability biases on drift rate, AIC values increasingly favour the model allowing the effect as the size of the effect is increased. Importantly, in each case, the model that disallows the effect in question is favoured when the effect is fully eradicated (x 0) in the simulated data. The model selection correctly concludes in all 4 cases when the size of the effect was commensurate with the effect sizes arising from the real data (x 1).

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Kelly, S.P., Corbett, E.A. & O’Connell, R.G. Neurocomputational mechanisms of prior-informed perceptual decision-making in humans. Nat Hum Behav 5, 467–481 (2021). https://doi.org/10.1038/s41562-020-00967-9

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