Theories and computational models of decision-making usually focus on how strongly different attributes are weighted in choice, for example, as a function of their importance or salience to the decision-maker. However, when different attributes affect the decision process is a question that has received far less attention. Here, we investigated whether the timing of attribute consideration has a unique influence on decision-making by using a time-varying drift diffusion model and data from four separate experiments. Experimental manipulations of attention and neural activity demonstrated that we can dissociate the processes that determine the relative weighting strength and timing of attribute consideration. Thus, the processes determining either the weighting strengths or the timing of attributes in decision-making can independently adapt to changes in the environment or goals. Quantifying these separate influences of timing and weighting on choice improves our understanding and predictions of individual differences in decision behaviour.
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The code for fitting the diffusion models and running the other analyses is openly available on the Open Science Framework at https://osf.io/g76fn/.
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This study has been supported by Swiss National Science Foundation (SNSF) grants 320030_143443 and 32003B_166566 (to C.C.R. and T.A.H.), a Marie Curie International Incoming Fellowship PIIF-GA-2012-327196 (to A.R.B.) and EU FP7 grant 607310 (to T.A.H.). C.C.R. received support from the SNSF (grant no. 100019L_173248) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725355, BRAINCODES). The funders had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript. The authors thank A. Makwana and A. Cubillo for contributing code for the food choice and control tasks, J. Price for help with data collection and documentation, and G. Lombardi for help with data collection and documentation as well as useful discussions about analysis strategies and implementation.
The authors declare no competing interests.
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Parameter recovery for the time-varying DDM with separate consideration onset times for tastiness and healthiness attributes. The plots in the first column show the distributions of all 272 generating and recovered relative weighting (a) and timing parameters (b). There was no significant difference between generating and recovered relative weighting (mean difference = 0.01, 95% HDI = [−0.36, 0.54], posterior probability of a difference > 0 = 0.662, Bayes factor = 0.140) or relative timing parameters (mean difference = −0.01, 95% HDI = [−0.03, 0.01], posterior probability of a difference > 0 = 0.105, Bayes factor = 0.024). The panels in the second column show the correlations between the generating and recovered relative weighting (c) and timing parameters (d). The red dotted line indicates the x = y identity line. Panel e) plots the error in relative weight recovery against the error in relative timing recovery. This plot shows that there is no significant correlation between the two types of error when fitting the model (r = − 0.1, 95% HDI = [−0.215; 0.018], posterior probability of observing a negative correlation = 0.95). The grey shaded area (panels c-e) signifies the 95% confidence interval.
Cumulative distributions for response times by participant type, choice outcome and data source. Participants estimated to consider taste or health first are plotted in the top and bottom rows, respectively. Response times for choices in favour of (91) less healthy but more tasty (LH_MT), (2) more healthy but less tasty (MH_LT), or (3) both more healthy and more tasty (MH_MT) outcomes are shown in columns 1-3, respectively. Choices in favour of the option rated as less healthy and less tasty were rarely made (less than 5% of trials) and are omitted for clarity. Responses generated by human participants are shown in green lines. Responses generated by simulated agents using the best-fitting sDDM, tDDM, and bDDM parameters are shown in orange, purple, and magenta lines respectively. All three models can recreate the RT patterns in the empirical data equally well when choice outcomes align with the attribute participants consider first. However, the sDDM and bDDM both generate response times that are too fast relative to the empirical data when participants that consider taste first ultimately choose in favour of a more healthy, but less tasty option (row 1, column 2) or if participants that consider health first ultimately choose in favour of a less healthy, but more tasty option (row 2, column 1). In contrast, the tDDM is able to reproduce the observed response time distributions in these cases well.
Relative start times in seconds for healthiness compared to tastiness for all participants in each study. Positive values indicate that tastiness is considered before healthiness and negative values that healthiness is considered before tastiness. In each column every dot is a separate participant. The thick black horizontal bars represent within-study means and the rectangular bands indicate the 95% highest density intervals (HDIs). Dataset abbreviations: MRT = data from the computer-mouse response trials in Sullivan et al 2015; IAC = data from the natural choice condition in Hare et al 2011; GFC = newly collected data from the first session/day of an experiment combining gambles and food choices; TDCS = newly collected data from the pre-stimulation baseline choices in our tDCS experiment.
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Maier, S.U., Raja Beharelle, A., Polanía, R. et al. Dissociable mechanisms govern when and how strongly reward attributes affect decisions. Nat Hum Behav 4, 949–963 (2020). https://doi.org/10.1038/s41562-020-0893-y
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