Impulsivity is characterized by impaired inhibition of thoughts and actions and a reduced ability to weigh the consequences of behavior. Importantly, impulsivity has been associated with altered reward processing and reward anticipation.
Methods
We aimed to investigate neurophysiological indices of impulsive behavior using an experiential delay discounting task with 64-channel EEG in neurotypical adults (n = 54). Impulsive behavior was operationalized as the preference for smaller, immediate rewards over larger, delayed rewards with the delay varied from 1 to 30 s in different blocks.
Results
We registered feedback-related fronto-central theta and centro-parietal delta power, although did not find any correlation between impulsive behavior and these neurophysiological measures of reward sensitivity. However, we found higher power spectral density in the alpha band over parieto-occipital region during reward anticipation in individuals exhibiting more impulsive behavior in our task.
Conclusion
These results suggest that impulsive individuals have less efficient mechanisms of reward anticipation, potentially resulting in lower tolerance for waiting.
delay discountingEEGexperiential delayimpulsivitytime-frequency analysisreward anticipationThe author(s) declared that financial support was received for this work and/or its publication. This work was supported by the grant of the state program of the Sirius Federal Territory “Scientific and Technological Development of the Sirius Federal Territory” (Agreement No. 3-03, date 18.02.2025).section-at-acceptanceCognitive ScienceIntroduction
Impulsive behavior is characterized by an impaired ability to inhibit thoughts or actions and to weigh the consequences of one’s behavior (Moeller et al., 2001). Importantly, impulsive behavior is a hallmark of multiple mental health disorders, such as substance use disorder (Kozak et al., 2019), gambling disorder (Ioannidis et al., 2019), and attention-deficit/hyperactivity disorder (ADHD) (Winstanley et al., 2006). Notably, impulsivity is a multifaceted construct that comprises different domains (Kozak et al., 2019) that do not always correlate with each other (Calluso et al., 2015; Friedman et al., 2020; Hedge et al., 2020). Thus, the mechanisms of impulsive behavior remain unclear, with studies suggesting they can be driven by impaired integration between regions involved in rewards evaluation and emotion regulation (Kerr et al., 2015), atypical processing of the waiting period (Scheres et al., 2007; Ströhle et al., 2008; Beck et al., 2009) or enhanced reward sensitivity (Herbort et al., 2016; Mechelmans et al., 2017).
With the use of methods that allow for non-invasive measurement of brain activity, such as electroencephalography (EEG), neural markers of reward processing and reward anticipation can be identified and compared across individuals. For example, a well-established electrophysiological measure of anticipatory activity is the decrease of power in the alpha (8–12 Hz) band over parieto-occipital regions, which has been linked to sustained attention (Bacigalupo and Luck, 2022) and inhibition of distractors (Jensen and Mazaheri, 2010; Doesburg et al., 2016; Woodman et al., 2022). Importantly, alpha power has been shown to decrease in response to cues preceding rewards (van den Berg et al., 2014) and is associated with a preference for larger delayed rewards (Pornpattananangkul and Nusslock, 2016). This suggests that a decrease in power in the alpha band may be an important index of the efficiency of reward anticipation. Additionally, there are consistently reported electrophysiological markers of reward processing, such as feedback-related increases in power in the delta (1–3 Hz) and theta (4–7 Hz) bands. For example, an increase in delta band power has been consistently reported in response to gains compared to losses (Nelson et al., 2017; Mei et al., 2018; Ethridge et al., 2020) and is sensitive to the magnitude of rewards (Watts and Bernat, 2018). Finally, feedback-related theta is another important oscillatory marker of reward processing that has also been shown to be sensitive to the magnitude of rewards (Paul et al., 2020) and to track cognitive control demands required for successful task performance (Cavanagh and Frank, 2014) and receiving rewards (Mei et al., 2018).
Delay Discounting (DD), also known as temporal discounting, is a widely described paradigm to study impulsivity, which manipulates the delay duration required to receive a larger rewards (Green et al., 1996; Story et al., 2014). In this regard, impulsive behavior manifests as a preference for a smaller but sooner reinforcement over a larger but delayed one (da Matta et al., 2012). Crucially, DD enables the study of not only the reward processing stage of decision-making but also reward anticipation. Existing studies utilizing DD with different designs have provided insights into impulsive decision-making in various contexts, such as food behavior (Skrynka and Vincent, 2019; Epstein et al., 2023), substance abuse (Yi et al., 2010; Robles et al., 2011), and normative behavior (Schwenke et al., 2022), suggesting that DD is a reliable paradigm for assessing the temporal discounting domain of impulsivity. To date, existing studies investigating DD using EEG have used designs with monetary incentives and very large delays ranging from weeks to months (Pornpattananangkul and Nusslock, 2016; Patalano et al., 2020; Wang et al., 2020) that cannot be directly experienced by participants in the form of reward anticipation. Moreover, monetary rewards, although studied more often, are not necessary to induce reward-related brain activity (Distefano et al., 2018; Tunison et al., 2019). Thus, there is a need for electrophysiological studies on DD with experiential, rather than hypothetical, delay and non-monetary incentives. Furthermore, such experiential design bridges the gap between human and animal studies, as only experiential delays can be used for the latter (Lukinova et al., 2019).
In this study, we used an experiential DD task with non-monetary rewards and EEG to investigate the electrophysiological correlates of impulsive decision-making. We hypothesized that individuals who make impulsive choices in the delay-discounting task would have increased sensitivity to rewards, as indexed by increased feedback-related power in the delta and theta bands. Additionally, we hypothesized that the preference for smaller, immediate rewards in impulsive decision-makers would be associated with a deficiency in reward anticipation, as indexed by relatively higher alpha band power in the parieto-occipital regions during the delay.
Materials and methodsParticipants
A total of 54 healthy adults (mean age = 25.50, SD = 6.14, 11 males, 43 females) participated in the study. Participants had no history of neuropsychiatric disorders and were not taking any psychopharmacological medications. The sample size for our study was determined using G*Power 3.1 (Faul et al., 2009) to be sufficient to detect the hypothesized effects of medium to large size (Cohen’s f2 = 0.2), as demonstrated in previous similar studies (Kamarajan et al., 2008; Leicht et al., 2013; Pornpattananangkul and Nusslock, 2016), with alpha error probability of 0.05 and 80% power.
Recording and procedure
During the experimental session, participants sat in a comfortable chair in a quiet room with dim lighting. The task was presented on a 24” LCD screen with a 120 Hz refresh rate. EEG data were obtained using a 64-channel Brain Products setup (actiCHamp Plus, Brain Products GmbH, Gilching, Germany). A standard 10-10 montage with an online reference at FCz and a ground electrode placed at FPz was used. The data were sampled at a rate of 500 Hz.
Task
An experiential DD task [adapted from Steele et al. (2019)] (Figure 1) was used in this study and developed using PsychoPy software (Peirce et al., 2019). Participants were instructed to play a game with the objective of maximizing their cumulative points represented as M&M’s candies, which were operationalized as non-consumable visual cues The use of candy imagery served to “gamify” the point accumulation, distinguishing it from standard monetary cues, but they remained hypothetical in nature. Furthermore, M&Ms also represent a palatable food stimulus, which is typically difficult to resist when readily available (French et al., 2001). The participants were informed that both the number of trials and game duration were fixed and unaffected by their choices, although it was not fully correct, as the duration of the experiment depended on the complex algorithm of the stair case procedure, which will be described below. This instruction served as a necessary methodological control designed to prevent participants from prematurely terminating the experiment. Given that we did not offer monetary compensation commensurate with the points collected, this instruction was intended to limit the number of participants who might speed through the experiment (by not choosing the larger delayed rewards, see below) and fail to pursue the stated goal of maximizing points.
Experimental paradigm depiction. The first line shows a presentation of the delay period that was demonstrated once before each block. Each trial of the main blocks started with the presentation of SS, which became available for selection immediately after the onset of the trial. Its presentation also marked the beginning of the delay period. LL was not displayed and was unavailable for choosing until its associated delay period elapsed. Importantly, if a participant selected the SS option at any point during the delay period, the delay was immediately aborted, and there was no post-choice enforced waiting period. If the participant refrained from choosing SS and waited for the full duration of the delay, LL appeared alongside the SS option. After that moment, participants could choose this option by pressing the associated button. In both scenarios, the selected option (SS or LL) was highlighted with a red circle for 1 s, and the trial moved to the 1-s feedback cue, after which the cumulative score was presented before initiating the next trial. Note that the image displayed in the feedback cue depended only on the selected option (SS or LL) and not on the duration of the delay period in a given task block. IP, indifference point (see the main text).
Flowchart illustrating a delay demonstration (top section, consecutive presentations of small and large reward for respective duration, occurring before each block) and trial structure for the tasks (bottom section). The main trial starts with a small reward presentation, available for choosing after 1 s, and large reward appears alongside and becomes available for choosing only after the respective delay period if the small award is not selected before. The respective choice is highlighted for 1 s and followed by a feedback cue and invitation to proceed to the next trail. The block is indeed after r eaching 6 indifference poireward.
On each trial, participants selected between two options: an immediate but smaller rewards (16 candies) or a larger but delayed rewards (32 candies) by pressing “1” or “2” on a standard QWERTY keyboard. The rewards were presented as a picture of M&Ms, with reward magnitudes additionally displayed beneath each option. For convenience, we will refer to these reward options as SS (small/sooner) and LL (large/later) further throughout the text. Before the four blocks of the main task, participants completed a 4-trial practice block to familiarize themselves with the key mapping and the two reward types (SS and LL). Trials in the practice block were identical to those in the main task, with the exception that the LL delay was 3 s. Each trial started with the presentation of SS, which became available for selection 1 s after the onset of the trial. The option for the LL reward was not displayed and was unavailable until its associated delay period elapsed. Importantly, if a participant selected the SS option at any point during the delay period, the delay was immediately aborted, and there was no post-choice enforced waiting period. If the participant refrained from choosing SS and waited for the full duration of the delay, LL appeared alongside the SS option and became available for selection. In both scenarios, the selected option (SS or LL) was highlighted with a red circle for 1 s and the trial moved to the 1-s feedback cue, after which the cumulative score was presented before initiating the next trial. Thus, the experimental design required participants to actively inhibit the immediately available SS option to secure the opportunity to choose the LL reward. We hypothesized that this requirement would make the waiting period difficult to tolerate for impulsive individuals, despite the virtual, non-monetary nature of the incentives, consistent with prior evidence showing that time preferences remain reliable across widely differing paradigms, including those utilizing immediate, symbolic reinforcers rather than real consumable rewards (Lukinova et al., 2019).
The entire game comprised four consecutive blocks, each with a different delay for the LL, presented in the following order: 1, 15, 30, and 7 s. At the beginning of each block, participants experienced the full delay duration as a demonstration of the delay period, separately for both SS and LL (Figure 1). Option positions (left/right) were counterbalanced across trials.
In our task, a staircase procedure was applied to adjust the amount of the SS. The SS amount gradually decreased (from initial 16 to a minimum of 1 candy) in subsequent trials if chosen, and gradually increased (to a maximum of 30 candies) if the LL was chosen. The amount of the LL remained constant throughout the task. Block termination occurred when either six indifference points (IPs) were reached within the block or the participant reached the minimum (1 candy) or maximum (30 candies) for the SS and maintained this preference for six consecutive choices, indicating a stable preference for a given reward option. IPs were calculated as the mean SS value across three consecutive trials featuring alternating choices (e.g., SS-LL-SS or LL-SS-LL), representing subjective equivalence between options.
The mean of the six IPs for each of four blocks, normalized by the maximum value reached for the SS within these blocks, was used to compute the discounting factor (DF). The DF served as an indicator of the strategy individuals adopted in the task (impulsive or self-controlled). The DF was calculated as the area under the curve (AUC) connecting the mean normalized IPs for each delay (Odum, 2011; Yeh et al., 2021). Accordingly, if a participant consistently chose the LL reward, the SS amount gradually increased, resulting in an increase in the DF (with a maximum DF of 1). Conversely, if the participant chose the SS, its amount gradually decreased, resulting in a decrease in the DF (with a minimum DF of 0).
Symbolic performance-contingent bonuses (a cup of coffee, a marshmallow, or a piece of cake) were awarded for reaching cumulative point thresholds (200, 400, or 600 points, respectively), with these thresholds visibly displayed during the task. Upon completion of all four blocks, participants viewed their total accumulated points, marking the end of the game.
Data processing
Electroencephalography preprocessing was performed using MNE Python (Gramfort et al., 2013; Larson et al., 2023). A 0.1–40 Hz band-pass filter was applied, and the data were re-referenced to the average activity across all electrodes. The data underwent visual inspection for bad channels and muscle artifacts. Independent component analysis (ICA) was performed, and artifacts were manually removed with reference to the ALICE algorithm (Soghoyan et al., 2021). All epochs exceeding a ±150 μV amplitude threshold were rejected (Mognon et al., 2011). For time-frequency analysis, the data were resampled to 250 Hz. Feedback-locked epochs were extracted in the [−1, 2 s] time window, with zero corresponding to the onset of the feedback cue (Figure 1). For reward anticipation epochs, the delay period (7, 15, or 30 s), beginning with the delay period (Figure 1), was segmented into 1-s epochs with 50% overlap. Time-frequency decomposition was performed using Morlet wavelets for frequencies between 1 and 35 Hz. Prior to averaging, time-frequency epochs were baseline-corrected using a window from −300 to −100 ms to prevent leakage of post-stimulus activity into the baseline period, which may occur at lower frequencies (Cohen, 2014).
Feedback-locked theta power (4–7 Hz) was computed at the fronto-central cluster (Fz, FC1, FC2) in a 200–400 ms window. Feedback-locked delta power (1–3 Hz) was computed at the centro-parietal cluster (Cz, CPz) for a 100–600 ms window. The time windows for feedback-related time-frequency measures were selected based on inspection of grand-average waveforms and previous studies (Pornpattananangkul and Nusslock, 2016; Paul and Pourtois, 2017; Ethridge et al., 2020). Due to the lower trial count for SS choices, only epochs for LL choices were included in the analysis. This ensured a sufficient number of epochs per participant after exclusion of two participants with an epoch number lower than 12 (n = 52, M = 27.52, SD = 8.09, range = 12–39).
In the main analysis of anticipatory alpha-band activity, only participants with at least one artifact-free trial in the 7-, 15-, or 30-s block were included (n = 52). Anticipatory alpha band (8–12 Hz) power spectral density (PSD) was computed at the parieto-occipital electrode site (POz) (Bacigalupo and Luck, 2022) as the average of corresponding 1-s epochs (number of epochs per participant per condition: M = 302.40, SD = 114.33, range = 29–539). Such an approach allowed us to normalize for different durations of the delay as well as to mitigate a low trial count. Thus, the minimum number of epochs contributed by any participant was 29, which exceeds the threshold previously shown to yield reliable EEG estimates (Pontifex et al., 2010).
Statistical analysis
The statistical analysis was performed in R (R Core Team, 2021) and included two generalized linear models (GLM) (relationship between DF and feedback-locked delta, and theta power) and a linear mixed model (LMM) (between DF and alpha PSD), with a random intercept fit for each participant. The use of LMM for the alpha PSD data, which contained multiple levels of delay per participant, ensured the robustness of our analysis to potential missing and unbalanced data and to asymmetrical distributions (Schielzeth et al., 2020). Prior to statistical modeling, outliers exceeding ±3 SD from the mean were removed.
The GLMs s included one continuous between-subject predictor, DF. The LMM for the anticipatory alpha band PSD included one categorical within-subject predictor, the duration of the delay (7/15/30 s), and one continuous between-subject predictor (DF).
If a significant main effect between groups was found in the full models, post-hoc tests adjusted for false discovery rate (FDR) (Benjamini and Hochberg, 1995) were conducted. If a significant continuous-categorical interaction was found in the full models, simple slopes were computed.
Additionally, we conducted a complementary analysis to examine the relationships between DF and task duration using Spearman’s correlation coefficient.
ResultsBehavioral results
To characterize the overall trend for the strategy participants followed in the DD task, we first present the descriptive statistics of DF. On average, participants tended to choose larger delayed (LL) rewards over smaller, immediate (SS) rewards, with a mean DF of 0.825 (SD = 0.226). Accordingly, the DF distribution in our sample was negatively skewed. The negative skewness of DF was likely caused by the instructions, which encouraged the majority of participants to consistently choose the LL option (as maximizing points was the explicit goal and task duration was perceived as fixed), thereby contributing to the observed ceiling effect. The mean duration of the task was 15.475 min (SD = 3.6). Our complementary analysis showed a significant positive correlation between DF and task duration (r = 0.586, p < 0.001). A visual depiction of the averaged DD curve DF distribution, as well as the relationships between the task duration and DF, is provided in the Supplementary Figures 1, 2.
Feedback-related theta and delta power
As we hypothesized that impulsive behavior would be associated with increased sensitivity to rewards, we examined the time-frequency measures of reward processing. We observed feedback-related increases in theta and delta band power in our task, as can be seen in Figure 2. However, neither band’s activity within the predefined time windows was related to individual strategy in the DD task. In the model with feedback-related delta power as the dependent variable, we did not find a significant effect of DF [F(1, 49) = 0.748, p = 0.391]. Similarly, we did not find a significant effect of DF on feedback-related theta power [F(1, 49) = 0.173, p = 0.679]. Therefore, in contrast to our hypotheses, none of the feedback-related measures showed significant associations with impulsive behavior in the DD task.
(A) Feedback-related theta at the frontal-central cluster (Fz, FC1, FC2), n = 51. (B) Feedback-related delta at the centroparietal cluster (CPz, Pz), n = 52. None of the measures showed a significant relationship with DF (p = 0.679 and p = 0.391, respectively). The regression line depicts the linear model fit, and the shaded region indicates the 95% confidence interval.
Two paired data visualizations showing frequency and power over time. Panel A: A heatmap on the left displays power across frequency (1 to 31.2 Hz) and time (minus 200 to 800 ms) for big reward conditions at electrodes Fz, FC1, FC2. The hottest area is around 200 to 600 ms. On the right, a scatter plot shows a slight negative correlation in theta band power (20 to 400 ms) versus DF, with a linear regression line. Panel B: A similar heatmap for electrodes Cz, CPz, with the hot spot also around 200 to 600 ms. The scatter plot for delta band power (100 to 600 ms) versus DF shows a slght negative trend.Anticipatory alpha PSD
In line with the original hypothesis, we tested the association between DF and reward anticipation indexed by the parieto-occipital alpha band. The full model with parieto-occipital alpha band PSD indicated a significant main effect of DF [F(1, 66) = 11.934, p = 0.001). In particular, individuals with higher DF (more self-controlled) exhibited reduced alpha PSD during reward anticipation (Figure 3). No main effect of delay [F(2, 110) = 2.014, p = 0.138] or delay*DF interaction [F(2, 109) = 0.601, p = 0.550] was found. For completeness, we also provide a figure depicting alpha PSD-DF relationships for each level of the delay, although they were qualitatively similar across all delay levels (Supplementary Figure 3).
Averaged parieto-occipital alpha band PSD (n = 52) during the LL anticipation across three delay levels (7, 15, 30 s). Individuals with higher DF (more self-controlled) exhibited reduced alpha PSD during reward anticipation. The regression line depicts the linear model fit, and the shaded region indicates the 95% confidence interval.
Topographic map and scatter plot related to Alpha Power Spectral Density (PSD) during reward anticipation for 52 participants. The map shows variations in brain activity, with red and blue areas indicating different levels of PSD with relative maximum at POz electrode. The scatter plot displays a significant negative correlation between DF and alpha PSD, with a trend line and confidence interval.Discussion
In this study, we investigated two processes underlying impulsive decision-making, as indexed by the delay discounting (DD) task: reward-related feedback processing and reward anticipation in the context of an experiential DD task with non-monetary rewards. Our main findings showed that individuals exhibiting greater self-control demonstrated reduced alpha band power during reward anticipation. Additionally, we showed that reward processing, as measured by feedback-related theta and delta power, did not correlate with impulsivity as measured by the experiential DD task.
One of the key characteristics of impulsivity, as reported at the trait (Torrubia et al., 2001), behavioral (Robles et al., 2011; Jones et al., 2015), and neural (Martin and Potts, 2004; Mechelmans et al., 2017) levels, is reward hypersensitivity. In our study, we hypothesized that neural indices associated with processing of feedback and rewards, namely, feedback-related delta and theta power, would be positively associated with impulsivity. In contrast to our hypotheses, we failed to find an effect of impulsivity (DF) on these measures. To the best of our knowledge, only one study has investigated feedback-locked delta power in relation to impulsive behavior, and it also did not find this association (Pornpattananangkul and Nusslock, 2016). In contrast, there is multiple evidence showing that feedback-locked theta activity correlates with self-reported impulsivity scores (Kamarajan et al., 2008; Leicht et al., 2013; Andreou et al., 2017) as well as behavioral performance during the DD task specifically (Pornpattananangkul and Nusslock, 2016). The absence of association between impulsivity and reward-related time-frequency measures in our data is likely attributable to the deterministic nature of our task design. In our paradigm, the outcome was known and certain upon choice, thus minimizing or eliminating any reward prediction error. The feedback, therefore, served as a confirmation of an expected event rather than a signal of unexpected gain. As already mentioned, our paradigm, in contrast, did not include loss or probabilistic conditions, as we were focused solely on the processing of rewards (gains) when they were known and expected. Thus, within the limited context of our design, we can only conclude that when reward prediction error is absent, impulsive individuals do not process the mere receipt of the rewards differently from self-controlled ones.
Finally, another critical process that contributes to impulsivity and decision-making, which our study investigated, is how well an individual can tolerate a delay preceding a reward. Accordingly, we assessed neural anticipatory mechanisms by measuring power spectral density (PSD) in the alpha band during anticipation of large rewards and observed increased parieto-occipital alpha PSD during reward anticipation in more impulsive individuals, in line with our hypotheses. In the context of our task design, we suggest that increased alpha PSD during the anticipation may capture a specific aspect of impulsive behavior: irrational delay intolerance. As our instructions discouraged participants from speeding through the experiment, participants with lower DFs chose the smaller rewards despite knowing it was sub-optimal and would not save them time. This suggests that for these individuals, the subjective difficulty of the wait or the immediate attraction of the SS rewards outweighed the rational instruction to maximize points. This interpretation is also in line with research that has consistently linked a decrease in alpha band power to enhanced sustained attention (Bacigalupo and Luck, 2022) and inhibitory gating (Jensen and Mazaheri, 2010; Doesburg et al., 2016; Woodman et al., 2022), which suggests less efficient mechanisms of attentional focusing ability to inhibit irrelevant stimuli. Furthermore, self-controlled individuals maintained this neural signature of focused attention (lower alpha PSD) despite the longer overall duration of their experiment, reinforcing the link between delay tolerance and sustained cognitive engagement. Another potential explanation for individual differences in the alpha band during the anticipation may involve differences in the subjective perception of time, an ability that has been linked to impulsivity (Wittmann and Paulus, 2008; Paasche et al., 2019) and is mediated by alpha oscillations (Rogachev and Sysoeva, 2023). The novelty of our findings relates to the use of experiential DD, as we are the first to show, using electrophysiology, that self-controlled individuals better tolerate delays due to being more alert during waiting, thus linking our findings with existing fMRI studies that used an experiential design (Tanaka et al., 2020) and providing a potential for translational research where hypothetical delays cannot be used (Lukinova et al., 2019).
Taken together, our results provide insights into the electrophysiological correlates of impulsive decision-making using an experiential DD task design and further elaborate on the evidence of less efficient mechanisms of reward anticipation in impulsivity as measured by DD.
Limitations and future directions
In our data, we were not able to find any electrophysiological measures of reward sensitivity that correlated with impulsivity as measured by DD, which may be partially attributable to the specific design of our experiential task. While ecologically valid for studying waiting, it was not optimized to elicit reward processing driven by reward prediction error, as the feedback-related responses indicated a mere fact of receiving the reward. Additionally, the design and instructions of our task (that the trial count and total duration were fixed), which encouraged participants to collect as many points as possible and intended to prevent too many participants from consistently choosing the SS option to rapidly complete the experiment, led to a substantial number of our participants reaching a ceiling effect and having close to maximum values of DF. This resulted in a low trial count for SS choices and consequently restricted our ability to include reward magnitude in the analysis of reward sensitivity measures and investigate its interaction with DF.
Future research utilizing experiential DD paradigms utilizing EEG should specifically address the design limitations outlined above, potentially by refining task instructions or mechanics to better disentangle reward sensitivity from session-duration strategies and mitigate low trial counts. Additionally, given the prevalence of digital technology among young adults, future work might benefit from incorporating other measures of individual differences, such as self-reported impulsivity scores and digital behavior patterns, which have been linked to delay discounting (van Endert and Mohr, 2020; Cheng et al., 2021; Weinsztok et al., 2021; Pancani et al., 2023).
Conclusion
In our study, we focused on two processes that contribute to impulsive decision-making in the experiential DD task: rewards processing and reward anticipation. We suggest that impulsive individuals exhibit less efficient mechanisms of rewards anticipation, as indexed by higher alpha band power in the parieto-occipital regions, which may explain their lower tolerance to delays. Additionally, we found no evidence for enhanced rewards processing in more impulsive individuals. Taken together, our results expand the electrophysiological evidence for altered mechanisms of reward anticipation, using the example of an experiential DD task.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in https://github.com/fzakirov/dd_eeg/.
Ethics statement
The studies involving humans were approved by Sirius University Ethic Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
We gratefully acknowledge the help of our data collection team: Anton Rogachev, Daria Kostonian, and Ali Adeli Koudehi for the help in developing and programming the experimental design. We also express our thanks to all participants who volunteered their time and effort to participate in this study.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1746734/full#supplementary-material
ReferencesAndreouC.FrielinghausH.RauhJ.MußmannM.VauthS.BraunP. (2017). Theta and high-beta networks for feedback processing: A simultaneous EEG–fMRI study in healthy male subjects.Transl. Psychiatry7:e1016. 10.1038/tp.2016.28728140398BacigalupoF.LuckS. J. (2022). Alpha-band EEG suppression as a neural marker of sustained attentional engagement to conditioned threat stimuli.Soc. Cogn. Affect. Neurosci.171101–1117. 10.1093/scan/nsac02935434733BeckA.SchlagenhaufF.WüstenbergT.HeinJ.KienastT.KahntT. (2009). Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics.NeuroImage47:S159. 10.1016/S1053-8119(09)71675-1BenjaminiY.HochbergY. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing.J. R. Stat. Soc. Ser. B Methodol.57289–300. 10.1111/j.2517-6161.1995.tb02031.xCallusoC.TosoniA.PezzuloG.SpadoneS.CommitteriG. (2015). Interindividual variability in functional connectivity as long-term correlate of temporal discounting.PLoS One10:e0119710. 10.1371/journal.pone.011971025774886CavanaghJ. F.FrankM. J. (2014). Frontal theta as a mechanism for cognitive control.Trends Cogn. Sci.18414–421. 10.1016/j.tics.2014.04.01224835663ChengY.-S.KoH.-C.SunC.-K.YehP.-Y. (2021). The relationship between delay discounting and internet addiction: A systematic review and meta-analysis.Addict. Behav.114:106751. 10.1016/j.addbeh.2020.10675133310692CohenM. X. (2014). Analyzing neural time series data: Theory and practice.Cambridge, MA: The MIT Press, 10.7551/mitpress/9609.001.0001da MattaA.GonçalvesF. L.BizarroL. (2012). Delay discounting: Concepts and measures.Psychol. Neurosci.5135–146. 10.3922/j.psns.2012.2.03DistefanoA.JacksonF.LevinsonA. R.InfantolinoZ. P.JarchoJ. M.NelsonB. D. (2018). A comparison of the electrocortical response to monetary and social reward.Soc. Cogn. Affect. Neurosci.13247–255. 10.1093/scan/nsy00629373743DoesburgS. M.BedoN.WardL. M. (2016). Top-down alpha oscillatory network interactions during visuospatial attention orienting.NeuroImage132512–519. 10.1016/j.neuroimage.2016.02.07626952198EpsteinL. H.RizwanA.PaluchR. A.TempleJ. L. (2023). Delay discounting and the income-food insecurity-obesity paradox in mothers.J. Obes.2023:8898498. 10.1155/2023/889849837766882EthridgeP.AliN.RacineS. E.PruessnerJ. C.WeinbergA. (2020). Risk and resilience in an acute stress paradigm: Evidence from salivary cortisol and time-frequency analysis of the reward positivity.Clin. Psychol. Sci.8872–889. 10.1177/2167702620917463FaulF.ErdfelderE.BuchnerA.LangA.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses.Behav. Res. Methods411149–1160. 10.3758/BRM.41.4.114919897823FrenchS. A.StoryM.JefferyR. W. (2001). Environmental influences on eating and physical activity.Annu. Rev. Public Health22309–335. 10.1146/annurev.publhealth.22.1.30911274524FriedmanN. P.HatoumA. S.GustavsonD. E.CorleyR. P.HewittJ. K.YoungS. E. (2020). Executive functions and impulsivity are genetically distinct and independently predict psychopathology: Results from two adult twin studies.Clin. Psychol. Sci. J. Assoc. Psychol. Sci.8519–538. 10.1177/216770261989881433758683GramfortA.LuessiM.LarsonE.EngemannD. A.StrohmeierD.BrodbeckC. (2013). MEG and EEG data analysis with MNE-Python.Front. Neurosci.7:267. 10.3389/fnins.2013.0026724431986GreenL.MyersonJ.LichtmanD.RosenS.FryA. (1996). Temporal discounting in choice between delayed rewards: The role of age and income.Psychol. Aging1179–84. 10.1037//0882-7974.11.1.798726373HedgeC.PowellG.BompasA.SumnerP. (2020). Self-reported impulsivity does not predict response caution.Personal. Individ. Differ.167:110257. 10.1016/j.paid.2020.11025733273749HerbortM. C.SochJ.WüstenbergT.KrauelK.PujaraM.KoenigsM. (2016). A negative relationship between ventral striatal loss anticipation response and impulsivity in borderline personality disorder.NeuroImage Clin.12:724. 10.1016/j.nicl.2016.08.01127766203IoannidisK.HookR.WickhamK.GrantJ. E.ChamberlainS. R. (2019). Impulsivity in gambling disorder and problem gambling: A meta-analysis.Neuropsychopharmacology441354–1361. 10.1038/s41386-019-0393-930986818JensenO.MazaheriA. (2010). Shaping functional architecture by oscillatory alpha activity: Gating by inhibition.Front. Hum. Neurosci.4:186. 10.3389/fnhum.2010.0018621119777JonesC. G. A.FearnleyH.PanagiotopoulosB.KempR. I. (2015). Delay discounting, self-control, and substance use among adult drug court participants.Behav. Pharmacol.26:447. 10.1097/FBP.000000000000014926110226KamarajanC.RangaswamyM.ChorlianD. B.ManzN.TangY.PandeyA. K. (2008). Theta oscillations during the processing of monetary loss and gain: A perspective on gender and impulsivity.Brain Res.123545–62. 10.1016/j.brainres.2008.06.05118616934KerrK. L.AveryJ. A.BarcalowJ. C.MosemanS. E.BodurkaJ.BellgowanP. S. F. (2015). Trait impulsivity is related to ventral ACC and amygdala activity during primary reward anticipation.Soc. Cogn. Affect. Neurosci.1036–42. 10.1093/scan/nsu02324526181KozakK.LucatchA. M.LoweD. J. E.BalodisI. M.MacKillopJ.GeorgeT. P. (2019). The neurobiology of impulsivity and substance use disorders: Implications for treatment.Ann. N. Y. Acad. Sci.145171–91. 10.1111/nyas.1397730291624LarsonE.GramfortA.EngemannD. A.LeppakangasJ.BrodbeckC.JasM. (2023). MNE-Python.Andover, MI: Zendo. 10.5281/zenodo.7671973LeichtG.TroschützS.AndreouC.KaramatskosE.ErtlM.NaberD. (2013). Relationship between oscillatory neuronal activity during reward processing and trait impulsivity and sensation seeking.PLoS One8:e83414. 10.1371/journal.pone.008341424376698LukinovaE.WangY.LehrerS. F.ErlichJ. C. (2019). Time preferences are reliable across time-horizons and verbal versus experiential tasks.eLife8:e39656. 10.7554/eLife.3965630719974MartinL. E.PottsG. F. (2004). Reward sensitivity in impulsivity.Neuroreport151519–1522. 10.1097/01.wnr.0000132920.12990.b915194887MechelmansD. J.StrelchukD.DoñamayorN.BancaP.RobbinsT. W.BaekK. (2017). Reward sensitivity and waiting impulsivity: Shift towards reward valuation away from action control.Int. J. Neuropsychopharmacol.20:971. 10.1093/ijnp/pyx07229020291MeiS.LiQ.LiuX.ZhengY. (2018). Monetary incentives modulate feedback-related brain activity.Sci. Rep.8:11913. 10.1038/s41598-018-30294-z30093674MoellerF. G.BarrattE. S.DoughertyD. M.SchmitzJ. M.SwannA. C. (2001). Psychiatric aspects of impulsivity.Am. J. Psychiatry1581783–1793. 10.1176/appi.ajp.158.11.178311691682MognonA.JovicichJ.BruzzoneL.BuiattiM. (2011). ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.Psychophysiology48229–240. 10.1111/j.1469-8986.2010.01061.x20636297NelsonB. D.InfantolinoZ. P.KleinD. N.PerlmanG.KotovR.HajcakG. (2017). Time-Frequency reward-related delta prospectively predicts the development of adolescent-onset depression.Biol. Psychiatry Cogn. Neurosci. Neuroimag.3:41. 10.1016/j.bpsc.2017.07.00529397078OdumA. L. (2011). Delay discounting: I’m a k, You’re a k.J. Exp. Anal. Behav.96427–439. 10.1901/jeab.2011.96-42322084499PaascheC.WeibelS.WittmannM.LalanneL. (2019). Time perception and impulsivity: A proposed relationship in addictive disorders.Neurosci. Biobehav. Rev.106182–201. 10.1016/j.neubiorev.2018.12.00630529361PancaniL.PetilliM. A.RivaP.RusconiP. (2023). I can’t live without you: Delay discounting in smartphone usage.J. Cogn. Psychol.35441–455. 10.1080/20445911.2023.2195031PatalanoA. L.LolliS. L.SanislowC. A. (2020). Brief report on the relationship between temporal discount rate and error related negativity for immediate versus future choice options.Int. J. Psychophysiol.1511–6. 10.1016/j.ijpsycho.2020.02.00632057780PaulK.PourtoisG. (2017). Mood congruent tuning of reward expectation in positive mood: Evidence from FRN and theta modulations.Soc. Cogn. Affect. Neurosci.12765–774. 10.1093/scan/nsx01028199707PaulK.VassenaE.SeveroM. C.PourtoisG. (2020). Dissociable effects of reward magnitude on fronto-medial theta and FRN during performance monitoring.Psychophysiology57:e13481. 10.1111/psyp.1348131578739PeirceJ.GrayJ. R.SimpsonS.MacAskillM.HöchenbergerR.SogoH. (2019). PsychoPy2: Experiments in behavior made easy.Behav. Res. Methods51195–203. 10.3758/s13428-018-01193-y30734206PontifexM. B.ScudderM. R.BrownM. L.O’LearyK. C.WuC.-T.ThemansonJ. R. (2010). On the number of trials necessary for stabilization of error-related brain activity across the life span.Psychophysiology47767–773. 10.1111/j.1469-8986.2010.00974.x20230502PornpattananangkulN.NusslockR. (2016). Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards.Neuropsychologia91141–162. 10.1016/j.neuropsychologia.2016.07.03727477630R Core Team (2021). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available online at: https://www.R-project.org/RoblesE.HuangB. E.SimpsonP. M.McMillanD. E. (2011). Delay discounting, impulsiveness, and addiction severity in opioid-dependent patients.J. Subst. Abuse Treat.41:354. 10.1016/j.jsat.2011.05.00321741198RogachevA. O.SysoevaO. V. (2023). Dynamics of the parieto-occipital alpha rhythm of the brain on comparison of the durations of time intervals.Neurosci. Behav. Physiol.531551–1558. 10.1007/s11055-023-01550-1ScheresA.MilhamM. P.KnutsonB.CastellanosF. X. (2007). Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder.Biol. Psychiatry61720–724. 10.1016/j.biopsych.2006.04.04216950228SchielzethH.DingemanseN. J.NakagawaS.WestneatD. F.AllegueH.TeplitskyC. (2020). Robustness of linear mixed-effects models to violations of distributional assumptions.Methods Ecol. Evol.111141–1152. 10.1111/2041-210X.13434SchwenkeD.WehnerP.ScherbaumS. (2022). Effects of individual and dyadic decision-making and normative reference on delay discounting decisions.Cogn. Res. Princ. Implic.7:71. 10.1186/s41235-022-00422-535900639SkrynkaJ.VincentB. T. (2019). Hunger increases delay discounting of food and non-food rewards.Psychon. Bull. Rev.261729–1737. 10.3758/s13423-019-01655-031520252SoghoyanG.LedovskyA.NekrashevichM.MartynovaO.PolikanovaI.PortnovaG. (2021). A toolbox and crowdsourcing platform for automatic labeling of independent components in electroencephalography.Front. Neuroinform.15:720229. 10.3389/fninf.2021.72022934924988SteeleC. C.GwinnerM.SmithT.YoungM. E.KirkpatrickK. (2019). Experience matters: The effects of hypothetical versus experiential delays and magnitudes on impulsive choice in delay discounting tasks.Brain Sci.9:379. 10.3390/brainsci912037931888218StoryG. W.VlaevI.SeymourB.DarziA.DolanR. J. (2014). Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective.Front. Behav. Neurosci.8:76. 10.3389/fnbeh.2014.0007624659960StröhleA.StoyM.WraseJ.SchwarzerS.SchlagenhaufF.HussM. (2008). Reward anticipation and outcomes in adult males with attention-deficit/hyperactivity disorder.NeuroImage39966–972. 10.1016/j.neuroimage.2007.09.04417996464TanakaD.AokiR.SuzukiS.TakedaM.NakaharaK.JimuraK. (2020). Self-Controlled choice arises from dynamic prefrontal signals that enable future anticipation.J. Neurosci.409736–9750. 10.1523/JNEUROSCI.1702-20.202033188069TorrubiaR.ÁvilaC.MoltóJ.CaserasX. (2001). The Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) as a measure of Gray’s anxiety and impulsivity dimensions.Personal. Individ. Differ.31837–862. 10.1016/S0191-8869(00)00183-5TunisonE.SylvainR.SterrJ.HileyV.CarlsonJ. M. (2019). No money, no problem: Enhanced reward positivity in the absence of monetary reward.Front. Hum. Neurosci.13:41. 10.3389/fnhum.2019.0004130809138van den BergB.KrebsR. M.LoristM. M.WoldorffM. G. (2014). Utilization of reward-prospect enhances preparatory attention and reduces stimulus conflict.Cogn. Affect. Behav. Neurosci.14561–577. 10.3758/s13415-014-0281-z24820263van EndertT. S.MohrP. N. C. (2020). Likes and impulsivity: Investigating the relationship between actual smartphone use and delay discounting.PLoS One15:e0241383. 10.1371/journal.pone.024138333206673WangG.LiJ.LiS.ZhuC. (2020). Neural dynamics of the combined discounting of delay and probability during the evaluation of a delayed risky reward.Front. Psychol.11:576460. 10.3389/fpsyg.2020.57646033132984WattsA. T. M.BernatE. M. (2018). Effects of reward context on feedback processing as indexed by time-frequency analysis.Psychophysiology55:e13195. 10.1111/psyp.1319529750361WeinsztokS.BrassardS.BalodisI.MartinL. E.AmlungM. (2021). delay discounting in established and proposed behavioral addictions: A systematic review and meta-analysis.Front. Behav. Neurosci.15:786358. 10.3389/fnbeh.2021.78635834899207WinstanleyC. A.EagleD. M.RobbinsT. W. (2006). Behavioral models of impulsivity in relation to ADHD: Translation between clinical and preclinical studies.Clin. Psychol. Rev.26:379. 10.1016/j.cpr.2006.01.00116504359WittmannM.PaulusM. P. (2008). Decision making, impulsivity and time perception.Trends Cogn. Sci.127–12. 10.1016/j.tics.2007.10.00418042423WoodmanG. F.WangS.SuttererD. W.ReinhartR. M. G.FukudaK. (2022). Alpha suppression indexes a spotlight of visual-spatial attention that can shine on both perceptual and memory representations.Psychon. Bull. Rev.29681–698. 10.3758/s13423-021-02034-434877635YehY.-H.MyersonJ.GreenL. (2021). Delay discounting, cognitive ability, and personality: What matters?Psychon. Bull. Rev.28686–694. 10.3758/s13423-020-01777-w33219456YiR.MitchellS. H.BickelW. K. (2010). “Delay discounting and substance abuse-dependence,” in Impulsivity: The behavioral and neurological science of discounting, edsMaddenG. J.BickelW. K. (Washington, DC: American Psychological Association), 191–211. 10.1037/12069-007
Edited by: Evgeniya Lukinova, University of Nottingham, United Kingdom
Reviewed by: Lidia Belles, Université de Genève, Switzerland
Masanaga Ikegami, Asahikawa Medical University, Japan