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. 2026 Feb 26;90(2):43. doi: 10.1007/s00426-026-02266-w

Positive affect increases aperiodic brain activity

Xiaolei Xu 1,#, Xianyun Zhu 1,#, Meiyun Jiang 1, Zhe Li 1, Xiaoqi Fu 1, Lorenza Colzato 1,✉, Bernhard Hommel 1,✉
PMCID: PMC12945908  PMID: 41746401

Abstract

Metacontrol refers to the capacity to effectively balance persistence and flexibility according to situational circumstances. Recent studies suggest a crucial role of cortical noise in metacontrol by showing that the aperiodic exponent, a measure of aperiodic neural activity, varies systematically with metacontrol requirements. Given the behavioral evidence that affect and metacontrol are related, we predicted that affect could also induce systematic changes of the aperiodic exponent. We thus analyzed aperiodic activity in response to affective facial expressions of negative, neutral, or positive valence in a 2-back task from two separate datasets. Our results showed that, at the whole-brain level, aperiodic activity was higher for positive faces than for both neutral and negative faces, but only if valence was blocked (i.e., was the same over consecutive trials). Subsequent analysis further showed that the difference was significant in electrodes widely spread across the frontal, parietal, temporal, and occipital lobes. In conclusion, the present study demonstrates that aperiodic activity changed with affective valence and was more pronounced when processing positive, as compared to neutral or negative stimuli.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00426-026-02266-w.

Introduction

The human ability to regulate the processing of external and internal stimuli according to current goals and contextual demands is typically ascribed to cognitive control or executive functions (Diamond, 2013; Menon & D’Esposito, 2022). Cognitive control enables individuals to selectively attend to task-relevant information, suppress irrelevant inputs, and maintain goal-directed behavior under internal or external challenges. Recent theoretical work, however, emphasizes that cognitive control encompasses not only the maintenance of stable processing (persistence) but also the capacity to adaptively shift attention and behavior in response to changing goals or opportunities (flexibility; see Hommel and Colzato 2017a, b). This dual-function perspective suggests that effective control involves balancing stable, focused processing (persistence) with the ability to adjust to context-dependent alternatives (flexibility), allowing the cognitive system to dynamically and adaptively optimize behavior in complex environments. Once the control concept is extended to encompass both this flexibility function and the traditional persistence function, the question emerges how people achieve a balance between persistence and flexibility according to situational circumstances—an ability that has been coined metacontrol (Hommel and Colzato 2017a, b; Eppinger et al. 2021; Hommel et al. 2024).

The metacontrol framework suggests that a persistent mode is marked by lower variability and more focused processing, whereas a flexible mode involves more variable neural activation and broader processing (Hommel and Colzato 2017a, b; Hommel & Wiers, 2017). These two modes relate to the brain’s excitation/inhibition (E/I) balance. Increased excitatory activity enhances signal variability, promoting a flexible mode, while stronger inhibitory activity produces more focused and stable processing, thereby sustaining a persistent mode (Gao et al., 2017; Lombardi et al., 2017). Thus, the E/I balance may serve as a core neural mechanism underlying the metacontrol.

EEG studies suggest that the E/I balance comes with increased aperiodic neural activity – non-oscillatory components of EEG signals, which in turn can be quantified by means of the aperiodic exponent (Gao et al., 2017; Lombardi et al., 2017). More specifically, high aperiodic activity is reflected by a lower aperiodic exponent, whereas lower aperiodic activity corresponds to a higher aperiodic exponent. This somewhat confusing inverse relationship reflects the way the aperiodic exponent is calculated: It is derived from the regression line across the power of frequency-sorted periodic activities, so that a high exponent reflects a steep regression line (i.e., large differences between low- and high-frequency bands and a relatively stronger dominance of low frequency bands) while a low exponent reflects a shallow regression line (i.e., small differences and a less pronounced dominance of low frequency bands).

A shallower line indicates greater cortical noise, arising from heightened excitatory drive and weaker inhibitory control, and more pronounced variability in neuronal firing, causing asynchronous activity, amplifying signal fluctuations, and diminished efficiency in signal transmission. Conversely, a steep line corresponds to a high exponent, where periodic low-frequency activity dominates and periodic high-frequency components are suppressed. This is likely to establish a more stable neural state, characterized by stronger inhibitory processes that reduce excitatory interference. Enhanced inhibition fosters synchronized neuronal firing, minimizing unnecessary fluctuations in cortical activity. As a result, aperiodic activity declines, reflecting lower cortical noise and more structured neuronal dynamics that support persistent and efficient cognitive processing (Donoghue et al., 2020; Gao et al., 2017; Lombardi et al., 2017). Importantly, whereas high cortical noise is likely to disrupt neural information transmission, this does not necessarily need to impair performance. In fact, there is evidence that performance in tasks that require novelty or switching to new states benefit from high noise levels (e.g., Yan et al., 2024), suggesting that both increases and decreases of aperiodic activity may be seen as adjustments to task demands.

Given the strong link between E/I balance, cortical noise, and metacontrol, the aperiodic exponent has emerged as a key neurophysiological marker of their dynamic regulation (Gao et al., 2024; Jia et al., 2024; Pi et al., 2024; Yan et al., 2024; Zhang et al., 2023). Increasing evidence shows systematic changes in the aperiodic exponent across varying metacontrol task demands (Gao et al., 2024, 2025; Pi et al., 2024; Yan et al., 2024; Zhang et al., 2023). Specifically, these studies demonstrate that the exponent is not a static trait marker but dynamically adjusts in relation to task difficulty and control demands, and the need to balance persistence with flexibility. For example, lower exponents have been observed under conditions requiring rapid adaptation or increased distractor resistance (Yan et al., 2024), whereas higher exponents are linked to more stable, persistence-oriented control states (Gao et al., 2024, 2025; Pi et al., 2024; Zhang et al., 2023). Together, this growing body of evidence positions the aperiodic exponent as a sensitive index of how neural E/I balance supports metacontrol in real time.

Recently, metacontrol has been empirically and theoretically (Hommel, 2025) linked to affect. For instance, the induction of positive affect was found to hamper selectivity and focus in conflict tasks (Dreisbach et al., 2019), and engaging in a flexibility-heavy creativity task was found to induce positive affect (Akbari Chermahini and Hommel 2012a). Further, inducing a positive affective state has been shown to hinder focused attention in tasks resembling the Stroop paradigm, where it is essential to suppress irrelevant information (Dreisbach et al., 2018) . However, the same affective enhancement appears to facilitate performance in tasks that demand cognitive flexibility, such as integrating diverse information or switching between mental sets (e.g., Zhang & Hommel, 2022; Zwosta et al., 2013). These findings indicate that conflict monitoring and metacontrol persistence tend to be weakened under positively valenced affective states. This means that affect might impact cortical noise and the E/I balance, and thus measures that have been found to be related to metacontrol—like the aperiodic exponent. Given that flexibility-heavy tasks benefit from positive affect, and vice versa (Akbari Chermahini and Hommel 2012a), while the opposite is true for persistence-heavy tasks (Dreisbach et al., 2019), and that persistence-heavy tasks increase, but flexibility-heavy tasks reduce the aperiodic exponent, one would expect that positive affect induces to a lower aperiodic exponent than negative affect. Moreover, given that the link between positive affect and metacontrol (i.e., facilitation of flexibility-heavy tasks and/or impairment of persistence-heavy tasks) is much better established than the link between negative affect and metacontrol (Akbari Chermahini and Hommel 2012b), one might speculate that it is easier to demonstrate an impact of positive than of negative affect.

Note that assuming a link between affect and metacontrol is consistent with neurochemical approaches to metacontrol. Both affect and metacontrol have been associated with dopaminergic modulation of prefrontal–striatal circuits (Bertolino et al., 2009; Cools, 2015; Frank et al., 2009). Dopamine has been shown to influence the balance between stability and flexibility in cognitive control and to interact with affective signals, providing a mechanistic route through which affective states may bias metacontrol-related neural dynamics. Importantly, affective experience is also supported by a broader set of neurobiological systems—including serotonergic, noradrenergic, and limbic pathways—which likely interact with dopaminergic mechanisms to shape affect and cognitive control. Thus, dopamine is highlighted here as an empirically supported contributor to the affect–metacontrol link, while recognizing that it is one component of a larger, multi-system framework.

In the present study, we considered two data sets that both included the same social-affective 2-back task. In this task, participants were presented with a stream of facial expressions with negative, neutral and positive valence and required to identify whether the current stimulus matched the one presented 2 trials earlier. The stimuli were presented in blocks containing either stimuli of the same valence or stimuli of randomly varying valence—so to see whether this would impact the behavioral performance. We no specific prediction regarding the impact of blocking on the aperiodic exponent, even though one may speculate that aperiodic activity might be more pronounced with random (i.e., “noisy”) variation of stimuli, since this design may need more cognitive flexibility to switch between different-valence stimuli (Hommel et al. 2024; Hommel and Colzato 2017a, b). The first dataset (Sample 1) was collected for other purposes and was reported by Xu et al. (2024). The second dataset (Sample 2) was a replication of the relevant conditions of the Xu et al. experiment, to see whether our findings are robust. The aperiodic activity was indexed by the aperiodic exponent derived from the FOOOF toolbox (Donoghue et al., 2020).

In sum, given the strong link between E/I balance, cortical noise, and metacontrol, the aperiodic exponent provides a theoretically informative index of neural dynamics that may support the balance between persistence and flexibility. Given the previously obtained behavioral evidence for a link between metacontrol and affect, we expected that affect would also be systematically related to the aperiodic exponent. Specifically, we expected that positive facial expressions, relative to negative ones, would be associated with higher cortical noise (i.e., a lower aperiodic exponent), as had been previously found with more flexible metacontrol states.

Methods

Participants

A total of 76 participants from two separate samples were recruited for this study. Sample 1 included 36 participants and to examine the repeatability of the aperiodic findings observed from Sample 1, another 40 participants were recruited as Sample 2 in the current study. Due to technical issues, outliers exceeding three standard deviations in accuracy, or a high proportion of rejected epochs, 4 participants from Sample 2 were excluded leading to a final sample of 36 participants (male = 17, mean ± sd age = 20.53 ± 1.56 years) in Sample 1 and 36 participants (male = 18, mean ± sd age = 20.03 ± 1.34 years) in Sample 2. The study was approved by the Ethics Committee of Shandong Normal University and the procedure was in accordance with the latest version of the Declaration of Helsinki. All participants were given the written informed consent before the experiment and paid 50RMB after finishing it.

Experimental task

A validated social-affective 2-back paradigm was employed to examine the social working memory updating and details of the experimental task have been published elsewhere (Xu et al., 2024). A total of 30 faces including 10 sad, 10 happy and 10 neutral expressions from 5 female and 5 male actors were used as stimuli in this task. The positive and negative stimuli were matched in both recognition and intensity and have been used in our previous studies (Xu et al., 2019, 2022; Ma et al., 2022). Briefly, this task included two orders to present the facial stimuli with negative (sad), positive (happy) or neutral valences. In one, the facial expressions with same valence were presented in each block (same-valence block) and in the other, faces with different valence were randomly mixed in each block (different-valence block). This task included six blocks with valence being blocked and six blocks with valence varying randomly, each block contained 90 valid trials. Participants were required to judge whether the identity of the current face matched the one presented 2-items earlier (Fig. 1).

Fig. 1.

Fig. 1

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The trial procedure of the affective facial 2-back task. In the same-valence blocks, faces with same affective valences were presented and in the different-valence blocks, faces with different affective valences were presented. Participants were instructed to identify whether the identity of the current face match the one appeared 2-item earlier

Electroencephalography (EEG) recording and preprocessing

The EEG signals were recorded by a 64-channel amplifier with a sampling rate of 1000 Hz (Neuroscan system, https://compumedicsneuroscan.com/) and collected with electrode impedance kept below 5 kΩ. Offline data were preprocessed using the EEGLAB toolbox (Delorme & Makeig, 2004) with re-referencing to the average signal of all electrodes, filtering with 0.1–30 Hz band-pass, segmenting into epochs from 200ms before and 1000ms after the onset of the stimulus and baseline-correcting with respect to the mean voltage over the − 200ms before the onset of the stimulus. An independent component analysis (ICA) was conducted to reject temporal or spatial discontinuities and regular artifacts including eye, muscle and cardiac interferences. Epochs including artifacts exceeding ± 100µV were also rejected after visual inspection confirmation. Trials with incorrect responses were removed and the time widow for analysis was 0-1000ms when the stimulus was presented. A pre-trial period of 1.4s before the onset of the stimulus was also analyzed (please see Supplementary Information for results). Subjects with an average trial retention rate below 65% were excluded. For the final EEG analysis, the average number of trials across different conditions ranged from 69 to 76 (see SI for details).

Parameterization of spectral data

The power spectral density (PSD) for each experimental condition, electrode, and participant was calculated using the ‘pwelch’ function implemented in Matlab and further parameterized using the Python-based Fitting Oscillations & One-Over-F (FOOOF; version 1.0.0; https://fooof-tools.github.io/fooof/), a toolbox developed to parameterize neural power spectra, to estimate aperiodic activity by decomposing the signal into periodic and aperiodic components (detailed overview of this approach see: Donoghue et al., 2020; Adelhöfer et al., 2021).

The FOOOF algorithm models the power spectrum as a combination of aperiodic activity [L(f)] and periodic (oscillatory) activity [Gn(f)]. Specifically, the formula for the model is:

graphic file with name d33e450.gif

Here, \f\ denotes the frequency. The power spectral density (PSD) is comprised of a linear combination of the aperiodic component, \L(f)\, along with a total of \n\ Gaussian distributions. The aperiodic component is modeled as a function spanning the complete range of the spectrum that is being fitted. The function for the aperiodic component, \L(f)\, is described as follows:

graphic file with name d33e455.gif

In this context, \b\ indicates the aperiodic offset, which accounts for the shift in broadband power, while \x\ represents the aperiodic exponent, equivalent to the slope of the line when fitted to the power spectrum in a log-log space. The periodic, or oscillatory, components are identified as frequency regions where the power exceeds the aperiodic component. Each oscillatory component, commonly called a ‘peak’, is represented using a Gaussian profile, determined by three specific parameters. Each Gaussian fit can be described as:

graphic file with name d33e460.gif

Inline graphic, Inline graphic and Inline graphic represent the amplitude, center frequency, and bandwidth of each component, respectively.

To achieve a reliable estimation of the aperiodic component, the power spectra data were fitted over a broad frequency range from 3 to 30 Hz, in line with the FOOOF documentation guidelines. The FOOOF algorithm was adjusted with the following settings: {aperiodic mode = ‘fixed’, peak width limits = [2 8], maximum number of peaks = 8, and minimum peak height = 0.05, using default settings for all other parameters}. Power spectra fitting was conducted for each electrode, participant, and task condition (see Figure SI for model fit). The average R2 of the spectral fits was above 0.95 for Sample 1 (N = 36), above 0.95 for Sample 2 (N = 36), and above 0.94 for the entire group of participants (N = 72).

Aperiodic exponent

First, we determined the ‘global’ aperiodic-only signal for each electrode and participant (Hill et al., 2022) and then calculated the average of the exponent values across 60 electrodes for each participant (Hill et al., 2022), so to identify the general pattern of variation. Then, to explore how the aperiodic components were distributed across the scalp, we performed an additional cluster-based permutation test, which led to statistically significant results on a global level. The non-parametric cluster-based permutation test, as proposed by Maris and Oostenveld in 2007, is an approach used to detect effects in the dimensions of space, frequency, and time, while addressing the issue of multiple comparisons in high-dimensional EEG/MEG datasets. In the current study, clusters were created by linking adjacent sample-level F-values that surpassed a threshold (α = 0.005). The sum of F-values within each cluster defined the cluster-level statistics. To identify significant clusters, 10,000 Monte Carlo random samples were employed with a significance level set at 0.05.

Statistical analysis

The behavioral accuracy, reaction time (RT) and exponent data were analyzed using SPSS 24.0. A 3 (affect: negative/neutral/positive) × 2 (block: same-valence/different-valence) repeated-measures ANOVA was conducted. Greenhouse–Geisser correction was used to adjust the ANOVA results, and Bonferroni correction was applied to Post hoc tests. All descriptive statistics include reporting the mean and the standard error of the mean (SEM).

Results

Behavior

The behavioral results from sample 1 have been reported elsewhere (Xu et al., 2024). Briefly, we found that the accuracy was lower for neutral faces (82.53 ± 1.0%) than for negative (85.63 ± 1.1%, p < 0.001) and positive faces (85.14 ± 1.1%, p = 0.007, Fig. 2a), and the accuracy tended to be higher in same-valence blocks (85.48 ± 1.1%) than in different-valence blocks (83.39 ± 1.2%, p = 0.07, Fig. 2b). Consistently, the behavioral results from Sample 2 also showed significant main effects of affect (F2,70 = 6.12, p = 0.002, ηp2 = 0.16) and block (F1,35 = 36.74, p < 0.001, ηp2 = 0.51) with higher accuracy for both negative (81.11 ± 1.10%) and positive faces (81.36 ± 1.12%) than for neutral faces (79.26 ± 1.07%, vs. negative p = 0.02; vs. positive p = 0.01) and higher accuracy in same-valence (84.57 ± 1.20%) than that in different-valence blocks (76.59 ± 1.24%, p < 0.001). After pooling the two samples together, they showed significant main effects of affect (F2,142 = 15.18, p < 0.001, ηp2 = 0.18) and block (F1,71 = 33.35, p < 0.001, ηp2 = 0.32). Pairwise comparisons indicated that accuracies for negative (83.49 ± 0.84%) and positive (83.44 ± 0.82%) faces were both higher than for neutral faces (81.09 ± 0.85%, ps < 0.001, Fig. 2c) and accuracy was higher in same-valence (85.38 ± 0.87%) than different-valence blocks (79.97 ± 0.96%, p < 0.001; Fig. 2d).

Fig. 2.

Fig. 2

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Results of behavioral accuracy in sample 1 (a, b) and the pooled sample (c, d). Higher accuracy for both negative and positive than neutral faces (a, c) and in same-valence than different-valence blocks (b, d). #p = 0.07, *p < 0.05, **p < 0.01, ***p < 0.001

Brain-wide aperiodic exponents

As predicted, results from Sample 1 showed a significant main effect of affect (F2,70 = 11.46, p < 0.001, ηp2 = 0.25) with lower exponents (i.e., higher cortical noise and E/I ratios) for positive (1.52 ± 0.03) than both neutral (1.56 ± 0.03, p = 0.008) and negative faces (1.58 ± 0.04, p < 0.001). The interaction between affect and block was also significant (F2,70 = 9.97, p < 0.001, ηp2 = 0.22) and post-hoc Bonferroni-corrected comparisons indicated that the lower exponent for positive than neutral and negative faces was obtained in same-valence block (ps < 0.004) but not in different-valence block (ps > 0.30). In addition, the exponent for positive faces was significantly higher in different-valence blocks(1.55 ± 0.04) than in same-valence blocks (1.48 ± 0.03, p = 0.03).

These findings were replicated in Sample 2, which also showed a significant main effect of affect (F2,70 = 6.57, p = 0.02, ηp2 = 0.16) with a lower exponent for positive (1.65 ± 0.04) than both neutral (1.70 ± 0.04, p = 0.003) and negative faces (1.69 ± 0.04, p = 0.056). The interaction between affect and block was also significant (F2,70 = 5.87, p = 0.004, ηp2 = 0.14) and post-hoc Bonferroni-corrected comparisons indicated that the effect of a lower exponent for positive than neutral and negative faces was observed in same-valence blocks (ps < 0.03) but not in different-valence blocks (ps > 0.8). In addition, the exponent for positive faces was significantly higher in different-valence blocks (1.68 ± 0.05) than that in same-valence blocks (1.61 ± 0.04, p = 0.05).

Given the results were consistent across two samples, we pooled them together. This resulted in significant main effects of affect (F2,142 = 16.82, p < 0.001, ηp2 = 0.19), with the exponent being significantly lower for positive faces (1.58 ± 0.03) than for both neutral (1.629 ± 0.027, p < 0.001, Fig. 3a) and negative faces (1.632 ± 0.028, p < 0.001). The interaction effect was also significant (F2,142 = 15.38, p < 0.001, ηp2 = 0.18) and post-hoc Bonferroni-corrected comparisons indicated that the exponent was lower for positive faces than for neutral and negative faces in same-valence blocks (ps < 0.001) but not in different-valence blocks (ps > 1, Fig. 3b).

Fig. 3.

Fig. 3

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The main effects of affect with lower aperiodic exponents for positive than both neutral and negative faces (a) and interactions indicating the effect of valence only in same-valence blocks (b). **p < 0.005, ***p < 0.001

Cluster-Based permutation analysis

Following previous published studies (Gao et al., 2024, 2025; Pi et al., 2024, 2025; Yan et al., 2024: Yan et al., 2025), the cluster-based permutation test was performed to identify the specific electrodes that contributed to the main effects of affect in the whole-brain sample. Given brain-wide analysis revealed that the differences only occurred in same-valence block design, in the subsequent electrode-specific analysis, we only focus on the exponents in this design.

We found four significant clusters including F1, Fz, F2 (cluster 1, p = 0.03), F6, F8, F12, FT11 (cluster 2, p = 0.02), FC3, FC1, FCz, FC2, FC4, FC6, FT12 (cluster 3, p = 0.003) and C1, Cz, C2-4, T8, TP7, TP8, all CP-, P-, PO-electrodes and O1 (cluster 4, p < 0.001) when comparing negative and positive faces and five significant clusters including F1, Fz, F2 (cluster 1, p = 0.03), F8, F12, FT11 (cluster 2, p = 0.03), FC3, FC1, FCz, FC2 (cluster 3, p = 0.005), C3, C1, Cz, C2, C4 (cluster 4, p = 0.003), and T8, TP7, TP8, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO3, Poz, PO4, PO8, and O1 (cluster 5, p < 0.001) comparing neutral and positive faces. No other significant clusters were found when comparing negative vs. neutral faces (Fig. 4).

Fig. 4.

Fig. 4

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Scalp topography when comparing negative to positive faces (left) and neutral to positive faces (right) with colors denoting the sum of cluster-level T-values

Discussion

The aim of this study was to find out whether human affect is systematically related to aperiodic activity as indicated by the aperiodic exponent. Before interpreting our findings in terms of metacontrol, it is important to clarify that the present task did not directly manipulate persistence versus flexibility. Cognitive control demands were held constant at a 2-back load, and affective valence was task-irrelevant. Accordingly, we use the metacontrol framework as a conceptual lens to interpret how affective states may bias neural dynamics toward more persistent or flexible processing, rather than as a directly instantiated experimental manipulation. The prediction that this might be the case was derived from theoretical triangulation hypothesized by Hommel (2025): metacontrol, the balancing between cognitive persistence and flexibility, has been reported to be systematically associated with both aperiodic activity and affective states, suggesting that affective states may also be associated with aperiodic activity. Given that positive affect has been theoretically linked to metacontrol flexibility (Dreisbach et al. 2019; Akbari Chermahini and Hommel 2012a, b; Zhang & Hommel, 2022; Zwosta et al., 2013; Dreisbach et al., 2018), and that persistence was found to be associated with a lower degree of aperiodic activity (i.e., higher values of the aperiodic exponent) than flexibility (Gao et al., 2024; Pi et al., 2024; Yan et al., 2024; Zhang et al., 2023), we predicted that the processing of positive, neutral, or negative stimuli would be associated with changes in cortical noise and the E/I balance, thereby shaping metacontrol dynamics. Processing positively valenced stimuli was expected to be associated with lower aperiodic exponents, reflecting increased neural variability that may be consistent with flexibility-related neural states. In contrast, negative affect may raise the aperiodic exponent, reinforce inhibition and promote sustained, focused cognitive processing. The findings provide substantial evidence for the first hypothesis, but not for the second—which fits with previous observations that the positive-going effects tend to be stronger and more reliable than negative-going effects (Akbari Chermahini and Hommel 2012b).

However, our prediction for positive affect only held when valence was blocked, but not when valence was randomly varied. This pattern may reflect sustained valence-related neural modulation across blocked trials, rather than purely phasic, stimulus-locked effects, without implying a verified change in subjective mood. On the one hand, this observation is consistent with behavioral findings showing that affective context can bias cognitive control styles in ways that are consistent with our predictions—i.e., positive mood coming with flexibility biases or better performance in flexibility-heavy tasks and negative mood coming with persistence biases or better performance in persistence-heavy tasks (Dreisbach et al. 2019; Akbari Chermahini and Hommel 2012a). On the other hand, however, the absence of valence-related effects with random variation may suggest that phasic, stimulus-driven influences on aperiodic activity were attenuated in this design. Given that behavioral studies do suggest the possibility of such effects (e.g., Sperlich & Unkelbach, 2025), and given that phasic effects on aperiodic activity have been frequently reported (Gao et al., 2024, 2025; Pi et al., 2024), this could be taken to challenge our hypothesis of a strong link between affect and metacontrol. However, there are reasons not to overinterpret the null finding with random presentation of valence.

For one, the task we considered was rather complex and likely to include many strategical switches from memory maintenance to the intake of new information and back. As these different strategies affect aperiodic activity in different ways (Zhang et al., 2025), phasic effects of affective stimuli may have been easily overwritten. For another, the n-back task required the active suppression of the immediately preceding stimulus to come to the right conclusions, which may also have affected the way affective content was impacting aperiodic activity. In other words, the present task might have been suboptimal for assessing phasic effects. While we interpret the block-level differences in aperiodic activity as consistent with mood-like or sustained valence effects, we acknowledge that our data do not provide direct evidence for underlying dopaminergic mechanisms, flexibility networks, or clinical outcomes. Any links to neuromodulatory pathways or clinical implications should therefore be considered theoretical and exploratory, highlighting directions for future research rather than established conclusions.

While we had no theoretically guided predictions regarding the block factor, the eventual outcome fits with our speculation: varying the valence of stimuli randomly yielded a higher degree of aperiodic activity than the blocked presentation of valence. This might be a first indication that the randomness of stimuli has a systematic relationship to the aperiodic activity of the brain, in the sense that external randomness can entrain internal randomness to some degree. The findings suggest that stimulus unpredictability may systematically modulate aperiodic activity, with potential implications for metacontrol-related neural states. When stimuli are presented unpredictably, the brain’s intrinsic variability, as indicated by aperiodic activity, shows an increase. This heightened neural variability suggests that the cognitive system becomes more adaptable, shifting toward a more flexible metacontrol state. This is another point of departure for further empirical investigation. Nevertheless, we emphasize that random-valence blocks do not constitute a direct manipulation of metacontrol or flexibility, as the core 2-back task remained constant and valence was task-irrelevant. Rather, these blocks introduce increased contextual variability, which may bias neural processing toward higher variability and flexibility-related states, providing indirect, theoretically motivated evidence linking environmental unpredictability to aperiodic activity. Further, although we observed higher aperiodic exponents (less cortical noise) for positive faces in same-valence blocks compared with random-valence blocks, any interpretation that this reflects “randomness entraining internal noise” remains speculative. This pattern is post-hoc and hypothesis-generating, suggesting a possible influence of stimulus predictability on neural variability, but direct evidence is needed to confirm this mechanism.

The behavioral affective enhancement effect, with faster or more accurate responses to positive stimuli, aligns with the observed decreases in aperiodic exponent for the same conditions. This suggests that valence-related changes in cortical activity may support more flexible processing, providing a potential neural correlate of the behavioral facilitation. The cluster-based permutation test revealed that the difference in aperiodic activity between positive and negative/neutral stimuli was supported by a broad distribution of electrodes across frontal, parietal, temporal and occipital regions. While our findings for positive valence were robust, it is important to note that the relationship between negative valence and persistence (higher aperiodic exponents) is less consistently established in the literature. Accordingly, interpretations regarding negative stimuli should be considered tentative and viewed as exploratory in the context of our study. The obtained spatial pattern aligns with previous neuroimaging studies, which have identified multiple large-scale networks—such as the frontoparietal network, cognitive control network, and default mode network—as being engaged in the n-back task (Finc et al., 2020; Weigard et al., 2024). Specifically, the frontoparietal network has been implicated in stimulus decoding and attentional control, while temporo-occipital regions are thought to support the rehearsal of task-relevant information (Rossi et al., 2023). Moreover, the frontoparietal network, salience network and primary visual network are involved in cognitive flexibility (Chen et al., 2016; Yin et al., 2020; Uddin, 2021).

Our observation of increased aperiodic activity across widespread electrodes located within these networks in response to positive valence may reflect enhanced neural variability consistent with flexibility-related processing, in line with predictions derived from the metacontrol framework. From a physiological perspective, dopaminergic signaling has been implicated in both affective processing and the modulation of neural excitability, which may in turn influence the balance of excitation and inhibition in cortical circuits (Badgaiyan et al., 2009; Salimpoor et al., 2011; Gao et al., 2024). While our study does not directly measure dopamine release or manipulate catecholamines, these prior findings provide a theoretically motivated framework suggesting that valence-related neural modulations, as reflected in aperiodic EEG activity, could be influenced by neuromodulatory systems including dopamine. Importantly, our data support only the observation of valence-related changes in cortical activity, without making claims about causal molecular mechanisms. Dopamine increases the signal-to-noise ratio (SNR) of neuronal activity (Servan-Schreiber et al., 1990; Winterer & Weinberger, 2004) by improving the differentiation between irrelevant neural noise and significant neural input. This enhancement of gain control is demonstrated by a steeper slope in the sigmoid input-output function (Warren et al., 2016). This suggests that factors known to elicit dopaminergic signaling, such as affective processing, may in principle be reflected in alterations of this slope, indicating that both the slope and the associated measurement of cortical noise (as reflected by aperiodic activity) could act as novel neurophysiological markers in disorders characterized by impaired affective processing.

It is important to note that, in the present study, aperiodic exponents were computed per condition and averaged across trials, reflecting sustained, block-level neural states rather than trial-by-trial or within-block adjustments. As such, our results capture context-dependent biases in neural activity associated with affective valence, rather than dynamic metacontrol adjustments in the strict sense. Positive valence was associated with increased aperiodic activity, consistent with a bias toward neural variability and cognitive flexibility, whereas negative or neutral valence corresponded to lower aperiodic activity, reflecting more stable, persistent processing. From a theoretical perspective, these block-level associations align with predictions derived from the metacontrol framework, suggesting that affective states can bias neural excitability and the balance of persistence versus flexibility, even if they do not directly induce adaptive, trial-by-trial control shifts. We consider these findings an indirect demonstration of metacontrol-relevant neural dynamics, providing a first empirical indication that affective context systematically modulates aperiodic activity.

There were several limitations of our study. First, the facial stimuli that we used were rated for valence but not arousal, so that we cannot exclude an impact of arousal . Second, the positive and negative stimuli in the present study only included happy and sad faces, other positive stimuli such as positive surprise as well as negative stimuli such as anger or disgust may show different outcome patterns. Third, a limitation of the present study is that affective valence was manipulated at the stimulus level and no independent measure of subjective mood was obtained. Accordingly, our interpretations are restricted to valence-related modulations of aperiodic activity, and links to affective states or metacontrol are intended as theoretically motivated rather than empirically demonstrated. Finally, the FOOOF algorithm constrains the calculation of the aperiodic exponent to a single, predefined time window. To advance our understanding of the relationship between affect and aperiodic activity over time, future research should employ Spectral Parameterization Resolved in Time (SPRiNT; Wilson et al., 2022). This novel approach enables the continuous tracking of fluctuations in aperiodic parameters, offering valuable insights into the temporal dynamics and progression of affective states. In any case, we take the present outcome as a first indication that affective valence embedded in task stimuli is systematically associated with aperiodic activity in the human brain. Minimally, this suggests that aperiodic activity can serve as an indicator of the valence of affective states. However, it may also be the case that the degree of aperiodic activity is an actual means that reflects the functional purpose of emotions. Contemporary research increasingly emphasizes the integration of emotion and cognition within neural systems. From this perspective, affective processes may influence the balance between persistence and flexibility in cognitive control, potentially shaping metacontrol dynamics (Hommel, 2019). Notably, both metacontrol and affective processes have been associated with overlapping frontal–striatal dopaminergic pathways (Cardinal et al., 2002; Nieoullon & Coquerel, 2003). While our study does not provide direct evidence for a functional interaction, this convergence is theoretically consistent with the idea that affective context could influence neural states relevant to the regulation of persistence and flexibility in cognitive control.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (DOCX 331 KB) (331.1KB, docx)

Author contributions

Conceptualization: Bernhard Hommel, Lorenza Colzato, Xiaolei Xu; Data collection: Meiyun Jiang, Xiaoqi Fu; Data analysis: Xianyun Zhu, Xiaolei Xu, Meiyun Jiang, Zhe Li; Writing: Xiaolei Xu, Xianyun Zhu, Lorenza Colzato, Bernhard Hommel.

Funding

This work was financially supported by a One case, one policy grant from the Shandong province to Bernhard Hommel, and by grants from the Taishan Scholars Program (No. tsqn202306156), the Natural Science Foundation of Shandong Province (ZR2024MC187) and aNational Natural Science Foundation of China (NSFC, 32100887) award to Xiaolei Xu.

Data availability

The data underlying this article will be shared on reasonable request to the first author.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Xiaolei Xu and Xianyun Zhu contributed equally in this work.

Contributor Information

Lorenza Colzato, Email: colzato@bhommel.onmicrosoft.com.

Bernhard Hommel, Email: bh@bhommel.onmicrosoft.com.

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Supplementary Materials

Supplementary Material 1 (DOCX 331 KB) (331.1KB, docx)

Data Availability Statement

The data underlying this article will be shared on reasonable request to the first author.

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