Comparison of three behavioral cardioception tasks and heartbeat evoked potentials in the same group of healthy volunteers

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Methods for determining cardioception are still under discussion. In the present study, we considered metrics for interoceptive accuracy (IAcc) assessments in three behavioral cardioception tasks − (1) the heartbeat tapping (HTT), (2) the heartbeat discrimination (HDT), and (3) the heartbeat counting (HCT) - and heartbeat evoked potentials (HEP) recorded by an electroencephalography during resting state and the tasks. The study included forty-eight healthy volunteers (25 females, 36 ± 7 age). The IAcc in the HTT assessed using various metrics, except for the metric based on the circular variation between heartbeat and press timing, positively correlated both with each other and with the IAcc in the HCT. The HDT showed no correlation with the other tasks. However, none of the metrics showed a clear advantage over the others in their association with the neurophysiological marker of interoception, the mean HEP amplitude, during task performance. During all three tasks, the HEP amplitude (1) did not differ between individuals with high and low IAcc, (2) was not different from the HEP amplitude during the resting state, (3) was lower during the HDT compared to the HCT. Thus, our results contribute to the debate on the interaction between behavioral cardioception tasks and the HEP. Biological sciences/Neuroscience/Cognitive neuroscience/Perception Biological sciences/Psychology/Human behaviour Biological sciences/Physiology/Neurophysiology Cardioception heartbeat evoked potentials heartbeat tapping task heartbeat discrimination task heartbeat counting task Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Interoception is a complex phenomenon conceptualized as perception, processing and integration of the internal bodily signals. Cardioception is gaining increasing attention in various research fields. In healthy individuals cardioception was shown to be associated with somatosensory perception and attention 1 , motor cortical excitability 2 , emotional processing and decision-making 3 4 , 5 . In clinical populations interoceptive processing is actively studied in patients with various psychoneurological and developmental disorders 6 – 10 , endocrine 11 , 12 , cardiological diseases 13 , 14 , and others 15 . Currently the most popular tasks for cardioception are: counting heartbeats during given time intervals (HCT) 16 , pressing a button at the moment of heartbeat sensation (HTT) 17 , determining whether the presented series of sound signals is synchronous or asynchronous to heartbeats (HDT) 18 , 19 (Fig. 1 ). A major prerequisite for evaluating and comparing any tasks is to assess their reliability and validity. The reliability of the HCT was previously demonstrated in several studies 20 – 24 . For the HTT, the IAcc metric, reflecting the presses occurring within a specific delay after the heartbeat 25 , also showed stability from the beginning to the end of the task. For the HDT, reliability has been demonstrated in several studies: using split-half reliability for two repeated sessions run intermittently²⁶, odd-even reliability²³, test-retest reliability with a one-week interval, and by correlating conditions with 1-, 5-, and 10-tone sequences²⁷. The review suggests a link between IAcc and interoceptive questionnaires on body awareness, which may implicitly confirm that they measure a single construct (convergent validity) 28 . At the same time, a short longitudinal study did not find this relationship 29 . Current research on the association between other questionnaires evaluating depression, anxiety, alexithymia and interoception presents conflicting results – negative 30 , 31 , positive 32 , or nonsignificant 33 associations. Noteworthy, all cardioception tasks face significant criticism for various reasons 23 , 34 . Thus, some researchers suggest that the existing tasks are unrelated to genuine cardioception 35 and are prone to biased results 36 . For instance, some authors suggest that HCT and HTT results, when measured as the difference between estimated and recorded heartbeats, may be influenced by participants’ awareness of their normal heart rate 35 , 37 . Others highlight the insensitivity of the HCT to heart rate changes induced by a pacemaker 38 or to changes in posture 37 . Another criticism has been directed at the HTT and the HDT, suggesting that the task structure could interfere with cardioception due to competition for attentional resource 23 . Moreover, different metrics assessing IAcc in the HTT 4 , 9 , 22 , 36 , and HDT 39 19, 40 , 41 have been proposed, each with its own distinctive features. Tables 1 – 2 provide a brief description of the most common IAcc metrics and their abbreviations. The delay-based and CAcmotor metrics require participants to tap within a set time limit but do not penalize extra taps, leading to inflated scores due to response frequency bias. The d_mod metric addresses this issue by penalizing false alarms, but it still relies on an arbitrarily defined response window, which does not account for individual temporal differences in attentional processes. The resVec metric overcomes this limitation by assessing the phase consistency between heartbeats and motor responses without rewarding frequent tapping and selecting a specific window 25 . Similarly, the md metric mitigates response bias by comparing response and cardiac frequencies across overlapping time windows rather than single time spans, making it robust against subjective heart rate estimates and arbitrary response classifications while capturing dynamic behavioral adjustments 39 . Fittipaldi et al. demonstrated that for the HTT, md was more reliable than the other two mainstream metrics ( mSI and d_mod ) because md was explained by markers of interoception such as HEP, fMRI functional connectivity within interoceptive hubs, and socio-demographics characteristics 39 . Abrevaya et al. revealed that md may be a distinguishing feature between groups with cardiac or neurological disorders in terms of interoception, in contrast to other metrics in the HTT, such as mSI , d_mod , and delay-based . 9 . However, no such analysis has been performed for recent metrics such as those from Körmendi et al. 22 . Table 1 Dictionary of the used metrics to assess interoceptive accuracy (IAcc) in the heartbeat tapping (HTT) task Task Abbreviation Description of IAcc metrics Reference HTT delay-based Ratio of the total number of presses that fell within a heart rate-dependent time window (delay) after the nearest preceding R peak to all recorded heartbeats in the task. Ranges from 0 to 1, with 1 indicating high cardioception. Refs. 9 , 62 , 88 – 92 mSI Modified Schandry’s classic index, ratio of the total number of presses to all recorded heartbeats in the task. Ranges from 0 to 1, with 1 indicating high cardioception. Refs. 9 , 39 , 62 md Mean distance, a measure of the mean synchrony between the press frequency and the heartbeat frequency in overlapping time windows starting from each R peak. Had no defined limits, higher values indicated greater cardioception. Refs. 9 , 39 , 62 d_mod Modified from the classic d-prime based on signal detection theory, a measure of sensitivity to feel a heartbeat in the heart rate-dependent time window (delay) after the nearest preceding R peak and not to feel outside the window. Had no defined limits, higher values indicated greater cardioception. Refs. 9 , 39 resVec Mean resultant vector, a measure of the variation of a time delay between the press and the nearest preceding R peak normalized by the corresponding inter-beat interval to that R peak. Ranges from 0 to 1, with 1 indicating high cardioception. Refs. 4 , 22 CAcmotor Ratio of the total number of presses that fell within a 350–650 ms time window after the nearest preceding R peak to all recorded heartbeats in the task. Ranges from 0 to 1, with 1 indicating high cardioception. Ref. 22 Table 2 Dictionary of the used metrics to assess interoceptive accuracy (IAcc) in the heartbeat discrimination (HDT) and in the heartbeat counting (HCT) tasks Task Abbreviation Description of IAcc metrics Reference HDT ncorrect Ratio of correct synchronicity judgments to total number of trials. Ranges from 0 to 1, with 1 indicating high cardioception. Refs. 93 , 94 d d-prime based on signal detection theory, a measure of the sensitivity to synchronous and asynchronous auditory signals delivered after the R peak with small and large delays, respectively. Had no defined limits, higher values indicated greater cardioception. Refs. 19 , 95 с value Criterion, propency to answer "yes" or "no" in judgments about the synchronicity of the series of tones with the heartbeats. Refs. 88 HCT SI Schandry’s classic index, mean ratio of counted heartbeats to all recorded heartbeats in the time interval. Ranges from 0 to 1, with 1 indicating high cardioception. Ref. 16 corSI Corrected Schandry’s classic index, taken into account when the number of counted heartbeats was much higher than the recorded heartbeats; mean ratio of counted heartbeats to half of the sum of all recorded heartbeats in the time interval and the counted heartbeats. Ranges from − 1 to 1, with 1 indicating high cardioception. Ref. 96 Therefore, in addition to evaluating the reliability and validity of tasks, the extent to which behavioral tasks measure cardioception should be assessed by examining their association with an objective neurophysiological proxy for cardioception, such as the HEP. Schandry et al. was the first group who reported the existence of the HEP and revealed that the latency of the cortical activity peak, occurring 200–300 ms after the R peak, may be influenced by the direction of attention towards internal or external stimuli 42 . Later, intracranial EEG studies confirmed the existence of genuine neural sources of HEP, proving that this is not an artifact due to volume conduction from ECG 43 , 44 . Besides physiological pathways and mechanisms underlying HEP discussed by Park et al. 44 recent studies provided more information for our understanding of brain-heart communications 45 , 46 . Building upon Schandry et al. research 42 , Pollatos and Schandry demonstrated significant correlation between the IAcc in the HCT and the average amplitude of HEP within the 250–300 ms period 47 . In their later work, they did not show this outcome 48 . A recent meta-analysis 49 emphasized that the relationship between HEP and interoception remains unclear, and the few existing studies report controversial results of correlations between the HEP and IAcc in tasks. To our knowledge, our study is the first to analyze cardioception in the same subjects using (1) the three most frequently used cardioception tasks, along with both commonly used and novel metrics to assess IAcc, and (2) relating these measures to the objective neurophysiological marker of interoception, HEP. We emphasize the importance of conducting such task-based studies within the same group of participants, as clinical characteristics are known to influence cardiac interoceptive accuracy 33 , 50 , 51 . If these characteristics are not accounted for, it becomes challenging to generalize findings from one group to another. Results 4.1 Cardioception tasks comparison The different metrics in the HTT were correlated with each other as well as with the metrics in the HCT, HDT. The aim was to identify groups of metrics that potentially measure a similar construct or share the same biases, and to explore the relationship between three behavioral cardioception tasks. Figure 2 shows the results of a pairwise correlation analysis. Metrics in the HTT were positively correlated with each other. Specifically, d_mod and md were moderately correlated ( n = 40, r = .55, p = .01), while the remaining metrics showed strong correlations ( r ≥ .7, p < .001). Metrics in the HTT were also correlated with corSI from the HCT ( n = 48, r ≥ .7, p < .001 for delay-based , mSI , and CAcmotor ; r ≥ .6, p < .001 for md and d_mod with n = 40 and n = 48, respectively). The exception was resVec , which did not show a significant correlation either with other metrics in the HTT or with metrics in other tasks. ( p = n.s.). For the HDT, ncorrect and d were strongly correlated ( n = 30, r = .94, p < .001). There were no correlations between IAcc in the HTT and HDT, or between the HCT and HDT. A negative correlation was found between the c value in the HDT and several metrics in the HTT: delay-based ( r = − .64, p = .01), mSI ( r = − .6, p = .02), d_mod ( r = − .65, p = .005), and CAcmotor ( r = − .63, p = .01) ( n = 30 for all correlations). Additionally, a negative correlation was observed between c value in the HDT and IAcc in the HCT ( n = 30, r = − .58, p = .03). IAcc was compared between detectors and non-detectors to identify metrics that differentiate between groups with different levels of cardioception (Table 3 ). Detectors ( n = 31) had higher md and resVec in the HTT compared to non-detectors ( n = 9). There were no significant differences between the groups regarding sex (Chi-squared < 1, p = 1; non-detectors: 16 females, 15 males; detectors: 5 females, 4 males), age ( p = .163; non-detectors: 36.61 ± 7.11 years; detectors: 32.89 ± 5.67 years), or BMI ( p = .35; non-detectors: 23.94 ± 3.56; detectors: 24.87 ± 3.16). Table 3 Interoceptive accuracy (IAcc) in the heartbeat tapping (HTT), heartbeat discrimination (HDT), and heartbeat counting (HCT) tasks in groups with nonuniform (non-detectors) and uniform (detectors) presses circular distribution relative to R peak in ECG (unpaired Wilcoxon test with Bonferroni correction) Task IAcc Non-detectors ( M ± SD (n)) Detectors ( M ± SD (n)) W p HTT delay-based .29 ± .23 (31) .53 ± .26 (9) 207 .28 mSI .36 ± .28 (31) .66 ± .32 (9) 211 .20 md 0.4 ± 0.31 (31) 0.74 ± 0.29 (9) 233 .02 d_mod 0.87 ± 0.58 (31) 1.11 ± 0.93 (9) 198 .59 resVec .2 ± .24 (31) .33 ± .22 (9) 224 .049 CAcmotor .12 ± .1 (31) .21 ± .1 (9) 206 .31 HDT ncorrect .45 ± 0.11 (21) .47 ± 0.11 (3) 38.5 1 d -0.35 ± 0.61 (21) -0.07 ± 1.02 (3) 37 1 c -0.28 ± 0.74 (21) -0.9 ± 0.35 (3) 10 .66 HCT corSI .16 ± .67 (31) .72 ± .28 (9) 216 .12 Note. Significant results are highlighted in bold font. We decided to retain three metrics in the HTT for further analysis: md , resVec , and CAcmotor . md was retained due to its significant correlation with other metrics in both the HTT and HCT. resVec was retained because it showed no correlation with the other metrics; moreover, both md and resVec differed significantly between detectors and non-detectors. CAcmotor was retained because, like resVec , it is not a mainstream metric. For the HDT we retained d due to its correlation with ncorrect. As an additional characteristic we retained c value due to its negative correlation with several metrics in HTT and HCT. Finally, we retained corSI in the HCT. The purpose of the measuring internal consistency was to determine how similar the metrics are in the assessment of the level of cardioception. Cronbach's alphas for the normalized versions of the selected metrics in three tasks ( n = 30) were as follows: α = 0.60 for the combination of md_norm , Pc2IFC , and SI; α = 0.45 for the combination of resVec , Pc2IFC , and SI; α = 0.48 for the combination of CAcmotor , Pc2IFC , and SI. 4.2 HEP amplitude comparison within conditions in the whole sample and in groups with different levels of cardioception. Figure 3 illustrates the HEP amplitude in channel groups. The comparison aimed to assess (1) whether different cardioception tasks influence interoception level expressed as a pattern of HEP amplitude, (2) whether patterns differ from the off-task condition, and (3) whether patterns differ in task within people with good and poor cardioceptive abilities (divided by the level of IAcc in the corresponding task into high and low IAcc groups) and based on the press circular distribution in the HTT (detector and non-detector groups). A significant effect of condition in the whole sample was observed when comparing the HEP amplitudes recorded during the HDT and HCT ( n = 30, p = .046). A significant cluster, spanning from 124 to 296 ms, included the following channels: Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, C3, Cz, C4, and CP3 (Fig. 4 a-c). An effect of condition was also found in a group of participants with high c value for the HDT and the resting state comparison ( n = 15, p = .033). A significant cluster, occurring between 320 and 492 ms, included channels F7, F3, Fz, F8, FT7, FC3, FCz, FC4, C3, Cz, C4, TP7, CP3, CPz, CP4, T5, P3, Pz, P4, P5, PO3, POz, PO4, P6, PO7, O1, Oz, O2, and PO8 (Fig. 4 g-i). We did not find any significant difference between the HEP amplitude during other behavioral cardioception tasks within the whole sample, between the HEP amplitude during tasks and resting state within the whole sample, within groups with high and low IAcc (see Supplementary Fig. S1 -S2) and within detectors and non-detectors groups (see Supplementary Fig. S3). A summary of the results is presented in Supplementary Figure S4. 4.3 HEP amplitude modulation comparison between groups with different levels of cardioception The purpose of the comparison was to assess whether task modifications produce different levels of interoception modulation expressed as a pattern of task-rest difference of HEP amplitude. Comparisons were performed between people with good and poor cardioceptive abilities divided by the level of IAcc in the corresponding task (high and low IAcc groups) and by the presses’ circular distribution in the HTT (detector and non-detector groups). There were no significant clusters when comparing the HEP amplitude modulation between the high and low IAcc groups (see Supplementary Fig. S1 -S2), or between the detector and non-detector groups (see Supplementary Fig. S3). 4.4 Association between mean HEP amplitude and IAcc within each cardioception task 4.4.1 Mean HEP amplitude in channels, ROIs and IAcc There were no correlations between the HEP amplitude averaged over channels belonging to significant cluster within the 124–296 ms time range and the corresponding IAcc in HDT and HCT (Fig. 4 d-f). There were several significant correlations between IAcc and mean HEP amplitude both at rest (Fig. 5 a) and during the task (Fig. 5 b). However, after correction for multiple comparisons using PCA none of the results survived (significant trend between md and HEP amplitude in TP8 channel in HTT r = − .45, p = .003, p_corr after correction was equal to .06, n = 40). Multivariate analyses revealed that HEP amplitude in HDT in left frontal ROI was a significant positive predictor of d before BH correction but not after ( n = 30, β = 1.73, p = .02, p BH =.13). There were no associations between mean HEP amplitude in ROIs during other tasks and resting state and IAcc in tasks. 4.4.2 Mean HEP amplitude in spatio-temporal clusters and IAcc There were no clusters with significant correlations based on the spatio-temporal permutation cluster test on correlation. Discussion The current work provides, for the first time, an evaluation of the three most commonly used behavioral tasks for cardioception and their comparison with the electrophysiological marker of interoception - HEP - in a group of healthy volunteers. Our results demonstrate that not all metrics from three cardioception tasks correlate with each other. In the HTT, we found that five metrics were significantly correlated: based on (1) the heart rate-dependent time delay ( delay-based ), (2) the synchrony of response frequency and heartbeat frequency ( md ), (3) the modified Schandry index ( mSI ), (4) the sensitivity to feel a heartbeat within a heart rate-dependent time window ( d_mod ), and (5) the CAcmotor metric recently suggested by Körmendi et al. 22 . The strong correlations observed among delay-based , mSI , md , d_mod , and CAcmotor suggest that these metrics may share common biases rather than measuring the same underlying construct. Of particular concern is the high correlation of d_mod and md with these metrics, as this challenges the assumption that they are resistant to response frequency bias. Furthermore, the association of delay-based , md , mSI , d_mod , and CAcmotor with the metrics of HCT, which are biased by guessing strategies, raises additional concerns. On the contrary, the sixth metric, resVec (based on the circular variation between heartbeat and press timing), did not significantly correlate with any of the other metrics in HTT and HCT, suggesting it is less susceptible to response biases, guessing strategies and predefined time windows. In a previous study, resVec was associated with mSI and CAcmotor , showing a negative correlation 25 . We suggest that metrics need further refinement and validation and caution should be exercised when using any of the five metrics in the HTT ( delay-based , md , mSI , d_mod , or CAcmotor ), while resVec is a promising measure of cardiac interoceptive accuracy. In addition, we compared these metrics between individuals with good and poor cardioceptive abilities. In previous studies such division was performed mainly for the HCT (summarized by Coll et al. 49 ) and the HDT 27 , 52 . We followed the approach proposed by Körmendi et al. 22 , in which participants were divided into detectors and non-detectors based on the uniformity of the time between heartbeats and button presses. We found that 22.5% of participants fell into the detector group, which is higher than the 12% reported by Kormendi and colleagues. Detectors showed a higher resVec metric, which can be attributed to the fact that both the detector classification and resVec are based on variations in pressing time. Among the remaining metrics, only the md metric in the HTT showed a significant difference between the detector and non-detector groups, independent of sex, age, and BMI. These results align with Fittipaldi et al. 39 , who suggested that md also may be a better proxy for cardioception in the HTT. Our findings are consistent with previous studies showing that only a minority of healthy participants are good heartbeat perceivers in other tasks (have an accuracy above the median). In the HCT, 35% of participants had an IAcc above 0.85 53 , while in the HDT, 30% of participants were able to perceive their heartbeat 54 . We performed a correlation analysis to explore the relationship between three behavioral cardioception tasks. Although a recent meta-analysis 55 identified a weak relationship between IAcc in the HCT and HDT (R² = 0.044), results from various studies differ, ranging from moderate to small correlations 40 , 56 to no correlation 57 – 59 , which is consistent with the findings of the present study. Our results align with those of Körmendi et al. 25 , who found a correlation between IAcc in the HTT and HCT. We expand on their work by showing a correlation not only between one metric ( CAcmotor ) but between five HTT metrics and the HCT. Studies comparing IAcc between the HTT and HDT are limited. One early study by Pennebaker and Hoover 60 reported no correlation as in the present study. Furthermore, we observed a negative correlation between the c value in the HDT and IAcc in the other two tasks, suggesting that participants with poor heartbeat perception were more likely to respond "no" when asked about the synchronicity of tones with their heartbeat. The variability of performance from task to task may also be an important characteristic of the stability of a person's attention and conscientiousness when completing a task. If we assume that three behavioral tasks evaluate the same phenomenon, cardioception, the IAcc across tasks should reflect a relatively stable characteristic of the individual. However, the Cronbach’s alpha for three tasks was less than .6 and did not show internal consistency, which may indicate that these tasks probe different aspects of cardioceptive ability. Indeed, there is ongoing debate regarding the utility of different approaches to measuring cardioception (for details see Körmendi et al. 61 ). A growing body of evidence suggests a relationship between interoceptive ability and the HEP (for review, see Coll et al. 49 ), making HEP amplitude a potential electrophysiological marker of interoception 44 . Most studies examining the associations between behavioral task IAcc and HEP parameters implemented a maximum of two behavioral tasks 9 , 62 – 65 . The current study aimed to investigate the relationship between IAcc in three tasks and HEP in the same sample of participants. This represents a key aspect of the novelty of our research, as comparing results across studies that use different behavioral tasks is often challenging due to methodological heterogeneity (as discussed by Coll et al. 49 ) and the multimodal nature of interoceptive ability, which is influenced by various factors such as sex and body composition 12 , age 33 , 66 and cardiac hemodynamics 67 . Fittipaldi et al. 39 found that the higher the cardioception, as measured by md (but not by mSI or d ), the more negative was the amplitude of the HEP in HTT (but not for mSI or d ). However, our study did not reveal any such correlation for these IAcc. One possible explanation could be the difference in the age range of participants. Fittipaldi et al. 's study included participants aged 17 to 84 years, whereas our study focused on those aged 20 to 50 years. The authors found that the md metric did not correlate with age in a univariate analysis. However, in a multivariate model that included electrophysiological, hemodynamic, and socio-emotional features, md could be predicted by age. It is possible that a wider age range might contribute to the relationship between IAcc in the HTT and the HEP amplitude, as age is one of the factors that may influence interoceptive abilities 33 , 66 . In contrast to our study, an earlier study showed a significant negative correlation between IAcc in the HDT and the amplitude of the evoked potential (the authors called it 'N1') locked to the heartbeat 68 . However, this correlation was demonstrated for another IAcc metric (the standard deviation of the mean time delay that participants preferred to judge as synchronous with the heartbeat). Banellis and Cruse found that HEP amplitude differed between stimulus anticipation of synchronous and asynchronous trials, but reported lack of correlation between d and the HEP amplitude 69 . The authors provided three arguments explaining this result: the misperception of both conditions as mostly asynchronous by poor perceiving participants, individual differences in time perception and the greater latency of the HEP. Our data support the idea of misperception as indicated by high c value, while IAcc was low. Another study 70 also reinforces our findings. The authors found a marginally significant suppression of N1 auditory evoked potentials in central-frontal electrodes during the presentation of synchronous tones with different latencies relative to the exteroceptive condition. Authors also noted that there was no association between suppression and percentage of correct answers in the task, which was at chance level across participants. Thus, IAcc in the HDT does not reflect sensory suppression in the processing of heartbeat-related information. Although a positive correlation between IAcc in the HCT and HEP amplitude during the task was previously demonstrated 47 , 71 , other studies have found no correlation between behavioral and neurophysiological data for this task, which aligns with our results 48 , 63 – 65 , 72 , 73 . The lack of association observed in our study contributes to the ongoing discussion regarding the ability of the HCT to reliably indicate interoception. A recent large-scale study 74 demonstrated that IAcc in the HCT has a low correlation with the actual number of heartbeats and exhibits non-linearity across IAcc quantiles. One potential approach to increasing the utility of the HEP amplitude during behavioral tasks may involve accounting for the breathing cycle. Zaccaro et al. 73 demonstrated that the HEP amplitude varies between inhalation and exhalation during interoceptive tasks. Another factor complicating the interpretation of results from different studies using the HCT is the variation in instructions given to participants. In earlier studies, participants were asked to count heartbeats even if they did not feel them, relying on intuition 4 , 47 . More recent studies, however, instruct participants to report only perceived heartbeats, which helps to reduce the influence of estimation and better capture interoceptive ability 73 , 75 , 76 . We used the latter approach. Interestingly, Desmedt et al. 76 compared both instructions and demonstrated that the second approach (report only perceived, not estimated heartbeats number) reduced IAcc. During tasks, the HEP amplitude did not differ from the resting state. This contradicts the expectation that HEP is modulated as the participant entered an interoceptive state during the task. Couto et al. 4 demonstrated, that the HEP amplitude differed between conditions when participants were (1) instructed to “think freely and pay attention to nothing in particular” (in the current study the same instruction was given for the resting EEG data acquisition), (2) to pay attention to heartbeats (which is analogous to the performance in the behavioral tasks in our experimental procedure), and (3) to count sounds (which corresponds to the exteroceptive condition of the HTT in our study). Yoris et al. 13 demonstrated decreased HEP amplitude modulation between exteroceptive and interoceptive conditions in hypertensive patients compared to controls. Similarly, Schulz et al. 65 found higher HEP amplitude during the HCT compared to the resting state in control individuals, but not in patients with depersonalization disorders. HEP amplitude did not differ either between individuals with high and low IAcc (except for c value) or between detectors and non-detectors. The presence of HEP amplitude modulation was exclusively observed in the group with high c value. As previously mentioned, a higher c value indicates a bias towards negative responses regarding the synchronicity of stimuli in this task. Participants who responded with a tendency to answer no had low IAcc (there was a negative correlation between metrics and c value). One of the assumptions might be that it indicated their shift of attention to external stimuli and an inability to perceive interoceptive signals. In the HDT, they did not hear the heart ( corSI was low – they claimed few contractions) and listened only to sounds, i.e. exteroception was predominant. Performing a predominantly exteroceptive task disguises the HEP relative to the resting state over the head 77 , 78 . An alternative assumption could be that people with poor cardioceptive abilities made more effort to perform the task, which was reflected in the negative deflection of HEP. We also observed a difference in HEP amplitudes during the HDT and HCT in the frontal and central channels while this was not accompanied by a correlation between IAcc and HEP in these channels in the tasks (Fig. 4 e-f). This result aligns with the discussion by Schulz et al. 65 , suggesting that HEP during the HDT may be influenced by auditory-evoked potentials. Also Baess 79 showed N1 auditory evoked potential suppression in center-frontal regions for self-initiated sounds triggered by a participant’s button press. In recent years, new methods have been developed for measuring cardioception. In a narrative review by Körmendi et al., methods that involve a combination of tracking, detection, and discrimination are referred to as mixed methods (for details, see the review 61 ). Although we applied the traditional HDT methodology, rather than one of the new mixed methods, our data confirm that this task, compared to HCT and HTT, demands more attentional effort from the participant. The study's limitations may include an insufficient sample size to detect weak to moderate effect sizes, the inclusion of only HEP without psychological questionnaire data or other canonical markers of interoception, working in the sensor domain rather than the source domain, relying primarily on correlation analyses instead of multivariate analyses, and using a specific montage scheme (for details see Supplementary Fig. S5). Conclusion In conclusion, this study aimed to add evidence to the debate on task selection for the assessment of cardioception. We compared a set of different IAcc metrics (1) within tasks, (2) between tasks and (3) with the electrophysiological marker of interoception - HEP amplitude during resting state and task conditions. Although most metrics in the HTT were highly correlated, only resVec remained independent, suggesting it was not influenced by the same biases as the other metrics. Additionally, md effectively differentiated individuals with high and low cardioceptive ability, highlighting its potential for assessing interoceptive accuracy. HEP amplitude was lower in the HDT in comparison with resting state and HCT, which may indicate the predominance of the exteroceptive state over the interoceptive and complexity of HDT in comparison with two other tasks. The lack of association between IAcc and HEP amplitudes contributes to the growing body of literature suggesting that HEP parameters may reflect multiple processes related to cardioception, attention, respiration, etc., and thus may not necessarily show a simple association with the accuracy of heartbeat perception. Although the association between HEP amplitude and IAcc did not survive correction, it is worth discussing this result as potentially significant for task selection, but requiring further investigation of our highlighted metrics. Our findings could drive future theoretical developments and clinical progress in interoception research.. Methods 7.1 Participants The study included 48 healthy volunteers (25 females; M [Q25, Q75] = 36 [29, 43] years old; M [Q25, Q75] = 24.67 [21.98, 26.90] body mass index (BMI)). After checking the biofeedback quality, as outlined in the Experimental Procedure section of the Methods, 30 participants were included in the HDT analysis. Eight participants who did not press in the HTT were excluded from the analysis of md and resVec , as these metrics could not be calculated without responses. Healthy participants were recruited through a local mailing list. We used G*Power 3.1.9.7 to calculate the sample sizes needed to achieve 80% power with a two-tailed alpha level of 0.05 for detecting correlations. Correlations from weak (.28) to strong (.8) were previously reported between tasks, and between HEP and tasks (see Supplementary Information for details). We therefore concluded that, when comparing all three conditions, a sample size of 48 participants was adequate. See Supplementary Information for the exclusion criteria and medical examinations undergone by the participants. The study followed the ethical norms outlined in the 1964 Declaration of Helsinki. Participation in the study was voluntary. Informed consent was obtained from all participants. The consent written by the participants was approved by the local Ethics Committee at the National Medical Research Center for Therapy and Preventive Medicine in accordance with the Declaration of Helsinki. The experimental protocol (No. 02–02/21 of 25.02.2021) was approved by the local Ethics Committee at the National Medical Research Center for Therapy and Preventive Medicine in accordance with the Declaration of Helsinki. 7.2 Experimental procedure Experiment was implemented in an open source software package PsychoPy (v2022.2.4, https://www.psychopy.org ) 80 . The participants were sitting quietly with their eyes open. At the beginning, five minutes of resting EEG data were recorded, during which participants were instructed to focus on the fixation cross in the middle of the screen in front of them and to avoid moving and excessive blinking. Before each of the tasks, presented in random order, participants had a training session and were instructed to focus on their internal sensations. Instructions were shown on a computer, and participants' responses were recorded using a keyboard and mouse. Figure 1 shows the task procedures. During all three tasks, participants were asked to focus solely on actual heartbeats sensations, without palpating their pulse or guessing. They were, however, permitted to consider any weak heartbeat sensations they experienced. Participants were allowed not to press the button and report zero counts if they did not perceive any heartbeats. The HDT consisted of 43 trials, with the first three serving as training trials and were not included in the analysis 81 . S250 condition should be perceived as synchronous with the heartbeat according to previous work 82 where delay between 200 and 300 ms got more synchronous judgements compared to 500 ms. S550 condition should be perceived as asynchronous to the heartbeats. Each trial consisted of 10 auditory signals (440 Hz, 100 ms) 27 . Technical details of the task implementation can be found in the Supplementary Information. We excluded trials with poor quality of the biofeedback (see Supplementary Information for quality criteria). As a result, participants had an average of 17.6 ± 3.42 synchronous and 17.17 ± 2.44 asynchronous trials. 7.3 EEG, EMG, ECG, EOG recording Electrophysiological data were recorded using the NVX-52 EEG amplifier (Medical Computer Systems, Ltd. (MCS)) with 36 Ag/AgCl electrodes placed on elastic EEG caps according to the international 10–20 system. Channels T3 and T4 were used as online ipsilateral references. The T3 reference was used for electrodes on the left side, the T4 reference for the right side, and the average of (T3 + T4)/2 for electrodes on the central sagittal line. The impedance across all channels during recording was generally around 10 kΩ and did not exceed 20 kΩ. Surface EMG was recorded from the first dorsal interosseous muscle using a belly-tendon montage. ECG was recorded using three pairs of electrodes in a bipolar montage: the first pair on the anterior forearm, the second pair 2 cm below the clavicles in the infraclavicular fossa, and the third pair on the right and left sides of the neck as it was used previously 67 . Two EOG electrodes were placed on the lateral sides of the eyes. The sampling frequency was set at 500 Hz, and the data were filtered at 0.1–70 Hz with a 50 Hz notch filter. 7.4 Data analysis ECG and EEG data were processed and analyzed using custom-made scripts in Python (v3.11, https://www.python.org ) and the MNE-Python toolbox (v1.6.0, https://mne.tools/stable/index.html ) 83 . Both ECG and EEG data were notch-filtered at 50 Hz and then bandpass filtered from 0.5 to 45 Hz using a zero-phase FIR filter with a Hamming window using the raw.filter function. 7.4.1 EMG, ECG data analysis EMG data were used to assess the precise timing of the button press (see Supplementary Information for details). R peaks were detected from the ECG lead with the fewest artifacts (chest lead below the clavicles for most participants) using the MNE-Python. ECG data were always visually inspected by cardiologists to verify the accuracy of R peak detection and to identify any extrasystoles. Both sinus rhythm R peaks and extrasystoles were considered when analyzing IAcc during the tasks. However, epochs time-locked to extrasystoles were removed from further analysis of HEP (see the EEG analysis section below). 7.4.2 EEG data analysis We removed eye-movements and cardiac-field artifacts using independent component analysis (ICA) (fastica algorithm) to find components explaining 99% of the variance. We selected and deleted two EOG and one ECG components. See Supplementary Information for components’ selection protocol. These components were then projected from the EEG data. EEG data were filtered with a high-pass at 0.5 Hz and a low-pass at 20 Hz 71 . Bad channels (two at max) were interpolated. EEG data were segmented into epochs time-locked to R peaks, ranging from − 200 to 600 ms, with baseline correction from − 200 to -100 ms. We dropped epochs time-locked to the extrasystoles, two epochs before and one after the extrasystoles and epochs corresponding to the interval between R peaks less than 600 ms. We used the AutoReject algorithm 84 to delete and/or interpolate bad epochs. We visually inspected the EEG epochs and excluded any containing excessive noise, limiting the total exclusion to no more than 10% of the data. The final number of epochs was 157 ± 20 in the exteroceptive condition in the HTT, 152 ± 22 in the interoceptive condition in HTT, 374 ± 51 in the HDT, 228 ± 34 in the HCT, and 301 ± 46 in the resting state. We divided channels into spatial ROIs as it was done previously 47 , 64 : left frontal (Fp1, F3, FC3, C3, F7, FT7), central frontal (Fpz, Fz, FCz, Cz), right frontal (Fp2, F4, FC4, C4, F8, FT8), left parietal (TP7, CP3, P3, T5, P5, PO7), central occipital (CPz, Pz, POz, Oz, PO3, O1, PO4, O2), and right parietal (TP8, CP4, P4, T6, P6, PO8). HEP amplitude among the channels and ROIs were obtained from the within-channel and within-ROI epoch-wise averaging of the HEP amplitude respectively. Mean HEP amplitude over channels and ROIs were obtained in the time range 200–600 ms after R peak. Additionally, our analysis incorporated not only HEP amplitude during the tasks but also HEP modulation, which was calculated by subtracting the resting state HEP amplitude across channels from the HEP amplitude recorded during the tasks. 7.5 Behavioral cardioception tasks analysis For the comparison of IAcc between tasks we used the normalized versions of the metrics: Pc2IFC for d , md_norm for md ( derived from md after min-max normalization), and SI for corSI . For further analyses we divided the sample into groups with different levels of cardioception using two approaches. First, individuals were catrgorised depending on whether their metric was higher or lower than the median for this metric (high and low IAcc groups) 86 . This process was repeated separately for each metric, resulting in a number of high and low IAcc groups equal to the number of metrics used. Table 4 shows IAcc in groups with high and low IAcc in tasks. Second, participants were categorised into the detector and non-detector groups based on the uniform or non-uniform presses circular distribution in the HTT, respectively. For the estimation of the uniformity we applied the Rayleigh uniformity test 22 . See Supplementary Information for the calculation of the presses circular distribution. Table 4 Interoceptive accuracy (IAcc) in the heartbeat tapping (HTT), heartbeat discrimination (HDT), and heartbeat counting (HCT) tasks in group with IAcc below the median (LOW) and group with IAcc above than median (HIGH) Test IAcc median N in each group LOW group ( M ± SD ) HIGH group ( M ± SD ) HTT md 0.53 20 0.18 ± 0.18 0.77 ± 0.12 resVec .15 20 .08 ± .04 .38 ± .26 CAcmotor .1 24 .03 ± .04 .21 ± .07 HDT d -0.2 15 -0.69 ± 0.39 0.22 ± 0.43 c -0.2 15 -0.75 ± 0.47 0.52 ± 0.85 HCT corSI .41 24 − .37 ± .54 .76 ± .17 7.6 Statistical analysis The Fig. 6 presents a scheme of the statistical analysis. 7.6.1 Cardioception tasks comparison We conducted a pairwise correlation analysis between metrics across the tasks using Spearman’s correlation with Bonferroni correction. The comparison of metrics between detectors and non-detectors was performed using an unpaired Wilcoxon test with Bonferroni correction. The internal consistency of the three tasks was tested by Cronbach's alphas. The analysis was performed using the open-source RStudio environment (v4.3.1, https://posit.co/download/rstudio-desktop/ ). 7.6.2 HEP amplitude comparison within conditions in the whole sample and in groups with different levels of cardioception. We compared HEP amplitude between three behavioral cardioception tasks (HTT (interoceptive condition) vs. HDT, HTT (interoceptive condition) vs. HCT, HDT vs. HCT); between two conditions in the HTT (HTT (interoceptive condition) vs. HTT (exteroceptive condition)) within the whole sample. We also compared HEP amplitude between tasks and resting state (HTT (interoceptive condition) vs. resting state, HDT vs. resting state, HCT vs. resting state) within the whole sample, within groups with high and low IAcc, within detectors and non-detectors groups. A nonparametric spatio-temporal permutation test of the MNE-Python was used to compare HEP amplitude. Test implemented nonparametric analysis 87 to compare dependent data (using a two-tailed paired t-test) with temporal and spatial dimensions. See Supplementary Information for details on how this test worked. 7.6.3 HEP amplitude modulation comparison between groups with different levels of cardioception We used a nonparametric spatio-temporal permutation test from MNE-Python (see above) for independent (with one-way ANOVA) data to compare HEP amplitude modulation between groups with high and low IAcc in tasks, between detectors and non-detectors. 7.6.4 Association between mean HEP amplitude and IAcc within each cardioception task We analysed an association of HEP amplitude during task (HTT (interoceptive condition), HDT, HCT) and each IAcc metric in the corresponding task: 1) for each channel separately; 2) for the mean HEP amplitude for the six ROIs; 3) using a spatiotemporal cluster permutation test for a correlation. The cluster test was additionally used to address the problem of multiple comparisons that arises when dealing with channels and regions of interest. The cluster test allowed us to broaden or narrow the group of channels and the time period when it was significantly associated with IAcc. Mean HEP amplitude in channels, ROIs and IAcc The analysis was performed using the RStudio. Spearman’s correlation with correction for multiple comparisons was performed between (1) mean HEP amplitude in channels during task and IAcc within each cardioception task, (2) mean HEP amplitude in channels during rest and IAcc within each cardioception task. The multiple comparison correction relied on the assumption that IAcc within tasks had high association and HEP amplitude within channels also had high association. Therefore, the calculation of the number of genuine independent comparisons made in the correlation analysis was based on the number of principal components explaining 95% of the variance in the principal component analysis (PCA). PCA was computed on the matrix where participants were represented as rows and mean HEP amplitude in channels during the task as columns. The number of components explaining 95% of the variance was 17 for the HTT, 15 for the HDT, 15 for the HCT and 17 for the resting state and used for Bonferroni correction. We used a multivariate analysis to explore how HEP amplitude among ROIs during this task and during resting state predicted each IAcc metric in the corresponding task. Generalized Linear Models models were: A1-A3. IAcc in HTT ∈ { md , resVec , CAmotor } ~ HEP in ROIs ∈ {HTT} A4-A5. IAcc in HDT ∈ { d , c -value} ~ HEP in ROIs ∈ {HDT} A6. IAcc in HCT ∈ { corSI } ~ HEP in ROIs ∈ {HCT} B1-B3. IAcc in HTT ∈ { md , resVec , CAmotor } ~ HEP in ROIs ∈ {resting state} B4-B5. IAcc in HDT ∈ { d , c -value} ~ HEP in ROIs ∈ {resting state} B6. IAcc in HCT ∈ { corSI } ~ HEP in ROIs ∈ {resting state} We used a BH correction on the p-values obtained for ROIs within each of the A1-A6 and B1-B6 models. Mean HEP amplitude in spatio-temporal clusters and IAcc The spatio-temporal permutation cluster test on correlation between IAcc and HEP amplitude was performed using the MNE-Python with Monte-Carlo statistics. See Supplementary Information for parameters we set. The analysis was the same as in Maris and Oostenveld 87 but Spearman’s correlation between IAcc and HEP amplitude over samples in channels was converted to t-statistic. Declarations Data availability statement The datasets generated and analysed during the current study and code we used are available in the Open Science Framework webpage. Please see https://osf.io/c3qws/?view_only=ea629fa512d34097976def3331354fe4. Competing interests The authors declare no competing interests. Grants and funding The research was funded by the Russian Science Foundation (project No. 22-15-00507). Author Contribution I.M. conducted data acquisition, formal analysis, developed the experiment software, and wrote the manuscript. A.L. contributed to conceptualization, funding acquisition, design, data acquisition, participant recruitment and selection, EEG data analysis, and writing. A.S. contributed to data acquisition and EEG data analysis. V.K. and I.M. performed the statistical analysis. M.N. contributed to conceptualization, funding acquisition, and manuscript revision. A.E. contributed to conceptualization, funding acquisition, design, participant recruitment and selection, study supervision, and manuscript revision. O.D. contributed to conceptualization and study supervision. All authors reviewed and approved the submitted version. 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Behavioral and Brain Functions 9 , 47 (2013). Sedeño, L. et al. How Do You Feel when You Can’t Feel Your Body? Interoception, Functional Connectivity and Emotional Processing in Depersonalization-Derealization Disorder. PLoS One 9 , e98769 (2014). Yoris, A. et al. The roles of interoceptive sensitivity and metacognitive interoception in panic. Behavioral and Brain Functions 11 , 14 (2015). Yoris, A. et al. The inner world of overactive monitoring: neural markers of interoception in obsessive–compulsive disorder. Psychol Med 47 , 1957–1970 (2017). García-Cordero, I. et al. Feeling, learning from and being aware of inner states: Interoceptive dimensions in neurodegeneration and stroke. Philosophical Transactions of the Royal Society B: Biological Sciences 371 , (2016). Herman, A. M., Rae, C. L., Critchley, H. D. & Duka, T. Interoceptive accuracy predicts nonplanning trait impulsivity. Psychophysiology 56 , (2019). Hina, F. & Aspell, J. E. Altered interoceptive processing in smokers: Evidence from the heartbeat tracking task. International Journal of Psychophysiology 142 , 10–16 (2019). Ewing, D. L. et al. Sleep and the heart: Interoceptive differences linked to poor experiential sleep quality in anxiety and depression. Biol Psychol 127 , 163–172 (2017). Garfinkel, S. N., Seth, A. K., Barrett, A. B., Suzuki, K. & Critchley, H. D. Knowing your own heart: Distinguishing interoceptive accuracy from interoceptive awareness. Biol Psychol 104 , 65–74 (2015). Additional Declarations No competing interests reported. Supplementary Files HeartBrainSupplementaryInformationrevisionv6.pdf Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 May, 2025 Reviews received at journal 01 May, 2025 Reviews received at journal 16 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 05 Apr, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5124302","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439180096,"identity":"8db7b949-26b5-408c-9963-4f31f1c72b7a","order_by":0,"name":"Irina Minenko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNiA+AMQGDOwNUC7xWngOEKkFBgwYJBKI1MLHfjrxcEHFHWN+yTeGnwvKbBIbpM8Y4HcYT+6GwzPOPDOTnJ1jLD3jXJoxA18OAS0MQC28bYdtDG7nGEgDGXIMPDwEtPC/BWr5B9Ry84zxb962/zyEtUiAbGk4bGZwg8cMaMsBImyRANrCc+ywsWRPWpk1z7lkYzYetgK8WuT7czd/5qk5bNjPfnjzbZ4yu8R+HuYNeLUgAQ6Ie0iJTfYHJCgeBaNgFIyCkQQAo10/grk8+y8AAAAASUVORK5CYII=","orcid":"","institution":"National Medical Research Center for Therapy and Preventive Medicine","correspondingAuthor":true,"prefix":"","firstName":"Irina","middleName":"","lastName":"Minenko","suffix":""},{"id":439180097,"identity":"a2d6b2a8-2056-4bbe-b100-7eb002f965ec","order_by":1,"name":"Alena Limonova","email":"","orcid":"","institution":"National Medical Research Center for Therapy and Preventive Medicine","correspondingAuthor":false,"prefix":"","firstName":"Alena","middleName":"","lastName":"Limonova","suffix":""},{"id":439180098,"identity":"e4f5c0f3-b8a8-4e8e-8e2b-89e44aacf225","order_by":2,"name":"Anastasia Sukmanova","email":"","orcid":"","institution":"National Medical Research Center for Therapy and Preventive Medicine","correspondingAuthor":false,"prefix":"","firstName":"Anastasia","middleName":"","lastName":"Sukmanova","suffix":""},{"id":439180099,"identity":"601c032a-010c-4558-bc5b-c8c91f804e72","order_by":3,"name":"Vladimir Kutsenko","email":"","orcid":"","institution":"National Medical Research Center for Therapy and Preventive Medicine","correspondingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"","lastName":"Kutsenko","suffix":""},{"id":439180100,"identity":"911d1227-8892-429d-85e9-20985cfde9e4","order_by":4,"name":"Maria Nazarova","email":"","orcid":"","institution":"National Research University Higher School of Economics","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Nazarova","suffix":""},{"id":439180101,"identity":"ac6c4753-cbde-4bac-bcb6-b9d973a7fc40","order_by":5,"name":"Alexandra Ershova","email":"","orcid":"","institution":"National Medical Research Center for Therapy and Preventive Medicine","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Ershova","suffix":""},{"id":439180102,"identity":"64216ff0-77b8-4b64-b209-23579a82f395","order_by":6,"name":"Oksana Drapkina","email":"","orcid":"","institution":"National Medical Research Center for Therapy and Preventive Medicine","correspondingAuthor":false,"prefix":"","firstName":"Oksana","middleName":"","lastName":"Drapkina","suffix":""}],"badges":[],"createdAt":"2024-09-20 14:25:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5124302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5124302/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08779-5","type":"published","date":"2025-10-14T15:58:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80117600,"identity":"d7b77a11-7847-4b5d-a7c2-0d203c8721e3","added_by":"auto","created_at":"2025-04-08 06:49:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61902,"visible":true,"origin":"","legend":"\u003cp\u003eThe experimental procedure of the HTT, HDT and HCT. (a) HTT involved identifying the moment of a heartbeat sensation and reporting it via a button press. The task included two conditions lasting 150 s preceded by a 10-s training trial: (1) an exteroceptive condition in which participants pressed the \"space\" key each time they heard a sound signal (1 Hz, 500 ms), and (2) an interoceptive condition in which participants pressed the button when they felt a heartbeat. (b) HDT used biofeedback in the form of auditory signals delivered with specific delay after the online-registered R peak. Participants were instructed to determine whether the signals were simultaneous (synchronous) with their heartbeat. Two conditions were used in the task: S250 (250 ms delay) and S550 (550 ms delay). The conditions were presented randomly, with 20 trials of each condition. (c) HCT\u003cstrong\u003e \u003c/strong\u003erequired participants to count their heartbeat sensations during six randomly presented time intervals. The main task was preceded by a 25-s training session.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/740d41d146d44dd2df13aac0.jpg"},{"id":80118530,"identity":"8a0d5a1d-6191-498b-a817-cca5aa708e60","added_by":"auto","created_at":"2025-04-08 06:57:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124362,"visible":true,"origin":"","legend":"\u003cp\u003eIAcc metrics assessed within and between the HTT, HDT, and HCT (Spearman’s correlation with Bonferroni correction). The 95% confidence band is drawn.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/6e2a0a795246b4f164c0d398.jpg"},{"id":80117601,"identity":"94976c39-9933-4948-bcf7-6354e5ed10ce","added_by":"auto","created_at":"2025-04-08 06:49:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65945,"visible":true,"origin":"","legend":"\u003cp\u003eGrand averages of HEP amplitude during the interoceptive and exteroceptive conditions in heartbeat tapping (HTT), heartbeat discrimination (HDT), heartbeat counting (HCT) tasks and resting state over six regions of interest (ROIs). The positions of the sensors belonging to the ROIs are shown by the dots on the head map.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/9d19d278e814497d25545cd5.jpg"},{"id":80117613,"identity":"8260c636-9ba4-46ab-88e0-102e00bd90a1","added_by":"auto","created_at":"2025-04-08 06:49:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76356,"visible":true,"origin":"","legend":"\u003cp\u003eHEP amplitude comparison (nonparametric permutation paired t-test). (a) Topography of the difference in HEP amplitude between the heartbeat discrimination (HDT) and heartbeat counting (HCT) tasks. HEP amplitude was averaged within the significant time range of 124–296 ms (\u003cem\u003en\u003c/em\u003e = 40, \u003cem\u003ep\u003c/em\u003e = .046; significant cluster channels are highlighted). (b) Grand averages of HEP amplitude during the HDT and HCT within the channels comprising the significant cluster (the significant time range is highlighted in gray). (c) Distribution of HEP amplitude averaged over the cluster channels in the significant time range during the HDT and HCT. Paired Wilcoxon signed-rank test results are shown. (d-f) Spearman’s correlation between HEP amplitude averaged over the cluster channels in the significant time range and interoceptive accuracy metric (d) \u003cem\u003ed\u003c/em\u003e in the HDT, (e) \u003cem\u003ec\u003c/em\u003e value in the HDT, (f) \u003cem\u003ecorSI\u003c/em\u003e in the HCT. (g) Topography of the difference in HEP amplitude between the HDT and the resting state in participants with high \u003cem\u003ec\u003c/em\u003e value (HIGH(c)). HEP amplitude was averaged within the significant time range of 320–492 ms (\u003cem\u003en\u003c/em\u003e = 15, \u003cem\u003ep \u003c/em\u003e= .033; significant cluster channels are highlighted). (h) Grand averages of HEP amplitude during the HDT and resting state in the HIGH(c) group within the channels comprising the significant cluster (the significant time range is highlighted in gray). (i) Distribution of HEP amplitude averaged over the cluster channels in the significant time range during the HDT and resting state. Paired Wilcoxon signed-rank test results are shown.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/915467fd88f9cb45e737da27.jpg"},{"id":80117606,"identity":"a842cea6-ef8d-44f3-9e50-60e6d5de7001","added_by":"auto","created_at":"2025-04-08 06:49:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104864,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman’s correlation between interoceptive accuracy (IAcc) in the HTT, HDT, and HCT and mean HEP amplitude in the 200-600 ms time range in channels recorded during (a) resting state and (b) during the tasks. Only results with significant p-values before correction for multiple comparisons based on PCA are shown. The 95% confidence band is drawn.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/c5a0d48e8f83fb011d86e9a9.jpg"},{"id":80118535,"identity":"02156141-ef92-4508-8d58-9c126f8af602","added_by":"auto","created_at":"2025-04-08 06:57:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":109224,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of the statistical analysis\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/0ce19cd5b75670f890eddf81.jpg"},{"id":93956115,"identity":"a3f74fbb-1b51-4ec0-8e32-6dd99679e398","added_by":"auto","created_at":"2025-10-20 16:10:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2038198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/b1c51d32-35da-4e2c-9330-f41ca1043f01.pdf"},{"id":80117604,"identity":"fb634b25-2103-4fec-8136-eb7359b6b991","added_by":"auto","created_at":"2025-04-08 06:49:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1075203,"visible":true,"origin":"","legend":"","description":"","filename":"HeartBrainSupplementaryInformationrevisionv6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5124302/v1/7bfaab2d096b3fe32c0f16ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of three behavioral cardioception tasks and heartbeat evoked potentials in the same group of healthy volunteers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInteroception is a complex phenomenon conceptualized as perception, processing and integration of the internal bodily signals. Cardioception is gaining increasing attention in various research fields. In healthy individuals cardioception was shown to be associated with somatosensory perception and attention\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, motor cortical excitability\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, emotional processing and decision-making\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In clinical populations interoceptive processing is actively studied in patients with various psychoneurological and developmental disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, endocrine\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, cardiological diseases\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and others\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Currently the most popular tasks for cardioception are: counting heartbeats during given time intervals (HCT)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, pressing a button at the moment of heartbeat sensation (HTT)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, determining whether the presented series of sound signals is synchronous or asynchronous to heartbeats (HDT)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA major prerequisite for evaluating and comparing any tasks is to assess their reliability and validity. The reliability of the HCT was previously demonstrated in several studies\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For the HTT, the IAcc metric, reflecting the presses occurring within a specific delay after the heartbeat\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, also showed stability from the beginning to the end of the task. For the HDT, reliability has been demonstrated in several studies: using split-half reliability for two repeated sessions run intermittently\u0026sup2;⁶, odd-even reliability\u0026sup2;\u0026sup3;, test-retest reliability with a one-week interval, and by correlating conditions with 1-, 5-, and 10-tone sequences\u0026sup2;⁷. The review suggests a link between IAcc and interoceptive questionnaires on body awareness, which may implicitly confirm that they measure a single construct (convergent validity)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. At the same time, a short longitudinal study did not find this relationship\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Current research on the association between other questionnaires evaluating depression, anxiety, alexithymia and interoception presents conflicting results \u0026ndash; negative\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, positive\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, or nonsignificant\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e associations.\u003c/p\u003e \u003cp\u003eNoteworthy, all cardioception tasks face significant criticism for various reasons\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Thus, some researchers suggest that the existing tasks are unrelated to genuine cardioception\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and are prone to biased results\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. For instance, some authors suggest that HCT and HTT results, when measured as the difference between estimated and recorded heartbeats, may be influenced by participants\u0026rsquo; awareness of their normal heart rate\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Others highlight the insensitivity of the HCT to heart rate changes induced by a pacemaker\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e or to changes in posture \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Another criticism has been directed at the HTT and the HDT, suggesting that the task structure could interfere with cardioception due to competition for attentional resource\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Moreover, different metrics assessing IAcc in the HTT\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003c/sup\u003e and HDT \u003csup\u003e39 19, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e have been proposed, each with its own distinctive features. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide a brief description of the most common IAcc metrics and their abbreviations. The \u003cem\u003edelay-based\u003c/em\u003e and \u003cem\u003eCAcmotor\u003c/em\u003e metrics require participants to tap within a set time limit but do not penalize extra taps, leading to inflated scores due to response frequency bias. The \u003cem\u003ed_mod\u003c/em\u003e metric addresses this issue by penalizing false alarms, but it still relies on an arbitrarily defined response window, which does not account for individual temporal differences in attentional processes. The \u003cem\u003eresVec\u003c/em\u003e metric overcomes this limitation by assessing the phase consistency between heartbeats and motor responses without rewarding frequent tapping and selecting a specific window\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Similarly, the \u003cem\u003emd\u003c/em\u003e metric mitigates response bias by comparing response and cardiac frequencies across overlapping time windows rather than single time spans, making it robust against subjective heart rate estimates and arbitrary response classifications while capturing dynamic behavioral adjustments\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Fittipaldi et al. demonstrated that for the HTT, \u003cem\u003emd\u003c/em\u003e was more reliable than the other two mainstream metrics (\u003cem\u003emSI\u003c/em\u003e and \u003cem\u003ed_mod\u003c/em\u003e) because \u003cem\u003emd\u003c/em\u003e was explained by markers of interoception such as HEP, fMRI functional connectivity within interoceptive hubs, and socio-demographics characteristics\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Abrevaya et al. revealed that \u003cem\u003emd\u003c/em\u003e may be a distinguishing feature between groups with cardiac or neurological disorders in terms of interoception, in contrast to other metrics in the HTT, such as \u003cem\u003emSI\u003c/em\u003e, \u003cem\u003ed_mod\u003c/em\u003e, and \u003cem\u003edelay-based\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, no such analysis has been performed for recent metrics such as those from K\u0026ouml;rmendi et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDictionary of the used metrics to assess interoceptive accuracy (IAcc) in the heartbeat tapping (HTT) task\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription of IAcc metrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003edelay-based\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatio of the total number of presses that fell within a heart rate-dependent time window (delay) after the nearest preceding R peak to all recorded heartbeats in the task. Ranges from 0 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan additionalcitationids=\"CR89 CR90 CR91\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emSI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified Schandry\u0026rsquo;s classic index, ratio of the total number of presses to all recorded heartbeats in the task. Ranges from 0 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emd\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean distance, a measure of the mean synchrony between the press frequency and the heartbeat frequency in overlapping time windows starting from each R peak. Had no defined limits, higher values indicated greater cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed_mod\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified from the classic d-prime based on signal detection theory, a measure of sensitivity to feel a heartbeat in the heart rate-dependent time window (delay) after the nearest preceding R peak and not to feel outside the window. Had no defined limits, higher values indicated greater cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eresVec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean resultant vector, a measure of the variation of a time delay between the press and the nearest preceding R peak normalized by the corresponding inter-beat interval to that R peak. Ranges from 0 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCAcmotor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatio of the total number of presses that fell within a 350\u0026ndash;650 ms time window after the nearest preceding R peak to all recorded heartbeats in the task. Ranges from 0 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDictionary of the used metrics to assess interoceptive accuracy (IAcc) in the heartbeat discrimination (HDT) and in the heartbeat counting (HCT) tasks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription of IAcc metrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003encorrect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatio of correct synchronicity judgments to total number of trials. Ranges from 0 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed-prime based on signal detection theory, a measure of the sensitivity to synchronous and asynchronous auditory signals delivered after the R peak with small and large delays, respectively. Had no defined limits, higher values indicated greater cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eс value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriterion, propency to answer \"yes\" or \"no\" in judgments about the synchronicity of the series of tones with the heartbeats.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefs.\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchandry\u0026rsquo;s classic index, mean ratio of counted heartbeats to all recorded heartbeats in the time interval. Ranges from 0 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecorSI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrected Schandry\u0026rsquo;s classic index, taken into account when the number of counted heartbeats was much higher than the recorded heartbeats; mean ratio of counted heartbeats to half of the sum of all recorded heartbeats in the time interval and the counted heartbeats. Ranges from \u0026minus;\u0026thinsp;1 to 1, with 1 indicating high cardioception.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTherefore, in addition to evaluating the reliability and validity of tasks, the extent to which behavioral tasks measure cardioception should be assessed by examining their association with an objective neurophysiological proxy for cardioception, such as the HEP. Schandry et al. was the first group who reported the existence of the HEP and revealed that the latency of the cortical activity peak, occurring 200\u0026ndash;300 ms after the R peak, may be influenced by the direction of attention towards internal or external stimuli\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Later, intracranial EEG studies confirmed the existence of genuine neural sources of HEP, proving that this is not an artifact due to volume conduction from ECG\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Besides physiological pathways and mechanisms underlying HEP discussed by Park et al.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e recent studies provided more information for our understanding of brain-heart communications\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Building upon Schandry et al. research\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, Pollatos and Schandry demonstrated significant correlation between the IAcc in the HCT and the average amplitude of HEP within the 250\u0026ndash;300 ms period\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. In their later work, they did not show this outcome \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. A recent meta-analysis \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e emphasized that the relationship between HEP and interoception remains unclear, and the few existing studies report controversial results of correlations between the HEP and IAcc in tasks.\u003c/p\u003e \u003cp\u003eTo our knowledge, our study is the first to analyze cardioception in the same subjects using (1) the three most frequently used cardioception tasks, along with both commonly used and novel metrics to assess IAcc, and (2) relating these measures to the objective neurophysiological marker of interoception, HEP. We emphasize the importance of conducting such task-based studies within the same group of participants, as clinical characteristics are known to influence cardiac interoceptive accuracy \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. If these characteristics are not accounted for, it becomes challenging to generalize findings from one group to another.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Cardioception tasks comparison\u003c/h2\u003e \u003cp\u003eThe different metrics in the HTT were correlated with each other as well as with the metrics in the HCT, HDT. The aim was to identify groups of metrics that potentially measure a similar construct or share the same biases, and to explore the relationship between three behavioral cardioception tasks. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of a pairwise correlation analysis. Metrics in the HTT were positively correlated with each other. Specifically, \u003cem\u003ed_mod\u003c/em\u003e and \u003cem\u003emd\u003c/em\u003e were moderately correlated (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01), while the remaining metrics showed strong correlations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Metrics in the HTT were also correlated with \u003cem\u003ecorSI\u003c/em\u003e from the HCT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48, \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 for \u003cem\u003edelay-based\u003c/em\u003e, \u003cem\u003emSI\u003c/em\u003e, and \u003cem\u003eCAcmotor\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 for \u003cem\u003emd\u003c/em\u003e and \u003cem\u003ed_mod\u003c/em\u003e with \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40 and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48, respectively). The exception was \u003cem\u003eresVec\u003c/em\u003e, which did not show a significant correlation either with other metrics in the HTT or with metrics in other tasks. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;n.s.). For the HDT, \u003cem\u003encorrect\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e were strongly correlated (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). There were no correlations between IAcc in the HTT and HDT, or between the HCT and HDT. A negative correlation was found between the \u003cem\u003ec\u003c/em\u003e value in the HDT and several metrics in the HTT: \u003cem\u003edelay-based\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01), \u003cem\u003emSI\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), \u003cem\u003ed_mod\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005), and \u003cem\u003eCAcmotor\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30 for all correlations). Additionally, a negative correlation was observed between \u003cem\u003ec\u003c/em\u003e value in the HDT and IAcc in the HCT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIAcc was compared between detectors and non-detectors to identify metrics that differentiate between groups with different levels of cardioception (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Detectors (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31) had higher \u003cem\u003emd\u003c/em\u003e and \u003cem\u003eresVec\u003c/em\u003e in the HTT compared to non-detectors (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9). There were no significant differences between the groups regarding sex (Chi-squared\u0026thinsp;\u0026lt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1; non-detectors: 16 females, 15 males; detectors: 5 females, 4 males), age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.163; non-detectors: 36.61\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11 years; detectors: 32.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67 years), or BMI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.35; non-detectors: 23.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56; detectors: 24.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteroceptive accuracy (IAcc) in the heartbeat tapping (HTT), heartbeat discrimination (HDT), and heartbeat counting (HCT) tasks in groups with nonuniform (non-detectors) and uniform (detectors) presses circular distribution relative to R peak in ECG (unpaired Wilcoxon test with Bonferroni correction)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAcc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-detectors (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e (n))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetectors (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e (n))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003edelay-based\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.29\u0026thinsp;\u0026plusmn;\u0026thinsp;.23 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.53\u0026thinsp;\u0026plusmn;\u0026thinsp;.26 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emSI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.36\u0026thinsp;\u0026plusmn;\u0026thinsp;.28 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.66\u0026thinsp;\u0026plusmn;\u0026thinsp;.32 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emd\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed_mod\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eresVec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.2\u0026thinsp;\u0026plusmn;\u0026thinsp;.24 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.33\u0026thinsp;\u0026plusmn;\u0026thinsp;.22 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCAcmotor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.12\u0026thinsp;\u0026plusmn;\u0026thinsp;.1 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.21\u0026thinsp;\u0026plusmn;\u0026thinsp;.1 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003encorrect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecorSI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.16\u0026thinsp;\u0026plusmn;\u0026thinsp;.67 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.72\u0026thinsp;\u0026plusmn;\u0026thinsp;.28 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e Significant results are highlighted in bold font.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe decided to retain three metrics in the HTT for further analysis: \u003cem\u003emd\u003c/em\u003e, \u003cem\u003eresVec\u003c/em\u003e, and \u003cem\u003eCAcmotor\u003c/em\u003e. \u003cem\u003emd\u003c/em\u003e was retained due to its significant correlation with other metrics in both the HTT and HCT. \u003cem\u003eresVec\u003c/em\u003e was retained because it showed no correlation with the other metrics; moreover, both \u003cem\u003emd\u003c/em\u003e and \u003cem\u003eresVec\u003c/em\u003e differed significantly between detectors and non-detectors. \u003cem\u003eCAcmotor\u003c/em\u003e was retained because, like \u003cem\u003eresVec\u003c/em\u003e, it is not a mainstream metric. For the HDT we retained \u003cem\u003ed\u003c/em\u003e due to its correlation with \u003cem\u003encorrect.\u003c/em\u003e As an additional characteristic we retained \u003cem\u003ec\u003c/em\u003e value due to its negative correlation with several metrics in HTT and HCT. Finally, we retained \u003cem\u003ecorSI\u003c/em\u003e in the HCT.\u003c/p\u003e \u003cp\u003eThe purpose of the measuring internal consistency was to determine how similar the metrics are in the assessment of the level of cardioception. Cronbach's alphas for the normalized versions of the selected metrics in three tasks (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) were as follows: α\u0026thinsp;=\u0026thinsp;0.60 for the combination of \u003cem\u003emd_norm\u003c/em\u003e, \u003cem\u003ePc2IFC\u003c/em\u003e, and \u003cem\u003eSI;\u003c/em\u003e α\u0026thinsp;=\u0026thinsp;0.45 for the combination of \u003cem\u003eresVec\u003c/em\u003e, \u003cem\u003ePc2IFC\u003c/em\u003e, and \u003cem\u003eSI;\u003c/em\u003e α\u0026thinsp;=\u0026thinsp;0.48 for the combination of \u003cem\u003eCAcmotor\u003c/em\u003e, \u003cem\u003ePc2IFC\u003c/em\u003e, and \u003cem\u003eSI.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2 HEP amplitude comparison within conditions in the whole sample and in groups with different levels of cardioception.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the HEP amplitude in channel groups. The comparison aimed to assess (1) whether different cardioception tasks influence interoception level expressed as a pattern of HEP amplitude, (2) whether patterns differ from the off-task condition, and (3) whether patterns differ in task within people with good and poor cardioceptive abilities (divided by the level of IAcc in the corresponding task into high and low IAcc groups) and based on the press circular distribution in the HTT (detector and non-detector groups).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA significant effect of condition in the whole sample was observed when comparing the HEP amplitudes recorded during the HDT and HCT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.046). A significant cluster, spanning from 124 to 296 ms, included the following channels: Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, C3, Cz, C4, and CP3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). An effect of condition was also found in a group of participants with high \u003cem\u003ec\u003c/em\u003e value for the HDT and the resting state comparison (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.033). A significant cluster, occurring between 320 and 492 ms, included channels F7, F3, Fz, F8, FT7, FC3, FCz, FC4, C3, Cz, C4, TP7, CP3, CPz, CP4, T5, P3, Pz, P4, P5, PO3, POz, PO4, P6, PO7, O1, Oz, O2, and PO8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg-i). We did not find any significant difference between the HEP amplitude during other behavioral cardioception tasks within the whole sample, between the HEP amplitude during tasks and resting state within the whole sample, within groups with high and low IAcc (see Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2) and within detectors and non-detectors groups (see Supplementary Fig. S3). A summary of the results is presented in Supplementary Figure S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e4.3 HEP amplitude modulation comparison between groups with different levels of cardioception\u003c/h2\u003e \u003cp\u003eThe purpose of the comparison was to assess whether task modifications produce different levels of interoception modulation expressed as a pattern of task-rest difference of HEP amplitude. Comparisons were performed between people with good and poor cardioceptive abilities divided by the level of IAcc in the corresponding task (high and low IAcc groups) and by the presses\u0026rsquo; circular distribution in the HTT (detector and non-detector groups). There were no significant clusters when comparing the HEP amplitude modulation between the high and low IAcc groups (see Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2), or between the detector and non-detector groups (see Supplementary Fig. S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Association between mean HEP amplitude and IAcc within each cardioception task\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Mean HEP amplitude in channels, ROIs and IAcc\u003c/h2\u003e \u003cp\u003eThere were no correlations between the HEP amplitude averaged over channels belonging to significant cluster within the 124\u0026ndash;296 ms time range and the corresponding IAcc in HDT and HCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f). There were several significant correlations between IAcc and mean HEP amplitude both at rest (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and during the task (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). However, after correction for multiple comparisons using PCA none of the results survived (significant trend between \u003cem\u003emd\u003c/em\u003e and HEP amplitude in TP8 channel in HTT \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003, \u003cem\u003ep_corr\u003c/em\u003e after correction was equal to .06, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40). Multivariate analyses revealed that HEP amplitude in HDT in left frontal ROI was a significant positive predictor of \u003cem\u003ed\u003c/em\u003e before BH correction but not after (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, β\u0026thinsp;=\u0026thinsp;1.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eBH\u003c/sub\u003e =.13). There were no associations between mean HEP amplitude in ROIs during other tasks and resting state and IAcc in tasks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Mean HEP amplitude in spatio-temporal clusters and IAcc\u003c/h2\u003e \u003cp\u003eThere were no clusters with significant correlations based on the spatio-temporal permutation cluster test on correlation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current work provides, for the first time, an evaluation of the three most commonly used behavioral tasks for cardioception and their comparison with the electrophysiological marker of interoception - HEP - in a group of healthy volunteers.\u003c/p\u003e \u003cp\u003eOur results demonstrate that not all metrics from three cardioception tasks correlate with each other. In the HTT, we found that five metrics were significantly correlated: based on (1) the heart rate-dependent time delay (\u003cem\u003edelay-based\u003c/em\u003e), (2) the synchrony of response frequency and heartbeat frequency (\u003cem\u003emd\u003c/em\u003e), (3) the modified Schandry index (\u003cem\u003emSI\u003c/em\u003e), (4) the sensitivity to feel a heartbeat within a heart rate-dependent time window (\u003cem\u003ed_mod\u003c/em\u003e), and (5) the \u003cem\u003eCAcmotor\u003c/em\u003e metric recently suggested by K\u0026ouml;rmendi et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The strong correlations observed among \u003cem\u003edelay-based\u003c/em\u003e, \u003cem\u003emSI\u003c/em\u003e, \u003cem\u003emd\u003c/em\u003e, \u003cem\u003ed_mod\u003c/em\u003e, and \u003cem\u003eCAcmotor\u003c/em\u003e suggest that these metrics may share common biases rather than measuring the same underlying construct. Of particular concern is the high correlation of \u003cem\u003ed_mod\u003c/em\u003e and \u003cem\u003emd\u003c/em\u003e with these metrics, as this challenges the assumption that they are resistant to response frequency bias. Furthermore, the association of \u003cem\u003edelay-based\u003c/em\u003e, \u003cem\u003emd\u003c/em\u003e, \u003cem\u003emSI\u003c/em\u003e, \u003cem\u003ed_mod\u003c/em\u003e, and \u003cem\u003eCAcmotor\u003c/em\u003e with the metrics of HCT, which are biased by guessing strategies, raises additional concerns. On the contrary, the sixth metric, \u003cem\u003eresVec\u003c/em\u003e (based on the circular variation between heartbeat and press timing), did not significantly correlate with any of the other metrics in HTT and HCT, suggesting it is less susceptible to response biases, guessing strategies and predefined time windows. In a previous study, \u003cem\u003eresVec\u003c/em\u003e was associated with \u003cem\u003emSI\u003c/em\u003e and \u003cem\u003eCAcmotor\u003c/em\u003e, showing a negative correlation \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We suggest that metrics need further refinement and validation and caution should be exercised when using any of the five metrics in the HTT (\u003cem\u003edelay-based\u003c/em\u003e, \u003cem\u003emd\u003c/em\u003e, \u003cem\u003emSI\u003c/em\u003e, \u003cem\u003ed_mod\u003c/em\u003e, or \u003cem\u003eCAcmotor\u003c/em\u003e), while \u003cem\u003eresVec\u003c/em\u003e is a promising measure of cardiac interoceptive accuracy.\u003c/p\u003e \u003cp\u003eIn addition, we compared these metrics between individuals with good and poor cardioceptive abilities. In previous studies such division was performed mainly for the HCT (summarized by Coll et al. \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e) and the HDT\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. We followed the approach proposed by K\u0026ouml;rmendi et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, in which participants were divided into detectors and non-detectors based on the uniformity of the time between heartbeats and button presses. We found that 22.5% of participants fell into the detector group, which is higher than the 12% reported by Kormendi and colleagues. Detectors showed a higher \u003cem\u003eresVec\u003c/em\u003e metric, which can be attributed to the fact that both the detector classification and \u003cem\u003eresVec\u003c/em\u003e are based on variations in pressing time. Among the remaining metrics, only the \u003cem\u003emd\u003c/em\u003e metric in the HTT showed a significant difference between the detector and non-detector groups, independent of sex, age, and BMI. These results align with Fittipaldi et al. \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, who suggested that \u003cem\u003emd\u003c/em\u003e also may be a better proxy for cardioception in the HTT. Our findings are consistent with previous studies showing that only a minority of healthy participants are good heartbeat perceivers in other tasks (have an accuracy above the median). In the HCT, 35% of participants had an IAcc above 0.85 \u003csup\u003e53\u003c/sup\u003e, while in the HDT, 30% of participants were able to perceive their heartbeat \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe performed a correlation analysis to explore the relationship between three behavioral cardioception tasks. Although a recent meta-analysis\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e identified a weak relationship between IAcc in the HCT and HDT (R\u0026sup2; = 0.044), results from various studies differ, ranging from moderate to small correlations \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e to no correlation \u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, which is consistent with the findings of the present study. Our results align with those of K\u0026ouml;rmendi et al. \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, who found a correlation between IAcc in the HTT and HCT. We expand on their work by showing a correlation not only between one metric (\u003cem\u003eCAcmotor\u003c/em\u003e) but between five HTT metrics and the HCT. Studies comparing IAcc between the HTT and HDT are limited. One early study by Pennebaker and Hoover \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e reported no correlation as in the present study. Furthermore, we observed a negative correlation between the \u003cem\u003ec\u003c/em\u003e value in the HDT and IAcc in the other two tasks, suggesting that participants with poor heartbeat perception were more likely to respond \"no\" when asked about the synchronicity of tones with their heartbeat.\u003c/p\u003e \u003cp\u003eThe variability of performance from task to task may also be an important characteristic of the stability of a person's attention and conscientiousness when completing a task. If we assume that three behavioral tasks evaluate the same phenomenon, cardioception, the IAcc across tasks should reflect a relatively stable characteristic of the individual. However, the Cronbach\u0026rsquo;s alpha for three tasks was less than .6 and did not show internal consistency, which may indicate that these tasks probe different aspects of cardioceptive ability. Indeed, there is ongoing debate regarding the utility of different approaches to measuring cardioception (for details see K\u0026ouml;rmendi et al. \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eA growing body of evidence suggests a relationship between interoceptive ability and the HEP (for review, see Coll et al.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e), making HEP amplitude a potential electrophysiological marker of interoception \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Most studies examining the associations between behavioral task IAcc and HEP parameters implemented a maximum of two behavioral tasks \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR63 CR64\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The current study aimed to investigate the relationship between IAcc in three tasks and HEP in the same sample of participants. This represents a key aspect of the novelty of our research, as comparing results across studies that use different behavioral tasks is often challenging due to methodological heterogeneity (as discussed by Coll et al.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e) and the multimodal nature of interoceptive ability, which is influenced by various factors such as sex and body composition \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, age \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and cardiac hemodynamics \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFittipaldi et al. \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e found that the higher the cardioception, as measured by \u003cem\u003emd\u003c/em\u003e (but not by \u003cem\u003emSI\u003c/em\u003e or \u003cem\u003ed\u003c/em\u003e), the more negative was the amplitude of the HEP in HTT (but not for \u003cem\u003emSI\u003c/em\u003e or \u003cem\u003ed\u003c/em\u003e). However, our study did not reveal any such correlation for these IAcc. One possible explanation could be the difference in the age range of participants. Fittipaldi et al. 's study included participants aged 17 to 84 years, whereas our study focused on those aged 20 to 50 years. The authors found that the \u003cem\u003emd\u003c/em\u003e metric did not correlate with age in a univariate analysis. However, in a multivariate model that included electrophysiological, hemodynamic, and socio-emotional features, \u003cem\u003emd\u003c/em\u003e could be predicted by age. It is possible that a wider age range might contribute to the relationship between IAcc in the HTT and the HEP amplitude, as age is one of the factors that may influence interoceptive abilities \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast to our study, an earlier study showed a significant negative correlation between IAcc in the HDT and the amplitude of the evoked potential (the authors called it 'N1') locked to the heartbeat \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. However, this correlation was demonstrated for another IAcc metric (the standard deviation of the mean time delay that participants preferred to judge as synchronous with the heartbeat). Banellis and Cruse found that HEP amplitude differed between stimulus anticipation of synchronous and asynchronous trials, but reported lack of correlation between \u003cem\u003ed\u003c/em\u003e and the HEP amplitude \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. The authors provided three arguments explaining this result: the misperception of both conditions as mostly asynchronous by poor perceiving participants, individual differences in time perception and the greater latency of the HEP. Our data support the idea of misperception as indicated by high \u003cem\u003ec\u003c/em\u003e value, while IAcc was low. Another study \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e also reinforces our findings. The authors found a marginally significant suppression of N1 auditory evoked potentials in central-frontal electrodes during the presentation of synchronous tones with different latencies relative to the exteroceptive condition. Authors also noted that there was no association between suppression and percentage of correct answers in the task, which was at chance level across participants. Thus, IAcc in the HDT does not reflect sensory suppression in the processing of heartbeat-related information.\u003c/p\u003e \u003cp\u003eAlthough a positive correlation between IAcc in the HCT and HEP amplitude during the task was previously demonstrated \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, other studies have found no correlation between behavioral and neurophysiological data for this task, which aligns with our results \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. The lack of association observed in our study contributes to the ongoing discussion regarding the ability of the HCT to reliably indicate interoception. A recent large-scale study \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e demonstrated that IAcc in the HCT has a low correlation with the actual number of heartbeats and exhibits non-linearity across IAcc quantiles. One potential approach to increasing the utility of the HEP amplitude during behavioral tasks may involve accounting for the breathing cycle. Zaccaro et al. \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e demonstrated that the HEP amplitude varies between inhalation and exhalation during interoceptive tasks. Another factor complicating the interpretation of results from different studies using the HCT is the variation in instructions given to participants. In earlier studies, participants were asked to count heartbeats even if they did not feel them, relying on intuition \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. More recent studies, however, instruct participants to report only perceived heartbeats, which helps to reduce the influence of estimation and better capture interoceptive ability \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. We used the latter approach. Interestingly, Desmedt et al. \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e compared both instructions and demonstrated that the second approach (report only perceived, not estimated heartbeats number) reduced IAcc.\u003c/p\u003e \u003cp\u003eDuring tasks, the HEP amplitude did not differ from the resting state. This contradicts the expectation that HEP is modulated as the participant entered an interoceptive state during the task. Couto et al.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e demonstrated, that the HEP amplitude differed between conditions when participants were (1) instructed to \u0026ldquo;think freely and pay attention to nothing in particular\u0026rdquo; (in the current study the same instruction was given for the resting EEG data acquisition), (2) to pay attention to heartbeats (which is analogous to the performance in the behavioral tasks in our experimental procedure), and (3) to count sounds (which corresponds to the exteroceptive condition of the HTT in our study). Yoris et al. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e demonstrated decreased HEP amplitude modulation between exteroceptive and interoceptive conditions in hypertensive patients compared to controls. Similarly, Schulz et al. \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e found higher HEP amplitude during the HCT compared to the resting state in control individuals, but not in patients with depersonalization disorders.\u003c/p\u003e \u003cp\u003eHEP amplitude did not differ either between individuals with high and low IAcc (except for \u003cem\u003ec\u003c/em\u003e value) or between detectors and non-detectors. The presence of HEP amplitude modulation was exclusively observed in the group with high c value. As previously mentioned, a higher \u003cem\u003ec\u003c/em\u003e value indicates a bias towards negative responses regarding the synchronicity of stimuli in this task. Participants who responded with a tendency to answer no had low IAcc (there was a negative correlation between metrics and c value). One of the assumptions might be that it indicated their shift of attention to external stimuli and an inability to perceive interoceptive signals. In the HDT, they did not hear the heart (\u003cem\u003ecorSI\u003c/em\u003e was low \u0026ndash; they claimed few contractions) and listened only to sounds, i.e. exteroception was predominant. Performing a predominantly exteroceptive task disguises the HEP relative to the resting state over the head \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. An alternative assumption could be that people with poor cardioceptive abilities made more effort to perform the task, which was reflected in the negative deflection of HEP.\u003c/p\u003e \u003cp\u003eWe also observed a difference in HEP amplitudes during the HDT and HCT in the frontal and central channels while this was not accompanied by a correlation between IAcc and HEP in these channels in the tasks (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-f). This result aligns with the discussion by Schulz et al. \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, suggesting that HEP during the HDT may be influenced by auditory-evoked potentials. Also Baess \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e showed N1 auditory evoked potential suppression in center-frontal regions for self-initiated sounds triggered by a participant\u0026rsquo;s button press. In recent years, new methods have been developed for measuring cardioception. In a narrative review by K\u0026ouml;rmendi et al., methods that involve a combination of tracking, detection, and discrimination are referred to as mixed methods (for details, see the review \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e). Although we applied the traditional HDT methodology, rather than one of the new mixed methods, our data confirm that this task, compared to HCT and HTT, demands more attentional effort from the participant.\u003c/p\u003e \u003cp\u003eThe study's limitations may include an insufficient sample size to detect weak to moderate effect sizes, the inclusion of only HEP without psychological questionnaire data or other canonical markers of interoception, working in the sensor domain rather than the source domain, relying primarily on correlation analyses instead of multivariate analyses, and using a specific montage scheme (for details see Supplementary Fig. S5).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study aimed to add evidence to the debate on task selection for the assessment of cardioception. We compared a set of different IAcc metrics (1) within tasks, (2) between tasks and (3) with the electrophysiological marker of interoception - HEP amplitude during resting state and task conditions. Although most metrics in the HTT were highly correlated, only \u003cem\u003eresVec\u003c/em\u003e remained independent, suggesting it was not influenced by the same biases as the other metrics. Additionally, \u003cem\u003emd\u003c/em\u003e effectively differentiated individuals with high and low cardioceptive ability, highlighting its potential for assessing interoceptive accuracy. HEP amplitude was lower in the HDT in comparison with resting state and HCT, which may indicate the predominance of the exteroceptive state over the interoceptive and complexity of HDT in comparison with two other tasks. The lack of association between IAcc and HEP amplitudes contributes to the growing body of literature suggesting that HEP parameters may reflect multiple processes related to cardioception, attention, respiration, etc., and thus may not necessarily show a simple association with the accuracy of heartbeat perception. Although the association between HEP amplitude and IAcc did not survive correction, it is worth discussing this result as potentially significant for task selection, but requiring further investigation of our highlighted metrics. Our findings could drive future theoretical developments and clinical progress in interoception research..\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Participants\u003c/h2\u003e \u003cp\u003eThe study included 48 healthy volunteers (25 females; \u003cem\u003eM\u003c/em\u003e [Q25, Q75]\u0026thinsp;=\u0026thinsp;36 [29, 43] years old; \u003cem\u003eM\u003c/em\u003e [Q25, Q75]\u0026thinsp;=\u0026thinsp;24.67 [21.98, 26.90] body mass index (BMI)). After checking the biofeedback quality, as outlined in the Experimental Procedure section of the Methods, 30 participants were included in the HDT analysis. Eight participants who did not press in the HTT were excluded from the analysis of \u003cem\u003emd\u003c/em\u003e and \u003cem\u003eresVec\u003c/em\u003e, as these metrics could not be calculated without responses.\u003c/p\u003e \u003cp\u003eHealthy participants were recruited through a local mailing list. We used G*Power 3.1.9.7 to calculate the sample sizes needed to achieve 80% power with a two-tailed alpha level of 0.05 for detecting correlations. Correlations from weak (.28) to strong (.8) were previously reported between tasks, and between HEP and tasks (see Supplementary Information for details). We therefore concluded that, when comparing all three conditions, a sample size of 48 participants was adequate. See Supplementary Information for the exclusion criteria and medical examinations undergone by the participants. The study followed the ethical norms outlined in the 1964 Declaration of Helsinki. Participation in the study was voluntary. Informed consent was obtained from all participants. The consent written by the participants was approved by the local Ethics Committee at the National Medical Research Center for Therapy and Preventive Medicine in accordance with the Declaration of Helsinki. The experimental protocol (No. 02\u0026ndash;02/21 of 25.02.2021) was approved by the local Ethics Committee at the National Medical Research Center for Therapy and Preventive Medicine in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Experimental procedure\u003c/h2\u003e \u003cp\u003eExperiment was implemented in an open source software package PsychoPy (v2022.2.4, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.psychopy.org\u003c/span\u003e\u003cspan address=\"https://www.psychopy.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e80\u003c/sup\u003e. The participants were sitting quietly with their eyes open. At the beginning, five minutes of resting EEG data were recorded, during which participants were instructed to focus on the fixation cross in the middle of the screen in front of them and to avoid moving and excessive blinking. Before each of the tasks, presented in random order, participants had a training session and were instructed to focus on their internal sensations. Instructions were shown on a computer, and participants' responses were recorded using a keyboard and mouse. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the task procedures. During all three tasks, participants were asked to focus solely on actual heartbeats sensations, without palpating their pulse or guessing. They were, however, permitted to consider any weak heartbeat sensations they experienced. Participants were allowed not to press the button and report zero counts if they did not perceive any heartbeats.\u003c/p\u003e \u003cp\u003eThe HDT consisted of 43 trials, with the first three serving as training trials and were not included in the analysis \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. S250 condition should be perceived as synchronous with the heartbeat according to previous work \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e where delay between 200 and 300 ms got more synchronous judgements compared to 500 ms. S550 condition should be perceived as asynchronous to the heartbeats. Each trial consisted of 10 auditory signals (440 Hz, 100 ms)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Technical details of the task implementation can be found in the Supplementary Information. We excluded trials with poor quality of the biofeedback (see Supplementary Information for quality criteria). As a result, participants had an average of 17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42 synchronous and 17.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44 asynchronous trials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e7.3 EEG, EMG, ECG, EOG recording\u003c/h2\u003e \u003cp\u003eElectrophysiological data were recorded using the NVX-52 EEG amplifier (Medical Computer Systems, Ltd. (MCS)) with 36 Ag/AgCl electrodes placed on elastic EEG caps according to the international 10\u0026ndash;20 system. Channels T3 and T4 were used as online ipsilateral references. The T3 reference was used for electrodes on the left side, the T4 reference for the right side, and the average of (T3\u0026thinsp;+\u0026thinsp;T4)/2 for electrodes on the central sagittal line. The impedance across all channels during recording was generally around 10 kΩ and did not exceed 20 kΩ. Surface EMG was recorded from the first dorsal interosseous muscle using a belly-tendon montage. ECG was recorded using three pairs of electrodes in a bipolar montage: the first pair on the anterior forearm, the second pair 2 cm below the clavicles in the infraclavicular fossa, and the third pair on the right and left sides of the neck as it was used previously \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Two EOG electrodes were placed on the lateral sides of the eyes. The sampling frequency was set at 500 Hz, and the data were filtered at 0.1\u0026ndash;70 Hz with a 50 Hz notch filter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Data analysis\u003c/h2\u003e \u003cp\u003eECG and EEG data were processed and analyzed using custom-made scripts in Python (v3.11, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org\u003c/span\u003e\u003cspan address=\"https://www.python.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the MNE-Python toolbox (v1.6.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mne.tools/stable/index.html\u003c/span\u003e\u003cspan address=\"https://mne.tools/stable/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Both ECG and EEG data were notch-filtered at 50 Hz and then bandpass filtered from 0.5 to 45 Hz using a zero-phase FIR filter with a Hamming window using the raw.filter function.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e7.4.1 EMG, ECG data analysis\u003c/h2\u003e \u003cp\u003eEMG data were used to assess the precise timing of the button press (see Supplementary Information for details). R peaks were detected from the ECG lead with the fewest artifacts (chest lead below the clavicles for most participants) using the MNE-Python. ECG data were always visually inspected by cardiologists to verify the accuracy of R peak detection and to identify any extrasystoles. Both sinus rhythm R peaks and extrasystoles were considered when analyzing IAcc during the tasks. However, epochs time-locked to extrasystoles were removed from further analysis of HEP (see the EEG analysis section below).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e7.4.2 EEG data analysis\u003c/h2\u003e \u003cp\u003eWe removed eye-movements and cardiac-field artifacts using independent component analysis (ICA) (fastica algorithm) to find components explaining 99% of the variance. We selected and deleted two EOG and one ECG components. See Supplementary Information for components\u0026rsquo; selection protocol. These components were then projected from the EEG data. EEG data were filtered with a high-pass at 0.5 Hz and a low-pass at 20 Hz \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Bad channels (two at max) were interpolated. EEG data were segmented into epochs time-locked to R peaks, ranging from \u0026minus;\u0026thinsp;200 to 600 ms, with baseline correction from \u0026minus;\u0026thinsp;200 to -100 ms. We dropped epochs time-locked to the extrasystoles, two epochs before and one after the extrasystoles and epochs corresponding to the interval between R peaks less than 600 ms. We used the AutoReject algorithm \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e to delete and/or interpolate bad epochs. We visually inspected the EEG epochs and excluded any containing excessive noise, limiting the total exclusion to no more than 10% of the data. The final number of epochs was 157\u0026thinsp;\u0026plusmn;\u0026thinsp;20 in the exteroceptive condition in the HTT, 152\u0026thinsp;\u0026plusmn;\u0026thinsp;22 in the interoceptive condition in HTT, 374\u0026thinsp;\u0026plusmn;\u0026thinsp;51 in the HDT, 228\u0026thinsp;\u0026plusmn;\u0026thinsp;34 in the HCT, and 301\u0026thinsp;\u0026plusmn;\u0026thinsp;46 in the resting state.\u003c/p\u003e \u003cp\u003eWe divided channels into spatial ROIs as it was done previously \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e: left frontal (Fp1, F3, FC3, C3, F7, FT7), central frontal (Fpz, Fz, FCz, Cz), right frontal (Fp2, F4, FC4, C4, F8, FT8), left parietal (TP7, CP3, P3, T5, P5, PO7), central occipital (CPz, Pz, POz, Oz, PO3, O1, PO4, O2), and right parietal (TP8, CP4, P4, T6, P6, PO8). HEP amplitude among the channels and ROIs were obtained from the within-channel and within-ROI epoch-wise averaging of the HEP amplitude respectively. Mean HEP amplitude over channels and ROIs were obtained in the time range 200\u0026ndash;600 ms after R peak. Additionally, our analysis incorporated not only HEP amplitude during the tasks but also HEP modulation, which was calculated by subtracting the resting state HEP amplitude across channels from the HEP amplitude recorded during the tasks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Behavioral cardioception tasks analysis\u003c/h2\u003e \u003cp\u003eFor the comparison of IAcc between tasks we used the normalized versions of the metrics: \u003cem\u003ePc2IFC\u003c/em\u003e for \u003cem\u003ed\u003c/em\u003e, \u003cem\u003emd_norm\u003c/em\u003e for \u003cem\u003emd\u003c/em\u003e ( derived from \u003cem\u003emd\u003c/em\u003e after min-max normalization), and \u003cem\u003eSI\u003c/em\u003e for \u003cem\u003ecorSI\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFor further analyses we divided the sample into groups with different levels of cardioception using two approaches. First, individuals were catrgorised depending on whether their metric was higher or lower than the median for this metric (high and low IAcc groups)\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. This process was repeated separately for each metric, resulting in a number of high and low IAcc groups equal to the number of metrics used. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows IAcc in groups with high and low IAcc in tasks. Second, participants were categorised into the detector and non-detector groups based on the uniform or non-uniform presses circular distribution in the HTT, respectively. For the estimation of the uniformity we applied the Rayleigh uniformity test\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. See Supplementary Information for the calculation of the presses circular distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteroceptive accuracy (IAcc) in the heartbeat tapping (HTT), heartbeat discrimination (HDT), and heartbeat counting (HCT) tasks in group with IAcc below the median (LOW) and group with IAcc above than median (HIGH)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAcc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN in each group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLOW group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHIGH group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003emd\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eresVec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e.08\u0026thinsp;\u0026plusmn;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e.38\u0026thinsp;\u0026plusmn;\u0026thinsp;.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCAcmotor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e.03\u0026thinsp;\u0026plusmn;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e.21\u0026thinsp;\u0026plusmn;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e-0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e-0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecorSI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.37\u0026thinsp;\u0026plusmn;\u0026thinsp;.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e.76\u0026thinsp;\u0026plusmn;\u0026thinsp;.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e7.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a scheme of the statistical analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e7.6.1 Cardioception tasks comparison\u003c/h2\u003e \u003cp\u003eWe conducted a pairwise correlation analysis between metrics across the tasks using Spearman\u0026rsquo;s correlation with Bonferroni correction. The comparison of metrics between detectors and non-detectors was performed using an unpaired Wilcoxon test with Bonferroni correction. The internal consistency of the three tasks was tested by Cronbach's alphas. The analysis was performed using the open-source RStudio environment (v4.3.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://posit.co/download/rstudio-desktop/\u003c/span\u003e\u003cspan address=\"https://posit.co/download/rstudio-desktop/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003e7.6.2 HEP amplitude comparison within conditions in the whole sample and in groups with different levels of cardioception.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe compared HEP amplitude between three behavioral cardioception tasks (HTT (interoceptive condition) vs. HDT, HTT (interoceptive condition) vs. HCT, HDT vs. HCT); between two conditions in the HTT (HTT (interoceptive condition) vs. HTT (exteroceptive condition)) within the whole sample. We also compared HEP amplitude between tasks and resting state (HTT (interoceptive condition) vs. resting state, HDT vs. resting state, HCT vs. resting state) within the whole sample, within groups with high and low IAcc, within detectors and non-detectors groups. A nonparametric spatio-temporal permutation test of the MNE-Python was used to compare HEP amplitude. Test implemented nonparametric analysis \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e to compare dependent data (using a two-tailed paired t-test) with temporal and spatial dimensions. See Supplementary Information for details on how this test worked.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e7.6.3 HEP amplitude modulation comparison between groups with different levels of cardioception\u003c/h2\u003e \u003cp\u003eWe used a nonparametric spatio-temporal permutation test from MNE-Python (see above) for independent (with one-way ANOVA) data to compare HEP amplitude modulation between groups with high and low IAcc in tasks, between detectors and non-detectors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e7.6.4 Association between mean HEP amplitude and IAcc within each cardioception task\u003c/h2\u003e \u003cp\u003eWe analysed an association of HEP amplitude during task (HTT (interoceptive condition), HDT, HCT) and each IAcc metric in the corresponding task: 1) for each channel separately; 2) for the mean HEP amplitude for the six ROIs; 3) using a spatiotemporal cluster permutation test for a correlation. The cluster test was additionally used to address the problem of multiple comparisons that arises when dealing with channels and regions of interest. The cluster test allowed us to broaden or narrow the group of channels and the time period when it was significantly associated with IAcc.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMean HEP amplitude in channels, ROIs and IAcc\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe analysis was performed using the RStudio. Spearman\u0026rsquo;s correlation with correction for multiple comparisons was performed between (1) mean HEP amplitude in channels during task and IAcc within each cardioception task, (2) mean HEP amplitude in channels during rest and IAcc within each cardioception task. The multiple comparison correction relied on the assumption that IAcc within tasks had high association and HEP amplitude within channels also had high association. Therefore, the calculation of the number of genuine independent comparisons made in the correlation analysis was based on the number of principal components explaining 95% of the variance in the principal component analysis (PCA). PCA was computed on the matrix where participants were represented as rows and mean HEP amplitude in channels during the task as columns. The number of components explaining 95% of the variance was 17 for the HTT, 15 for the HDT, 15 for the HCT and 17 for the resting state and used for Bonferroni correction.\u003c/p\u003e \u003cp\u003eWe used a multivariate analysis to explore how HEP amplitude among ROIs during this task and during resting state predicted each IAcc metric in the corresponding task. Generalized Linear Models models were:\u003c/p\u003e \u003cp\u003eA1-A3. IAcc in HTT \u0026isin; {\u003cem\u003emd\u003c/em\u003e, \u003cem\u003eresVec\u003c/em\u003e, \u003cem\u003eCAmotor\u003c/em\u003e} ~ HEP in ROIs \u0026isin; {HTT}\u003c/p\u003e \u003cp\u003eA4-A5. IAcc in HDT \u0026isin; {\u003cem\u003ed\u003c/em\u003e, \u003cem\u003ec\u003c/em\u003e-value} ~ HEP in ROIs \u0026isin; {HDT}\u003c/p\u003e \u003cp\u003eA6. IAcc in HCT \u0026isin; {\u003cem\u003ecorSI\u003c/em\u003e} ~ HEP in ROIs \u0026isin; {HCT}\u003c/p\u003e \u003cp\u003eB1-B3. IAcc in HTT \u0026isin; {\u003cem\u003emd\u003c/em\u003e, \u003cem\u003eresVec\u003c/em\u003e, \u003cem\u003eCAmotor\u003c/em\u003e} ~ HEP in ROIs \u0026isin; {resting state}\u003c/p\u003e \u003cp\u003eB4-B5. IAcc in HDT \u0026isin; {\u003cem\u003ed\u003c/em\u003e, \u003cem\u003ec\u003c/em\u003e-value} ~ HEP in ROIs \u0026isin; {resting state}\u003c/p\u003e \u003cp\u003eB6. IAcc in HCT \u0026isin; {\u003cem\u003ecorSI\u003c/em\u003e} ~ HEP in ROIs \u0026isin; {resting state}\u003c/p\u003e \u003cp\u003eWe used a BH correction on the p-values obtained for ROIs within each of the A1-A6 and B1-B6 models.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMean HEP amplitude in spatio-temporal clusters and IAcc\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe spatio-temporal permutation cluster test on correlation between IAcc and HEP amplitude was performed using the MNE-Python with Monte-Carlo statistics. See Supplementary Information for parameters we set. The analysis was the same as in Maris and Oostenveld \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e but Spearman\u0026rsquo;s correlation between IAcc and HEP amplitude over samples in channels was converted to t-statistic.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study and code we used are available in the Open Science Framework webpage. Please see https://osf.io/c3qws/?view_only=ea629fa512d34097976def3331354fe4.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eGrants and funding\u003c/h2\u003e \u003cp\u003eThe research was funded by the Russian Science Foundation (project No. 22-15-00507).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.M. conducted data acquisition, formal analysis, developed the experiment software, and wrote the manuscript. A.L. contributed to conceptualization, funding acquisition, design, data acquisition, participant recruitment and selection, EEG data analysis, and writing. A.S. contributed to data acquisition and EEG data analysis. V.K. and I.M. performed the statistical analysis. M.N. contributed to conceptualization, funding acquisition, and manuscript revision. A.E. contributed to conceptualization, funding acquisition, design, participant recruitment and selection, study supervision, and manuscript revision. O.D. contributed to conceptualization and study supervision. All authors reviewed and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful to Nikulin V.V. for counseling during the study conduction and manuscript preparation and to Huseynova K.A for assistance in the participants\u0026rsquo; selection and recruitment and visual ECG inspection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study and code we used are available in the Open Science Framework webpage. Please see https://osf.io/c3qws/?view_only=ea629fa512d34097976def3331354fe4.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl, E. \u003cem\u003eet al.\u003c/em\u003e Heart\u0026ndash;brain interactions shape somatosensory perception and evoked potentials. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 10575\u0026ndash;10584 (2020).\u003c/li\u003e\n\u003cli\u003eAl, E. \u003cem\u003eet al.\u003c/em\u003e Cardiac activity impacts cortical motor excitability. \u003cem\u003ePLoS Biol\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 1\u0026ndash;23 (2023).\u003c/li\u003e\n\u003cli\u003eZaki, J., Davis, J. I. \u0026amp; Ochsner, K. N. 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Knowing your own heart: Distinguishing interoceptive accuracy from interoceptive awareness. \u003cem\u003eBiol Psychol\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 65\u0026ndash;74 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardioception, heartbeat evoked potentials, heartbeat tapping task, heartbeat discrimination task, heartbeat counting task","lastPublishedDoi":"10.21203/rs.3.rs-5124302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5124302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardioception is the ability of the central nervous system to process signals from the heart. Methods for determining cardioception are still under discussion. In the present study, we considered metrics for interoceptive accuracy (IAcc) assessments in three behavioral cardioception tasks − (1) the heartbeat tapping (HTT), (2) the heartbeat discrimination (HDT), and (3) the heartbeat counting (HCT) - and heartbeat evoked potentials (HEP) recorded by an electroencephalography during resting state and the tasks. The study included forty-eight healthy volunteers (25 females, 36 ± 7 age). The IAcc in the HTT assessed using various metrics, except for the metric based on the circular variation between heartbeat and press timing, positively correlated both with each other and with the IAcc in the HCT. The HDT showed no correlation with the other tasks. However, none of the metrics showed a clear advantage over the others in their association with the neurophysiological marker of interoception, the mean HEP amplitude, during task performance. During all three tasks, the HEP amplitude (1) did not differ between individuals with high and low IAcc, (2) was not different from the HEP amplitude during the resting state, (3) was lower during the HDT compared to the HCT. Thus, our results contribute to the debate on the interaction between behavioral cardioception tasks and the HEP.\u003c/p\u003e","manuscriptTitle":"Comparison of three behavioral cardioception tasks and heartbeat evoked potentials in the same group of healthy volunteers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-08 06:49:01","doi":"10.21203/rs.3.rs-5124302/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-02T10:04:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T18:16:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T12:06:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168305596428192515650282958737298725232","date":"2025-04-07T01:13:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304928540605702868441673900084071246485","date":"2025-04-06T10:52:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-05T04:59:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T02:21:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-17T23:14:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"72801af5-99e8-4cee-95f5-12199b63f965","owner":[],"postedDate":"April 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46752393,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Perception"},{"id":46752394,"name":"Biological sciences/Psychology/Human behaviour"},{"id":46752395,"name":"Biological sciences/Physiology/Neurophysiology"}],"tags":[],"updatedAt":"2025-10-20T16:05:39+00:00","versionOfRecord":{"articleIdentity":"rs-5124302","link":"https://doi.org/10.1038/s41598-025-08779-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-14 15:58:46","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-04-08 06:49:01","video":"","vorDoi":"10.1038/s41598-025-08779-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-08779-5","workflowStages":[]},"version":"v1","identity":"rs-5124302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5124302","identity":"rs-5124302","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}