Temporal predictions shape somatosensory perception

Domain: neuroscience
Published: 2026-04-14
Editorial outcome: major_revision

Abstract
Although intensity expectations have been thoroughly studied in relation to pain, there has been a notable lack of investigation into temporal expectations. One important temporal pain effect, the so-called dread effect, suggests that future pain becomes more aversive with increasing delay. Here we investigated temporal expectations including the dread effect by presenting probabilistically cued painful heat and non-painful cold stimuli after different delay periods. Actual stimulus latency had no effect on perceived intensity in both non-painful cold and painful heat conditions. However, our data clearly show that the expectation of longer delays amplified somatosensory perception, indicating that the dread effect is related to expected and not to experienced delay. Electroencephalograph data show that temporal expectations modulate alpha/beta activity during cue presentation, but not during stimulation. Actual stimulus timing is represented in alpha-to-beta frequencies during heat and cold stimulation.

Introduction
Whenever we wait for a painful event, we generate expectations regarding the timing of the painful stimulus, making this a possible example of predictive coding. Predictive coding posits that stimulus expectations, such as intensity and timing, are fundamental to interoceptive perception1–3. From the lens of this framework, we consistently build a model of our world and compare it with actual sensory input. Mismatches between this model and the actual sensory information would result in prediction errors, which are utilized to form more accurate future models of the world. This mechanism would allow us to reduce the amount of processed information, which in turn leads to more efficient neural processing, and, in the context of the Free Energy Principle3–5, to a reduction in surprise/entropy. The recurrent nature of pain-related neural activity has led to the suggestion that pain processing can be investigated through the lens of predictive processing5–13.

Previously, pain processing has been shown to reflect the interaction of sensory input properties, expectations, and prediction errors7,12,14–20. However, these studies have primarily focused on demonstrating these effects within the context of predicting pain intensity. For example, if someone expects to feel low pain but suddenly experiences high pain instead, this creates a prediction error. This line of reasoning has led to the formulation of a Bayesian integration model of pain perception6,13,21–23. According to this model, precision-weighted prior expectations are combined with sensory information to generate an individual’s pain perception. This has been shown in many studies investigating the relationship of pain and pain intensity expectations7,12,15,24–31.

In contrast, few studies have investigated the properties of temporal expectations regarding pain, which raises the question of how temporal sensory input, expectations, and prediction errors are processed, for example, what if pain occurs earlier than expected? This specific form of predictive timing remains largely unexplored, highlighting the need for further investigation into these mechanisms to enhance our understanding of how temporal expectations influence pain processing.

One notable phenomenon related to timing in pain perception is the “dread effect,” which challenged traditional economic theories of temporal discounting32,33. In classical decision-making theories, it was posited that individuals tend to defer the experience of negative consequences while prioritizing the pursuit of positive outcomes34,35. However, these theories do not consider the possibility that anticipation itself can have an appetitive or aversive value. On the one hand, we experience a certain sense of joyful anticipation for a distant vacation, and this anticipation has intrinsic value. On the other hand, we experience a certain sense of dread in anticipation of a dentist appointment (as has been shown by Story et al.36). This clearly affects decision making: When presented with a decision regarding the waiting time for potential pain, most individuals tend to choose an earlier rather than a later outcome36,37. This phenomenon is known as the “dread effect”, as anticipation itself seems to be associated with negative effects of dread. Similar to this notion, longer anticipation periods have been linked to an increased perception of pain38,39, and are particularly relevant to investigate within the framework of predictive timing, as it may reflect how temporal expectations shape processing of pain during a waiting period. However, none of these studies have distinguished between an expected waiting period and the actual waiting period.

Building upon prior work demonstrating that pain perception is modulated by probabilistic expectations and prediction errors7,12,15,16, our study investigated how probabilistic temporal expectations and actual delays influence pain perception and associated brain (EEG) activity.

We designed an EEG experiment (adapted from Strube et al.12,40), in which we could induce temporal expectations (instant, early, late) regarding a somatosensory non-painful cold or painful heat stimulus (see Fig. 1 for an overview and Fig. 2 for the trial design). We deliberately included non-painful cold stimuli to ascertain whether these effects stem from general somatosensory processing or whether they are specific to painful outcomes. We aimed to specifically distinguish between an expected waiting period and an actual waiting period. This approach allowed us to explore several key questions: First, can we identify a distinct pattern in brain activity for predictive timing, i.e., temporal expectations and prediction errors? Second, what is the potential role of timing expectations versus actual delays with respect to the dread effect36,37?

Results
In this experiment, we initially presented a cue indicating whether the subsequent stimulus would result in an increase to (painful) 46.5°C heat or a decrease to (non-painful) 20.5°C cold from a baseline temperature of 30°C. A second cue indicated, probabilistically, if the next (painful) heat stimulus or (non-painful) cold stimulus started instantly, early or late (0s, 2s, and 4s after presentation of the temporal cue, respectively), inducing temporal expectations. After stimulation, participants were asked to rate the stimulus on a visual analog intensity scale (VAS), where 0–50 represented non-painful intensities, and 51–100 represented painful intensities.

Behavioral intensity ratings
We analyzed trial-wise intensity ratings using a linear mixed-effects (LME) model with fixed effects of stimulus modality (heat vs. cold), temporal expectation (i.e., expected latency based on predictive cues), stimulus latency (i.e., actual latency from cue-offset to stimulation-onset), and prediction error (i.e., absolute difference between expectation and latency), including all interactions with stimulus modality. The model included by-participant random intercepts and random slopes for all predictors. See “Methods” for a detailed description of the LME model (Fig. 1b). See Supplementary Information for full model output and diagnostics.

The model revealed a significant main effect of stimulus modality (t(10072)=16.88, p<.001, standardized β=1.69, SE=0.10, 95% CI [1.50, 1.89]), i.e., (non-painful) cold stimuli were rated with lower intensities than (painful) heat (see Fig. 3a, b for heat and cold ratings). In addition, we observed a significant main effect of temporal expectations (t(10072)=3.22, p=.001, standardized β=0.02, SE=0.006, 95% CI [0.008, 0.033]), indicating that intensity ratings increased with longer expected latency.

In contrast, the actual latency between cue and stimulation did not significantly modulate intensity ratings (t(10072)=0.14, p=.886, standardized β<0.001, SE=0.006, 95% CI [−0.011, 0.013]), i.e., longer latencies were not associated with differences in ratings. We also did not observe a significant prediction error effect, i.e., the absolute difference between temporal expectations and stimulus latency did not significantly modulate intensity ratings (t(10072)=−0.20, p=.843, standardized β=−0.001, SE=0.005, 95% CI [−0.011, 0.009]). Furthermore, no significant interactions of stimulus modality with latency, expectation, or prediction error were observed (all p>.66).

For painful heat stimulation (Fig. 3a), post hoc analyses using Bonferroni correction for multiple comparisons indicated that late temporal expectations lead to higher intensity VAS ratings (M=72.16, SD=11.66) than early latency expectations (M=70.55, SD=11.79, p<.001) and instant temporal expectations (M=70.85, SD=11.74; all p<.001). The difference between instant and early temporal expectations was not significant (p=.94).

In (non-painful) cold stimulation (Fig. 3b), post hoc analyses using Bonferroni correction for multiple comparisons indicated that late temporal expectations lead to higher VAS intensity ratings (M=22.14, SD=11.76) than instant temporal expectations (M=20.67, SD=11.05, p<.001). The difference between both, early (M=21.29, SD=11.03) and instant temporal expectations (p=.25) and early and late temporal expectations (p=.22) were not significant.

An additional supplementary analysis, including prediction errors at stimulation (see Fig. 1a, bottom row), also showed that the data are modulated by expected latency, but not by prediction errors or actual stimulus latency (see Supplementary Information and Supplementary Fig. 2).

This suggests that temporal expectations alone can modulate somatosensory perception, increasing its perceived intensity. This was observed for painful heat and non-painful cold stimuli, i.e., more intense heat and more intense cold perception. Our rating scale allowed us to dissociate painful (ratings between 51 and 100) from non-painful stimulation (ratings between 0 and 50). Heat stimuli ratings (M=71.19, SD=11.69) were clearly in the painful range, whereas cold stimuli (M=21.37, SD=11.21) were clearly in the non-painful range. Therefore, the observed effect of temporal expectations applies regardless of the painfulness of the stimulation. This is at odds with interpretations explaining increased pain by longer anticipation periods; instead, this suggests that the somatosensory expectation itself can drive this effect in both painful heat and non-painful cold conditions.

We additionally performed exploratory computational modeling within a Bayesian Pain Model framework6,30,41–44 to illustrate possible mechanisms behind the expected-delay-dependent intensity increase. Given a scenario in which intensity expectations during acute pain are below the sensory input, model evidence favored an expectation intensity shift model over an expectation precision modulation model. In all tested configurations, expectation intensity shift models outperformed expectation precision modulation models (i.e., protected exceedance probability was at φ≈1). Modeling details are reported in the Supplementary Information.

EEG results
For the time-frequency analysis of EEG data, we considered two separate time intervals to evaluate cue-locked and stimulus-locked effects. For cue-locked analyses, we set t=0 to the onset of the cue, probabilistically indicating the latency of the upcoming stimulus. In stimulus-locked analyses t=0 was set to the point when the thermode was triggered to increase/decrease the temperature to the target levels of 46.5°C for painful heat and to 20.5°C for non-painful cold. All tests were corrected for multiple comparisons using Monte Carlo cluster tests. At each sample an F-test was conducted for each respective contrast (i.e., heat vs cold stimulation, stimulation vs catch, stimulus latency, temporal expectations and prediction errors), and all samples exceeding the threshold of p<0.05 were clustered in connected sets on the basis of temporal (i.e., adjacent time intervals), spatial (i.e., neighboring electrodes), and spectral adjacency. This was adjusted using permutation testing. (see “Methods” for details; see Supplementary Fig. 7 for z-scored EEG power at pain stimulation conditions; see Supplementary Fig. 8 for z-scored EEG power at cold stimulation conditions).

Effect of pain and cold stimulation
In the first step, we investigated the EEG profile related to pain stimulation to check whether this follows canonical EEG patterns, namely an increase in theta power (4−8 Hz), a decrease in alpha-to-beta power (8−30 Hz), and an increase in gamma power (>30 Hz; see Ploner et al. for review)45.

Firstly, we conducted an F-test to assess the main effect of modality on EEG power by contrasting (correctly cued, to avoid the influence of prediction errors) non-painful cold and painful heat trials and identified clusters showing significant differences between cold and pain using a Monte Carlo cluster-based permutation approach. Differences were significant for stimulus-locked data (in a window of 0–4 s with t=0 at stimulus onset, 4–181 Hz), revealing two clusters of activity associated with painful heat vs non-painful cold stimulation (Fig. 4a). We observed a positive cluster (p=.004), including frequencies from 9.5 to 181 Hz in a time frame of 2.05–4 s, indicating an increase of EEG power in beta-to-gamma (>12 Hz) in painful heat stimulation vs non-painful cold stimulation. The highest F-value from the repeated-measures ANOVA was F(1,34)=31.82 (p<.001). This sample was observed after 3 s at 16 Hz and had a maximum at channel CP3. In the theta-to-alpha band (4–12 Hz), we found another significant positive cluster of activity (p=.034), showing higher EEG power with painful heat vs non-painful cold stimulation. This cluster included frequencies from 4 to 9.5 Hz in a time frame of 0.85–4 s. The highest parametric F-value from the repeated-measures ANOVA was F(1,34)=23.19 (p<.001). This sample was observed after 1.4 s at 8 Hz and had a maximum at channel FCz.

Secondly, we tested stimulation of (painful) heat and (non-painful) cold, separately, against respective catch trial periods (with the same modality and timing cues) without stimulation. To avoid potential confounds with prediction errors, only correctly cued trials were included in the analysis of stimulation effects on EEG.

For painful heat stimulation versus catch trials (Fig. 4b), differences were significant for stimulus-locked data (in a window of 0–4 s with t=0 at stimulus onset, 4–181 Hz), revealing two clusters of activity associated with painful heat stimulation. We observed a positive cluster (p=.047), including frequencies from 4 to 6.8 Hz in a time frame of 1.75–4 s, indicating an increase of EEG power in theta (4–8 Hz) in painful stimulation. The highest F-value from the repeated-measures ANOVA was F(1,34)=24.33 (p<.001). This sample was observed after 2.65 s at 4 Hz and had a maximum at channel C2. In the alpha-to-beta band (8–30 Hz), we found a significant negative cluster of activity (p<.001), showing desynchronization of EEG power with pain stimulation. This cluster included frequencies from 8 to 63 Hz in a time frame of 0–4 s. The highest parametric F-value from the repeated-measures ANOVA was F(1,34)=45.97 (p<.001). This sample was observed after 4 s at 22.6 Hz and had a maximum at channel Fz.

For non-painful cold stimulation (Fig. 4c), we observed one cluster of activity (p<.001), showing desynchronization in the theta, alpha-to-beta, and gamma bands from 4.8 to 180 Hz. The highest parametric F-value from the repeated-measures ANOVA was F(1,34)=49.18 (p<.001). This sample was found at 0.85 s and 19 Hz and had a maximum at channel C4.

Painful stimulation is typically associated with increases in the theta band (4–8 Hz), decreases in the alpha and beta band (8–30 Hz), and increases in the gamma band (>30 Hz) activity45. When contrasting painful heat with non-painful cold stimulation, we observed widespread increases in EEG power in the theta (4–8 Hz), alpha-to-beta (8–30 Hz), and gamma range (>30 Hz), but no significant alpha–to-beta (8–30 Hz) decreases for pain in the direct modality comparison. By contrast, when each stimulation type was compared against its respective catch condition, both painful heat and non-painful cold stimulation elicited significant alpha-to-beta (8–30 Hz) desynchronization, consistent with canonical somatosensory responses. For gamma activity (>30 Hz), painful heat stimulation did not show a significant increase relative to catch trials. However, in the direct pain vs. cold comparison, gamma power was significantly higher for pain, an effect that may be partly driven by reduced gamma activity during cold stimulation in the present data.

Stimulus latency
In this experiment, participants were exposed to either (painful) heat or (non-painful) cold stimuli, delivered at varying delays following a cue: instant (0 s), early (2 s), or late (4 s).

As a first step, we tested for the effects of actual stimulus latency (i.e., the duration between cue offset and stimulus onset) on EEG time-frequency data. We considered EEG data from stimulus onset (time-locked to 0 s) to stimulus-offset (time-locked to 4 s) and conducted F-tests contrasting instant, early, and late stimulation and testing its interaction with stimulus modality (heat vs cold).

For the main effect of stimulus latency (Fig. 5), a cluster-corrected dependent samples F-test revealed significant changes in stimulus-locked EEG power (0–4 s at stimulation onset, 4–181 Hz). Data shows a negative cluster (p<.001) predominantly in the alpha and beta bands (8–30 Hz), with significant samples ranging from 0 to 4 s, including frequencies from 4 to 181 Hz, exhibiting a negative association between longer stimulus latencies and EEG power during stimulation. The highest parametric F-value from the repeated-measures ANOVA was F(1,34)=85.89 (p<.001). This sample was found at 0.95 s at 16 Hz and had a maximum at channel PO8. A topography of statistical F-values indicates that the stimulus latency main effect originates from posterior regions. We found no significant cluster of activity associated with an interaction between stimulus latency and stimulus modality (all p>.05).

This represents actual temporal stimulus properties—i.e., the stimulus delay—, which appear to be predominately encoded at posterior regions in lower frequency bands (theta and alpha-to-beta).

Temporal expectations
As an next step, we evaluated how temporal expectations modulated EEG time-frequency patterns. In this study, a cue presented before stimulus presentation conveyed information (probabilistically) whether the next stimulus will be initiated instantly (0 s), early (2 s), or late (4 s), and thus provided information to induce temporal expectations regarding the subsequent stimulus. As this expectation signal might be present during both the cue presentation as well as during stimulus presentation, we conducted cluster-corrected dependent samples F-test both during cue presentation (0–1 s, 4–181 Hz; time-locked to the onset of the predictive cue) and during stimulation (0–4 s, 4–181 Hz; time-locked to the onset of the thermal stimulation).

In the timeframe of the presentation of the cue indicating instant, early or late stimulation, dependent samples F-tests (0–1 s, 4–181 Hz; time-locked to the onset of the predictive cue) revealed no significant main effect of expected latency (all p>.05). We observed a significant crossed modality x expected latency interaction in the alpha-to-beta bands (8–30 Hz; p=.048) with significant samples occurring from 0 to 1 s after cue onset at frequencies between 6.7 and 53.8 Hz, showing a positive association between expected longer latencies and EEG power during cue presentation for (painful) heat stimuli and a negative association between longer expected latencies and EEG power during cue presentation for (non-painful) cold stimuli (Fig. 6). The highest parametric F-value from the repeated-measures ANOVA was F(1,34)=21.42 (p<.001). This sample was found at 0.75 s after cue onset and 16 Hz and had a maximum at channel CP3.

The analysis of stimulus-locked data revealed no association between latency expectations and EEG data during stimulation (all p>.05). Also, our data revealed no significant interaction between stimulus modality and expected latency in stimulus-locked data (all p>.05).

The data presented here provides evidence that temporal expectations regarding subsequent temporal stimulus properties are encoded during cue presentation, whereas EEG activity during stimulus presentation was not modulated by latency expectations.

Temporal prediction errors
One aim of this study was to investigate whether discrepancies between temporal expectations and actual stimulus latencies would lead to measurable changes in time-frequency EEG data. Specifically, we examined absolute prediction errors, coding a late stimulus with instant latency expectations (a high prediction error) in the same way as an instant stimulus with late latency expectations (also a high prediction error), whereas a stimulus with instant latency expectations would not create a prediction error in instant expectation conditions (see Supplementary Fig. 4 for results with a prediction error coded based on the actual probability at stimulation; see Supplementary Fig. 5 for EEG analysis results with all datasets with 10 or more trials in each PE condition; see Supplementary Fig. 6 for additional time-frequency patterns of absolute prediction errors).

For the main effect of (absolute) temporal prediction errors (Fig. 7), we found an association between stimulus-locked EEG data (0–4 s at stimulation onset, 4–181 Hz) with absolute temporal prediction errors in beta-to-gamma frequencies (>12 Hz) predominately in the second half of stimulation. This positive cluster (p<.001) includes samples ranging from 0 to 4 s and frequencies from >6.7 Hz. The highest parametric F-value from the repeated-measures ANOVA was F(1,34)=94.13 (p<.001). This sample was observed at 4 s and 16 Hz and had a maximum at channel C3. We found no significant interaction between absolute prediction errors and stimulus modality (all p>.05).

It is typically theorized that prediction errors are encoded in gamma frequencies (>30 Hz; Bastos et al.). The data presented here demonstrate that temporal prediction errors are encoded predominately in beta-to-gamma frequencies (>12 Hz) for both (painful) heat and (non-painful) cold stimulation. This implies that a mismatch between expected latencies and actual stimulus latencies leads to increases in beta-to-gamma activity.

Methods
Subjects
35 healthy participants were enrolled (mean age 27.9, range 19–40 years, sex: 18 female/17 male). Note that no formal sex- or gender-specific analyses were conducted, as the study was not powered to detect reliable sex- or gender-dependent effects. All participants gave informed consent and were paid as compensation for their participation. Applicants were excluded if one of the following exclusion criteria applied: neurological, psychiatric, dermatological diseases, pain conditions, current medication, or substance abuse. Participants were recruited via convenience sampling using advertisements on a local job platform, targeting the local community and university population. Potential self-selection effects are inherent to this recruitment strategy. The study was approved by the Ethics Board of the Hamburg Medical Association.

The sample size was determined through a priori power analysis using G*Power (version 3.1.9.4). We informed the power analysis by behavioral effects observed in Strube et al.30, where expectations were manipulated under similar experimental conditions in the same laboratory. That study reported large effect sizes (Cohen’s f > 0.5) for both intensity expectation effects and modulations of pain ratings by agency. Assuming a low correlation among repeated measures (r=0.2), an alpha level of 0.05, and a desired power of 0.80, the analysis indicated that a sample size of 12 subjects would be sufficient to detect similar effects. However, considering potential differences in expectation modulation—specifically, temporal versus intensity expectations—we increased the planned sample size to 35 subjects. Accounting for an anticipated dropout of four subjects, a final sample size of 31 provides adequate power to detect medium effect sizes (f=0.3) in our within-subject design.

Stimuli and task
Thermal stimulation was performed using a 30×30 mm² Peltier thermode (CHEPS Pathway, Medoc) at two different intensities: (non-painful) cold (20.5°C) and (painful) heat (46.5°C) at the left radial forearm. The baseline temperature was set at 30°C and the rise rate to 8°C/s. After each block, the stimulated skin area was changed to avoid sensitization. To ensure a clear perceptual contrast between painful and non-painful stimulation, we used cold stimuli (20.5°C). Pilot testing indicated that warm non-painful stimuli near the neutral threshold (32–34°C) were perceived inconsistently across pilot participants and exhibited strong habituation effects. In contrast, cold stimuli were reliably perceived as non-painful but salient.

Each trial started with the presentation of a temperature cue (indicating correctly if the next stimulus is a heat or cold stimulus) by the display of “heat” or “cold” on the center of the screen for 1 s. Afterwards, a fractal was presented for 1 s as a temporal expectation cue and probabilistically associated with instant, early, or late stimulation (see Fig. 1a for a contingency table). In test trials, instant, early, and late stimulation (either heat or cold stimulation) was initiated 0, 2, or 4 s after the offset of the temporal expectation cue, respectively. In catch trials, after an anticipation period of 8 s (i.e., the whole duration of a late stimulation trial including anticipation and stimulation), participants were asked to report the information of both cues (temperature and temporal cue). The intertrial interval was set to 2 s.

Trials were presented in 5 blocks (including one initial training block). At the beginning of each block, the three different fractals associated with instant, early, and late stimulation were shown (fractals were randomized for each participant), and participants were asked to learn these associations. Participants were given time until they were sure that they learned the associations. Participants were informed that the temporal cue was correctly predicting the latency until the onset of the pain in most cases, but not in all cases. At the beginning of each block, we asked participants to rate the salience of 2 heat and 2 cold stimuli (4 s stimulation, 46.5°C for heat and 20.5°C for cold) on a visual analog scale (VAS) from 0 to 100, where 0 was labeled as “no stimulation” and 100 was rated as “very salient”.

During the training block, participants only experienced congruent trials and catch trials in a fixed order, e.g., an instant temporal expectation cue was followed by instant stimulation. The fixed order was as follows: Instant Cold, Instant Cold Catch, Instant Heat, Instant Heat Catch, Early Cold, Early Cold Catch, Early Pain, Early Pain Catch, Late Cold, Late Cold Catch, Late Pain, Late Pain Catch.

During 4 experimental blocks, trials were presented in a randomized order in two microblocks. Each microblock contained 4 congruent trials of each congruent cue-stimulus combination (e.g., early cold cue leads to actual early cold stimulation), 1 incongruent trial of each incongruent cue-stimulus combination (e.g., early heat cue leads to instant heat stimulation), and 2 catch trials for each potential cue-stimulus combination. For randomization, we used a sampling method to guarantee that there were no two consecutive trials of the same condition within one block. Catch trials were implemented to ensure that late latency stimulation (4 s) was still improbable in instant and early expectation trials (i.e., probability of stimulation was at 33%) during the progression of a trial, even though stimulation did not occur after 0 and 2 s in instant and early (0 and 2 s) expectation conditions. For example, if stimulation did not occur instantly after 0 s, this changes the probabilities of stimulation at later time points (2 or 4 s) as the trial temporally progresses.

Participants were allowed to take a break between each block without a time limitation. The whole experimental procedure (incl. EEG preparation, informed consent, etc.) had a duration of approximately 3.5–4 h. The participant, the experimenter, and, in some sessions, a trained research assistant were present in the testing room. The experimenter followed a standardized experimental protocol and did not provide any feedback regarding experimental conditions or hypotheses during data collection. Blinding of the experimenter to stimulus modality and timing conditions was not feasible due to the nature of the stimulation procedure; however, all task instructions, stimulus delivery, and data acquisition were fully automated, minimizing potential experimenter-related bias.

EEG recording
EEG data were acquired using a 64-channel Ag/AgCl active electrode system (ActiCap 64; Brain Products GmbH, Germany) placed according to the extended 10–20 system49. The EEG was sampled at 500 Hz, referenced at FCz, and grounded at Iz. For removal of ocular movement artifacts, the horizontal and vertical bipolar electrooculogram (EOG) were recorded using the 4 remaining electrodes.

EEG preprocessing
The data analysis was performed using the Fieldtrip toolbox for EEG/MEG analysis50. For preprocessing, data were epoched and time-locked to the onset of the cue signaling whether the upcoming stimulus was a (non-painful) cold or (painful) heat stimulus. Each epoch was centered (subtraction of the temporal mean) and included a time range of 3 s before and 12 s after trigger onset. In 7 subjects, channel P3 was interpolated using the average of the adjacent electrodes, as this channel was found to be affected by very low signal quality in these subjects. The data were re-referenced to a common average of all EEG channels, and the previous reference channel FCz was reused as a data channel.

EEG preprocessing steps were replicated from a previous study from this laboratory to ensure consistency between analyses30. We employed a preprocessing approach by Hipp et al.51, by splitting the data into 2 band-pass filtered sub-sets from 4 to 34 Hz for low frequencies and from 16 to 250 Hz for high frequencies. This enabled efficient separation of low- and high frequency artifacts in subsequent ICA analysis. EEG epochs were visually inspected, and trials contaminated by artifacts due to gross movements or large technical artifacts were removed. Trials contaminated by eye-blinks, heartbeat, muscle activity, technical artifacts, or movements were corrected using an independent component analysis (ICA) algorithm52–54 after careful inspection of topographies, power spectra, and relation of ICA time courses to the temporal structure of the experiment. Artifactual components were removed before the remaining components were back-projected and resulted in corrected data. Finally, epochs were visually screened and trials with remaining artifacts were excluded from analysis (see Supplementary Table 2 and Supplementary Table 3 for the number of trials included for each subject, for painful heat and non-painful cold conditions, respectively; see Supplementary Table 4 and Supplementary Table 5 for the number of trials included for each subject, for each trial combination representing stimulus latency (instant, early late), expected stimulus latency (instant, early, late) and PE (no, low, high) conditions for painful heat and non-painful cold, respectively).

EEG spectral analysis
Spectral analysis was adapted from Hipp et al.51, and replicates previous analysis from our laboratory30. This approach ensured homogenous sampling and smoothing in time and frequency space. We calculated spectral estimates for 23 logarithmically scaled frequencies ranging from 4 to 181 Hz (0.25 octave increments) for the whole epoch. Using the multitaper (DPSS) approach, we set the temporal and spectral smoothing to match 250 ms and 3/4 octave, respectively. For frequencies below 16 Hz, we employed 250 ms temporal windows and varied the number of Slepian tapers to approximate a 3/4 octave spectrum smoothing. We computed the frequency transform using high- and low-frequency data for frequencies above and below 25 Hz, respectively. Analysis was then continued with the combined spectral data after averaging of spectral estimates per block and condition over trials for each subject.

For the baseline correction of time-frequency data, the mean and standard deviation were estimated (for each subject/channel/frequency combination, separately) from −1000 to −500 ms before the onset of the modality cue (indicating if the subsequent stimulus was a cold or heat stimulus). The mean spectral estimate of the baseline was then subtracted from each data point, and the resulting baseline-centered values were divided by the baseline standard deviation55.

Before further data analysis was performed on the cleaned datasets, the time axis of single trials was shifted to create cue-locked and stimulus-locked data. For cue-locked data, we used the onset of the cue signaling the expected stimulus latency as t=0. For stimulus-locked data analyses, we used t=0 at the onset of the heat or cold stimulation, i.e., to the trigger signaling the thermode to initiate a temperature change. To test for a main effect of cold or heat stimulation, we also used EEG data from catch trials, which was locked to the exact time windows where stimulation would occur in stimulation trials, i.e., 0–4 s after cue offset in instant stimulation trials, 2–6 s after cue offset in early stimulation trials, and 4–8 s after cue offset in late stimulation trials.

Predictive timing model
Similar to previous fMRI and EEG studies with intensity expectations in pain12,15, our full model included three experimental within-subject factors (see Fig. 1b) to comprise a simple predictive timing coding model. Here, we adopted this model and translated stimulus intensity, intensity expectations, and prediction errors, and reframed these as stimulus latency, temporal expectations, and temporal prediction errors to represent a simple predictive timing model.

The stimulus latency factor (see Fig. 1b, left column) models the measured response with a simple linear function of the stimulus latency (−1, 0, and 1 for instant, early, and late stimulation, respectively). The temporal expectation factor was defined (see Fig. 1b, center column) linearly from the latency predicted by the cue. Again, conditions with an instant stimulation cue were coded with a −1, conditions with an early stimulation cue with a 0, and conditions with a late stimulation cue with a 1. The temporal prediction error factor (PE) resulted from the absolute difference of the temporal expectations and the actual stimulus latency (see Fig. 1b, right column).

Behavioral intensity ratings
Behavioral intensity ratings were analyzed at the single-trial level using linear mixed-effects models (LMEs). Trials were defined by the full combination of stimulus modality (cold vs. heat), predictive cue (instant: 0 s, early: 2 s, or late: 4 s), and actual stimulus latency (instant: 0 s, early: 2 s, or late: 4 s delay), allowing for prediction errors to occur (i.e., the absolute difference between expected and actual cue-stimulus latency). Temporal expectations were modeled as continuous predictors derived from the predictive cues, actual stimulus latency was included as a continuous variable, and absolute prediction error (|expected latency - actual latency|) was entered as an additional predictor. Stimulus modality (heat vs. cold) was included as a categorical effect. Analyses were performed in MATLAB (fitlme function, version 2025a, The MathWorks). LMEs included all predictors simultaneously, with random intercepts and random slopes for each predictor by subject, thus accounting for between-subject variability and accommodating unbalanced trial numbers across conditions. Post hoc comparisons were Bonferroni-corrected for multiple testing. Violin plots were created using the function daviolinplot of the MATLAB toolbox DataViz (v3.2.4)56.

EEG data analysis
All statistical tests in electrode space were corrected for multiple comparisons using non-parametrical permutation tests of clusters57. We wanted to explore positive and negative time-frequency patterns associated with our variations of stimulus latency, temporal expectations, and (absolute) temporal prediction errors, as well as their interactions with stimulus modality, using a repeated-measures ANOVA. A statistical value corresponding to a p-value of 0.05 (F[1,34]=4.13) obtained from the repeated-measures ANOVA F-statistics of the respective main effect was used for clustering. Samples exceeding the threshold of F[1,34]=4.13 were clustered in connected sets on the basis of temporal (i.e., adjacent time points), spatial (i.e., neighboring electrodes), and spectral adjacency. Further, clustering was restricted in a way that only samples were included in a cluster which had at least one significant neighbor in electrode space; that is, at least one neighboring channel also had to exceed the threshold for a sample to be included in the cluster. Neighbors were defined by a template provided by the Fieldtrip toolbox corresponding to the used EEG montage.

Subsequently, a cluster value was defined as the sum of all statistical values of included samples. Monte Carlo sampling was used to generate 1000 random permutations of the design matrix, and statistical tests were repeated in time–frequency space with the random design matrix. The probability of a cluster from the original design matrix (p-value) was calculated by the proportion of random design matrices producing a cluster with a cluster value exceeding the original cluster. This test was applied two-sided for negative and positive clusters, which were differentiated by the average slope of the estimated factors, i.e., an increase in EEG power with factor levels 1, 2, and 3 would be regarded as a positive cluster, whereas a decrease in EEG power with factor levels 1, 2, and 3 would be considered a negative cluster of activity.

The within-subject stimulus latency factor (which was coded as increasing linearly with increasing stimulus latency) was tested stimulus-locked from 0 to 4 s. The within-subject temporal expectation factor, which was coded as increasing linearly with the cued stimulus latency, was tested latency-cue-locked from 0 to 1 s and stimulus-locked from 0 to 4 s. The absolute prediction error was coded as the absolute difference between stimulus latency and temporal expectations (see Fig. 1b for details) and was tested during stimulus presentation. In addition, we tested correctly cued non-painful cold and painful heat stimuli, respectively, against the respective time periods of catch trials, as well as non-painful cold vs painful heat.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this paper, the authors examine the impact of temporal delay and temporal delay expectations/prediction errors on pain perception and associated EEG activity. I found this to be an excellent paper. There is limited research on the temporal processing of pain and pain expectation, and this work addresses an important gap. The computational modeling offers a compelling perspective, and the experimental design is both simple and robust. I thoroughly enjoyed reading it!\nMy comments are mostly minor and could be addressed by refining the introduction and discussion. The extent of rewriting required may vary depending on how much the authors are willing to revise, but this represents an opportunity to clarify and strengthen the overall message.\n1. Definition of the \"Dread\" Effect\nThe authors describe the dread effect as the increased pain perception associated with longer expected delays. However, traditionally, the dread effect refers to a preference for experiencing pain sooner to avoid the aversiveness of anticipation. It is tied to decision-making processes and the desire to minimize the discomfort of the anticipatory period. While the two phenomena are both related to the timing of painful events, they are conceptually distinct. The paper’s interpretation of dread as increased pain perception during longer delays is a labeling issue rather than a major conceptual error. However, certain interpretations in the discussion may overextend the definition of dread. For instance:\n“This suggests that modulations in the pain experience (which are typically associated with dread) are associated with somatosensory expectation effects, regardless of a “dreading” delay period. Furthermore, we found an increase in the intensity of the cold percept, which was clearly non-painful, which is also indicative of a top-down temporal expectation process associated with aversive stimulation driving an increased intensity of the percept, instead of the dread for pain.”\nI disagree with the claim that modulations in pain experience are \"typically associated with dread.\" If the authors are aware of specific references that support this, it would be helpful to include them. Otherwise, it may be more accurate to distinguish the reported effects from dread explicitly and consider introducing a different term. The relationship to dread can still be explored in the discussion.\nThat said, I found the observed effect of increased pain (and cold) perception for longer delays is fascinating, particularly when viewed through a Bayesian lens. Regardless of the terminology, this phenomenon is compelling and warrants publication.\n2. Conceptual Disconnect Between the Two Parts of the Paper\nThe paper appears to focus on two distinct aspects: (1) the increased perception of pain associated with longer expected delays and (2) prediction errors. These two parts are conceptually difficult to reconcile. Initially, one might expect that increased pain perception could be linked to prediction errors, but the results suggest otherwise. Prediction errors emerge as a separate phenomenon. The relationship between these two components could be clarified with a more cohesive narrative in the discussion.\n3. Definition of prediction error\nHowever, I think that there is a more fundamental problem with the way the authors have set their prediction errors as depending on a priori probabilities (right panel of figure 1b). Indeed, right panel of figure 1b is generated from “total probability” in table 1a. But total probability is just the a priori probabilistic design of the experiment. The probabilities that participants experience are the “probability at stimulation”. The inverse of those probabilities are the prediction errors that participants experience.\n4. Interaction in Figure 7\nThe interaction depicted in Figure 7 is puzzling. Why would expectations have opposite effects for heat and cold? This inconsistency is intriguing and merits additional discussion or clarification to help readers understand this unexpected result.", "reviewer_2": "This study examines how temporal expectations influence pain perception, showing that expected delays increase perceived intensity while actual delays do not. EEG results indicate that expected delays modulate alpha/beta activity during anticipation, while actual delays affect activity during stimulation. These findings underscore the role of alpha/beta oscillations in the top-down modulation of sensory processing. While the study’s focus on temporal expectations in the context of pain perception is new, its relevance within theoretical frameworks such as predictive coding or its implications for clinical pain states are not clear to me yet. Additionally, the explanation of statistical methods lacks sufficient detail and clarity, limiting the transparency and interpretability of the analyses.\nMajor Concerns\n- The significance of the predictive coding (PC) framework in the present study is unclear. While this framework is mentioned extensively throughout the manuscript, its significance seems limited to motivating the different contrasts, i.e., effects of actual delays, expected delays, and prediction errors. However, the presence of these effects alone does not provide evidence for a predictive coding mechanism. This would at least require demonstrating that prediction errors lead to changes of future predictions – an aspect not addressed in the study. Repeated reference to the PC framework is distracting and potentially misleading. For example, the claim “This represents a full predictive coding model, …” in the discussion is unwarranted and should be reconsidered or omitted.\n- The comparison of the expectation precision modulation model and the expectation intensity shift model is problematic for two key reasons: First, I understand that, according to Weber’s law, the absolute precision of time period estimates decreases with longer actual time periods. However, it is unclear how this implies that the precision of pain intensity expectations decreases with longer anticipated waiting periods. Pain intensity expectations are cue-induced, and this cue information is unlikely to “wear off” over short durations, such as those in the present experiment. Second, the methods used for constructing and comparing the expectation intensity shift- and expectation precision modulation models are inadequately explained. Bayes rule can be stated as p(θ│D)∝p(D│θ)p(θ). Here, the posterior p(θ│D) is a probability density functon(PDF) of model parameters θ given the data, the likelihood p(D│θ) is the PDF of the data given the model parameters θ, and the prior p(θ) is a PDF reflecting a priori beliefs about values model parameters can assume. Note that the posterior is proportional to the product of two PDFs of two distinct random variables, D and θ. By contrast, the posterior means and variances specified in Eq. 1-3 result from a mere multiplication of two PDFs of a single random variable. Without further explanation, this appears like a misapplication of Bayes rule. Greater mathematical rigor is needed to clarify how Bayes’ rule was applied, including explicit definitions of model parameters (θ), data (D), and the likelihood model. Given these conceptual and technical shortcomings - and considering the analysis is not central to the paper’s primary conclusions - I suggest removing this section.\n- The discussion claims that “previous EEG pain studies have consistently shown that cue-induced expectation effects do not modulate EEG signals during pain stimulation …”. This is not true. For instance, Bott et al. (2023, Science Advances) demonstrated that cue-induced expectations modulate interregional connectivity during pain stimulation. The study’s exclusive focus on signal power should be noted as a limitation.\n- The discussion states that the paradigm “eliminates the confound of expected waiting times and actual waiting times”, but this requires clarification. As I understand it, by presenting cues about the waiting time, expectations about waiting times and thus their confound with actual waiting times is introduced in the first place. However, it is also true that the paradigm allows for the disentanglement of the two factors.\n- The description of the repeated measure ANOVA in the results and methods sections is unclear (“One-way interaction” is not standard terminology. What are the ANOVA factors? How was the prediction error effect tested specifically? Why is the data summarized in a 2 x 35 x 9 “matrix”? While expected and actual durations are categorical variables with three levels, the phrase “measured response [is modeled] with a simple linear function of stimulus latency” implies these variables are treated as continuous). Providing the code for the analyses would greatly facilitate their understanding (see comment below).\n- To promote transparency and reproducibility, the analysis code and data should be made publicly available and easily accessible.\n- A priori sample size calculations should be provided. Note that lower power entails a lower likelihood of positive effects to reflect true effects.\nMinor concerns\n- Are predictive coding principles truly fundamental to all brain processes, or primarily relevant to perception-related processes? (Lines 43-44)\n- Timing may be a critical factor in clinical pain contexts, but it is unclear how this specifically motivates the present study (Lines 61-68).\n- Why were non-painful cold stimuli used instead of non-painful warm stimuli?\n- The coloring of the Table in Figure 1a (color intensity ~ actual latency) is inconsistent with that in Figure 1b (color intensity ~ expected latency).\n- Line 194: The phrase “…modulate sensory perception in a way that it is experienced as more intense” is awkward and suggests unintended links to consciousness research (“experience of perception”).\n- The bar graphs in Figures 3a and 3b reveal little about the data, and the lower cut-offs seem arbitrary. Consider using box plots or raincloud plots instead.\n- Line 293: Replace “time points” with “time intervals”?\n- For the assessment of stimulation effects on EEG, why were only correctly cued trials used?", "reviewer_3": "Key Results\nThis study operationalises a predictive timing paradigm to examine how actual and expected pain stimulus latency influence subjective pain intensity ratings across hot and 20.5-degree (cold) stimulus modalities. By implementing computational Bayesian modelling, the authors investigate the role of prior expectations and precision in shaping pain perception within a predictive coding framework, particularly in relation to the dread effect - the idea that prolonged anticipation amplifies pain perception. EEG data were used to explore the neural signatures of probabilistic inference processes. The results indicate that actual stimulus latency does not significantly affect ratings, but temporal expectations do, such that delayed stimulation was rated as more painful than instant stimulation. Bayesian model selection supported a prior expectation intensity shift model over a prior expectation precision modulation model, suggesting that the content of top-down predictions exerts a stronger influence on ratings than their certainty. The same pattern was observed for the 20.5 degree (“cold”) stimulation suggesting that the effect of temporal expectations is not pain-specific.\nThe key EEG data showed that temporal expectations modulated alpha-to-beta frequencies at cue presentation, but not at stimulus delivery, supporting the argument that they bias prior expectations rather than the processing of ascending nociceptive signal. Temporal prediction errors were expressed in the beta-to-gamma frequency range.\nThe key EEG data showed that temporal expectations modulated alpha-to-beta frequencies at cue presentation, with a crossed interaction where activity increased for heat and decreased for cold. No significant modulation of alpha-to-beta power was observed at stimulus delivery, supporting the argument that they bias prior expectations rather than the processing of ascending nociceptive signal. Temporal prediction errors were expressed in the beta-to-gamma frequency range, particularly in the gamma range, during the second half of stimulus presentation.\nValidity\nThe experiment offers a valid test of the hypotheses. The rationale is very clear and followed up well by comprehensive analyses, which closely follows the stated rationale. The behavioural modelling is particularly impressive, and all results are examined thoroughly, and interpreted fairly.\nA few minor corrections can help increase the validity:\n- The ‘cold’ stimulus is referred to as ‘aversive’ and ‘unpleasant’. Since 20 degrees is not a low temperature this designation should be removed unless it can be supported by evidence.\n- The main effect of stimulus intensity on the signal at stimulus delivery (line 368 details the statistical model) is not reported – was there any difference between ‘hot’ and ‘cold’?\n- The maximum cue-stimulus interval of 4 seconds in this study may not engage the prolonged affective anticipation necessary to elicit dread-related modulation of pain perception. The null effect of actual stimulus latency may therefore reflect an insufficient experimental latency window, a possibility which should be acknowledged as a caveat.\n- The modelling assumes that the mean and precision of prior expectations are static across trials of equal expectancy conditions. A more biologically plausible implementation would involve a hierarchical precision estimation process, where precision is continuously updated in response to ongoing sensory evidence and past prediction error rather than being pre-determined by task conditions. The authors may which to consider the assumption as a potential limitation.\nSignificance\nHow the effect of dread can be operationalised within predictive processing mechanisms, and how it is implemented computationally and neutrally, is a fascinating topic and one that the authors have done an excellent job addressing. The study contributes to our understanding of predictive coding in pain perception by addressing the topic of temporal predictions and prediction errors which has been relatively neglected.\nData and Methodology\nThe method and analysis are professionally executed.\nAnalytical Approach\nThe analysis was executed professionally. Given the core emphasis on Bayesian modelling and the theoretical significance of some null effects we were curious whether the authors might consider employing Bayesian rather than frequentist statistics.\nSuggested Improvements\n- The authors should report how many trials were included in each condition/participant\n- The behavioural analysis reports interactions with stimulus intensity (hot/cold); it should also unpack/explain this interaction.\n- Comparing hot/cold separately against catch trials is helpful, but titling Figure 5 ‘main effect’ could be misleading, because this term is typically reserved to the results of a factorial analysis. That figure could improve by including the main effect from the F test in line 368.\nClarity and context\nThe manuscript is well-structured and provides a clear account of the experimental approach and findings. However, the categorical treatment of thermos-sensation introduces potential conceptual and statistical ambiguities that are not explicitly acknowledged.\nReferences\nThe manuscript cites relevant literature on predictive coding, Bayesian inference, and pain perception, but the discussion on precision weighting could be better contextualised within the broader hierarchical inference literature. Specifically, research on error-driven precision updates and adaptive inference mechanisms should be referenced to clarify how the study’s approach differs from models that incorporate trial-by-trial learning of precision estimates.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point-by-point responses to the reviewers:\nReviewer #1:\nIn this paper, the authors examine the impact of temporal delay and temporal delay\nexpectations/prediction errors on pain perception and associated EEG activity. I found this to be an\nexcellent paper. There is limited research on the temporal processing of pain and pain expectation,\nand this work addresses an important gap. The computational modeling offers a compelling\nperspective, and the experimental design is both simple and robust. I thoroughly enjoyed reading it!\nMy comments are mostly minor and could be addressed by refining the introduction and discussion.\nThe extent of rewriting required may vary depending on how much the authors are willing to revise,\nbut this represents an opportunity to clarify and strengthen the overall message.\n1. Definition of the \"Dread\" Effect\nThe authors describe the dread effect as the increased pain perception associated with longer\nexpected delays. However, traditionally, the dread effect refers to a preference for experiencing pain\nsooner to avoid the aversiveness of anticipation. It is tied to decision-making processes and the desire\nto minimize the discomfort of the anticipatory period. While the two phenomena are both related to\nthe timing of painful events, they are conceptually distinct. The paper’s interpretation of dread as\nincreased pain perception during longer delays is a labeling issue rather than a major conceptual\nerror. However, certain interpretations in the discussion may overextend the definition of dread. For\ninstance:\n“This suggests that modulations in the pain experience (which are typically associated with dread) are\nassociated with somatosensory expectation effects, regardless of a “dreading” delay period.\nFurthermore, we found an increase in the intensity of the cold percept, which was clearly non-painful,\nwhich is also indicative of a top-down temporal expectation process associated with aversive\nstimulation driving an increased intensity of the percept, instead of the dread for pain.”\nI disagree with the claim that modulations in pain experience are \"typically associated with dread.\" If\nthe authors are aware of specific references that support this, it would be helpful to include them.\nOtherwise, it may be more accurate to distinguish the reported effects from dread explicitly and\nconsider introducing a different term. The relationship to dread can still be explored in the discussion.\nThat said, I found the observed effect of increased pain (and cold) perception for longer delays is\nfascinating, particularly when viewed through a Bayesian lens. Regardless of the terminology, this\nphenomenon is compelling and warrants publication.\nWe agree with the reviewer that our previous use of the term “dread” was not precise. We now\nconsistently refer to our observed effect as a \"temporal expectation effect\". We revised the\nintroduction and discussion accordingly to avoid overextension and clarify the observed effects.\n(See lines 19f., 86ff., 119f., 210ff., 563f.)\n2. Conceptual Disconnect Between the Two Parts of the Paper\nThe paper appears to focus on two distinct aspects: (1) the increased perception of pain associated\nwith longer expected delays and (2) prediction errors. These two parts are conceptually difficult to\nreconcile. Initially, one might expect that increased pain perception could be linked to prediction\nerrors, but the results suggest otherwise. Prediction errors emerge as a separate phenomenon. The\nrelationship between these two components could be clarified with a more cohesive narrative in the\ndiscussion.\nWe thank the reviewer for raising this conceptual point and changed the narrative in the discussion\naccordingly. In the revised discussion, we acknowledge that dread and prediction errors may\ninteract, as aversive anticipation could heighten sensitivity to temporal mismatch. We now\nexplicitly discuss this possibility in the revised manuscript. (See lines 527ff.)\n3. Definition of prediction error\nHowever, I think that there is a more fundamental problem with the way the authors have set their\nprediction errors as depending on a priori probabilities (right panel of figure 1b). Indeed, right panel\nof figure 1b is generated from “total probability” in table 1a. But total probability is just the a priori\nprobabilistic design of the experiment. The probabilities that participants experience are the “\nprobability at stimulation”. The inverse of those probabilities are the prediction errors that\nparticipants experience.\nAs requested, we have added supplementary analyses using the “probability at stimulation” metric\nto calculate prediction errors (see Supplementary Analysis 2). As can be seen the results remain\nalmost identical when using the probabilities at stimulation, which is expected as our task design\nwas optimized to keep both probabilities quite similar. Therefore, we would suggest to report\nprediction errors derived from a priori probabilities in the main results, while presenting the\nanalyses based on “probability at stimulation” in the supplementary material. (See lines 199ff.)\n4. Interaction in Figure 7\nThe interaction depicted in Figure 7 is puzzling. Why would expectations have opposite effects for\nheat and cold? This inconsistency is intriguing and merits additional discussion or clarification to help\nreaders understand this unexpected result.\nWe agree that this interaction is intriguing. We now include an expanded discussion of potential\nmechanisms, including differences in motivational salience of threatening / dreadful stimuli in the\ndiscussion. (See lines 599ff., 613ff.)\nReviewer #2:\nThis study examines how temporal expectations influence pain perception, showing that expected\ndelays increase perceived intensity while actual delays do not. EEG results indicate that expected\ndelays modulate alpha/beta activity during anticipation, while actual delays affect activity during\nstimulation. These findings underscore the role of alpha/beta oscillations in the top-down modulation\nof sensory processing. While the study’s focus on temporal expectations in the context of pain\nperception is new, its relevance within theoretical frameworks such as predictive coding or its\nimplications for clinical pain states are not clear to me yet. Additionally, the explanation of statistical\nmethods lacks sufficient detail and clarity, limiting the transparency and interpretability of the\nanalyses.\nMajor Concerns\n- The significance of the predictive coding (PC) framework in the present study is unclear. While this\nframework is mentioned extensively throughout the manuscript, its significance seems limited to\nmotivating the different contrasts, i.e., effects of actual delays, expected delays, and prediction\nerrors. However, the presence of these effects alone does not provide evidence for a predictive coding\nmechanism. This would at least require demonstrating that prediction errors lead to changes of\nfuture predictions – an aspect not addressed in the study. Repeated reference to the PC framework is\ndistracting and potentially misleading. For example, the claim “This represents a full predictive coding\nmodel, …” in the discussion is unwarranted and should be reconsidered or omitted.\nWe thank the reviewer for pointing out this issue. We have now removed overly strong claims such\nas “full predictive coding model” and instead emphasize that our model tests behavioral and\nneural signatures of temporal expectations and prediction errors, which are conceptually aligned\nwith—but not exclusive evidence for—predictive coding. The introduction now references our\nprevious predictive coding studies (e.g., Strube et al., 2021) as a motivation, but does not\noverextend on PC anymore. We also refrained from overextending on PC in the Results part. (See\nlines 86ff., 357ff., 489ff., 529, 794)\n- The comparison of the expectation precision modulation model and the expectation intensity shift\nmodel is problematic for two key reasons: First, I understand that, according to Weber’s law, the\nabsolute precision of time period estimates decreases with longer actual time periods. However, it is\nunclear how this implies that the precision of pain intensity expectations decreases with longer\nanticipated waiting periods. Pain intensity expectations are cue-induced, and this cue information is\nunlikely to “wear off” over short durations, such as those in the present experiment.\nSecond, the methods used for constructing and comparing the expectation intensity shift- and\nexpectation precision modulation models are inadequately explained. Bayes rule can be stated as p(θ\n│D)∝p(D│θ)p(θ). Here, the posterior p(θ│D) is a probability density functon(PDF) of model\nparameters θ given the data, the likelihood p(D│θ) is the PDF of the data given the model\nparameters θ, and the prior p(θ) is a PDF reflecting a priori beliefs about values model parameters\ncan assume. Note that the posterior is proportional to the product of two PDFs of two distinct random\nvariables, D and θ. By contrast, the posterior means and variances specified in Eq. 1-3 result from a\nmere multiplication of two PDFs of a single random variable. Without further explanation, this\nappears like a misapplication of Bayes rule. Greater mathematical rigor is needed to clarify how\nBayes’ rule was applied, including explicit definitions of model parameters (θ), data (D), and the\nlikelihood model. Given these conceptual and technical shortcomings - and considering the analysis\nis not central to the paper’s primary conclusions - I suggest removing this section.\nWe thank the reviewer for this important observation and agree that our initial phrasing may have\nled to confusion. In our formulation, we did not attempt to derive a full posterior over model\nparameters but rather implemented an analytical solution to the posterior over the sensory\nestimate (pain intensity) using a conjugate prior formulation under the assumption of normally\ndistributed priors and likelihoods. The equations presented in Eq. 1–3 correspond to the analytical\nform of the posterior mean and variance resulting from Bayesian integration over stimulus\nintensity, not over model parameters. Accordingly, the model parameters (e.g., pshift, pprecision,\npoffset) were estimated using variational Bayesian inference over trials, not derived via Bayes’\ntheorem in Eq. 1–3. We have now clarified this distinction and updated the text to reflect that Eq.\n1–3 describe the application of normal-normal conjugate inference on a cognitive model of\nperceived intensity, rather than an inferential Bayesian treatment of model parameter\ndistributions.\nIn our framework, μ_posterior and σ²_posterior do not reflect a posterior over model parameters\nθ, but the mean and variance of the subjective percept, computed through Bayesian integration of\na prior belief about pain intensity (based on the cue) and the sensory evidence (likelihood).\nAccordingly, the variables in our equations correspond to hypothetical internal distributions over\npain intensity, not statistical distributions over data or parameters. We regret any confusion and\nhave clarified this distinction in the revised manuscript at several points.\nAlthough modeling is not central to the primary findings, it provides a principled framework to\nformally contrast alternative mechanistic hypotheses (shift vs. precision modulation), and to\nexplore whether temporal aspects of expectation affect pain perception through distinct\ncomputational routes (i.e. via changes in intensity or precision). While Weber’s law originally refers\nto perception of time intervals, we here adopt its broader implication: that internal uncertainty\nincreases with longer intervals. Applied to expectation, we hypothesize that longer cue-stimulus\ndelays reduce the influence of the prior due to increased uncertainty in applying temporally distant\ncues, i.e. they would be “less reliable” – based on the larger uncertainty induced by Weber’s law.\nWe believe this contributes to the broader theoretical context and therefore retained the section\nin revised form. (See lines 234ff., 241, 249ff., 272ff., 288ff., 296ff., 304ff., 332ff., 580ff., 832ff., 857,\n865ff., 889ff., 928ff.)\n- The discussion claims that “previous EEG pain studies have consistently shown that cue-induced\nexpectation effects do not modulate EEG signals during pain stimulation …”. This is not true. For\ninstance, Bott et al. (2023, Science Advances) demonstrated that cue-induced expectations modulate\ninterregional connectivity during pain stimulation. The study’s exclusive focus on signal power should\nbe noted as a limitation.\nWe thank the reviewer for highlighting recent EEG work. We now explicitly acknowledge that while\nour EEG analysis focused on signal power, prior studies have shown expectation effects in\nconnectivity and phase-based metrics during pain stimulation. We also added this point in the\nlimitations. (See lines 600ff., 636ff.)\n- The discussion states that the paradigm “eliminates the confound of expected waiting times and\nactual waiting times”, but this requires clarification. As I understand it, by presenting cues about the\nwaiting time, expectations about waiting times and thus their confound with actual waiting times is\nintroduced in the first place. However, it is also true that the paradigm allows for the\ndisentanglement of the two factors.\nWe agree and expanded the discussion to clarify that while our design introduces expectations via\ncues, it also allows for their dissociation from actual delays, which is the key advantage for\nstudying predictive timing with the present design. (See lines 549ff.)\n- The description of the repeated measure ANOVA in the results and methods sections is unclear (“\nOne-way interaction” is not standard terminology. What are the ANOVA factors? How was the\nprediction error effect tested specifically? Why is the data summarized in a 2 x 35 x 9 “matrix”? While\nexpected and actual durations are categorical variables with three levels, the phrase “measured\nresponse [is modeled] with a simple linear function of stimulus latency” implies these variables are\ntreated as continuous). Providing the code for the analyses would greatly facilitate their understanding\n(see comment below).\nWe thank the reviewer for requesting clarification. We have now restructured the Results and\nMethods sections to clearly list all ANOVA factors: stimulus modality (heat/cold), expected delay\n(cue-based), actual delay (stimulus latency), and prediction error (absolute difference). We no\nlonger use “one-way interaction” terminology and provide the MATLAB code. (See lines 157f.,\n807ff.)\n- To promote transparency and reproducibility, the analysis code and data should be made publicly\navailable and easily accessible.\nWe agree and had already made data available at OSF (as mentioned in the original manuscript).\nWe have now added the updated scripts. (See lines 969ff.)\n- A priori sample size calculations should be provided. Note that lower power entails a lower\nlikelihood of positive effects to reflect true effects.\nWe based our sample size on previously observed behavioral effect sizes from our related study\n(Strube et al., 2023) and added a corresponding paragraph in the Methods section. (See lines\n655ff.)\nMinor concerns\n- Are predictive coding principles truly fundamental to all brain processes, or primarily relevant to\nperception-related processes? (Lines 43-44)\nThis is a heavily debated question, and our study was not designed to substantially add to this\ndiscussion. We therefore have rephrased this section accordingly. We now discuss Predictive\nCoding as a possibility, for perception-(and-pain-) related processes without claiming universality\nacross all brain functions. (See lines 39ff.)\n- Timing may be a critical factor in clinical pain contexts, but it is unclear how this specifically\nmotivates the present study (Lines 61-68).\nWe agree and have removed the clinical framing from the introduction.\n- Why were non-painful cold stimuli used instead of non-painful warm stimuli?\nWe have added an explanation to the Methods section stating that cold stimuli were chosen over\nwarm stimuli due to pilot results showing stronger perceptual clarity and lower habituation\neffects. Warm stimuli near the neutral threshold (32–34 °C) were perceived inconsistently (i.e.\nsome trials were missed), whereas cold stimuli (20.5 °C) showed clearer responses across trials.\n(672ff.)\n- The coloring of the Table in Figure 1a (color intensity ~ actual latency) is inconsistent with that in\nFigure 1b (color intensity ~ expected latency).\nThank you! We have corrected the color mapping in Figure 1a to align with Figure 1b\n- Line 194: The phrase “…modulate sensory perception in a way that it is experienced as more intense\n” is awkward and suggests unintended links to consciousness research (“experience of perception”).\nWe agree and have revised the sentence for clarity. (203f.)\n- The bar graphs in Figures 3a and 3b reveal little about the data, and the lower cut-offs seem\narbitrary. Consider using box plots or raincloud plots instead.\nWe appreciate the suggestion and have replaced the original bar plots with raincloud plots in\nFigures 3a and 3b to better visualize the data distribution.\n- Line 293: Replace “time points” with “time intervals”?\nWe have replaced “time points” with “time intervals”. (See line 341)\n- For the assessment of stimulation effects on EEG, why were only correctly cued trials used?\nWe used only correctly cued trials in the EEG analysis to avoid confounding effects of prediction\nerrors on neural responses. (See lines 362ff.)\nReviewer #3 (Remarks to the Author):\nKey Results\nThis study operationalises a predictive timing paradigm to examine how actual and expected pain\nstimulus latency influence subjective pain intensity ratings across hot and 20.5-degree (cold) stimulus\nmodalities. By implementing computational Bayesian modelling, the authors investigate the role of\nprior expectations and precision in shaping pain perception within a predictive coding framework,\nparticularly in relation to the dread effect - the idea that prolonged anticipation amplifies pain\nperception. EEG data were used to explore the neural signatures of probabilistic inference processes.\nThe results indicate that actual stimulus latency does not significantly affect ratings, but temporal\nexpectations do, such that delayed stimulation was rated as more painful than instant stimulation.\nBayesian model selection supported a prior expectation intensity shift model over a prior expectation\nprecision modulation model, suggesting that the content of top-down predictions exerts a stronger\ninfluence on ratings than their certainty. The same pattern was observed for the 20.5 degree (“cold”)\nstimulation suggesting that the effect of temporal expectations is not pain-specific.\nThe key EEG data showed that temporal expectations modulated alpha-to-beta frequencies at cue\npresentation, but not at stimulus delivery, supporting the argument that they bias prior expectations\nrather than the processing of ascending nociceptive signal. Temporal prediction errors were\nexpressed in the beta-to-gamma frequency range.\nThe key EEG data showed that temporal expectations modulated alpha-to-beta frequencies at cue\npresentation, with a crossed interaction where activity increased for heat and decreased for cold. No\nsignificant modulation of alpha-to-beta power was observed at stimulus delivery, supporting the\nargument that they bias prior expectations rather than the processing of ascending nociceptive\nsignal. Temporal prediction errors were expressed in the beta-to-gamma frequency range,\nparticularly in the gamma range, during the second half of stimulus presentation.\nValidity\nThe experiment offers a valid test of the hypotheses. The rationale is very clear and followed up well\nby comprehensive analyses, which closely follows the stated rationale. The behavioural modelling is\nparticularly impressive, and all results are examined thoroughly, and interpreted fairly.\nA few minor corrections can help increase the validity:\n- The ‘cold’ stimulus is referred to as ‘aversive’ and ‘unpleasant’. Since 20 degrees is not a low\ntemperature this designation should be removed unless it can be supported by evidence.\nWe agree and removed the labeling of 20.5°C cold stimuli as “aversive” or “unpleasant.” We now\nsimply describe them as “non-painful cold.”\n- The main effect of stimulus intensity on the signal at stimulus delivery (line 368 details the statistical\nmodel) is not reported – was there any difference between ‘hot’ and ‘cold’?\nWe now report the results of the main effect of modality (cold vs heat) in the Supplementary\nMaterials (Supplementary Figure 2). (See lines 361ff.)\n- The maximum cue-stimulus interval of 4 seconds in this study may not engage the prolonged\naffective anticipation necessary to elicit dread-related modulation of pain perception. The null effect\nof actual stimulus latency may therefore reflect an insufficient experimental latency window, a\npossibility which should be acknowledged as a caveat.\nIn our limitations sections, we now discuss that our 4-second maximum delay may not reflect the\ntemporal range of “true” dread-related affective buildup. (See lines 633ff.)\n- The modelling assumes that the mean and precision of prior expectations are static across trials of\nequal expectancy conditions. A more biologically plausible implementation would involve a\nhierarchical precision estimation process, where precision is continuously updated in response to\nongoing sensory evidence and past prediction error rather than being pre-determined by task\nconditions. The authors may which to consider the assumption as a potential limitation.\nWe agree that our model assumes fixed priors within conditions and now explicitly state this as a\nlimitation. We mention that a Kalman-filter or hierarchical Bayesian implementation could model\ntrial-by-trial precision updates and may be a future direction. (See lines 640ff.)\nSignificance\nHow the effect of dread can be operationalised within predictive processing mechanisms, and how it\nis implemented computationally and neutrally, is a fascinating topic and one that the authors have\ndone an excellent job addressing. The study contributes to our understanding of predictive coding in\npain perception by addressing the topic of temporal predictions and prediction errors which has been\nrelatively neglected.\nData and Methodology\nThe method and analysis are professionally executed.\nAnalytical Approach\nThe analysis was executed professionally. Given the core emphasis on Bayesian modelling and the\ntheoretical significance of some null effects we were curious whether the authors might consider\nemploying Bayesian rather than frequentist statistics.\nSuggested Improvements\n- The authors should report how many trials were included in each condition/participant\nWe now provide the number of trials per condition and participant in the Supplement\n(Supplemental Table 1 & 2). (See lines 752f.)\n- The behavioural analysis reports interactions with stimulus intensity (hot/cold); it should also\nunpack/explain this interaction.\nWe thank the reviewer for this pointing this out. Upon revisiting our analysis, we realized that a\nmistake had occurred due to incorrect centering of the factors. In particular, the modality factor\n(hot/cold) was not properly centered [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0] instead of [1 1 1 1 1 1 1 1 1 -\n1 -1 -1 -1 -1 -1 -1 -1 -1]. After correcting this issue and rerunning the repeated-measures ANOVA,\nwe found that the interaction effect is no longer statistically significant. As already outlined in the\nprevious version of the manuscript, this interaction was not central to our results. We have now\nupdated the manuscript and OSF files accordingly and now report the corrected version of the\nanalysis without a significant interaction effect. Nevertheless, we still report the results of the\npost-hoc tests for completeness and transparency. We apologize for the mistake and thank the\nreviewer for bringing this to our attention. (See lines 157ff.)\n- Comparing hot/cold separately against catch trials is helpful, but titling Figure 5 ‘main effect’ could\nbe misleading, because this term is typically reserved to the results of a factorial analysis. That figure\ncould improve by including the main effect from the F test in line 368.\nWe have renamed the figures and adjusted captions to avoid confusion. (See lines 355ff.)\nClarity and context\nThe manuscript is well-structured and provides a clear account of the experimental approach and\nfindings. However, the categorical treatment of thermos-sensation introduces potential conceptual\nand statistical ambiguities that are not explicitly acknowledged.\nWe now explicitly acknowledge this limitation in the revised manuscript, noting that future work\ncould explore continuous modeling of thermal intensity across a broader stimulus range. (See lines\n629ff.)\nReferences\nThe manuscript cites relevant literature on predictive coding, Bayesian inference, and pain perception,\nbut the discussion on precision weighting could be better contextualised within the broader\nhierarchical inference literature. Specifically, research on error-driven precision updates and adaptive\ninference mechanisms should be referenced to clarify how the study’s approach differs from models\nthat incorporate trial-by-trial learning of precision estimates.\nThank you for this helpful comment. We now clarify in the Limitations that our approach assumes\nstable prior expectations within blocks, due to the fully balanced block design. We now mention\nthat models with trial-by-trial learning would be a potential future direction and use appropriate\nreferences. (See lines 638ff.)\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nWe thank the co-reviewer for their contribution."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am satisfied with the authors' revisions and have no additional comments.", "reviewer_2": "With their revisions, the authors have substantially improved the manuscript. Below are my remaining comments.\n- The revised framing and reduced emphasis on Predictive Coding are now more appropriate.\n- The explanation of the Bayesian methodology is clearer, and the provided references are helpful. However, the manuscript would benefit from a more rigorous presentation of the approach, e.g., in the supplementary materials. Specifically, explicitly stating the prior, likelihood, and posterior densities (along the lines of Körding and Wolpert, 2004) together with their respective parameters (means and standard deviations) would aid understanding. Moreover, in my opinion, the following statement is problematic: “, where priors and likelihoods represent internal beliefs, not random variables in a statistical inference framework”. Priors and likelihoods, are, by definition, associated with random variables. It is these random variables which are used in conjunction with Bayes theorem to model internal beliefs and the subjective percept. Lastly, in line 924, the procedure for estimating the parameters should be described more precisely. E.g., explicitly list the parameters which are estimated and state the optimization criterion used.\n- Regarding the potential confound of expected and actual waiting times, the authors have added an explanatory paragraph, which makes their point clearer (lines 553 - 559). However, in its current form, the added text appears somewhat unconnected with the rest of the explanation. I suggest explicitly stating that the absence of expectation cues does not imply the absence of expectations, if this is what the authors mean.\n- The description of the statistical model has improved. However, should it not be “ratings were averaged within each combination of ” rather than “across all combinations of” (line 807)?\n- There is no code available under the provided link. Providing the study's code and would enhance its transparency and reproducibility.", "reviewer_3": "The available evidence from prior research suggests that participants tend to anticipate greater pain when it's expected to occur after a delay—possibly accounting for the so-called “dread effect,” in which people would rather experience pain sooner than later. It’s intriguing to consider whether this very expectation might amplify the subjective intensity of pain, and it’s worth exploring how expectations operate—whether by shifting the mean or altering the variance. Since the behavioral finding runs counter to intuition, the entire manuscript deserves a close, critical read. After reviewing the revised version, several significant concerns emerged.\nMajor Comments: Interpretation and Modeling\nThe mean shift model strikes me as a relatively straightforward application of predictive coding principles to these findings. Given that participants report more intense pain when they anticipate a longer delay—and considering that a prior expectation with a higher mean would naturally lead to a higher posterior—the idea of modeling this as a shift in the mean seems both logical and grounded. It offers a solid theoretical framework, especially valuable if the goal is to test alternative explanations of the data.\nThat said, I’m increasingly unconvinced that the precision-shift model offers a meaningful comparison. While I understand the premise that priors might become less precise with longer expected delays, it’s not clear how this imprecision could lead to a mean-shifted posterior. The authors could strengthen their case by more convincingly explaining why this model represents a plausible mechanism, rather than simply re-describing the ANOVA results through a more complex lens.\nThe discussion of temporal uncertainty should also be contextualized with prior work, particularly Clark et al. (2008), who demonstrated that elevated pain ratings weren't attributable to temporal uncertainty alone. Additionally, the authors suggest their model supports the idea that “cognitive/affective processes alter the valence of late-expected stimuli,” but given the lack of an interaction between expectation and modality (line 173), I’m unsure why a valence-based explanation is justified.\nThe EEG findings are informative in showing that expectations influence brain activity at the moment the cue is processed but not when the stimulation is processed. This interesting difference between the effect of expectations in two time points should be backed up by including cue and stimulus in the same analysis. However, the broader mechanism underlying the key behavioral effect remains unclear.\nMajor Comment: Manipulation Check\nThe experiment uses a factorial design, with modality as a critical variable. This factor is included in the behavioral analysis and again in analyzing temporal delay (line 412). But oddly, it’s missing as an independent variable in the immediately preceding EEG analysis. Given the study design, the appropriate test for whether EEG responses differ by modality would be a direct comparison of heat vs. non-painful stimulation, with that contrast featured prominently in the main text (rather than being relegated to the supplementary material).\nMore troubling, the manipulation check fails to replicate the findings the authors committed to replicating—namely, the pattern identified in Ploner et al.’s (2017) review: increased theta and decreased alpha-beta. While this pattern does emerge in the heat vs. catch trial comparison, that analysis is confounded by the mere presence versus absence of stimulation. The cleaner test—heat vs. control stimulation—does not replicate this expected result, even though it’s the more appropriate comparison for a factorial design involving modality.\nMajor Comment: Reporting Statistics\n• Lines 157, 167, and 814 suggest that just one ANOVA was conducted, but from lines 805–821, it’s still unclear exactly which statistical tests were run. Were all main effects entered simultaneously? If so, the design would likely be unbalanced (i.e., unequal numbers of trials across some of the 9 conditions). How was this addressed?\n• Including both prediction error and latency in the same analysis seems problematic, since the PE calculation combines different conditions (e.g., high PE for late onset/0s cue and early onset/4s cue). Did the authors run multiple two-factor models?\n• Lines 178 and 183 suggest that real and expected delay were entered together, yet the degrees of freedom reported for their interaction with modality are (1,34), not (2,34) as would be expected with 2 modalities and 3 levels of latency. This discrepancy needs clarification.\n• Results from normality tests and information on how violations of sphericity were handled should be reported.\nMinor Comments\n• Line 210 contains a typo: “This is at odds with interpretations explaining increased pain by higher, instead,” – sentence is incomplete or awkwardly phrased.\n• Figure 1: For “probability at stimulation,” the catch trials should list “N/A” instead of “100%,” since no stimulation occurs.\n• EEG trial counts: Some condition counts are reported as fewer than 10 trials. The authors should reassure readers that each condition included in Figure 8 had a sufficient number of trials for robust analysis—typically more than 10, and ideally over 20, as per common practice (see Supplementary Table 1).\n• While the paper rightly emphasizes the novel design element—separating expectation from actual delay via the cue—it would be valuable to relate the EEG results back to prior M/EEG studies that examined actual delay. That comparison would help clarify whether earlier findings were truly driven by expectations or were confounded by the delay itself."}, "author_response": "1\n3\n5\n8 revisions.\n10\n12 comments.\n13 - The revised framing and reduced emphasis on Predictive Coding are now more appropriate.\n15 appropriate.\n16 - The explanation of the Bayesian methodology is clearer, and the provided references are helpful.\n18 supplementary materials. Specifically, explicitly stating the prior, likelihood, and posterior densities\n19 (along the lines of Körding and Wolpert, 2004) together with their respective parameters (means and\n20 standard deviations) would aid understanding.\n22 more rigorous presentation of our Bayesian modeling approach. To illustrate interindividual\n23 variability and robustness, we include summary figures showing distributions of parameter\n24 estimates, as well as their correlations with the behavioral expectation effect. Together, these\n25 additions address the reviewer’s request for a more transparent and precise description of the\n29 represent internal beliefs, not random variables in a statistical inference framework”. Priors and\n30 likelihoods, are, by definition, associated with random variables. It is these random variables which\n31 are used in conjunction with Bayes theorem to model internal beliefs and the subjective percept.\n33 confusing and misleading. In the revised manuscript, we have removed this statement. (See line\n34 903)\n38 is achieved by a variational approach to approximate Bayesian inference and based on the\n39 minimization of variational free energy as implemented in variational Bayes and the VBA Toolbox\n40 (https://mbb-team.github.io/VBA-toolbox/). We have also listed the estimated parameters (p_shift,\n41 p_precision and p_offset) in the revised manuscript. (See lines 987ff.)\n42 - Regarding the potential confound of expected and actual waiting times, the authors have added an\n43 explanatory paragraph, which makes their point clearer (lines 553 - 559). However, in its current form,\n44 the added text appears somewhat unconnected with the rest of the explanation. I suggest explicitly\n45 stating that the absence of expectation cues does not imply the absence of expectations, if this is what\n46 the authors mean.\n50 expectations. We explain that expectations without cues are gradually shaped by elapsed time,\n51 whereas in our paradigm, expectations are formed immediately based on the cue and therefore\n52 disentangled from actual waiting times. (See lines 577ff.)\n53 - The description of the statistical model has improved. However, should it not be “ratings were\n54 averaged within each combination of ” rather than “across all combinations of” (line 807)?\n56 repeated-measures ANOVA with a single-trial Linear Mixed-Effects (LME) approach. This model\n57 does not rely on averaging across or within combinations of conditions, but instead considers all\n58 predictors simultaneously at the trial level, with subject-specific random intercepts and slopes. The\n59 full model specification and output are now provided in the Supplementary Information for\n60 transparency. (See Supplementary Information; see lines 153ff., 205ff., 872ff.)\n61 - There is no code available under the provided link. Providing the study's code and would enhance its\n62 transparency and reproducibility.\n64 scripts are located in the “Files” section of the repository, organized into several subfolders labeled\n65 “Analysis Code.” We apologize if accessing the code may have been unintuitive; we have tested the\n66 link and confirmed that all files can be retrieved. (See lines 1071f.)\n69 when it's expected to occur after a delay—possibly accounting for the so-called “dread effect,” in\n70 which people would rather experience pain sooner than later. It’s intriguing to consider whether this\n71 very expectation might amplify the subjective intensity of pain, and it’s worth exploring how\n72 expectations operate—whether by shifting the mean or altering the variance. Since the behavioral\n73 finding runs counter to intuition, the entire manuscript deserves a close, critical read. After reviewing\n74 the revised version, several significant concerns emerged.\n77 principles to these findings. Given that participants report more intense pain when they anticipate a\n78 longer delay—and considering that a prior expectation with a higher mean would naturally lead to a\n79 higher posterior—the idea of modeling this as a shift in the mean seems both logical and grounded. It\n80 offers a solid theoretical framework, especially valuable if the goal is to test alternative explanations of\n81 the data.\n83\n85 comparison. While I understand the premise that priors might become less precise with longer\n86 expected delays, it’s not clear how this imprecision could lead to a mean-shifted posterior. The authors\n87 could strengthen their case by more convincingly explaining why this model represents a plausible\n88 mechanism, rather than simply re-describing the ANOVA results through a more complex lens.\n90 precision-shift model was too brief and may not have sufficiently conveyed its theoretical\n91 motivation. In the revised manuscript, we now explain in more detail why the modulation of prior\n92 precision represents a plausible mechanism. Specifically, when temporal expectations become less\n93 precise, the sensory input receives relatively greater weight in the Bayesian integration process. If\n94 the actual sensory input is more painful than the expected value, this reweighting results in a\n95 posterior shifted toward higher pain intensity. Crucially, this account is not redundant with the\n96 mean-shift model: while both mechanisms predict a higher posterior mean, the precision-shift\n97 model additionally predicts a decrease in posterior precision (i.e., greater variability in perception).\n98\n100 (purple, top) and a likelihood precision modulation (green, bottom). In both cases, the posterior\n101 distribution (blue) is shifted toward lower VAS values, but the underlying mechanisms differ.\n102  With a prior shift, the posterior mean decreases because the prior itself is shifted.\n103  With a likelihood precision modulation, the likelihood becomes less precise, reducing its\n104 weight in the Bayesian integration. As a result, the posterior is drawn more strongly toward\n105 the relatively more precise prior.\n107 shift only affects the posterior mean, whereas a likelihood precision modulation affects both the\n108 posterior mean and posterior precision, leading to increased variability in the perceptual estimate.\n110 an illustrative example of a scenario in which the precision-modulation model outperforms\n111 alternative accounts, yielding not only a higher posterior mean but also a lower posterior precision\n112 by this mechanism. (See Supplementary Information; see lines 253ff., 322ff.)\n113\n116 uncertainty alone.\n118 the suggestions about the contextualization of our results within the literature which examined the\n119 effects of delay on pain.\n121 the valence of late-expected stimuli,” but given the lack of an interaction between expectation and\n122 modality (line 173), I’m unsure why a valence-based explanation is justified.\n125 the cue is processed but not when the stimulation is processed. This interesting difference between the\n126 effect of expectations in two time points should be backed up by including cue and stimulus in the\n127 same analysis. However, the broader mechanism underlying the key behavioral effect remains unclear.\n129 stimulus-related effects; however, within the framework of cluster-based permutation testing,\n130 concatenating cue and stimulus periods into a single analysis is not feasible. This approach\n131 requires temporal alignment across conditions, which is not possible here because the stimulus\n132 phase follows different waiting times depending on the cue condition. Concatenating the two phases\n133 would therefore violate the assumptions of the test and could yield uninterpretable results. Instead,\n134 we analyzed cue-locked and stimulus-locked data separately, which allows us to capture the\n135 temporally specific effects of expectations at the moment the predictive cue is processed and at the\n136 time of stimulation.\n139 the behavioral analysis and again in analyzing temporal delay (line 412). But oddly, it’s missing as an\n140 independent variable in the immediately preceding EEG analysis. Given the study design, the\n141 appropriate test for whether EEG responses differ by modality would be a direct comparison of heat\n142 vs. non-painful stimulation, with that contrast featured prominently in the main text (rather than being\n143 relegated to the supplementary material).\n145 replicating—namely, the pattern identified in Ploner et al.’s (2017) review: increased theta and\n146 decreased alpha-beta. While this pattern does emerge in the heat vs. catch trial comparison, that\n147 analysis is confounded by the mere presence versus absence of stimulation. The cleaner test—heat vs.\n148 control stimulation—does not replicate this expected result, even though it’s the more appropriate\n149 comparison for a factorial design involving modality.\n151 vs. cold control contrast in the main Results section (rather than only in the Supplement). We also\n152 updated the interpretation of our findings accordingly: while the heat vs. catch comparison\n153 reproduces canonical EEG patterns of pain (theta increase, alpha–beta decrease), the heat vs. cold\n154 contrast shows a different profile, with increased theta–alpha and beta–gamma activity for pain.\n156 cold stimuli. We have clarified this distinction in the Results and now also explicitly discuss it in the\n158 oscillatory responses. (See lines 356ff., 399ff., 411ff., 644ff.)\n160 • Lines 157, 167, and 814 suggest that just one ANOVA was conducted, but from lines 805–821, it’s\n161 still unclear exactly which statistical tests were run. Were all main effects entered simultaneously? If\n162 so, the design would likely be unbalanced (i.e., unequal numbers of trials across some of the 9\n163 conditions). How was this addressed?\n164 • Including both prediction error and latency in the same analysis seems problematic, since the PE\n165 calculation combines different conditions (e.g., high PE for late onset/0s cue and early onset/4s cue).\n167 • Lines 178 and 183 suggest that real and expected delay were entered together, yet the degrees of\n168 freedom reported for their interaction with modality are (1,34), not (2,34) as would be expected with 2\n169 modalities and 3 levels of latency. This discrepancy needs clarification.\n170 • Results from normality tests and information on how violations of sphericity were handled should be\n171 reported.\n173 a more comprehensive single-trial Linear Mixed-Effects (LME) models (see Methods &\n175 model subject-level random intercepts and slopes. This approach: (1) accommodates the\n176 unbalanced trial counts without excluding any subjects, (2) allows Expectation and Prediction\n178 (3) does not rely on the sphericity assumption since the within-subject covariance is explicitly\n179 modelled via random effects, and (4) provides appropriate degrees of freedom and p-values via\n180 mixed-model approximations.\n182 unchanged relative to the repeated-measures ANOVA: there was a robust main effect of Modality\n183 and a significant effect of Temporal Expectation, whereas Latency and PE did not significantly\n184 modulate ratings, and no interactions with Modality reached significance. (See lines 153ff., 872ff.)\n186 • Line 210 contains a typo: “This is at odds with interpretations explaining increased pain by higher,\n187 instead,” – sentence is incomplete or awkwardly phrased.\n189 is at odds with interpretations explaining increased pain by higher arousal; instead, this suggests\n190 that the somatosensory expectation itself can drive this effect in both painful heat and non-painful\n191 cold conditions.” (See lines 200ff.)\n192 • Figure 1: For “probability at stimulation,” the catch trials should list “N/A” instead of “100%,” since\n193 no stimulation occurs.\n195 trials, “probability at stimulation” is now indicated as “N/A” instead of “100%.” (See Figure 1)\n196 • EEG trial counts: Some condition counts are reported as fewer than 10 trials. The authors should\n197 reassure readers that each condition included in Figure 8 had a sufficient number of trials for robust\n198 analysis—typically more than 10, and ideally over 20, as per common practice (see Supplementary\n201 counts for all conditions shown in Figure 8 in the Supplementary Information (Supplementary\n203 10 trials per condition, and often substantially more.\n205 8 for a few participants. While this number is somewhat lower than the commonly cited threshold of\n206 10 trials, we note that it is still within the range considered acceptable for EEG analyses (see e.g.,\n208 given the overall consistency across participants, we decided to retain the full dataset for the main\n211 an additional analysis of the PE time-frequency effect including only participants with ≥10 trials in\n212 the high-PE condition. The results of this restricted analysis were fully consistent with the main\n213 analysis and are now reported in the Supplementary Information. This convergence confirms that\n214 our conclusions are not driven by a small subset of participants with lower trial counts. (See\n216 • While the paper rightly emphasizes the novel design element—separating expectation from actual\n217 delay via the cue—it would be valuable to relate the EEG results back to prior M/EEG studies that\n218 examined actual delay. That comparison would help clarify whether earlier findings were truly driven\n219 by expectations or were confounded by the delay itself.\n222 s) aligns with our finding of no effect for actual delays (0–4 s), while their P300 modulations by\n223 unpredictability parallel our EEG prediction-error effects without behavioral changes. Findings of\n225 pain probabilities, whereas our paradigm separates expected from actual delay and controls for\n226 probability. (See lines 588ff.)\n228 raised by the reviewers. Importantly, the additional analyses (e.g., LME, Bayesian modeling\n229 clarifications, EEG contrasts) converge with our original findings and strengthen the robustness\n230 and transparency of the study. We therefore hope the manuscript is now suitable for acceptance in"}, {"round": 3, "reviewer_comments": {"reviewer_3": "The revised manuscript represents a clear improvement aligned with reviewer feedback, but this increased clarity raises important concerns about the modelling.\nOn temporal uncertainty, they now reference Clark et al. (2008) and position their results in relation to that literature. They also further contextualise their EEG findings relative to Ploner et al. (2017), clarifying the basis for comparison in that review and discussing why a heat/cold contrast can yield a different profile without contradiction.\nThe revisions provide clearer language and interpretations of results and a cleaner distinction between actual and expected latency, making the central arguments easier to follow. The revision explains how separating expected from actual delay addresses prior designs that bundled these factors and clarifies why short-delay nulls can arise under such confounding.\nThe authors have added clarifications to how their precision-modulation account yields a mean shift in the posterior conditional on a prior < likelihood asymmetry, and helpfully further specified the assumptions accountable for its behaviour, making explicit the directional assumptions used in their illustrative figures.\nHowever, the implementation of the “expectation precision modulation model” is still problematic. The model makes a critical assumption that the expected mean pain (the prior mean) is lower than the sensory likelihood (via p_offset). The authors do not discuss this in any length, but this assumption is necessary to enable reduced prior precision to shift the posterior upward for longer expected delays. While it is entirely possible that sometimes expected pain would be lower, in other times it may be higher, exceeding input across trials. It is not obvious why Bayesian updating would not centre expectations exactly on the intensity of delivered pain, especially considering that uniform stimulation intensities are used (heat/cold).\nThe “expectation intensity shift model” directly shifts the prior mean upward with delay (via p_shift) to fit observed higher ratings, but provides no cognitive or theoretical basis for why expected latency would systematically inflate intensity expectations. Precision modulation has firm support in Bayesian perception accounts, where longer expected delays diminish prior precision under temporal uncertainty. The mean-shift mechanism lacks similar grounding, which makes it read as an add-on that enables a dread-like effect, but not derived from a specified generative link between delay cues and intensity expectations. Without an explicit rationale that elevates it to a general structural (dread-like) feature in line with the principal computational frameworks, its mechanistic and epistemic basis remains uncertain, and is further challenged by the same monotonic effect in the non-painful cold condition.\nThese issues seem to stem from an underlying epistemic oversight. Both models incorporate assumptions that presuppose that the dread effect exists in order to illustrate it in the data, rather than deriving a model structure from independent Bayesian principles that naturally reproduces it in line with predictive and Bayesian perception accounts. This arguably makes the modelling tautological, in that it is designed to fit behaviours while assuming the effect, rather than to fit the effect assuming a theory of cognition.\nAs a result, the modelling appears more like parameterisations describing data structure, rather than explaining why that structure occurs. It is recommended that the authors either 1) reframe the focus of the modelling from explanatory to descriptive, or 2) develop a strong rationale that can justify the assumption of a general prior-mean-shift mechanism in the context of Bayesian perception, and/or the assumption that a general mechanism may deflate pain expectations, moving the prior mean below the likelihood mean, so that the latency-dependent precision reduction may push posteriors upwards.\nThese constitute plausible challenges to the authors’ interpretation with respect to epistemic parsimony, with implications for hypothesis testing. An acknowledgement of this theoretical difficulty in the main text would hedge against strong critiques of the Bayesian model selection results on theoretical grounds."}, "author_response": "We thank the reviewer for the thoughtful assessment of our modelling approach. We agree that our\nprevious version unintentionally risked framing the modelling as mechanistic, even though the\nunderlying assumptions necessarily make it illustrative rather than explanatory. In the revised\nmanuscript, we therefore position the modelling explicitly as exploratory and descriptive, retaining\nonly a short summary in the main text. All model specifications, assumptions, parameters,\nsimulations, and Bayesian model comparisons have been moved to the Supplementary Information,\nwhere they can be consulted by readers interested in computational perspectives on pain. We believe\nthis restructuring prevents any over-interpretation while preserving transparency and the potential\nvalue of the modelling as an illustration of a possible mechanism. In particular, this could stimulate\nfuture research that directly addresses the assumptions we made in the context of our model.\nWe are genuinely grateful for this comment. It led to a clearer presentation of the modelling\napproach, and thus improved the conceptual precision of the manuscript. (See Supplementary\nInformation; see line 22; see lines 202ff.; see line 516f.)"}, {"round": 4, "reviewer_comments": {"reviewer_3": "The authors have addressed all of our comments."}, "author_response": ""}]

Decision letter