Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches.
decision making
drift diffusion model
electroencephalography
evidence accumulation
Journal
Wiley interdisciplinary reviews. Cognitive science
ISSN: 1939-5086
Titre abrégé: Wiley Interdiscip Rev Cogn Sci
Pays: United States
ID NLM: 101524169
Informations de publication
Date de publication:
May 2022
May 2022
Historique:
revised:
06
10
2021
received:
27
02
2021
accepted:
18
10
2021
pubmed:
3
12
2021
medline:
25
5
2022
entrez:
2
12
2021
Statut:
ppublish
Résumé
As interest in the temporal dynamics of decision-making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value-based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well-understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice-triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300-500 ms after stimulus onset. These findings can inform psychological theories of decision-making, providing empirical support for attribute weighting in value-based choice while suggesting theoretical alternatives to dual-process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment-by-moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision-Making.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1586Informations de copyright
© 2021 Wiley Periodicals LLC.
Références
Afacan-Seref, K., Steinemann, N. A., Blangero, A., & Kelly, S. P. (2018). Dynamic interplay of value and sensory information in high-speed decision making. Current Biology, 28(5), 795-802. https://doi.org/10.1016/j.cub.2018.01.071
Amasino, D. R., Sullivan, N. J., Kranton, R. E., & Huettel, S. A. (2019). Amount and time exert independent influences on intertemporal choice. Nature Human Behaviour, 3(4), 383-392. https://doi.org/10.1038/s41562-019-0537-2
Baron, J., & Gürçay, B. (2017). A meta-analysis of response-time tests of the sequential two-systems model of moral judgment. Memory and Cognition, 45(4), 566-575. https://doi.org/10.3758/s13421-016-0686-8
Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. NeuroImage, 76, 412-427. https://doi.org/10.1016/j.neuroimage.2013.02.063
Basten, U., Biele, G., Heekeren, H. R., & Fiebach, C. J. (2010). How the brain integrates costs and benefits during decision making. Proceedings of the National Academy of Sciences of the United States of America, 107(50), 21767-21772. https://doi.org/10.1073/pnas.0908104107
Berkman, E. T., Hutcherson, C. A., Livingston, J. L., Kahn, L. E., & Inzlicht, M. (2017). Self-control as value-based choice. Current Directions in Psychological Science, 26(5), 422-428. https://doi.org/10.1177/0963721417704394
Bhanji, J. P., & Beer, J. S. (2012). Taking a different perspective: Mindset influences neural regions that represent value and choice. Social Cognitive and Affective Neuroscience, 7(7), 782-793. https://doi.org/10.1093/scan/nsr062
Bode, S., Sewell, D. K., Lilburn, S., Forte, J. D., Smith, P. L., & Stahl, J. (2012). Predicting perceptual decision biases from early brain activity. Journal of Neuroscience, 32, 12488-12498. https://doi.org/10.1523/JNEUROSCI.1708-12.2012
Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113(4), 700-765. https://doi.org/10.1037/0033-295X.113.4.700
Bogacz, R., Wagenmakers, E.-J., Forstmann, B. U., & Nieuwenhuis, S. (2010). The neural basis of the speed-accuracy tradeoff. Trends in Neuroscience, 33(1), 10-16. https://doi.org/10.1016/j.tins.2009.09.002
Boswell, R. G., Sun, W., Suzuki, S., & Kober, H. (2018). Training in cognitive strategies reduces eating and improves food choice. Proceedings of the National Academy of Sciences, 115(48), E11238-E11247. https://doi.org/10.1073/pnas.1717092115
Brown, S. D., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57(3), 153-178. https://doi.org/10.1016/j.cogpsych.2007.12.002
Buschman, T. J., & Kastner, S. (2015). From behavior to neural dynamics: An integrated theory of attention. Neuron, 88(1), 127-144. https://doi.org/10.1016/j.neuron.2015.09.017
Busemeyer, J. R., Gluth, S., Rieskamp, J., & Turner, B. M. (2019). Cognitive and neural bases of multi-attribute, multi-alternative, value-based decisions. Trends in Cognitive Sciences, 23(3), 251-263. https://doi.org/10.1016/j.tics.2018.12.003
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432-459. https://doi.org/10.1037/0033-295X.100.3.432
Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407-420. https://doi.org/10.1038/nrn3241
Castegnetti, G., Tzovara, A., Khemka, S., Melinščak, F., Barnes, G. R., Dolan, R. J., & Bach, D. R. (2020). Representation of probabilistic outcomes during risky decision-making. Nature Communications, 11(1), 1-11. https://doi.org/10.1038/s41467-020-16202-y
Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18(8), 414-421. https://doi.org/10.1016/j.tics.2014.04.012
Cavanagh, J. F., Wiecki, T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S. J., & Frank, M. J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14(11), 1462-1467. https://doi.org/10.1038/nn.2925
Cisek, P., Puskas, G. A., & El-Murr, S. (2009). Decisions in changing conditions: The urgency-gating model. Journal of Neuroscience, 29(37), 11560-11571. https://doi.org/10.1523/JNEUROSCI.1844-09.2009
Clayton, M. S., Yeung, N., & Kadosh, R. C. (2015). The roles of cortical oscillations in sustained attention. Trends in Cognitive Sciences, 19(4), 188-195. https://doi.org/10.1016/j.tics.2015.02.004
Clithero, J. A., & Rangel, A. (2014). Informatic parcellation of the network involved in the computation of subjective value. Social Cognitive and Affective Neuroscience, 9(9), 1289-1302. https://doi.org/10.1093/scan/nst106
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review, 97(3), 332-361. https://doi.org/10.1037/0033-295X.97.3.332
Cohen, M. X. (2014). A neural microcircuit for cognitive conflict detection and signaling. Trends in Neurosciences, 37(9), 480-490. https://doi.org/10.1016/j.tins.2014.06.004
Coles, M. G., Gratton, G., Bashore, T. R., Eriksen, C. W., & Donchin, E. (1985). A psychophysiological investigation of the continuous flow model of human information processing. Journal of Experimental Psychology: Human Perception and Performance, 11(5), 529-553. https://doi.org/10.1037//0096-1523.11.5.529
de Lange, F. P., Rahnev, D. A., Donner, T. H., & Lau, H. (2013). Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. Journal of Neuroscience, 33(4), 1400-1410. https://doi.org/10.1523/JNEUROSCI.1094-12.2013
Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological Review, 125(2), 270-292. https://doi.org/10.1037/rev0000087
Dimigen, O., Sommer, W., Hohlfeld, A., Jacobs, A. M., & Kliegl, R. (2011). Coregistration of eye movements and EEG in natural reading: Analyses and review. Journal of Experimental Psychology: General, 140(4), 552-572. https://doi.org/10.1037/a0023885
Domenech, P., & Dreher, J.-C. (2010). Decision threshold modulation in the human brain. Journal of Neuroscience, 30(43), 14305-14317. https://doi.org/10.1523/JNEUROSCI.4010-11.2012
Donner, T. H., Siegel, M., Fries, P., & Engel, A. K. (2009). Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Current Biology, 19(18), 1581-1585. https://doi.org/10.1016/j.cub.2009.07.066
Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012). The cost of accumulating evidence in perceptual decision making. Journal of Neuroscience, 32(11), 3612-3628. https://doi.org/10.1523/JNEUROSCI.4010-11.2012
Engel, A. K., & Fries, P. (2010). Beta-band oscillations-Signalling the status quo? Current Opinion in Neurobiology, 20(2), 156-165. https://doi.org/10.1016/j.conb.2010.02.015
Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspective taking as egocentric anchoring and adjustment. Journal of Personality and Social Psychology, 87(3), 327-339. https://doi.org/10.1037/0022-3514.87.3.327
Evans, A. M., Dillon, K. D., & Rand, D. G. (2015). Fast but not intuitive, slow but not reflective: Decision conflict drives reaction times in social dilemmas. Journal of Experimental Psychology: General, 144(5), 951-966. https://doi.org/10.1037/xge0000107
Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223-241. https://doi.org/10.1177/1745691612460685
Forstmann, B. U., Anwander, A., Schäfer, A., Neumann, J., Brown, S., Wagenmakers, E.-J., Bogacz, R., & Turner, R. (2010). Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proceedings of the National Academy of Sciences, 107(36), 15916-15920. https://doi.org/10.1073/pnas.1004932107
Forstmann, B. U., Ratcliff, R., & Wagenmakers, E. J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual Review of Psychology, 67, 641-666. https://doi.org/10.1146/annurev-psych-122414-033645
Foxe, J. J., & Snyder, A. C. (2011). The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Frontiers in Psychology, 2, 154. https://doi.org/10.3389/fpsyg.2011.00154
Frank, M. J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T. V., Cavanagh, J. F., & Badre, D. (2015). fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. Journal of Neuroscience, 35(2), 485-494. https://doi.org/10.1523/JNEUROSCI.2036-14.2015
Fries, P. (2009). Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annual Review of Neuroscience, 32, 209-224. https://doi.org/10.1146/annurev.neuro.051508.135603
Gluth, S., Rieskamp, J., & Büchel, C. (2013). Classic EEG motor potentials track the emergence of value-based decisions. NeuroImage, 79, 394-403. https://doi.org/10.1016/j.neuroimage.2013.05.005
Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535-574. https://doi.org/10.1146/annurev.neuro.29.051605.113038
Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988). Pre-and poststimulus activation of response channels: A psychophysiological analysis. Journal of Experimental Psychology: Human Perception and Performance, 14(3), 331-344. https://doi.org/10.1037/0096-1523.14.3.331
Grice, G. R. (1968). Stimulus intensity and response evocation. Psychological Review, 75(5), 359-373. https://doi.org/10.1037/h0026287
HajiHosseini, A., & Hutcherson, C. A. (2021). Alpha oscillations and event-related potentials reflect distinct dynamics of attribute construction and evidence accumulation in dietary decision making. eLife, 10, e60874. https://doi.org/10.7554/elife.60874
Hanks, T. D., Kiani, R., & Shadlen, M. N. (2014). A neural mechanism of speed-accuracy tradeoff in macaque area LIP. eLife, 3, e02260. https://doi.org/10.7554/eLife.02260
Hanks, T. D., Kopec, C. D., Brunton, B. W., Duan, C. A., Erlich, J. C., & Brody, C. D. (2015). Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature, 520(7546), 220-223. https://doi.org/10.1038/nature14066
Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science, 324(5927), 646-648. https://doi.org/10.1126/science.1168450
Hare, T. A., Malmaud, J., & Rangel, A. (2011). Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice. Journal of Neuroscience, 31(30), 11077-11087. https://doi.org/10.1523/JNEUROSCI.6383-10.2011
Hare, T. A., Schultz, W., Camerer, C. F., O'Doherty, J. P., & Rangel, A. (2011). Transformation of stimulus value signals into motor commands during simple choice. Proceedings of the National Academy of Sciences, 108(44), 18120-18125. https://doi.org/10.1073/pnas.1109322108
Hari, R. (2006). Action-perception connection and the cortical mu rhythm. Progress in Brain Research, 159, 253-260. https://doi.org/10.1016/S0079-6123(06)59017-X
Harris, A., Adolphs, R., Camerer, C., & Rangel, A. (2011). Dynamic construction of stimulus values in the ventromedial prefrontal cortex. PLoS One, 6(6), e21074. https://doi.org/10.1371/journal.pone.0021074
Harris, A., Clithero, J. A., & Hutcherson, C. A. (2018). Accounting for taste: A multi-attribute neurocomputational model explains the neural dynamics of choices for self and others. Journal of Neuroscience, 38(37), 7952-7968. https://doi.org/10.1523/JNEUROSCI.3327-17.2018
Harris, A., Hare, T., & Rangel, A. (2013). Temporally dissociable mechanisms of self-control: Early attentional filtering versus late value modulation. Journal of Neuroscience, 33(48), 18917-18931. https://doi.org/10.1523/JNEUROSCI.5816-12.2013
Harris, A., & Lim, S. L. (2016). Temporal dynamics of sensorimotor networks in effort-based cost-benefit valuation: Early emergence and late net value integration. Journal of Neuroscience, 36, 7167-7183. https://doi.org/10.1523/JNEUROSCI.4016-15.2016
Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. Journal of Neuroscience, 35(6), 2476-2484. https://doi.org/10.1523/JNEUROSCI.2410-14.2015
Heekeren, H. R., Marrett, S., Bandettini, P. A., & Ungerleider, L. G. (2004). A general mechanism for perceptual decision-making in the human brain. Nature, 431(7010), 859-862. https://doi.org/10.1038/nature02966
Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences, 95(3), 781-787. https://doi.org/10.1073/pnas.95.3.781
Hofmann, W., Friese, M., & Strack, F. (2009). Impulse and self-control from a dual-systems perspective. Perspectives on Psychological Science, 4(2), 162-176. https://doi.org/10.1111/j.1745-6924.2009.01116.x
Hunt, L. T., Kolling, N., Soltani, A., Woolrich, M. W., Rushworth, M. F. S., & Behrens, T. E. J. (2012). Mechanisms underlying cortical activity during value-guided choice. Nature Neuroscience, 15(3), 470-476. https://doi.org/10.1038/nn.3017
Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic choice and its implications. Neuron, 87(2), 451-462. https://doi.org/10.1016/j.neuron.2015.06.031
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Strauss and Giroux.
Kelly, S. P., Corbett, E. A., & O'Connell, R. G. (2020). Neurocomputational mechanisms of prior-informed perceptual decision-making in humans. Nature Human Behaviour, 5, 467-481. https://doi.org/10.1038/s41562-020-00967-9
Kelly, S. P., & O'Connell, R. G. (2013). Internal and external influences on the rate of sensory evidence accumulation in the human brain. Journal of Neuroscience, 33(50), 19434-19441. https://doi.org/10.1523/JNEUROSCI.3355-13.2013
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2-3), 169-195. https://doi.org/10.1016/S0165-0173(98)00056-3
Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition-timing hypothesis. Brain Research Reviews, 53(1), 63-88. https://doi.org/10.1016/j.brainresrev.2006.06.003
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292-1298. https://doi.org/10.1038/nn.2635
Krajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015). Rethinking fast and slow based on a critique of reaction-time reverse inference. Nature Communications, 6(1), 1-9. https://doi.org/10.1038/ncomms8455
Larsen, T., & O'Doherty, J. P. (2014). Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: A combined fMRI-EEG study. Philosophical Transactions of the Royal Society B: Biological Sciences, 369, 20130473. https://doi.org/10.1098/rstb.2013.0473
Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2015). Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science, 349(6244), 184-187. https://doi.org/10.1126/science.aad3596
Lim, S.-L., Penrod, M. T., Ha, O.-R., Bruce, J. M., & Bruce, A. S. (2018). Calorie labeling promotes dietary self-control by shifting the temporal dynamics of health-and taste-attribute integration in overweight individuals. Psychological Science, 29(3), 447-462. https://doi.org/10.1177/0956797617737871
Lou, B., Li, Y., Philiastides, M. G., & Sajda, P. (2014). Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making. NeuroImage, 87, 242-251. https://doi.org/10.1016/j.neuroimage.2013.10.041
Loughnane, G. M., Newman, D. P., Bellgrove, M. A., Lalor, E. C., Kelly, S. P., & O'Connell, R. G. (2016). Target selection signals influence perceptual decisions by modulating the onset and rate of evidence accumulation. Current Biology, 26(4), 496-502. https://doi.org/10.1016/j.cub.2015.12.049
Luck, S. J. (2014). An introduction to the event-related potential technique. MIT Press.
Maier, S. U., Raja Beharelle, A., Polanía, R., Ruff, C. C., & Hare, T. A. (2020). Dissociable mechanisms govern when and how strongly reward attributes affect decisions. Nature Human Behaviour, 4(9), 949-963. https://doi.org/10.1038/s41562-020-0893-y
Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgment and Decision making, 5(6), 437-449. https://doi.org/10.2139/ssrn.1901533
Mostert, P., Kok, P., & De Lange, F. P. (2015). Dissociating sensory from decision processes in human perceptual decision making. Scientific Reports, 5(1), 1-13. https://doi.org/10.1038/srep18253
Mulder, M. J., Wagenmakers, E. J., Ratcliff, R., Boekel, W., & Forstmann, B. U. (2012). Bias in the brain: A diffusion model analysis of prior probability and potential payoff. Journal of Neuroscience, 32(7), 2335-2343. https://doi.org/10.1523/JNEUROSCI.4156-11.2012
Murphy, P. R., Boonstra, E., & Nieuwenhuis, S. (2016). Global gain modulation generates time-dependent urgency during perceptual choice in humans. Nature Communications, 7, 13526. https://doi.org/10.1038/ncomms13526
Nunez, M. D., Gosai, A., Vandekerckhove, J., & Srinivasan, R. (2019). The latency of a visual evoked potential tracks the onset of decision making. NeuroImage, 197, 93-108. https://doi.org/10.1016/j.neuroimage.2019.04.052
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76(Pt B, 117-130. https://doi.org/10.1016/j.jmp.2016.03.003
O'Connell, R. G., Dockree, P. M., & Kelly, S. P. (2012). A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nature Neuroscience, 15(12), 1729-1735. https://doi.org/10.1038/nn.3248
Palestro, J. J., Weichart, E., Sederberg, P. B., & Turner, B. M. (2018). Some task demands induce collapsing bounds: Evidence from a behavioral analysis. Psychonomic Bulletin & Review, 25(4), 1225-1248. https://doi.org/10.3758/s13423-018-1479-9
Palmeri, T. J., Love, B. C., & Turner, B. M. (2017). Model-based cognitive neuroscience. Journal of Mathematical Psychology, 76(B, 59-64. https://doi.org/10.1016/j.jmp.2016.10.010
Parnamets, P., Johansson, P., Hall, L., Spivey, M. J., & Richardson, D. C. (2014). Gaze and timing influences moral choices. Proceedings of the National Academy of Sciences, 112(13), 4170-4175. https://doi.org/10.1037/e512142015-275
Payne, L., & Sekuler, R. (2014). The importance of ignoring: Alpha oscillations protect selectivity. Current Directions in Psychological Science, 23(3), 171-177. https://doi.org/10.1177/0963721414529145
Philiastides, M. G., Biele, G., & Heekeren, H. R. (2010). A mechanistic account of value computation in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 107(20), 9430-9435. https://doi.org/10.1073/pnas.1001732107
Philiastides, M. G., Heekeren, H. R., & Sajda, P. (2014). Human scalp potentials reflect a mixture of decision- related signals during perceptual choices. Journal of Neuroscience, 34(50), 16877-16889. https://doi.org/10.1523/JNEUROSCI.3012-14.2014
Philiastides, M. G., & Sajda, P. (2006). Temporal characterization of the neural correlates of perceptual decision making in the human brain. Cerebral Cortex, 16(4), 509-518. https://doi.org/10.1093/cercor/bhi130
Pisauro, M. A., Fouragnan, E., Retzler, C., & Philiastides, M. G. (2017). Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous EEG-fMRI. Nature Communications, 8(1), 1-9. https://doi.org/10.1038/ncomms15808
Polanía, R., Krajbich, I., Grueschow, M., & Ruff, C. C. (2014). Neural oscillations and synchronization differentially support evidence accumulation in perceptual and value-based decision making. Neuron, 82(3), 709-720. https://doi.org/10.1016/j.neuron.2014.03.014
Polanía, R., Moisa, M., Opitz, A., Grueschow, M., & Ruff, C. C. (2015). The precision of value-based choices depends causally on fronto-parietal phase coupling. Nature Communications, 6(1), 1-10. https://doi.org/10.1038/ncomms9090
Purcell, B. A., Heitz, R. P., Cohen, J. Y., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2010). Neurally constrained modeling of perceptual decision making. Psychological Review, 117(4), 1113-1143. https://doi.org/10.1037/a0020311
Rae, B., Heathcote, A., Donkin, C., Averell, L., & Brown, S. (2014). The hare and the tortoise: Emphasizing speed can change the evidence used to make decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(5), 1226-1243. https://doi.org/10.1037/a0036801
Rand, D. G., Greene, J. D., & Nowak, M. A. (2012). Spontaneous giving and calculated greed. Nature, 489(7416), 427-430. https://doi.org/10.1038/nature11467
Rangel, A., & Clithero, J. A. (2014). The computation of stimulus values in simple choice. In Neuroeconomics: Decision making and the brain (pp. 125-148). Elsevier.
Ratcliff, R., Hasegawa, Y. T., Hasegawa, R. P., Smith, P. L., & Segraves, M. A. (2007). Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. Journal of Neurophysiology, 97(2), 1756-1774. https://doi.org/10.1152/jn.00393.2006
Ratcliff, R., Philiastides, M. G., & Sajda, P. (2009). Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proceedings of the National Academy of Sciences of the United States of America, 106(16), 6539-6544. https://doi.org/10.1073/pnas.0812589106
Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347-356. https://doi.org/10.1111/1467-9280.00067
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20, 260-281. https://doi.org/10.1016/j.tics.2016.01.007
Reeck, C., Wall, D., & Johnson, E. J. (2017). Search predicts and changes patience in intertemporal choice. Proceedings of the National Academy of Sciences, 114(45), 11890-11895. https://doi.org/10.1073/pnas.1707040114
Ridderinkhof, K. R., Ramautar, J. R., & Wijnen, J. G. (2009). To PE or not to PE: A P3-like ERP component reflecting the processing of response errors. Psychophysiology, 46(3), 531-538. https://doi.org/10.1111/j.1469-8986.2009.00790.x
Rodriguez, C. A., Turner, B. M., Van Zandt, T., & McClure, S. M. (2015). The neural basis of value accumulation in intertemporal choice. European Journal of Neuroscience, 42(5), 2179-2189. https://doi.org/10.1111/ejn.12997
Roux, F., & Uhlhaas, P. J. (2014). Working memory and neural oscillations: Alpha-gamma versus theta-gamma codes for distinct WM information? Trends in Cognitive Sciences, 18(1), 16-25. https://doi.org/10.1016/j.tics.2013.10.010
Ruff, C. C., & Huettel, S. A. (2014). Experimental methods in cognitive neuroscience. In P. W. Glimcher & E. Fehr (Eds.), Neuroeconomics: Decision making and the brain (2nd ed., pp. 77-108). Elsevier.
Saez, I., Lin, J., Stolk, A., Chang, E., Parvizi, J., Schalk, G., Knight, R. T., & Hsu, M. (2018). Encoding of multiple reward-related computations in transient and sustained high-frequency activity in human OFC. Current Biology, 28(18), 2889-2899. https://doi.org/10.1016/j.cub.2018.07.045
Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R. (2019). Beta oscillations in working memory, executive control of movement and thought, and sensorimotor function. Journal of Neuroscience, 39(42), 8231-8238. https://doi.org/10.1523/JNEUROSCI.1163-19.2019
Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of Neurophysiology, 86(4), 1916-1936. https://doi.org/10.1152/jn.2001.86.4.1916
Siegel, M., Donner, T. H., & Engel, A. K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience, 13(2), 121-134. https://doi.org/10.1038/nrn3137
Simen, P., Contreras, D., Buck, C., Hu, P., Holmes, P., & Cohen, J. D. (2009). Reward rate optimization in two-alternative decision making: Empirical tests of theoretical predictions. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1865-1897. https://doi.org/10.1037/a0016926
Smulders, F. T. Y., & Miller, J. O. (2012). The lateralized readiness potential. In S. J. Luck & E. S. Kappenman, The Oxford handbook of event-related potential components (pp. 209-229). Oxford University Press.
Spitzer, B., & Haegens, S. (2017). Beyond the status quo: A role for beta oscillations in endogenous content (re) activation. ENeuro, 4(4), ENEURO.0170. https://doi.org/10.1523/ENEURO.0170-17.2017
Steinhauser M., Yeung N. (2010). Decision Processes in Human Performance Monitoring. Journal of Neuroscience, 30(46), 15643-15653. https://doi.org/10.1523/jneurosci.1899-10.2010
Steinemann, N. A., O'Connell, R. G., & Kelly, S. P. (2018). Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy. Nature Communications, 9(1), 3627. https://doi.org/10.1038/s41467-018-06117-0
Sullivan, N., Hutcherson, C., Harris, A., & Rangel, A. (2015). Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed. Psychological Science, 26, 122-134. https://doi.org/10.1177/0956797614559543
Sun, P., & Landy, M. S. (2016). A two-stage process model of sensory discrimination: An alternative to drift-diffusion. Journal of Neuroscience, 36(44), 11259-11274.
Tajima, S., Drugowitsch, J., & Pouget, A. (2016). Optimal policy for value-based decision-making. Nature Communications, 7(1), 1-12. https://doi.org/10.1038/ncomms12400
Tamir, D. I., & Mitchell, J. P. (2013). Anchoring and adjustment during social inferences. Journal of Experimental Psychology: General, 142(1), 151-162. https://doi.org/10.1037/a0028232
Teoh, Y. Y., Yao, Z., Cunningham, W. A., & Hutcherson, C. A. (2020). Attentional priorities drive effects of time pressure on altruistic choice. Nature Communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-17326-x
Towal, R. B., Mormann, M., & Koch, C. (2013). Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proceedings of the National Academy of Sciences of the United States of America, 110(40), E3858-E3867. https://doi.org/10.1073/pnas.1304429110
Trueblood, J. S., Heathcote, A., Evans, N. J., & Holmes, W. R. (2021). Urgency, leakage, and the relative nature of information processing in decision-making. Psychological Review, 128(1), 160-186. https://doi.org/10.1037/rev0000255
Tsetsos, K., Gao, J., McClelland, J. L., & Usher, M. (2012). Using time-varying evidence to test models of decision dynamics: Bounded diffusion vs. the leaky competing accumulator model. Frontiers in Neuroscience, 6, 79. https://doi.org/10.3389/fnins.2012.00079
Turner, B. M., Van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: The neural drift diffusion model. Psychological Review, 122(2), 312-336. https://doi.org/10.1037/a0038894
Tusche, A., & Hutcherson, C. A. (2018). Cognitive regulation alters social and dietary choice by changing attribute representations in domain-general and domain-specific brain circuits. eLife, 7, e31185. https://doi.org/10.7554/eLife.31185
Twomey, D. M., Murphy, P. R., Kelly, S. P., & O'Connell, R. G. (2015). The classic P300 encodes a build-to-threshold decision variable. European Journal of Neuroscience, 42(1), 1636-1643. https://doi.org/10.1111/ejn.12936
Ullsperger, M., Fischer, A. G., Nigbur, R., & Endrass, T. (2014). Neural mechanisms and temporal dynamics of performance monitoring. Trends in Cognitive Sciences, 18(5), 259-267. https://doi.org/10.1016/j.tics.2014.02.009
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550-592. https://doi.org/10.1037/0033-295X.108.3.550
van Veen, V., Krug, M. K., & Carter, C. S. (2008). The neural and computational basis of controlled speed-accuracy tradeoff during task performance. Journal of Cognitive Neuroscience, 20(11), 1952-1965. https://doi.org/10.1162/jocn.2008.20146
van Vugt, M. K., Simen, P., Nystrom, L., Holmes, P., & Cohen, J. D. (2014). Lateralized readiness potentials reveal properties of a neural mechanism for implementing a decision threshold. PLoS One, 9(3), e90943. https://doi.org/10.1371/journal.pone.0090943
Van Zandt, T., & Ratcliff, R. (1995). Statistical mimicking of reaction time data: Single-process models, parameter variability, and mixtures. Psychonomic Bulletin & Review, 2(1), 20-54. https://doi.org/10.3758/BF03214411
Verleger, R. (2020). Effects of relevance and response frequency on P3b amplitudes: Review of findings and comparison of hypotheses about the process reflected by P3b. Psychophysiology, 57(7), e13542. https://doi.org/10.1111/psyp.13542
von Lautz, A., Herding, J., & Blankenburg, F. (2019). Neuronal signatures of a random-dot motion comparison task. NeuroImage, 193, 57-66. https://doi.org/10.1016/j.neuroimage.2019.02.071
Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5), 955-968. https://doi.org/10.1016/S0896-6273(02)01092-9
Womelsdorf, T., & Fries, P. (2007). The role of neuronal synchronization in selective attention. Current Opinion in Neurobiology, 17(2), 154-160. https://doi.org/10.1016/j.conb.2007.02.002
Wyart, V., De Gardelle, V., Scholl, J., & Summerfield, C. (2012). Rhythmic fluctuations in evidence accumulation during decision making in the human brain. Neuron, 76(4), 847-858. https://doi.org/10.1016/j.neuron.2012.09.015
Yau, Y., Hinault, T., Taylor, M., Cisek, P., Fellows, L. K., & Dagher, A. (2021). Evidence and urgency related EEG signals during dynamic decision-making in humans. Journal of Neuroscience, 41(26), 5711-5722. https://doi.org/10.1523/JNEUROSCI.2551-20.2021
Yu, H., Siegel, J., Clithero, J., & Crockett, M. (2021). How peer influence shapes value computation in moral decision-making. Cognition, 211, 104641. https://doi.org/10.31234/osf.io/hg79v