Is attentional discounting in value-based decision making magnitude sensitive?

References

• Ashby, N. J., Jekel, M., Dickert, S., & Glöckner, A. (2016). Finding the right fit: A comparison of process assumptions underlying popular drift-diffusion models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(12), 1982. https://doi.org/10.1037/xlm0000279
   Google Scholar
• 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
   Google Scholar
• Bose, T., Pirrone, A., Reina, A., & Marshall, J. A. (2020). Comparison of magnitude-sensitive sequential sampling models in a simulation-based study. Journal of Mathematical Psychology, 94, 102298. https://doi.org/10.1016/j.jmp.2019.102298
   Google Scholar
• Brunton, B. W., Botvinick, M. M., & Brody, C. D. (2013). Rats and humans can optimally accumulate evidence for decision-making. Science, 340(6128), 95–98. https://doi.org/10.1126/science.1233912
   Google Scholar
• Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143(4), 1476. https://doi.org/10.1037/a0035813
   Google Scholar
• Chen, W. J., & Krajbich, I. (2016). Pupil dilation and attention in value-based choice. Unpublished Manuscript, The Ohio State University.
   Google Scholar
• Dussutour, A., Ma, Q., & Sumpter, D. (2019). Phenotypic variability predicts decision accuracy in unicellular organisms. Proceedings of the Royal Society B, 286(1896), 20182825. https://doi.org/10.1098/rspb.2018.2825
   Google Scholar
• Gwinn, R., & Krajbich, I. (2020). Attitudes and attention. Journal of Experimental Social Psychology, 86, 103892. https://doi.org/10.1016/j.jesp.2019.103892
   Google Scholar
• Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.
   Google Scholar
• Hunt, L. T., Kolling, N., Soltani, A., Woolrich, M. W., Rushworth, M. F., & Behrens, T. E. (2012). Mechanisms underlying cortical activity during value-guided choice. Nature Neuroscience, 15(3), 470–476. https://doi.org/10.1038/nn.3017
   Google Scholar
• JASP Team. (2020). Jasp (version 0.12.2) [computer software]. https://jasp-stats.org/.
   Google Scholar
• Jeffreys, H. (1998). The theory of probability. OUP Oxford.
   Google Scholar
• 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
   Google Scholar
• Krajbich, I., Hare, T., Bartling, B., Morishima, Y., & Fehr, E. (2015). A common mechanism underlying food choice and social decisions. PLOS Computational Biology, 11(10), e1004371. https://doi.org/10.1371/journal.pcbi.1004371
   Google Scholar
• Krajbich, I., Lu, D., Camerer, C., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3, 193. https://doi.org/10.3389/fpsyg.2012.00193
   Google Scholar
• Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108(33), 13852–13857. https://doi.org/10.1073/pnas.1101328108
   Google Scholar
• Kvam, P. D., & Pleskac, T. J. (2016). Strength and weight: The determinants of choice and confidence. Cognition, 152, 170–180. https://doi.org/10.1016/j.cognition.2016.04.008
   Google Scholar
• Lee, M. D., & Wagenmakers, E.-J. (2014). Bayesian cognitive modeling: A practical course. Cambridge University Press.
   Google Scholar
• Louie, K., Khaw, M. W., & Glimcher, P. W. (2013). Normalization is a general neural mechanism for context-dependent decision making. Proceedings of the National Academy of Sciences, 110(15), 6139–6144. https://doi.org/10.1073/pnas.1217854110
   Google Scholar
• Lu, Z.-L., & Dosher, B. A. (2008). Characterizing observers using external noise and observer models: Assessing internal representations with external noise. Psychological Review, 115(1), 44. https://doi.org/10.1037/0033-295X.115.1.44
   Google Scholar
• 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.
   Google Scholar
• Pais, D., Hogan, P. M., Schlegel, T., Franks, N. R., Leonard, N. E., & Marshall, J. A. (2013). A mechanism for value-sensitive decision-making. PloS One, 8(9). https://doi.org/10.1371/journal.pone.0073216
   Google Scholar
• Pirrone, A., Azab, H., Hayden, B. Y., Stafford, T., & Marshall, J. A. (2018a). Evidence for the speed–value trade-off: Human and monkey decision making is magnitude sensitive. Decision, 5(2), 129. https://doi.org/10.1037/dec0000075
   Google Scholar
• Pirrone, A., & Gobet, F. (2020). Value-based decision making in naturalistic vs laboratory settings. Under Review.
   Google Scholar
• Pirrone, A., Stafford, T., & Marshall, J. A. (2014). When natural selection should optimize speed-accuracy trade-offs. Frontiers in Neuroscience, 8, 73. https://doi.org/10.3389/fnins.2014.00073
   Google Scholar
• Pirrone, A., Wen, W., & Li, S. (2018b). Single-trial dynamics explain magnitude sensitive decision making. BMC Neuroscience, 19(1), 1–10. https://doi.org/10.1186/s12868-018-0457-5
   Google Scholar
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59. https://doi.org/10.1037/0033-295X.85.2.59
   Google Scholar
• 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.2008.12-06-420
   Google Scholar
• Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20(4), 260–281. https://doi.org/10.1016/j.tics.2016.01.007
   Google Scholar
• Ratcliff, R., Voskuilen, C., & Teodorescu, A. (2018). Modeling 2-alternative forced-choice tasks: Accounting for both magnitude and difference effects. Cognitive Psychology, 103, 1–22. https://doi.org/10.1016/j.cogpsych.2018.02.002
   Google Scholar
• Smith, S. M., & Krajbich, I. (2018). Attention and choice across domains. Journal of Experimental Psychology: General, 147(12), 1810. https://doi.org/10.1037/xge0000482
   Google Scholar
• Smith, S. M., & Krajbich, I. (2019). Gaze amplifies value in decision making. Psychological Science, 30(1), 116–128. https://doi.org/10.1177/0956797618810521
   Google Scholar
• Smith, S. M., Krajbich, I., & Webb, R. (2019). Estimating the dynamic role of attention via random utility. Journal of the Economic Science Association, 5(1), 97–111. https://doi.org/10.1007/s40881-019-00062-4
   Google Scholar
• Steverson, K., Chung, H.-K., Zimmermann, J., Louie, K., & Glimcher, P. (2019). Sensitivity of reaction time to the magnitude of rewards reveals the cost-structure of time. Scientific Reports, 9(1), 1–14. https://doi.org/10.1038/s41598-019-56392-0
   Google Scholar
• Tavares, G., Perona, P., & Rangel, A. (2017). The attentional drift diffusion model of simple perceptual decision-making. Frontiers in Neuroscience, 11, 468. https://doi.org/10.3389/fnins.2017.00468
   Google Scholar
• Teodorescu, A. R., Moran, R., & Usher, M. (2016). Absolutely relative or relatively absolute: Violations of value invariance in human decision making. Psychonomic Bulletin & Review, 23(1), 22–38. https://doi.org/10.3758/s13423-015-0858-8
   Google Scholar
• Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550. https://doi.org/10.1037/0033-295X.108.3.550
   Google Scholar
• Webb, R. (2019). The (neural) dynamics of stochastic choice. Management Science, 65(1), 230–255. https://doi.org/10.1287/mnsc.2017.2931
   Google Scholar