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Deliberative control is more than just reactive: Insights from sequential sampling models
Published online by Cambridge University Press: 18 July 2023
Abstract
Activating relevant responses is a key function of automatic processes in De Neys's model; however, what determines the order or magnitude of such activation is ambiguous. Focusing on recently developed sequential sampling models of choice, we argue that proactive control shapes response generation but does not cleanly fit into De Neys's automatic-deliberative distinction, highlighting the need for further model development.
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- Copyright © The Author(s), 2023. Published by Cambridge University Press
References
Asplund, C. L., Todd, J. J., Snyder, A. P., & Marois, R. (2010). A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nature Neuroscience, 13(4), 507–512. https://doi.org/10.1038/nn.2509CrossRefGoogle ScholarPubMed
Braver, T. S. (2012). The variable nature of cognitive control: A dual mechanisms framework. Trends in Cognitive Sciences, 16(2), 106–113. https://doi.org/10.1016/j.tics.2011.12.010CrossRefGoogle ScholarPubMed
Braver, T. S., Gray, J. R., & Burgess, G. C. (2007). Explaining the many varieties of working memory variation: Dual mechanisms of cognitive control. Variation in Working Memory, 75, 106. http://doi.org/10.1093/acprof:oso/9780195168648.003.0004Google Scholar
Burianová, H., Ciaramelli, E., Grady, C. L., & Moscovitch, M. (2012). Top-down and bottom-up attention-to-memory: Mapping functional connectivity in two distinct networks that underlie cued and uncued recognition memory. NeuroImage, 63(3), 1343–1352. https://doi.org/10.1016/j.neuroimage.2012.07.057CrossRefGoogle ScholarPubMed
Ciaramelli, E., Grady, C. L., & Moscovitch, M. (2008). Top-down and bottom-up attention to memory: A hypothesis (AtoM) on the role of the posterior parietal cortex in memory retrieval. Neuropsychologia, 46(7), 1828–1851. https://doi.org/10.1016/j.neuropsychologia.2008.03.022CrossRefGoogle ScholarPubMed
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215. https://doi.org/10.1038/nrn755CrossRefGoogle ScholarPubMed
Cosme, D., Zeithamova, D., Stice, E., & Berkman, E. T. (2020). Multivariate neural signatures for health neuroscience: Assessing spontaneous regulation during food choice. Social Cognitive and Affective Neuroscience, 15(10), 1120–1134. https://doi.org/10.1093/scan/nsaa002CrossRefGoogle ScholarPubMed
Cunningham, W. A., & Zelazo, P. D. (2007). Attitudes and evaluations: A social cognitive neuroscience perspective. Trends in Cognitive Sciences, 11(3), 97–104. https://doi.org/10.1016/j.tics.2006.12.005CrossRefGoogle ScholarPubMed
Cunningham, W. A., Zelazo, P. D., Packer, D. J., & Van Bavel, J. J. (2007). The iterative reprocessing model: A multilevel framework for attitudes and evaluation. Social Cognition, 25(5), 736–760. http://doi.org/10.1521/soco.2007.25.5.736CrossRefGoogle Scholar
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.6383-10.2011CrossRefGoogle ScholarPubMed
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-yCrossRefGoogle ScholarPubMed
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https://doi.org/10.1162/neco.2008.12-06-420CrossRefGoogle ScholarPubMed
Roberts, I. D., Teoh, Y. Y., & Hutcherson, C. A. (2022). Time to pay attention? Information search explains amplified framing effects under time pressure. Psychological Science, 33(1), 90–104. https://doi.org/10.1177/09567976211026983CrossRefGoogle Scholar
Shadlen, M. N., & Shohamy, D. (2016). Decision making and sequential sampling from memory. Neuron, 90(5), 927–939. https://doi.org/10.1016/j.neuron.2016.04.036CrossRefGoogle ScholarPubMed
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(2), 122–134. https://doi.org/10.1177/0956797614559543CrossRefGoogle Scholar
Sullivan, N. J., & Huettel, S. A. (2021). Healthful choices depend on the latency and rate of information accumulation. Nature Human Behaviour, 5, 1698–1706. https://doi.org/10.1038/s41562-021-01154-0CrossRefGoogle ScholarPubMed
Teoh, Y. Y., & Hutcherson, C. A. (2022). The games we play: Prosocial choices under time pressure reflect context-sensitive information priorities. Psychological Science, 33(9), 1541–1556. https://doi.org/10.1177/0956797622109478CrossRefGoogle ScholarPubMed
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-xCrossRefGoogle ScholarPubMed
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.31185CrossRefGoogle ScholarPubMed
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