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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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/.
References
Smith, P. L. & Ratcliff, R. Psychology and neurobiology of simple decisions. Trends Neurosci. 27, 161–168 (2004).
Link, S. W. & Heath, R. A. A sequential theory of psychological discrimination. Psychometrika 40, 77–105 (1975).
Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873–922 (2008).
Heitz, R. P. The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Front. Neurosci. 8, 150 (2014).
Mulder, M. J., Wagenmakers, E.-J., Ratcliff, R., Boekel, W. & Forstmann, B. U. Bias in the brain: a diffusion model analysis of prior probability and potential payoff. J. Neurosci. 32, 2335–2343 (2012).
Rae, B., Heathcote, A., Donkin, C., Averell, L. & Brown, S. The hare and the tortoise: emphasizing speed can change the evidence used to make decisions. J. Exp. Psychol. Learn. Mem. Cogn. 40, 1226–1243 (2014).
Ratcliff, R., Smith, P. L., Brown, S. D. & McKoon, G. Diffusion decision model: current issues and history. Trends Cogn. Sci. 20, 260–281 (2016).
Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R. & Brown, S. D. Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. J. Neurosci. 35, 2476–2484 (2015).
Evans, N. J., Hawkins, G. E. & Brown, S. D. The role of passing time in decision-making. J. Exp. Psychol. Learn. Mem. Cogn. https://doi.org/10.1037/xlm0000725 (2019).
O’Connell, R. G., Shadlen, M. N., Wong-Lin, K. & Kelly, S. P. Bridging neural and computational viewpoints on perceptual decision-making. Trends Neurosci. 41, 838–852 (2018).
Dutilh, G. et al. The quality of response time data inference: A blinded, collaborative assessment of the validity of cognitive models. Psychon. Bull. Rev. https://doi.org/10.3758/s13423-017-1417-2 (2018).
Smith, P. L. & Lilburn, S. D. Vision for the blind: reconsidering blinded inference for decision models in light of visual psychophysics. Psychon. Bull. Rev. 27, 882–910 (2020).
Heathcote, A. & Hayes, B. Diffusion versus linear ballistic accumulation: different models for response time with different conclusions about psychological mechanisms? Can. J. Exp. Psychol. 66, 125–136 (2012).
Hanes, D. P. & Schall, J. D. Neural control of voluntary movement initiation. Science 274, 427–430 (1996).
Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
Donner, T. H., Siegel, M., Fries, P. & Engel, A. K. Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr. Biol. 19, 1581–1585 (2009).
van Vugt, M. K., Simen, P., Nystrom, L., Holmes, P. & Cohen, J. D. Lateralized readiness potentials reveal properties of a neural mechanism for implementing a decision threshold. PLoS ONE 9, e90943 (2014).
O’Connell, R. G., Dockree, P. M. & Kelly, S. P. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat. Neurosci. 15, 1729–1735 (2012).
Hanks, T. D. & Summerfield, C. Perceptual decision making in rodents, monkeys, and humans. Neuron 93, 15–31 (2017).
Purcell, B. A. et al. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 117, 1113–1143 (2010).
Hanks, T., Kiani, R. & Shadlen, M. N. A neural mechanism of speed-accuracy tradeoff in macaque area LIP. eLife 3, e02260 (2014).
Churchland, A. K., Kiani, R. & Shadlen, M. N. Decision-making with multiple alternatives. Nat. Neurosci. 11, 693–702 (2008).
Murphy, P. R., Boonstra, E. & Nieuwenhuis, S. Global gain modulation generates time-dependent urgency during perceptual choice in humans. Nat. Commun. 7, 13526 (2016).
Steinemann, N. A., O’Connell, R. G. & Kelly, S. P. Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy. Nat. Commun. 9, 3627 (2018).
Teichert, T., Grinband, J. & Ferrera, V. The importance of decision onset. J. Neurophysiol. 115, 643–661 (2016).
Bogacz, R., Wagenmakers, E.-J., Forstmann, B. U. & Nieuwenhuis, S. The neural basis of the speed-accuracy tradeoff. Trends Neurosci. 33, 10–16 (2010).
Voss, A., Rothermund, K. & Voss, J. Interpreting the parameters of the diffusion model: an empirical validation. Mem. Cogn. 32, 1206–1220 (2004).
Rinkenauer, G., Osman, A., Ulrich, R., Muller-Gethmann, H. & Mattes, S. On the locus of speed-accuracy trade-off in reaction time: inferences from the lateralized readiness potential. J. Exp. Psychol. Gen. 133, 261–282 (2004).
Arnold, N. R., Bröder, A. & Bayen, U. J. Empirical validation of the diffusion model for recognition memory and a comparison of parameter-estimation methods. Psychol. Res. 79, 882–898 (2015).
Donkin, C., Brown, S., Heathcote, A. & Wagenmakers, E.-J. Diffusion versus linear ballistic accumulation: different models but the same conclusions about psychological processes? Psychon. Bull. Rev. 18, 61–69 (2011).
Ho, T. et al. The optimality of sensory processing during the speed-accuracy tradeoff. J. Neurosci. 32, 7992–8003 (2012).
Heathcote, A. & Love, J. Linear deterministic accumulator models of simple choice. Front. Psychol. 3, 292 (2012).
de Lange, F. P., Rahnev, D. A., Donner, T. H. & Lau, H. Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. J. Neurosci. 33, 1400–1410 (2013).
Dmochowski, J. P. & Norcia, A. M. Cortical components of reaction-time during perceptual decisions in humans. PLoS ONE 10, e0143339 (2015).
Devine, C. A., Gaffney, C., Loughnane, G. M., Kelly, S. P. & O'Connell, R. G. The role of premature evidence accumulation in making difficult perceptual decisions under temporal uncertainty. eLife 8, e48526 (2019).
Vandekerckhove, J. & Tuerlinckx, F. Diffusion model analysis with MATLAB: a DMAT primer. Behav. Res. Methods 40, 61–72 (2008).
Kelly, S. P. & O’Connell, R. G. The neural processes underlying perceptual decision making in humans: recent progress and future directions. J. Physiol. Paris 109, 27–37 (2015).
Kelly, S. P. & O’Connell, R. G. Internal and external influences on the rate of sensory evidence accumulation in the human brain. J. Neurosci. 33, 19434–19441 (2013).
Twomey, D. M., Kelly, S. P. & O’Connell, R. G. Abstract and effector-selective decision signals exhibit qualitatively distinct dynamics before delayed perceptual reports. J. Neurosci. 36, 7346–7352 (2016).
Afacan-Seref, K., Steinemann, N. A., Blangero, A. & Kelly, S. P. Dynamic interplay of value and sensory information in high-speed decision making. Curr. Biol. 28, 795–802 (2018).
Heitz, R. P. & Schall, J. D. Neural mechanisms of speed-accuracy tradeoff. Neuron 76, 616–628 (2012).
Serences, J. T. Value-based modulations in human visual cortex. Neuron 60, 1169–1181 (2008).
Anderson, B. A., Laurent, P. A. & Yantis, S. Value-driven attentional capture. Proc. Natl Acad. Sci. USA 108, 10367–10371 (2011).
Ramkumar, P., Dekleva, B., Cooler, S., Miller, L. & Kording, K. Premotor and motor cortices encode reward. PLoS ONE 11, e0160851 (2016).
Spieser, L., Servant, M., Hasbroucq, T. & Burle, B. Beyond decision! Motor contribution to speed-accuracy trade-off in decision-making. Psychon. Bull. Rev. 24, 950–956 (2017).
Purcell, B. A., Schall, J. D., Logan, G. D. & Palmeri, T. J. From salience to saccades: multiple-alternative gated stochastic accumulator model of visual search. J. Neurosci. 32, 3433–3446 (2012).
Coles, M. G., Gratton, G., Bashore, T. R., Eriksen, C. W. & Donchin, E. A psychophysiological investigation of the continuous flow model of human information processing. J. Exp. Psychol. Hum. Percept. Perform. 11, 529–553 (1985).
Stanford, T. R., Shankar, S., Massoglia, D. P., Costello, M. G. & Salinas, E. Perceptual decision making in less than 30 milliseconds. Nat. Neurosci. 13, 379–385 (2010).
Gibbon, J. Scalar expectancy theory and Weber’s law in animal timing. Psychol. Rev. 84, 279–325 (1977).
Jazayeri, M. & Shadlen, M. N. Temporal context calibrates interval timing. Nat. Neurosci. 13, 1020–1026 (2010).
Dunovan, K. E., Tremel, J. J. & Wheeler, M. E. Prior probability and feature predictability interactively bias perceptual decisions. Neuropsychologia 61, 210–221 (2014).
Ratcliff, R. & Smith, P. L. A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111, 333–367 (2004).
de Gee, J. W. et al. Dynamic modulation of decision biases by brainstem arousal systems. eLife 6, e23232 (2017).
Urai, A. E., de Gee, J. W., Tsetsos, K. & Donner, T. H. Choice history biases subsequent evidence accumulation. eLife 8, e46331 (2019).
Katsimpokis, D., Hawkins, G. E. & van Maanen, L. Not all speed-accuracy trade-off manipulations have the same psychological effect. Comput. Brain Behav. https://doi.org/10.1007/s42113-020-00074-y (2020).
Purcell, B. A. & Palmeri, T. J. Relating accumulator model parameters and neural dynamics. J. Math. Psychol. 76, 156–171 (2017).
Wang, X.-J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).
Niyogi, R. K. & Wong-Lin, K. Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making. PLoS Comput. Biol. 9, e1003099 (2013).
Atiya, N. A. A., Rañó, I., Prasad, G. & Wong-Lin, K. A neural circuit model of decision uncertainty and change-of-mind. Nat. Commun. 10, 2287 (2019).
Loughnane, G. M. et al. Target selection signals influence perceptual decisions by modulating the onset and rate of evidence accumulation. Curr. Biol. 26, 496–502 (2016).
Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).
Wetherill, G. B. & Levitt, H. Sequential estimation of points on a psychometric function. Br. J. Math. Stat. Psychol. 18, 1–10 (1965).
Ratcliff, R. & Tuerlinckx, F. Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability. Psychon. Bull. Rev. 9, 438–481 (2002).
Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).
Widmann, A. & Schröger, E. Filter effects and filter artifacts in the analysis of electrophysiological data. Front. Psychol. 3, 233 (2012).
Kayser, J. & Tenke, C. E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin. Neurophysiol. 117, 348–368 (2006).
Twomey, D. M., Murphy, P. R., Kelly, S. P. & O’Connell, R. G. The classic P300 encodes a build-to-threshold decision variable. Eur. J. Neurosci. 42, 1636–1643 (2015).
Gratton, G., Coles, M. G., Sirevaag, E. J., Eriksen, C. W. & Donchin, E. Pre- and poststimulus activation of response channels: a psychophysiological analysis. J. Exp. Psychol. Hum. Percept. Perform. 14, 331–344 (1988).
Vidal, F., Grapperon, J., Bonnet, M. & Hasbroucq, T. The nature of unilateral motor commands in between-hand choice tasks as revealed by surface Laplacian estimation. Psychophysiology 40, 796–805 (2003).
Miller, J., Ulrich, R. & Schwarz, W. Why jackknifing yields good latency estimates. Psychophysiology 46, 300–312 (2009).
Pfurtscheller, G. & Lopes da Silva, F. H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857 (1999).
Busemeyer, J. R. & Diederich, A. Cognitive Modeling (SAGE, 2010).
Nelder, J. A. & Mead, R. A simplex method for function minimization. Comput. J. 7, 308–313 (1965).
Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).
Resulaj, A., Kiani, R., Wolpert, D. M. & Shadlen, M. N. Changes of mind in decision-making. Nature 461, 263–266 (2009).
Murphy, P. R., Robertson, I. H., Harty, S. & O'Connell, R. G. Neural evidence accumulation persists after choice to inform metacognitive judgments. eLife 4, e11946 (2015).
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.
Author information
Authors and Affiliations
Contributions
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information: Primary Handling Editor: Marike Schiffer.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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).
Supplementary information
Supplementary Information
Supplementary Tables 1–11 and Supplementary Figs. 1–7.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41562-020-00967-9
This article is cited by
-
Intracranial electroencephalography reveals effector-independent evidence accumulation dynamics in multiple human brain regions
Nature Human Behaviour (2024)
-
Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models
Computational Brain & Behavior (2024)
-
Proactive response preparation contributes to contingency learning: novel evidence from force-sensitive keyboards
Psychological Research (2024)
-
The interplay of contextual and immediate uncertainty: evidence for a common processing mechanism in prediction and cognitive control
Current Psychology (2024)
-
Movement characteristics impact decision-making and vice versa
Scientific Reports (2023)