Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Dec 6:7:e40583.
doi: 10.7554/eLife.40583.

Promoting subjective preferences in simple economic choices during nap

Sizhi Ai #  1   2 , Yunlu Yin #  3   4 , Yu Chen  1 , Cong Wang  5   6   7 , Yan Sun  1 , Xiangdong Tang  8 , Lin Lu  1   5   7   9 , Lusha Zhu  3   5   7   10 , Jie Shi  1   11   12   13
Affiliations

Promoting subjective preferences in simple economic choices during nap

Sizhi Ai et al. Elife. .

Abstract

Sleep is known to benefit consolidation of memories, especially those of motivational relevance. Yet, it remains largely unknown the extent to which sleep influences reward-associated behavior, in particular, whether and how sleep modulates reward evaluation that critically underlies value-based decisions. Here, we show that neural processing during sleep can selectively bias preferences in simple economic choices when the sleeper is stimulated by covert, reward-associated cues. Specifically, presenting the spoken name of a familiar, valued snack item during midday nap significantly improves the preference for that item relative to items not externally cued. The cueing-specific preference enhancement is sleep-dependent and can be predicted by cue-induced neurophysiological signals at the subject and item level. Computational modeling further suggests that sleep cueing accelerates evidence accumulation for cued options during the post-sleep choice process in a manner consistent with the preference shift. These findings suggest that neurocognitive processing during sleep contributes to the fine-tuning of subjective preferences in a flexible, selective manner.

Keywords: decision-making; human; neuroscience; simple economic choices; sleep; subjective preferences.

PubMed Disclaimer

Conflict of interest statement

SA, YY, YC, CW, YS, XT, LL, LZ, JS No competing interests declared

Figures

Figure 1.
Figure 1.. Task Illustration.
(A) BDM auction. In each trial, participants were presented with a familiar snack item on a computer screen for 4 s, accompanied by the spoken name of the item delivered over a speaker once. Then, participants were asked to report their willingness to pay for the item. (B) Binary choice task. Participants were presented with two snack items that were matched by baseline WTP (i.e. WTP1) and differed by whether they had or had not been named during verbal stimulation. (C) Timeline of the experiment. Subjects first indicated their baseline WTP (i.e. WTP1) for a set of 60 familiar snacks in the first BDM auction. After a break, subjects entered a verbal stimulation session, during which they were randomly divided into the sleep (N = 47) or wake (N = 45) group. Sleep group participants took a 90-min nap. The names of 8 snacks were broadcast during N2 sleep, each with ten repetitions. Wake group participants received the same stimulation for the same number of snacks at approximately the same time that verbal cues were delivered to the sleep group. After a break, all subjects underwent a second BDM auction followed by a binary choice task.
Figure 2.
Figure 2.. Effects of verbal cueing on preferences and choices.
(A) Effects of cueing on WTP. ΔWTP is defined as the paired difference of an item’s WTP elicited by the first and second BDM auction (i.e. WTP2 – WTP1). Relative to WTP1 (¥7.02 ± ¥0.16), cueing during sleep promotes preferences by 8.3% (±1.3%) for cued items more than for items not externally cued, whereas during wakefulness cueing increases WTP by 3.5% (±2.1%) for cued relative to uncued items. (B) Effects of cueing on choices in the binary decision task. Histograms illustrate the percentage of participants for each bin of choice likelihood. The average frequency of selecting the cued item was higher in the sleep group than in the wake group (Insert). Error bars indicate S.E.M.
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Results of permutation test for the effect of verbal cueing on ΔWTP.
Histograms depict the average difference in ΔWTP for cued and uncued items between the sleep and wake group based on 1000 permutations of the dataset in which the cohort labels of sleep and wake were randomly shuffled across subjects. The black line represents the actual data.
Figure 2—figure supplement 2.
Figure 2—figure supplement 2.. Detailed effects of verbal cueing on ΔWTP.
ΔWTP is defined as the paired difference of an item’s WTP before and after the verbal stimulation (i.e., WTP2 – WTP1). Each dot indicates a subject. Dot color represents different types of snack items, including items that had been cued during the verbal stimulation (green), items that had not been cued but matched with cued items and presented in the binary decision task (blue), and items that had neither been cued nor been presented in the binary decision task (orange). When taking filler items (orange) into consideration, repeated measures ANOVA revealed similar results as reported in Figure 2. There was no significant main effect in cohort (sleep vs. wake; F1,90 = 0.085, p = 0.772), yet a significant within-subject main effect in the cueing condition (cued vs. uncued vs. filler: F2,180 = 11.63, p = 1.8 × 10−5), and a significant cohort by condition interaction (F2,180 = 3.07, p = 0.049). Post-hoc analysis showed that ΔWTP was significantly higher for cued relative to other items in the sleep group (cued vs. uncued: t46 = 6.081, p = 6.6 × 10−7; cued vs. filler: t46 = 3.047, = 0.015, Bonferroni corrected), but not in the wake group (cued vs. uncued: t46 = 6.081, p = 0.413; cued vs. filler: t46 = 0.558, p = 1, Bonferroni corrected).
Figure 3.
Figure 3.. Effects of verbal cueing at the subject and item levels.
(A) Density plot of individual-level biases in WTP1 (top) and WTP2 (middle) in the binary decision task. The individual WTP1(2) bias is computed by subtracting WTP1(2) of an uncued item from the competing cued items that a participant chose between in the binary decision task. (Bottom) Individuals who demonstrated stronger biases in WTP2 toward cued items were more likely to choose cued over uncued items in both sleep and wake groups, with no significant group difference between slopes, suggesting that the cueing-specific effect was not driven by differential level of choice randomness across sleep and wake groups. Each dot represents a subject. (B) Density plot of item-level biases in WTP1 (top) and WTP2 (middle). The item-level WTP1(2) bias is computed by averaging the WTP1(2) difference associated with the same cued item across all subjects and all uncued items paired in the binary decision task. (Bottom) Cued items that were associated with higher WTP2 biases were more likely to be selected under both sleep and wake protocols, with no significant group differences between slopes. Each triangle represents a cued item, and the size of the triangle is proportional to the number of observations.
Figure 4.
Figure 4.. Neurophysiological responses to verbal stimuli during sleep and their contributions to post-sleep behavior (N = 23).
(A) Event-related potential (ERP) and event-related changes in spectral power (ERSPs) evoked by the onset of verbal cues. EEG analysis only encompasses 23 participants in the sleep group. The heatmap demonstrates the event-related changes in ERSPs and black line demonstrates the grand average ERP across participants and trials at the representative electrode F3. ERP topographies for other electrodes and the detailed statistical analyses are shown in Figure 4—figure supplement 1A and Supplementary file 1, Table 1F (also see Materials and methods). (B–C) Individual- and (D–E) item-level correlations between cue-induced power increased in low-frequency bands and ΔWTP for items presented during sleep.
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Neurophysiological responses to verbal cueing during sleep (N = 23).
(A) Mean ERP amplitude averaged across all verbal stimuli and all subjects in six electrodes. By averaging ERP amplitudes measured over the interval from −200 to 1800 ms with respect to the onset of each verbal cue, we observed the cue-evoked neural response at the frontal electrodes and central electrodes during N2 sleep, which is similar to the well-established K-complexes (KCs) waveform that has been associated with the processing of sensory stimuli, including auditory cues, during N2 sleep (Supplementary file 1, Table 1F). (B) Mean spectrograms in the baseline period (left) and the period of stimulus presentation (right) measured at the representative electrode F3 during N2.
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Linear ballistic accumulation model fit.
Observed reaction time (A) and probability of choosing cued over uncued items (B) plotted as a function of model prediction (RT: r = 0.995, p = 2.2 × 10−16; choice: r = 0.984, p = 2.2 × 10−16). No significant difference in the explanatory power was found between sleep and wake groups (RT: rsleep = 0.992, rwake = 0.997, F1,88 = 0.42, p = 0.52; choices: rsleep = 0.973, rwake = 0.988, F1,88 = 0.50, p = 0.480). Dashed line represents a perfect model fit. Each circle indicates a subject.
Figure 4—figure supplement 3.
Figure 4—figure supplement 3.. Bar plots for estimated LBA parameters of the sleep and wake group.
Drift rate estimates derived from LBA based on RT and choices in binary decisions demonstrated a pattern similar to that of ΔWTP as shown in Figure 2A. Besides non-decision time, there is no significant main effect in treatment. All Ps are Bonferroni corrected. Error bars represent S.E.M, *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 4—figure supplement 4.
Figure 4—figure supplement 4.. Across-subject differences in LBA estimates.
(A) Drift rate biases can be predicted by biases in WTP2 across all subjects (r = 0.37, p = 0.001), with no significant difference in the correlation coefficient across cohorts (sleep: r = 0.28; wake: r = 0.35; F1,88 = 0.50, p = 0.480). (B) Among sleep group participants with EEG recordings, the estimated drift rate bias was scaled with the amount of memory reactivation occurring during N2, as indexed by the duration of N2 multiplied by the degree of increase in delta band power evoked by verbal cues presented during N2 (r = 0.499, p = 0.015). (C) In both sleep and wake group, non-decision time estimates are correlated with the change of self-reported hunger before and after the stimulation session (r = 0.27, p = 0.009), with no significant difference in the correlation coefficient across cohorts (sleep: 0.51; wake: r = 032; F1,88 = 0.23, p = 0.633). Each circle indicates a subject.
Figure 4—figure supplement 5.
Figure 4—figure supplement 5.. Posterior predictive simulation revealed that the between-subject variability in the estimated drift rates was related to the variation observed in RT distributions.
(A) Compared with subjects associated with low drift rate bias (median split within N = 23 subjects), those with high drift rate bias presented a significantly higher degree of reactivation during N2 sleep, as indexed by the product of N2 duration and the degree of increase in delta band power evoked by verbal stimulation during N2 (Wilcoxon rank-sum test, Z = 2.09, p = 0.036). (B–C) Simulated (for 1000 simulation runs) and actual (N = 23) probability density of RT for subjects with high vs. low drift rate bias choosing cued and uncued items (detailed percentiles of RT distributions are shown in Supplementary file 1, Table 1I).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ai SZ, Chen J, Liu JF, He J, Xue YX, Bao YP, Han F, Tang XD, Lu L, Shi J. Exposure to extinction-associated contextual tone during slow-wave sleep and wakefulness differentially modulates fear expression. Neurobiology of Learning and Memory. 2015;123:159–167. doi: 10.1016/j.nlm.2015.06.005. - DOI - PubMed
    1. Antony JW, Gobel EW, O'Hare JK, Reber PJ, Paller KA. Cued memory reactivation during sleep influences skill learning. Nature Neuroscience. 2012;15:1114–1116. doi: 10.1038/nn.3152. - DOI - PMC - PubMed
    1. Armel KC, Beaumel A, Rangel A. Biasing simple choices by manipulating relative visual attention. Judgment and Decision Making. 2008;3:396–403.
    1. Backhaus J, Junghanns K. Daytime naps improve procedural motor memory. Sleep Medicine. 2006;7:508–512. doi: 10.1016/j.sleep.2006.04.002. - DOI - PubMed
    1. Bakkour A, Lewis-Peacock JA, Poldrack RA, Schonberg T. Neural mechanisms of cue-approach training. NeuroImage. 2017;151:92–104. doi: 10.1016/j.neuroimage.2016.09.059. - DOI - PMC - PubMed

Publication types

Grants and funding

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.