Time Estimation Task Research Articles

Predictive brain activity related to auditory information is associated with performance in speech comprehension tasks in noisy environments.

Understanding speech in noisy environments is challenging even for individuals with normal hearing, and it poses a significant challenge for those with hearing impairments or listening difficulties. There are limitations associated with the current methods of evaluating speech comprehension in such environments, especially in individuals with peripheral hearing impairments. According to the predictive coding model, speech comprehension is an active inference process that integrates sensory information through the interaction of bottom-up and top-down processing. Therefore, in this study, we aimed to examine the role of prediction in speech comprehension using an electrophysiological marker of anticipation: stimulus-preceding negativity (SPN). We measured SPN amplitude in young adults with normal hearing during a time-estimation task with auditory feedback under both quiet and noisy conditions. The results showed that SPN amplitude significantly increased in noisy environments. Moreover, individual differences in SPN amplitude correlated with performance in a speech-in-noise test. The increase in SPN amplitude was interpreted as reflecting the increased requirement for attentional resources for accurate prediction of speech information. These findings suggest that SPN could serve as a noninvasive neural marker for assessing individual differences in top-down processing involved in speech comprehension in noisy environments.

Investigating psychobiological causes and mechanisms in functional seizures and functional motor symptoms: Study protocol.

Advances have been made in understanding the aetiology of functional neurological disorder (FND); however, its pathophysiological mechanisms have not been definitively demonstrated. Evidence suggests interacting roles for altered emotional processing and interoception, elevated autonomic arousal, and dissociation, but there is limited evidence demonstrating their causal influence on specific FND symptoms. Our superordinate aim is to elucidate potentially shared and distinct aetiological factors and mechanisms in two common FND subtypes, functional seizures (FS) and functional motor symptoms (FMS). This study has a multimodal, mixed between- and within-groups design. The target sample is 50 individuals with FS, 50 with FMS, 50 clinical controls (anxiety/depression), and 50 healthy controls. Potential aetiological factors (e.g., adverse life events, physical/mental health symptoms, dissociative tendencies, interoceptive insight/sensibility) will be assessed with a detailed medical history interview and self-report questionnaires. A laboratory session will include a neurocognitive battery, psychophysiological testing, cardiac interoception and time estimation tasks and an isometric handgrip task. A subsample will undergo magnetic resonance imaging, including structural, resting-state and task-based scans combined with psychophysiological recording. Remote monitoring with ecological momentary assessment and wearables will measure variability in FND symptoms and their potential predictors/correlates for ≥2 weeks in patients' daily lives. Longitudinal follow-ups at 3, 6, and 12-months will monitor longer-term outcomes in the clinical groups. This study employs multimodal research methods to rigorously examine several putative mechanisms in FND, at subjective/experiential, behavioural, and physiological levels. The study will test causal hypotheses about the role of altered emotional processing, autonomic arousal, dissociation and interoception in the initiation or exacerbation of FND symptoms, directly comparing these processes in FS and FMS to healthy and clinical controls. This is the first study of its kind, with potential to reveal important targets for prevention and treatment of FND in future.

Multi-task learning for hand heat trace time estimation and identity recognition

The thermal imager can capture the invisible thermal trace, and the potential information contained in the thermal trace can be extracted through the identification and analysis of the infrared thermal trace, such as the identity information and time information of the trace. This technology has great application value in criminal investigation, military and other fields. However, with the passage of time, the heat traces will gradually fade, and the intra-class distances will gradually increase. For hand heat trace, the trace may exhibit significant variations at different time points or in different states. This will pose challenges for extracting potential information and present significant challenges for model recognition. Therefore, we propose a multi task framework using deep convolutional neural networks (MTLHand) to jointly handle the two tasks of heat trace identification and heat trace departure time estimation. It can learn invariant representations of thermal trace identities, better capturing the dynamic changes in palmprint data, to enhance the accuracy of identity recognition. Specifically, soft threshold is inserted as nonlinear transformation layers into an improved ResNet to extract deep mixed hand trace features. The mixed features are decomposed into identity related features and time related features through spatial attention and channel attention. Use the multi task training method to carry out the corresponding learning tasks. In addition, we collected and constructed an infrared hand heat trace dataset containing labels such as departure time, gender, hand posture, identity category, etc., to promote the development of heat trace research. Experiments on this dataset show that our method is superior to other deep learning methods for the specific task of infrared heat trace recognition, and the identification accuracy can reach 83.48 %. In the trace leaving time estimation task, the recognition error rate is 18 % if the residual value between the estimated time and the real time is less than 60 s, and 3.55 % if the residual value is less than 120 s.

Give it a second try? The influence of feedback and performance in the decision of reattempting

Feedback evaluation can affect behavioural continuation or discontinuation, and is essential for cognitive and motor skill learning. One critical factor that influences feedback evaluation is participants' internal estimation of self-performance. Previous research has shown that two event-related potential components, the Feedback-Related Negativity (FRN) and the P3, are related to feedback evaluation. In the present study, we used a time estimation task and EEG recordings to test the influence of feedback and performance on participants' decisions, and the sensitivity of the FRN and P3 components to those factors. In the experiment, participants were asked to reproduce the total duration of an intermittently presented visual stimulus. Feedback was given after every response, and participants had then to decide whether to retry the same trial and try to earn reward points, or to move on to the next trial. Results showed that both performance and feedback influenced participants' decision on whether to retry the ongoing trial. In line with previous studies, the FRN showed larger amplitude in response to negative than to positive feedback. Moreover, our results were also in agreement with previous works showing the relationship between the amplitude of the FRN and the size of feedback-related prediction error (PE), and provide further insight in how PE size influences participants' decisions on whether or not to retry a task. Specifically, we found that the larger the FRN, the more likely participants were to base their decision on their performance – choosing to retry the current trial after good performance or to move on to the next trial after poor performance, regardless of the feedback received. Conversely, the smaller the FRN, the more likely participants were to base their decision on the feedback received.

Life in the fast lane: the role of temporal processing in risk-taking behaviors

An existing theoretical framework proposes that aberrant temporal processing and a fast internal clock, denoted by overestimation and under-reproduction of time, increases the likelihood of engagement in risky behaviors (ERB). The primary aim of this project was to improve our understanding of the relationship between temporal processing and ERB in college students. The present study used the Wittmann and Paulus (2008) theoretical framework to examine the associations between temporal processing and ERB in college students. College student participants (N = 215) completed self-report measures of ERB, delay aversion, inhibitory control, ADHD symptoms and objective cognitive time estimation and time reproduction tasks. Time estimation accuracy was significantly associated with lower engagement in sexual risk behaviors (OR = .988; 95% CI: .979, .996; p = .006) and aggressive behaviors (OR = .989; 95% CI: .980, .998; p = .018). Time reproduction was not significantly associated with ERB. The present study established preliminary support for the associations between aberrant temporal processing, namely aberrant time estimation, and ERB among college students.

Temporal binding and sense of agency in major depression.

Alterations in the experience of controlling oneself and one's environment are of high relevance to understanding the psychopathology of depression. This study investigated the relationship between Temporal Binding for action-event sequences, sense of agency, self-efficacy and symptom severity in Major Depressive Disorder. We employed the Sense of Agency Scale (SoAS) and the General Self-Efficacy Scale (GSE) to assess explicit Sense of Agency and self-efficacy in a group of 42 persons diagnosed with Major Depressive Disorder (MDD) [20 identifying as female, 19 as male; mean age 37.8 years (± 13.3)] and 40 control persons without a psychiatric diagnosis (CG) [22 identifying as female, 20 as male; mean age 38.0 years ( ± 13.3)]. Depressive symptom severity was measured using the BDI-II. We additionally performed a temporal binding paradigm as a potential correlate to Sense of Agency. Participants partook in a time estimation task judging three intervals (250ms, 450ms, 650ms) while either observing or causing stimulus presentations. The underestimation of intervals following intentional actions causing stimulus presentations (compared to merely observing the stimulus presentation) is interpreted as temporal binding. SoAS scores demonstrated an inverse correlation with depressive symptoms (CG: p=.032, R2=.113; MDD: p<.001, R2=.260) and a positive correlation with GSE scores (CG: p<.001, R2=.379; MDD: p<.001, R2=.254). We found distinct differences in temporal binding between healthy participants and the Major Depressive Disorder group without significant correlation between temporal binding and the SoAS or GSE scores. The data suggest group differences in time estimation particular pertaining to time intervals involving intentional action and increasingly complex multisensory stimuli. We investigated parameters of subjective control, namely Sense of Agency and Self Efficacy. Here, we were able to reveal their inverse relationship with depressive symptoms in patients with major depressive disorder, highlighting a profound experience of loss of control with increasing symptom load. Deficits in experiencing control, particularly involving intentional motor actions (and more complex multisensory stimuli), appear to be more pronounced in Major Depressive Disorder, involving not only negative self-efficacy expectations but also an altered Sense of Agency and temporal binding. Temporal binding and SoAS scores did not correlate, adding to the growing evidence that the two measures may not be directly related. We propose that future research be directed at this contiguous relationship between Sense of Agency and Self Efficacy in Major Depressive Disorder.

Query execution time estimation in graph databases based on graph neural networks

Query execution time estimation is an essential task for databases, accurate estimation results can help administrators to manage and monitor systems. This study proposes an interaction-aware and dependency-aware query execution time estimation approach that utilizes graph neural networks to capture dependence and interaction relationships. We divide graph query execution time estimation tasks into three stages: workload generation and running, graph-based feature modeling and representation, training and estimation. Specifically, we generate query workloads and run them to collect the database and plan information when queries are executed. Then, the collected plan and database components are modeled into vertexes, the interaction and dependency between them are modeled into edges of graph-based feature representation. We develop an estimation model based on graph neural networks, in which the vertex embedding network is proposed to deal with the vertex heterogeneity, and the message passing network is proposed to aggregate the local representation into the global representation to obtain an embedding that can represent the higher-order feature information of the whole graph, and the estimation network is proposed to estimate execution times. The experiment results on datasets show that our estimation approach can improve estimation quality and outperform other estimation approaches in terms of estimation accuracy.

Insular and prelimbic cortices control behavioral accuracy and precision in a temporal decision-making task in rats

The anterior insular cortex (AIC) comprises a region of sensory integration. It appears to detect salient events in order to guide goal-directed behavior, code tracking errors, and estimate the passage of time. Temporal processing in the AIC may be instantiated by the integration of representations of interoception. Projections between the AIC and the medial prefrontal cortex (mPFC) - found both in rats and humans - also suggest a possible role for these structures in the integration of autonomic responses during ongoing behavior. Few studies, however, have investigated the role of AIC and mPFC in decision-making and time estimation tasks. Moreover, their findings are not consistent, so the relationship between temporal decision-making and those areas remains unclear. The present study employed bilateral inactivations to explore the role of AIC and prelimbic cortex (PL) in rats during a temporal decision-making task. In this task, two levers are available simultaneously (but only one is active), one predicting reinforcement after a short, and the other after a long-fixed interval. Optimal performance requires a switch from the short to the long lever after the short-fixed interval elapsed and no reinforcement was delivered. Switch behavior from the short to the long lever was dependent on AIC and PL. During AIC inactivation, switch latencies became more variable, while during PL inactivation switch latencies became both more variable and less accurate. These findings point to a dissociation between AIC and PL in temporal decision-making, suggesting that the AIC is important for temporal precision, and PL is important for both temporal accuracy and precision.

Deep soft threshold feature separation network for infrared handprint identity recognition and time estimation

With the development of hardware devices, infrared technology has become an important detection method in criminal investigation, military and other fields. Infrared technology can capture the thermal infrared information of targets, achieve efficient and rapid individual identification, provide important technological support for crime investigation, improve case solving rate and public safety level. However, infrared technology still faces some difficulties. Due to factors such as environmental temperature changes and heat transfer, the infrared images captured over time gradually blur, resulting in highly aliased image features and difficulty in extracting effective information. This article addresses this issue by combining the specific tasks of infrared fingerprint identity recognition and time estimation. By analyzing the captured thermal traces to predict the target identity and departure time, a deep soft threshold feature separation (DSTFS) network is proposed. This network effectively improves the accuracy of identity recognition and time estimation by separating the characteristics of identity information and time information. Specifically, this article is based on the ResNet backbone network, extracting common features through shallow residual convolution blocks, extracting corresponding features for different tasks using branch residual convolution blocks, and introducing spatial and channel attention mechanisms to solve the problems of deep blur and feature separation. In order to enhance the model's expressive power, the soft threshold activation function SPRelu was adopted. By dynamically balancing the separation of features to learn weights, the weight balancing problem is solved, and periodic validation is used to evaluate task performance. According to the task requirements, we collected infrared fingerprint images of multiple testers and constructed an infrared hand heat trace dataset with labels such as departure time, gender, hand posture, and identity category as experimental data. Through the analysis of experimental results, our proposed deep feature separation network has better identity prediction results compared to other deep learning models in infrared fingerprint identity recognition and time estimation tasks. At the same time, it performs well in time estimation tasks, with lower error rates and higher stability.

Is providing choices always a good thing? the backfire effect of providing choices on competence restoration

Grounded in self-determination theory (SDT), the purpose of this research is to investigate the influence of providing choices following competence frustration on one’s intrinsic motivation in a follow-up task. Study 1 conducted a between-group EEG experiment with 50 participants and used a component of event-related potentials (ERPs) to represent intrinsic motivation. Study 2 was a behavioural experiment with 149 participants, adopting the self-report method to measure intrinsic motivation. The stimuli and procedure in Study 1 are identical to Study 2. All participants were asked to complete a high-difficult time-estimation (TE) task during sessions 1–2, and a moderate-difficult stopwatch (SW) task during session 3 (no choices in the control group vs. providing choices in the experimental group). In Study 1, we observed a smaller reward positivity (RewP) difference wave in the experimental (vs. control) group during session 3. In Study 2, participants’ intrinsic motivation in the experimental (vs. control) group is significantly lower. The results suggest that providing choices impairs the competence-frustrated participants’ intrinsic motivation in the follow-up task and hinders competence restoration. Thus, the current research contributes original neuroscientific and subjective evidences for the adverse influence of providing choices on the competence-frustrated individual’s intrinsic motivation, and suggests important practical implications.

An adaptive deep multi-task learning approach for citywide travel time collaborative estimation

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network.

A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance.

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

The role of cerebellum in timing processing: A contingent negative variation study

Time management is an important aspect of human behaviour and cognition. Several brain regions are thought to be involved in motor timing and time estimation tasks. However, subcortical regions such as the basal nuclei and cerebellum seem to play a role in timing control.The aim of this study was to investigate the role of the cerebellum in temporal processing. For this purpose, we transitorily inhibited cerebellar activity by means of cathodal transcranial direct current stimulation (tDCS) and studied the effects of this inhibition on contingent negative variation (CNV) parameters elicited during a S1-S2 motor task in healthy subjects.Sixteen healthy subjects underwent a S1-S2 motor task prior to and after cathodal and sham cerebellar tDCS in separate sessions. The CNV task consisted of a duration discrimination task in which subjects had to determine whether the duration of a probe interval trial was shorter (800 ms), longer (1600 ms), or equal to the target interval of 1200 ms.A reduction in total CNV amplitude emerged only after cathodal tDCS for short and target interval trials, while no differences were detected for the long interval trial. Errors were significantly higher after cathodal tDCS than at baseline evaluation of short and target intervals. No reaction time differences were found for any time interval after the cathodal and sham sessions.These results point to a role of the cerebellum in time perception. In particular, the cerebellum seems to regulate temporal interval discrimination for second and sub-second ranges.

Practice Schedules Affect How Learners Correct Their Errors: Secondary Analysis From a Contextual Interference Study

Contextual interference is an established phenomenon in learning research; random practice schedules are associated with poorer performance, but superior learning, compared with blocked practice schedules. We present a secondary analysis of N = 84 healthy young adults, replicating the contextual interference effect in a time estimation task. We used the determinant of a correlation matrix to measure the amount of order in participant responses. We calculated this determinant in different phase spaces: trial space, the determinant of the previous five trials (lagged constant error 0–4); and target space, the determinant of the previous five trials of the same target. In trial space, there was no significant difference between groups (p = .98) and no Group × Lag interaction (p = .54), although there was an effect of Lag (p &lt; .01). In target space, there were effects of Group (p = .02), Lag (p &lt; .01), and a Group × Lag interaction (p = .03). Ultimately, randomly scheduled practice was associated with adaptive corrections but positive correlations between errors from trial to trial (e.g., overshoots followed by smaller overshoots). Blocked practice was associated with more adaptive corrections but uncorrelated responses. Our findings suggest that random practice leads to the retrieval and updating of the target from memory, facilitating long-term retention and transfer.

The contralateral effects of anticipated stimuli on brain activity measured by ERP and fMRI.

The present study examined the effects of unilateral stimulus presentation on the right hemisphere preponderance of the stimulus-preceding negativity (SPN) in the event-related potential (ERP) experiment, and aimed to elucidate whether unilateral stimulus presentation affected activations in the bilateral anterior insula in the functional magnetic resonance imaging (fMRI) experiment. Separate fMRI and ERP experiments were conducted using visual and auditory stimuli by manipulating the position of stimulus presentation (left side or right side) with the time estimation task. The ERP experiment revealed a significant right hemisphere preponderance during left stimulation and no laterality during the right stimulation. The fMRI experiment revealed that the left anterior insula was activated only in the right stimulation of auditory and visual stimuli whereas the right anterior insula was activated by both left and right stimulations. The visual condition retained a contralateral dominance, but the auditory condition showed a right hemisphere dominance in a localized area. The results of this study indicate that the SPN reflects perceptual anticipation, and also that the anterior insula is involved in its occurrence.

Neural responses to social partners' facial expressions are modulated by their social status in an interactive situation

Recently, several studies have found a recognition advantage for facial expressions, particularly angry expressions, when they appear on high-status faces rather than low-status faces. In the present study, we used event-related potentials (ERPs) to explore the influence of social status on the neural responses to others' facial expressions in a context of performance monitoring. Specifically, we used an interactive rank-inducing task (i.e., time estimation task) to manipulate social partners' status (high versus low) and then told participants that they would receive social feedback (i.e., happy, neutral, or angry) from social partners with high or low status after completing the task. ERP results revealed the preferential processing of high-status targets at both early (P1/N170/FRN) and late (P3) temporal stages of facial expression processing. Notably, larger FRN amplitudes elicited by feedback from high-status partners were observed in happy, neutral, and angry expression contexts, whereas larger P3 amplitudes elicited by feedback from high-status partners were only evident in both neutral and angry expression contexts but not in happy expression context. Together, the present study extended previous studies by showing that the perception of facial expressions could be modulated by target status at multiple stages.

Temporal binding of social events less pronounced in individuals with Autism Spectrum Disorder

Differences in predictive processing are considered amongst the prime candidates for mechanisms underlying different symptoms of autism spectrum disorder (ASD). A particularly valuable paradigm to investigate these processes is temporal binding (TB) assessed through time estimation tasks. In this study, we report on two separate experiments using a TB task designed to assess the influence of top-down social information on action event related TB. Both experiments were performed with a group of individuals diagnosed with ASD and a matched group without ASD. The results replicate earlier findings on a pronounced social hyperbinding for social action-event sequences and extend them to persons with ASD. Hyperbinding however, is less pronounced in the group with ASD as compared to the group without ASD. We interpret our results as indicative of a reduced predictive processing during social interaction. This reduction most likely results from differences in the integration of top-down social information into action-event monitoring. We speculate that this corresponds to differences in mentalizing processes in ASD.

Time estimation and arousal responses in dopa-responsive dystonia

Dopa-responsive dystonia (DRD) is caused by an impaired dopamine biosynthesis due to a GTP-cyclohydrolase-1 (GCH1) deficiency, resulting in a combination of dystonia and parkinsonism. However, the effect of GCH1 mutations and levodopa treatment on motor control beyond simple movements, such as timing, action preparation and feedback processing, have not been investigated so far. In an active time estimation task with trial-by-trial feedback, participants indicated a target interval (1200 ms) by a motor response. We compared 12 patients tested (in fixed order) under their current levodopa medication ("ON") and after levodopa withdrawal ("OFF") to matched healthy controls (HC), measured twice to control for repetition effects. We assessed time estimation accuracy, trial-to-trial adjustment, as well as task- and feedback-related pupil-linked arousal responses. Patients showed comparable time estimation accuracy ON medication as HC but reduced performance OFF medication. Task-related pupil responses showed the reverse pattern. Trial-to-trial adjustments of response times were reduced in DRD, particularly OFF medication. Our results indicate differential alterations of time estimation accuracy and task-related arousal dynamics in DRD patients as a function of dopaminergic medication state. A medication-independent alteration of task repetition effects in DRD cannot be ruled out with certainty but is discussed as less likely.

Exploring psychopathy traits on intertemporal decision-making, neurophysiological correlates, and emotions on time estimation in community adults

There are certain characteristics of psychopathy that may be related to changes in intertemporal choices. Specifically, traits such as impulsivity or lack of inhibitory control may be associated with a more pronounced discounting function in intertemporal choices (IC) and, in turn, this function may be based on changes in the basic mechanisms of time estimation (TE). Therefore, this study aimed to examine potential differences in neurophysiological correlates, specifically through N1, P3, and LPP measurements, which may be related to TE and IC, examining their modulation according to psychopathic traits, different emotional conditions, and different decision-making conditions. This experimental study included 67 adult participants (48 women) from the northern region of Portugal, who performed an intertemporal decision-making task and, of those, 19 participants (16 women), with a mean age of 25 years (SD = 5.41) and a mean of 16 years of schooling (SD = 3.37) performed the time estimation task. The instruments/measures applied were MoCA, used as a neurocognitive screening tool; the Triarchic Psychopathy Measure (TriPM), a self-report instrument with 58 items that map the core features of psychopathy along three facets – boldness, meanness, and disinhibition – and considers them continuously distributed among the general population; intertemporal decision-making and time estimation tasks – for the time estimation task, the stimuli consisted of 45 color images extracted from the Nencki Affective Picture System (NAPS). In the TE task, there was an almost significant effect of disinhibition on the values of θ, with higher values on this variable associated with greater values of θ in the unpleasant emotional condition. In the IC task, there were no significant effects of any psychopathy measure on the values of the gains and losses ratios. In addition, the analysis of the neurophysiological correlates of the IC task did not reveal a main effect of the decision-making condition, nor effects of any psychopathy measure on the N1 and P3 amplitudes. The analysis of the neurophysiological correlates of the TE task revealed that higher meanness values are associated with smaller N1 amplitude in the pleasant emotional condition, whereas higher disinhibition values are associated with greater N1 amplitude in the pleasant emotional condition. Still in this task, higher disinhibition values were associated with a smaller LPP amplitude in the unpleasant emotional condition. The increase in the distribution of attention resources towards time and/or the increase in activation states, including those originated by responses to emotional stimuli, may be the main factor that alters the way impulsive individuals and, presumably, individuals with high psychopathy, consider time when making decisions.

Disentangling performance-monitoring signals encoded in feedback-related EEG dynamics

The feedback-related negativity (FRN) is a well-established electrophysiological correlate of feedback-processing. However, there is still an ongoing debate whether the FRN is driven by negative or positive reward prediction errors (RPE), valence of feedback, or mere surprise. Our study disentangles independent contributions of valence, surprise, and RPE on the feedback-related neuronal signal including the FRN and P3 components using the statistical power of a sample of N = 992 healthy individuals. The participants performed a modified time-estimation task, while EEG from 64 scalp electrodes was recorded. Our results show that valence coding is present during the FRN with larger amplitudes for negative feedback. The FRN is further modulated by surprise in a valence-dependent way being more positive-going for surprising positive outcomes. The P3 was strongly driven by both global and local surprise, with larger amplitudes for unexpected feedback and local deviants. Behavioral adaptations after feedback and FRN just show small associations. Results support the theory of the FRN as a representation of a signed RPE. Additionally, our data indicates that surprising positive feedback enhances the EEG response in the time window of the P3. These results corroborate previous findings linking the P3 to the evaluation of PEs in decision making and learning tasks.