Identifying Cognitive Resources in a Decision-Making during Gambling

Identifying Cognitive Resources in a Decision-Making during Gambling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying Cognitive Resources in a Decision-Making during Gambling Amin Mohammad, Elias, Narjes, Hamid This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5754204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the learning stage of reward processing, the presence of an event-related potential (ERP) denoted as the Feedback-Related Negativity (FRN) is vastly mentioned which is elicited 200–350 milliseconds after feedback onset. Previous studies have confirmed Reinforcement Learning theory's prediction that a significant correlation exists between dopamine neuron responses (which generate the FRN) and the disparity between actual and expected outcomes where the expected outcome is determined by the probability and magnitude of rewards. Although previous studies have extensively illustrated the impact of reward probability on the FRN, the demonstration of the impact of reward magnitude on the FRN has not been established conclusively and still remains a matter of debate. Here in this study, we wanted to assess the effects that reward magnitude has on the FRN and its generator(s) as well in an isolated context. We recruited 24 participants and recorded 65-channel High-Density EEG signals with simultaneous fMRI, while they engaged in a modified task designed to control reward probability and evaluate the effects of reward magnitude. In our findings, firstly, a substantial positive correlation is observed between the ERP amplitude within the temporal window of FRN and the magnitude of outcomes, and through dipole fitting and distributed source localization, the source of FRN, regardless of magnitude, was located in the Medial Frontal Cortex. Our findings reveal strong connections among brain regions involved in error monitoring, memory, attention, and visual processing, with the dorsal Anterior Cingulate Cortex serving as a central hub. No significant differences were found between connectivity of win- and loss-related FRN’s brain sources. Additionally, participants demonstrated varying risk-taking behaviors across trials, favoring higher-risk options and transitioning towards more cautious decisions over consecutive trials during the experiment. The analysis also revealed increased risk-taking following losses compared to gains, highlighting contextual influences on decision-making. Cognitive Neuroscience Event-Related Potential (ERP) EEG Feedback-Related Negativity (FRN) Gambling Reinforcement Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction Vast studies indicate that human, as an adaptive biological system in balance with the environment, desires to minimize their non-compliance with the prevailing environment’s order and cope with the progressive disorganization of the surrounding system 1 . Our understanding of how human life is shaped by both small and significant decisions has given rise to a significant subfield within decision-making, the study of its neurophysiology, and the neural basis of brain functioning 2 . Understanding the neural basis of brain functioning requires knowledge about the temporal and spatial aspects of information processing. Electrophysiology, specifically electroencephalography (EEG), constitutes to be a major tool for studying the neural basis of brain functioning. Contrary to imaging techniques, EEG provides high temporal resolution and records the electrical activity of the brain in the order of milliseconds, using electrodes positioned on the scalp. EEG signal processing methods are applied to determine the quantitative parameters on the spectrum of frequencies, amplitudes, and coherence 3 . MRI, on the other hand, represents a morphological view of the brain with a significant spatial resolution 4 , 5 . It provides a multiparametric evaluation of the brain tissue with respect to both its structural and functional properties. In this context, similar to EEG but at different temporal scales (seconds vs. milliseconds), functional MRI (fMRI) 6 offers the possibility for examining the brain functional activation non-invasively both during resting state and task execution, expanding the panel of parameters obtainable by MRI [e.g., structural connectivity evaluated by diffusion tensor imaging (DTI), metabolites concentrations evaluated by magnetic resonance spectroscopy (MRS), and perfusion evaluated by arterial spin labeling (ASL)] 5 . Complementarity of information is highlighted in multimodal recording methods that are designed to overcome the single modality shortcomings and to enhance the patient’s treatment experience. These methods show promising potential in providing a more thorough understanding of the underlying mechanisms of neural activities, addressing brain function and dysfunction 5 . Recording EEG and fMRI simultaneously combines the optimal spatial and temporal data of the two modalities and overcomes the restrictions of single modalities. This method used to be mostly used to identify epileptic foci 4 , 7 – 12 , but today it is used to identify cognitive foci in various tasks 13 – 16 . Especially, when the goal is to identify active areas of the brain in a cognitive task, these tools together constitute one of the most efficient methods 5 , 17 . Therefore, simultaneous acquisition of EEG-fMRI can be useful in locating cognitive foci in gambling-based tasks, decision- making tasks and, reward processing stages 5 , 18 . Employing the two modalities together enhances our ability to study complex brain activities in depth. One such intricate brain activity that remains a matter of debate in the literature and requires further investigation is the learning substage of decision-making. In this context, a negative event-related potential (ERP) component, Feedback Related Negativity (FRN) has been observed which is usually sensitive to individual reliance on external feedback to determine whether the responses are correct. 3 , 19 This component is well-observed when the feedback stimuli revolved around monetary gains or losses 19 . The FRN is believed to reflect the influence of dopamine signals on the anterior cingulate cortex. A decrease in dopamine activity disinhibits anterior cingulate neurons, leading to a more negative FRN. Conversely, an increase in dopamine activity inhibits anterior cingulate neurons, resulting in a more positive FRN. Nevertheless, understanding the behavior of the mesencephalic dopaminergic system has given rise to the principle of several Reinforcement Learning (RL) algorithms such as the RL theory of Error-Related Negativity (RL-ERN). 20 – 22 The RL algorithms are based on the idea that the dopamine system evaluates outcomes to assess whether they align with expectations. When there is a positive prediction error, dopamine firing rates increase, and when there is a negative prediction error, dopamine firing rates decrease. The Substantia Nigra pars compacta (SNc) and Ventral Tegmental Area (VTA) transmit prediction errors to the basal ganglia for expectation adjustment. The VTA also communicates prediction errors to the anterior cingulate, where they are used to link reward information with action selection. 23 Studies have revealed a noteworthy correlation between the phasic responses of dopamine neurons and the differences between the real outcome and the expected outcome. This critical link underscores that the anticipation of outcomes is pivotal in understanding these neural responses. Furthermore, the determination of this expected outcome relies on two key factors: the probability and the magnitude of rewards. 24 With regards to the initial factor, reward probability, it indicates that the FRN tends to be more prominent when dealing with unlikely outcomes as opposed to likely ones. A multitude of research studies has consistently revealed a negative correlation between the amplitude of the FRN and the probability of an outcome. RL-ERN takes it a step further by anticipating that these event-related potentials (ERPs) will exhibit a more positive amplitude following improbable wins compared to probable ones, while they will display a more negative amplitude following improbable losses as opposed to likely losses. 25 – 28 The second factor, the magnitude of reward, although according to RL-ERN must show the same interaction with FRN amplitudes, the few studies which have been done to resolve this matter were unable to obtain this interaction. The RL-ERN posits that the amplitude difference in FRN between significant losses and wins will be greater than the amplitude difference between minor losses and wins. From a neurobiological standpoint, changes in the magnitude of wins and losses, as well as their valence, should be detectable within the time frame of the FRN occurrence. Logically, more substantial negative feedback must lead to a larger absolute value of FRN amplitude than lower-magnitude negative feedback; the same is true about the positive feedback. More substantial positive feedback should result in a more pronounced positive ERP amplitude within the temporal window of FRN compared to lower-magnitude positive feedback. This alignment with RL-ERN is due to the fact that the potential sizes of wins or losses must be reflected in phasic variations in dopaminergic signals. 29 The concept that an interaction between the feedback magnitude and amplitude of FRN should be observable within the FRN time frame is derived from a 2015 meta-analysis conducted by Sambrook and Goslin, providing supportive evidence for this relationship. Specifically, the authors noted in their studies that the disparity between positive and negative feedback was more pronounced when the feedback was of greater magnitude than when it was of smaller magnitude. 30 In some studies, such as Kamarajan's work, 31 the interaction between the feedback magnitude and the FRN amplitude was observed solely in male subjects, with no specific relationship found in female subjects. The inability of these experiments to establish this connection, specifically the impact of reward magnitude on the amplitude of the FRN, likely stems from the way they designed their tasks. This is because the feedback presented to participants in these experiments lacked information about the magnitude of wins or losses. For example, they only displayed a win of (+ $ 0.1) or a loss of (- $ 0.5) as output, which repeated and the effect of magnitude becomes weakened. Therefore, the results could have been more insightful if they could change the magnitude of feedback or show the differences between two choices as feedback. In this regard, we conducted an experiment aimed at assessing the relationship or correlation between the deflection of ERP signal amplitude in the FRN time window and feedback magnitude to assess FRN behavior regarding RL-ERN predictions and also to evaluate that FRN inherits its generator, the mesencephalic dopamine system behavior. We focused on the Feedback-Related Negativity (FRN), so we primarily concentrated on the EEG component of the simultaneous EEG-fMRI dataset that we recorded. We recruited 24 participants and recorded 64-channel HD EEG signals with simultaneous fMRI, while they engaged in a modified task designed to control expectations and avoid its interference. The task introduced random variations in both wins and losses states, with our primary focus being the isolation of the interplay between feedback magnitude (win and loss sizes) and the FRN amplitude. By focusing on EEG, we leveraged its high temporal resolution to capture the rapid neural dynamics associated with FRN during cognitive tasks. This approach allowed us to precisely monitor the timing and direct neural responses to feedback, which is essential for understanding the decision-making processes underlying FRN. While fMRI provided the structural and broader functional context simultaneously, the analysis and results of fMRI were not the main focus of this paper. Instead, we concentrated on the detailed temporal insights provided by EEG to explore the cognitive foci involved in a gambling-based task, specifically targeting the neural mechanisms underlying FRN. We assessed the EEG data on a single trial basis and categorized the feedback magnitude into four distinct levels. Through this approach, we were able to observe and analyze the interaction between these two variables. Our results show a positive correlation between the win magnitude and the positivity of ERP signal amplitude within the FRN time window and a direct correlation between the negativity of ERP signal amplitude within the FRN time scope and the loss magnitude. Moreover, using dipole fitting and distributed source localization, the source of FRN in both loss and win conditions was identified in the Medial Frontal Cortex. Our findings show robust connections among brain regions associated with error monitoring, memory, attention, and visual processing, with the dorsal Anterior Cingulate Cortex acting as a central hub. The lack of significant differences between win- and loss-related FRNs indicates that similar neural processes are involved in both types of feedback, regardless of their valence. In the behavior analysis, our results showcase that the participants displayed different risk-taking behaviors throughout the trials, initially favoring higher-risk options and gradually shifting to more cautious decisions as the experiment progressed. The analysis also showed that the participants took more risks following losses than after gains, emphasizing the impact of context on decision-making. 2. Material and Methods The experiment conducted, as mentioned, was a monetary gambling task. While gambling involves a significant element of chance and sometimes lacks enticements, verbal inquiries at the end of the study asked participants about their level of arousal during the experiment, proving that participant engagement in the tasks was substantial, possibly due to the enticing and provocative nature of the monetary stakes involved in the task. Further details of the materials and methods are described in the following subsections, where additional information are provided on the gambling task and its execution, how the data was facilitated, including participant selection, methods, equipment used for data collection, and data acquisition procedures. 2.1. Participants A total of twenty-four individuals were recruited. Before acquiring simultaneous EEG-fMRI data, the individuals filled out two-page questionnaires for evaluation and exclusion from participation in the experiment. In fact, the questionnaires and participants were examined by a neurologist to be admitted to or excluded from the experiment in case of any background major illness or psychiatric diseases. All participants had normal or corrected-to-normal visions and had no history of psychiatric, medical, or neurologic illness. In addition, they denied regular use of substances that might affect the central nervous system. The participants consist of eleven females and thirteen males with the age range of 21–43 years (mean 30) (Table 1 ). All participants provided written informed consent, and ethical approval was received from the local ethics committee of the Iran University of Medical Sciences, Tehran, Iran. Table 1 Demographic data of the participants. Order Date of acquisition Date of birth Age Sex SUB01 6–11 - 2021 1987 34 M SUB02 27–12 - 2021 1996 25 M SUB03 12–1 - 2022 1983 38 M SUB04 13–1 - 2022 1981 40 M SUB05 19–1 - 2022 2000 21 M SUB06 20–1 - 2022 1997 24 M SUB07 23–1 - 2022 1990 31 F SUB08 30–1 - 2022 1995 26 F SUB09 6–2 - 2022 1985 36 M SUB10 8–2 - 2022 1997 24 F SUB11 17–2 - 2022 1991 30 F SUB12 20–2 - 2022 1998 23 F SUB13 21–2 - 2022 1986 35 F SUB14 23–2 - 2022 1995 26 F SUB15 24–2 - 2022 2000 21 F SUB16 2–3 - 2022 1988 33 F SUB17 5–3 - 2022 1994 27 M SUB18 8–3 - 2022 1986 35 F SUB19 9–3 - 2022 1986 35 M SUB20 10–3 - 2022 1987 34 M SUB21 14–3 - 2022 1992 29 M SUB22 17–3 - 2022 1978 43 M SUB23 4–4 - 2022 2000 21 F SUB24 16–5 - 2022 1983 38 M Average: 30.3 11 F + 13 M Standard Deviation: 6.4 2.2. Experimental Setup The experiment utilized a gambling task that underwent optimization based on several previous experimental tasks 31 – 33 . The task was implemented using a new format designed in the MATLAB environment with Psychtoolbox-3 34 . In this task, participants were initially provided with a real budget, displayed as their account at the beginning of the experiment and after each trial. During each trial, participants were presented with two options, integer numbers ranging from -100 to 100, on a screen for a fixed duration of 2 seconds. This is where there is a pseudo control over the outcomes of wins or losses for the participant, which helps to sustain the participant's engagement in the task. After choosing an option, the selected number was revealed as a reward or punishment within the next 2 seconds. In this regard, the selected number displayed again was highlighted in green or red to indicate a win or a loss. This display lasted for a fixed duration of 2 seconds. This part of the trial is where the participant's reward system becomes highly engaged with the task. Moreover, to increase the motivational properties of the monetary incentives, cash in the amount of the cumulative total was kept on the table as their account balance and incremented or decremented after each block of trials. Therefore, the amount of money won or lost was added or subtracted from the participant's account. Subsequently, their account balance which was updated was shown for a fixed duration of 2 seconds. For example, as shown in Fig. 1 , when the first trial block resulted in a loss (- $ 25), that total was subtracted from the take-home amount delivered at the end of the trial. Conversely, when the second trial block resulted in a net gain (+ $ 90), the total was added to the take-home amount delivered at the end of the trial. These procedures (the presence of the cash and the steadily increasing or decreasing amount of money in the account) were used to increase the participants' motivation to attend to the task. Next, a fixation cue (+) was represented for 2 seconds, as a break after displaying the participant's account (see Fig. 1 ). It is important to highlight that the entire task comprised 100 trials, and the outcomes of winning or losing were entirely determined by chance with equal probability. In the event of a missed trial (resulting from the participant not providing an answer), no amount was credited to their account; there was neither an increase nor a decrease. Such these trials were disregarded and categorized as ignored instances in the experimental procedure. 2.3. Electrophysiological Recording and Preprocessing EEG was acquired using the custom-built electrode system, Brainproduct cap (BrainProducts GmbH, Gilching, Germany) equipped with 66 sintered Ag/AgCl Gel-based passive electrodes and multitrodes that are compatible with the Magnetic Resonance Imaging systems. The device operated at a sampling rate of 5000 Hz and EEG signals were amplified with a resolution of 0.5 µV and online highpass (cutoff 0.01 Hz) and low-pass (cutoff 500 Hz) filtered using the BrainVision Recorder 2.0 (Brain Products). The electrode array included AF3/4/7/8, Afz, Fp1/2, FPz, F1/2/3/4/5/6/7/8, Fz, FC1/2/3/4/5/6, FCz, FT7/8/9/10, C1/2/3/4/5/6, Cz, T7/8, TP7/8/9/10, CP1/2/3/4/5/6, CPz, PO3/4/7/8, POz, P1/2/3/4/5/6/7/8, Pz, O1/2, Oz, and ECG. In addition to the 63 EEG channels, three supplementary channels were included: one FCz channel as a reference (Ref), one Afz channel as ground (Grnd) and one ECG channel placed on the back. The electrode names and their corresponding locations on the head adhere to the 10/10 system. Additionally, throughout the experiment and across all subjects, the impedance of all electrodes was kept below 5 kΩ. The ribbon cable, which connects the electrode wires to the amplifiers, was fixed using sandbags on foam cushions. This setup was designed to minimize artifacts resulting from the scanner's vibrations. EEG preprocessing was conducted in the MATLAB environment using the EEGLAB Toolbox 35 . Initially, the gradient artifact was mitigated using the FMRI Artifact Slice Template Removal (FASTR) 36 method, facilitated by the FMRIB plug-in from the University of Oxford Centre for Functional MRI of the Brain (FMRIB) 36 , 37 , at EEGLAB in MATLAB. This comprehensive approach involved several critical steps: aligning slice-timing triggers, subtracting local slice artifact templates, removing residual gradient artifacts using Optimal Basis Sets (OBS) and Adaptive Noise Cancellation (ANC) 38 . Then, the data were low-pass filtered using a finite impulse response (FIR) filter with a 70 Hz cutoff in EEGLAB. Again, we used fMRIB plug-in to reduce the ballistocardiographic (BCG) artifact—caused by potentials induced by blood movement in the magnetic field during the cardiac cycle and related head and scalp movements 39 , 40 . The QRS peaks in the ECG channel were identified using an algorithm in the FMRIB plug-in and verified visually. When automatic detection failed to achieve satisfactory accuracy, QRS events were semi-automatically set in BrainVision Analyzer. Once QRS detection was finalized, the data were downsampled to 500 Hz. For correction, EEG data from each channel were aligned to the BCG artifact based on the detected QRS peaks, and principal component analysis was performed. The first four principal components were used as an Optimal Basis Set (OBS) to describe the artifact, which was then fitted to and subtracted from each QRS event segment 36 . Next, a low-pass finite impulse response (FIR) filter ranging from 1 to 40 Hz was applied to the data. This filter aimed to maintain the data's phase and general characteristics within the specified range (1–40 Hz) while attenuating frequencies outside this band, which were not the focus of this study. In the subsequent step, identification and visual rejection of noise and artifacts were carried out. They could result from activities such as swallowing saliva, head movement, and muscle contractions. Such non-stereotyped artifacts may introduce a variety of unique scalp patterns into the EEG data, which may in turn confound and compromise ICA decompositions. Therefore, it is important to identify and discard them from the data before running ICA, as was done here. Following this, channels with improper data or significant prolonged noise, whose time series did not align with the EEG data, were manually excluded. The criteria for designating a channel as 'bad' involved evaluating its time series and, if deemed necessary, inspecting the frequency spectrum. If the frequency spectrum did not exhibit a 1/f pattern, the corresponding channel was labeled as 'bad' and removed. Subsequently, manual ICA application in EEGLAB was performed to identify and remove eye-related components and heartbeat residuals. Once the data were devoid of artifacts and noise, the next step involved reconstructing the removed bad channels based on their positions on the electrode channel location. This reconstruction involves using data from other channels through a weighted sum of the surrounding channels' data in a circular radius manner. After channel restoration, the data were segmented, isolating EEG data from half a second before to one second after the stimulus trigger (-0.5 to 1 second). This ensured that the relevant stimulus-related information was organized systematically and accurately. In the following step, segments that still exhibited residual artifacts or noise, despite previous steps, were discarded. The data were then re-referenced to the average of all channels, a method where the average signal across all EEG electrodes for each time point is subtracted from the EEG signal in each channel. This method, known as the average reference, assumes that the head can be approximated as a sphere and leverages the idea that the sum of all potentials recorded across the entire sphere is zero, providing electrically a neutral reference for all channels. 41 2.4. Data Analysis For obtaining the FRN, the data were segmented into epochs time-locked to the onset of win or loss feedback presentation. Separate EEG epochs of 1,200 ms were baseline-corrected by subtracting the average activity of that channel during the − 200 to 0 ms baseline period from each sample. In regard of obtaining a comprehensive understanding of FRN, the FRNs were calculated in three modes: Grand Average (GA) mode, Moving Average (MA) mode, and Single Trial (ST) mode. Grand Average (GA) mode denotes a scenario in which all trials of all subjects are averaged. The significance of this approach lies in gaining a comprehensive understanding of FRN properties as a collective, and subsequently, the time window/interval for FRN occurrence determined through this method. By calculating this interval, we were able to fit bilateral dipoles. The dipole fitting process enables the localization of the neural generators responsible for the FRN. This localization is crucial for understanding the underlying neural mechanisms and can contribute to further research on the functional significance of the FRN. Moving Average (MA) mode refers to calculating an average across all trials of each participant. This mode provides a more detailed analysis compared to the GA mode by preserving individual variations and nuances in the data, allowing for a more granular examination of the FRN properties within each subject. In the MA mode, each participant's data is averaged separately. This approach can highlight individual differences and potentially identify unique patterns that might be obscured in the GA mode, where the data is averaged across all subjects. As we know, the FRN is a fronto-central ERP component elicited between 200–350 ms following feedback onset and has time variability within participants 42 . By focusing on individual averages, we were able to better understand how FRN manifests differently across participants and explore the variability in neural responses. Armed with this knowledge, the occurrence time of the FRN was manually and separately obtained for each subject by identifying the exact negative peak preceding the P300 using these MAs. These FRN occurrence times were useful for obtaining the Single-Trial (ST) mode, in which the FRN in each trial was extracted. To obtain more in-depth information, the FRNs in the ST mode were computed, through isolating an interval of 40 milliseconds centered around the occurrence time of the FRN in the MA mode. It is important to note that, following the calculation of the FRNs in the ST mode, the overall average of the amplitudes within the trials in each EEG channel (e.g., 107.1 mV) was subtracted from the amplitudes of the FRNs in the ST mode. This shift ensures that the amplitudes do not retain a DC or offset value. Finally, the average amplitude of the 40-millisecond interval was considered as the FRN amplitude in that trial. Subsequently, we categorized these amplitudes into two groups based on their association with win or loss states. Then, in both win and loss scenarios, we further divided the amplitudes into low-value feedbacks (ranging from 1 to 19) and high-value feedbacks (ranging from 81 to 99). This process resulted in the creation of four distinct classes: low-value win, high-value win, low-value loss, and high-value loss, for each subject. 3. Experimental Results 3.1. Moving Average mode The EEG data across all trials within each subject, spanning from − 200 to 1000 milliseconds after the feedback stimulus, were consolidated. Then, by calculating the average of all trials from each subject, the Moving Average mode was derived. By this approach, we were able to smooth out anomalies, noise, and artifact fluctuations, resulting in a more reliable and accurate depiction of the FRN. The occurrence time of FRN in Moving Average mode for each individual was then manually calculated from the respective Moving Averages (MAs) as shown in Fig. 3 . Table 2lists the FRN occurrence times for each subject extracted from the Moving Average method. These obtained times will be utilized to compute the single trials of FRNs. Nevertheless, it is important to mention that as it is demonstrated in the Table 1 , the FRN peak occurred on average around 293.4 milliseconds after the stimulus. Table 2 The occurrence time of the FRN observed for each participant in the MA mode. SUB01 337 ms SUB13 256 ms SUB02 269 ms SUB14 280 ms SUB03 286 ms SUB15 244 ms SUB04 276 ms SUB16 284 ms SUB05 379 ms SUB17 291 ms SUB06 309 ms SUB18 355 ms SUB07 262 ms SUB19 233 ms SUB08 313 ms SUB20 246 ms SUB09 264 ms SUB21 246 ms SUB10 292 ms SUB22 356 ms SUB11 319 ms SUB23 358 ms SUB12 236 ms SUB24 351 ms Mean 293.4 ms STD 42.85 ms 3.2. Grand Average mode Next, the segmented data from all subjects were normalizedand averaged (see Fig. 5 ). It is important to note that this averaged data, referred to as the Grand Average, consistently revealed the occurrence of the FRN at a specific time, which closely aligned with the average FRN peak observed in the MA mode. In the GA mode, the FRN amplitude peaked at 298 milliseconds after the stimulus. The topography and latency of Grand average are depicted in Fig. 4 . 3.3. Single-trial FRN The Single Trial FRN comprises a 40 ms period of the data within each trial, centered around the manually calculated occurrence time of the FRN in the MA mode. Subsequently, we categorized each Single Trial FRN into two groups based on its feedback valence: win and loss. The average of the trials within the win group was then compared to the average of the trials within the loss group, individually and for all subjects. This comparative analysis is depicted in Fig. 5 . Upon comparing the amplitudes of all trials linked to the win feedback with those associated with the loss states, a significant result was observed. The paired t-test indicated a noteworthy difference (p < 0.01), (Table 3 ), demonstrating that the amplitudes for the loss trials were lower than those for the win trails. The distinction between the two groups is readily apparent in Fig. 5 B-C and the impact of valence on FRN amplitudes is discernible and comprehensible from the information conveyed by these illustrations. Table 3 The outcomes of the comparison among the four modes: high win, low win, high loss, and low loss, using paired t-tests. Variables P-value Win – Loss < 0.01 Low Value Win – Low Value Loss < 0.01 Low Value Loss – High Value Loss < 0.01 Low Value Win – High Value Win < 0.05 In the next step, we categorized the loss and win trials further based on the quantity of feedback received. Feedback scores ranging from 1 to 19 were considered low value, while scores from 81 to 99 were regarded as high value. We segregated the trials corresponding to these categories. Consequently, for each subject experiencing both winning and losing scenarios, four classes were formed: low-value loss, high-value loss, low-value win, and high-value win. Utilizing paired t-tests, we identified a notable distinction between high-value losses and low-value losses (p < 0.01), (Table 3 ), suggesting that larger losses correlate with a more pronounced drop or a great negativity in the FRN time window compared to baseline (Fig. 6 ). Furthermore, a significant contrast was observed between high-value wins and low-value wins (p < 0.05), (Table 3 ), indicating that greater winnings result in a less deflection/drop and deviation or a broader positivity range compared to the baseline (Fig. 7 ). In the following, for a more comprehensive view, these four subgroups (low-value loss, high-value loss, low-value win, and high-value win) were visualized together in one plot. Figure 8 provides a detailed depiction of the FRN amplitudes and their distribution across all subgroups. 3.4. Source of FRN A) Dipole fitting: For revisiting the source of FRN and as there are a debate regarding the generator of FRN, we used dipole modeling with these ST mode data (40 ms period of data within each trial) to identify which cortical region was most likely to generate the FRN observed at the scalp. It worth mentioning The results of the modeling were consistent with a source in the medial frontal cortex, in or near the anterior cingulate cortex (ACC) (Fig. 9 ) 43 , 44 . B) Distributed Source Localization: To further investigate and to confirm the source of FRN, we also employed distributed source localization techniques in addition to dipole fitting. This method allows for a more comprehensive mapping of the brain regions contributing to the observed FRN by estimating the distribution of neural activity across the cortex. Using the same ST mode data (40 ms period of data within each trial), we applied this advanced technique, ‘exact’ low-resolution electromagnetic tomography eLORETA, which was embedded in the ROI connect 45 a plug-in in EEGLAB to identify potential sources of FRN. The eLORETA has been shown to outperform other linear solutions in localization precision 45 , 46 . The results corroborated the dipole modeling findings, highlighting significant activity in the medial frontal cortex, particularly in or near the anterior cingulate cortex (ACC). This complementary approach provides a more detailed and robust confirmation of the cortical origins of FRN (Fig. 10 ) 3.5. Brain Network associated with FRN Using the source activity data derived from the ST mode, we calculated undirected Functional Connectivity (FC), Coherency (COH), among the sources as clusters. These calculations were performed using the ROIconnect 45 plug-in in EEGLAB. FC was analyzed separately for each group (loss and win) as well as for all trials combined. A paired T-test revealed no significant differences between the groups, suggesting that the neural networks responsible for generating the loss-related FRN and win-related FRN are likely the same. As illustrated in Figs. 11 , 12 and 13 the connectivity matrices were performed among 15 clusters or brain regions: Anterior inferior parietal lobe, Angular gyrus, Dorsal anterior cingulate cortex, Dorsal anterior prefrontal cortex, Dorsal lateral prefrontal cortex, Frontal eye field, Frontal midline cortex, Frontal operculum, Hippocampus, Insular cortex, Intraparietal sulcus region, Parietal lobe, Post cingulate cortex, Posterior parietal cortex, Prefrontal cortex, Thalamus, Visual cortex. In the following analysis, we visualized brain network connectivity across all trials combined (Fig. 14 ). A graph was created where nodes represent brain regions and edges represent the connections between them, retaining only significant connections above a specified threshold (threshold ≥ 0.24). Node sizes were adjusted based on label length to enhance readability, while edge thickness and color were scaled according to the strength of connectivity, visually indicating the relative importance of each connection. The positions of the nodes in the graph schematically represent the regions' spatial locations in a top-down view of their 3D brain coordinates, as shown in Table 4 . Table 4 Coordinates of Brain Regions, This table shows the coordinates of various brain regions in this study in both the left and right hemispheres, as well as the average coordinates for each region. Region L_X L_Y L_Z R_X R_Y R_Z Avg_X Avg_Y Avg_Z Anterior inferior parietal lobe -49.94 -51.07 33.94 50.73 -50.8 32.86 0.39 -50.93 33.40 Angular gyrus -48.8 -66.6 21.4 47.64 -65.97 21.58 -0.57 -66.28 21.49 Dorsal anterior cingulate cortex -5.01 15.78 28.05 6.11 16.33 27.81 0.54 16.06 27.93 Dorsal anterior prefrontal cortex -23.02 41.77 25.09 24.91 42.07 25.18 0.94 41.92 25.13 Dorsal lateral prefrontal cortex -25 41.05 24.59 26.96 41.38 24.53 0.97 41.21 24.56 Frontal eye field -21.05 27.68 49.07 24.0 26.4 49.0 1.47 27.04 49.03 Frontal midline cortex -24.41 14.06 47.52 27.20 14.83 46.67 1.39 14.45 47.09 Frontal operculum -63.37 -24.12 12.37 63.5 -22.9 12.2 0.06 -23.51 12.28 Hippocampus -30.3 -25.7 -11.6 31.3 -22.9 -13.7 0.5 -24.3 -12.65 Insular cortex -38.72 -6.91 5.81 40.11 -6.86 7.68 0.69 -6.89 6.74 Intraparietal sulcus region -14.85 -63.73 51.21 15.37 -63.93 49.89 0.26 -63.83 50.55 Parietal lobe -35.55 -54.15 41.70 36.66 -53.90 40.66 0.55 -54.02 41.18 Post cingulate cortex -7.04 -49.73 30.55 7.42 -48.8 30.55 0.18 -49.26 30.55 Posterior parietal cortex -35.64 -56.23 40.98 37.34 -55.77 39.31 0.85 -56 40.14 Prefrontal cortex -4.93 14.95 20.06 6.13 15.98 18.14 0.59 15.46 19.10 Thalamus -3.89 -18.0 3.33 4.89 -18.0 12.66 0.5 -18.0 7.99 Visual cortex -23.65 -80.13 6.55 24.61 -78.41 6.55 0.48 -79.27 6.55 This thresholding revealed strong connections between the dorsal anterior cingulate cortex, frontal midline cortex, hippocampus, and parietal lobe; between the dorsolateral prefrontal cortex and insular and visual cortex; and between the posterior cingulate cortex and intraparietal sulcus region. 3.6. Impact of previous outcomes on subsequent choices Our observation showcases that the FRN amplitude relates to the feedback valence and its value, we observed that the monetary prize or the monetary loss both could be seen in the amount of the drop or deviation in the signal or the amount of negative peak preceding the P300, (Fig. 15 ) and this is completely aligned with RL theories we discussed. In our task, there was no explicit reward probability; therefore, participants did not have specific expectations of winning or losing, and as a result, there was no clear prediction or expectation of future outcomes. This indicates that participants could not learn consistent rules to guarantee rewards. Thus, the primary determinant of the FRN was the direction and value of the outcome (whether it was a loss or a gain and its size). Specifically, the valence of the feedback significantly influenced the FRN amplitude (P = 0.00068). Furthermore, participants exhibited distinct patterns of risk-taking and risk-avoiding behaviors, encountering varying degrees of risk across different trials. In trials where there were significant differences between the presented options, participants faced choices with higher and lower risk levels. Risk-taking was defined as selecting the option with the greater value when the difference between options exceeded 40, indicating a higher potential reward but also greater risk. For example, choosing 80 over 30 represented a risky decision. Conversely, if the difference was greater than 40 and the participant selected the smaller option, this choice was considered a cautious decision, reflecting a preference for minimizing potential loss despite the larger potential gain. In contrast, when the difference between options did not exceed 40, these trials were excluded from our analysis. This was to ensure that our focus remained on trials with clear and substantial risk differentials, allowing for a more precise examination of risk-related decision-making behavior. Upon closer examination of participants’ behaviours, it became evident that a predominant strategy involved initiating each block of 100 trials with riskier choices, gradually transitioning towards more cautious decisions. In the initial quarter of the block, the mean proportion of risky choices was 0.59, decreasing to 0.43 by the final quarter (P = 0.027). This strategic approach aimed to safeguard gains obtained early in the block. Further analysis unveiled additional factors influencing participants' risk preferences. Studies on decision-making have indicated that contexts emphasizing losses tend to promote risk-seeking behaviour. To explore this, we compared choices following losses versus gains. Results showed a higher proportion of risky choices after a loss compared to after a gain (P = 0.00083). 4. Discussion In this experiment, we reported our observation of neural processing that occurs within 293 milliseconds after feedback stimuli, which illustrate early human perception of gains and losses in a gambling task. A negative-polarity event-related brain potential, known as Feedback-Related Negativity (FRN), likely emanates from dopaminergic neurons within a part of the mesencephalic dopaminergic system called the medial-frontal region in or near the anterior cingulate cortex. This potential showed greater deflection in amplitude when a participant's choice between two alternatives resulted in a loss compared to when it resulted in a gain. Furthermore, we observed distinct differences in FRN amplitude not only between wins and losses but also within more detailed classes of feedback. We discerned specific variations in FRN amplitude among four classes of feedback—high-value losses, low-value losses, high-value wins, and low-value wins—based on feedback value. Specifically, larger losses correlated with a more pronounced negativity/drop in the FRN time window compared to baseline. Conversely, larger winnings resulted in less deflection, drop or deviation, and often a broader positivity amplitude compared to the baseline. This is in line with RL theories, which predict that the FRN is influenced by the expected outcome, comprising the probability and magnitude of rewards. In this experiment, the probability is effectively zero due to the absence of rules or previous expectations regarding winning or losing. Thus, the primary factors influencing the amplitude of the FRN are the magnitude of the reward and its valence. We propose that this magnitude remains in working memory until the results are revealed, indicating a close interaction between the network generating the FRN and working memory. This is supported by our connectivity analyses showing that regions like the dorsal Anterior Cingulate Cortex, dorsolateral Prefrontal Cortex, insula, and parietal lobe, which are implicated in working memory 47 , also participate in the FRN-related network. According to the literature, the dopaminergic system becomes activated or deactivated upon winning or losing, with the degree of this activation or deactivation depending on the value and valence of the chosen option 48 . Our suggestion is in our experiment, this activation or deactivation excites or inhibits dopaminergic neurons in the ACC the source of FRN, leading to variations in FRN amplitude. With a detailed analysis of participants’ behavior, the findings demonstrate that participants are more inclined to choose riskier options following losses and exhibit greater caution after previous wins. This helps to underscore how medial-frontal computations may influence mental states involved in higher-level decision-making, including economic choices. These findings have several theoretical implications. Normative theories of judgment and decision making posit that the context in which a choice occurs—such as the sequence of recent gains and losses or the aspirations of a decision maker—should not affect the choice. A great deal of evidence, however, suggests that individuals deviate from normative behaviour, making decisions that depend on the status quo or other nonnormative reference points. A critical issue for psychological theories of choice behaviour is how cognitive and affective processing drive behaviours in nonnormative ways. Our data suggest that a rapid assessment of the motivational impact of an event participates in the evaluation of outcomes. In decision-making behaviour, such processing could affect nonnormative decision making by mediating the role that outcome events play in choices. In particular, the processing represented by the FRN could contribute to the experience that Kahneman refers to as “instant utility,” which is the momentary mental state resulting from the continuous evaluation of events along a good-bad dimension 49 . Such a computation can contribute to decision making by influencing the emotional state that individuals anticipate will occur upon making a choice 50 , or it may affect the emotional state that drives behaviour at the moment of the choice itself 51 . In previous studies, researchers have utilized EEG signal source localization techniques to identify the source of the FRN. The findings from these studies indicate that the origin of FRN lies within the anterior cingulate cortex (ACC) 19 , 28 , 32 , 52 – 56 . However, other studies using these localization methods, also have introduced alternative neural sources for FRN, in addition to the ACC. Some studies suggest that FRN is generated in the posterior cingulate 53 , 54 , 57 – 60 , .some research indicates that both the anterior and posterior cingulate cortex contribute jointly to FRN generation. Given the reciprocal connectivity between the anterior and posterior cingulate cortex, this finding is plausible, and studies have shown that the posterior cingulate cortex also exhibits reward properties 54 , 61 – 64 and response error characteristics 65 . Interestingly, there are other studies suggest that the FRN signal originates in the ventral striatum and posterior insula 33 , 66 , 67 . Our results suggest that the anterior cingulate cortex (ACC) likely plays a role in the neural circuitry underlying the generation of FRN. The consistent medial-frontal scalp distribution of the FRN aligns with an ACC origin, as supported by dipole localization modelling and distributed source localization method, eLORETA. Furthermore, the processes associated with losses and wins that lead to the FRN, as well as the relationship between FRN and risky behaviour, reflect a close functional association between the affective and behavioural control functions of the ACC 50 , 68 . Specifically, there is evidence indicating that ACC activity is sensitive to reductions in reward or penalties 69 , 70 . On the other hand, we mentioned that the phasic responses of dopamine neurons scale with the difference between actual and expected outcomes 24 , and the primary claim of the RL-ERN theory is that the range of FRN depends on the difference between the actual and expected outcome values. Expected value, in turn, depends on probability and reward magnitude. We previously stated that the relationship between reward probability and FRN amplitude has been thoroughly investigated in studies 23 , 26 . As we know, observations by researchers have indicated that the FRN amplitude is greater for improbable outcomes compared to probable outcomes. Many other studies have also demonstrated an inverse relationship between the range of FRN and the probability of the outcome being probable 25 – 28 . Nevertheless, besides the impact of reward probability on FRN ranges, the RL-ERN theory suggests that the range of rewards and losses affects FRN amplitudes. It should be noted that many experiments were unable to observe this relationship 31 , 71 . We pointed out that inappropriate task design and a lack of comprehensive and sufficient study with modern High-Density EEG (HD-EEG) which provides suitable temporal resolution for examining the nature of ERP quality and good spatial resolution for evaluating the neurobiology of activity pathways, have been effective reasons for this observation (the lack of observation of the effect of the feedback magnitudes on FRN amplitude). In this experiment, with a HD-EEG and with the appropriate design of a gambling task, a significant difference was found in the amplitude of FRNs in four levels of feedbacks (in the form of reward or punishment). Eventually, our observation demonstrates that the magnitude of wins and losses has a significant effect on FRN as a single measure. Furthermore, the Feedback-Related Negativity (FRN) is produced by the combined work of different brain networks, primarily the salience network (SN) and the cognitive control network. The salience network is crucial in detecting behaviorally important events 72 and initiating cognitive control, maintaining and implementing task sets, and coordinating behavioral responses 73 . The SN consists of three main cortical areas: the dorsal anterior cingulate cortex (dACC), the left and right anterior insula (aRI), and the adjacent inferior frontal gyri 72 . Increased SN activity is observed when it is important to change behavior, such as after errors and change monitoring 74 , which often signal the need for behavioral adaptation 75 . The FRN can help define the actual cognitive operation performed by this monitoring system, whether it is monitoring for explicit behavioral errors, the outcomes of choices, or a more general monitoring function. In addition to the dACC, other brain areas like the supplementary motor area (SMA), dorsolateral PFC (dlPFC), ventrolateral PFC (vlPFC), inferior parietal lobule (IPL), and the amygdala are involved in error processing or monitoring. These regions form an error-monitoring network where the dACC acts as a central hub, monitoring conflicts and signaling the need for increased cognitive control 76 , 77 . The dACC also interacts with lateral prefrontal structures to implement behavioral changes 76 . Our findings of strong connections between the dorsal anterior cingulate cortex, frontal midline cortex, hippocampus, and parietal lobe, as well as between the dorsolateral prefrontal cortex and the insular and visual cortex, and between the posterior cingulate cortex and intraparietal sulcus region, provide additional insights into how these networks interact during the occurrence of FRN. These strong connections suggest that the dACC not only serves as a hub for error monitoring but also integrates information from memory-related regions like the hippocampus and regions involved in spatial attention and visual processing, such as the parietal lobe and visual cortex. The connections between the dlPFC, insular cortex, and visual cortex further emphasize the role of cognitive control and error processing in guiding visual and attentional processes. The link between the posterior cingulate cortex and the intraparietal sulcus underscores the involvement of these regions in orienting attention and processing feedback, contributing to the adaptive changes in behavior following feedbacks. In addition, our findings emphasize that there are no significant differences between the neural activity and the networks that generate win-related FRN (sometimes referred to as Reward Positivity, or RewP 78 ) and loss-related FRN. This helps clarify ongoing debates in previous studies and suggests that similar neural processes may be involved in feedback monitoring, potentially guiding behavioral responses in the same way. Together, these findings enhance our understanding of the complex neural networks involved in producing the FRN and their role in monitoring and adapting behavior based on feedback. 5. Conclusion People gain knowledge from the consequences of their actions. Thorndike's law of effect, developed in 1911, explains that actions leading to satisfaction tend to be repeated, while those resulting in unfavorable outcomes are less likely to recur. This principle of reinforcement learning has been expanded upon in artificial intelligence, giving rise to algorithms that train autonomous systems to operate independently in complex and uncertain environments 79 , 80 . The FRN, or Feedback-Related Negativity, is a dip, curvature, or negativity observed in the ERP signal occurring typically between 200 to 350 milliseconds after the stimulus. It's important to note that while the term "negativity" is used, the signal does not actually drop below zero; rather, within this timeframe, the ERP wave appears relatively more negative compared to its surrounding peaks. This negativity "rides" on a larger, more positive wave known as the P300, which is evoked by stimulus processing 81 . Studies show a significant correlation between the phasic responses of dopamine neurons the generator of FRN and the difference between actual and expected outcomes 24 , emphasizing the importance of outcome anticipation in neural responses. The expected outcome is influenced by two key factors: reward probability and reward magnitude 24 . For reward probability, the FRN is more pronounced with unlikely outcomes, showing a negative correlation with outcome probability. According to the RL-ERN model, ERPs are expected to be more positive after improbable wins and more negative after improbable losses. The second factor, reward magnitude, should interact with FRN amplitudes similarly, according to RL-ERN. Despite numerous experiments failing to establish this relationship 31 , 71 (the failure to demonstrate the effect of win and loss amounts on FRN amplitude), this experiment aimed to address this through the design of a suitable task and the implementation of a controlled experiment. Finally, a significant distinction was obtained in the magnitude of the FRN amplitudes across four modes: low win value feedback, high win value feedback, low loss value feedback, and high loss value feedback. It was observed that both the valence and the magnitude of wins and losses exert an effect on FRN as a singular measure. We observed strong connections between the dACC, frontal midline cortex, hippocampus, and parietal lobe, as well as between the dorsolateral prefrontal cortex, insular cortex, and visual cortex, and between the posterior cingulate cortex and intraparietal sulcus during the monetary feedback task. Notably, no significant differences were found between the neural activity generating win- and loss-related FRN, suggesting similar processes in feedback monitoring for both types of feedback. This finding helps clarify ongoing debates in previous studies and indicates a consistent network for monitoring and adapting behavior regardless of the feedback's valence. 5.1. Future Directions From a neurobiological perspective, alterations in the quantity and magnitude of wins and losses are expected to manifest within the time frame of the FRN. Specifically, it is reasonable to anticipate that a greater magnitude of negative feedback would elicit a more pronounced negativity, resulting in a larger FRN, compared to a lesser degree of negative feedback. Similarly, positive feedback with a greater magnitude should yield a more prominent positivity, leading to a smaller FRN, than positive feedback with a smaller magnitude. This concept aligns entirely with RL theories and is attributed to the scaling of reward potential with phasic changes in dopaminergic signaling 29 . A thorough investigation, utilizing cutting-edge equipment like simultaneous EEG-fMRI, is essential for examining the neurobiological pathways of activities in this manner with comparable hypotheses and tasks, also assessing the brain networks and neurobiological pathways in all these four states (low-value loss, high-value loss, low-value win, and high-value win) could provide valuable insight regarding the fact that there is a great debate on FRN sources. Nevertheless, this cutting-edge equipment provides suitable temporal resolution for analyzing ERP characteristics simultaneously with an excellent spatial resolution for assessing neurobiological sources and networks. It is worth mentioning that however, several studies 29 , 82 – 84 employing this equipment, solely focused on investigating the reward probability aspect of RL theories, with differing objectives. Presently, the precise determinants influencing the delay of FRN and its associated properties remain incompletely understood. It is imperative for future research to delve into the underlying causes of this delay and explore its relationship with various other factors impacting FRN properties. Expanding our understanding in this area will not only enhance our comprehension of FRN dynamics but also contribute to broader insights into cognition and decision-making. Declarations Conflict of Interest The authors declare that they have no conflict of interest. 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Published online 2001. 10.1002/1097-0193 Foti D, Weinberg A, Dien J, Hajcak G (2011) Event-related potential activity in the basal ganglia differentiates rewards from nonrewards: Temporospatial principal components analysis and source localization of the feedback negativity. Hum Brain Mapp 32(12):2207–2216. 10.1002/HBM.21182 Martin LE, Potts GF, Burton PC, Montague PR (2009) Electrophysiological and Hemodynamic Responses to Reward Prediction Violation. NeuroReport 20(13):1140. 10.1097/WNR.0B013E32832F0DCA Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 4(6):215–222. 10.1016/S1364-6613(00)01483-2 Knutson B, Westdorp A, Kaiser E, Hommer D (2000) FMRI Visualization of Brain Activity during a Monetary Incentive Delay Task. NeuroImage 12(1):20–27. 10.1006/NIMG.2000.0593 Bush G, Vogt BA, Holmes J et al (2002) Dorsal anterior cingulate cortex: A role in reward-based decision making. Proc Natl Acad Sci 99(1):523–528. 10.1073/PNAS.012470999 San Martín R (2012) Event-related potential studies of outcome processing and feedback-guided learning. Front Hum Neurosci . ;6(OCTOBER 2012):1–40. 10.3389/fnhum.2012.00304 Seeley WW, Menon V, Schatzberg AF et al (2007) Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. J Neurosci 27(9):2349. 10.1523/JNEUROSCI.5587-06.2007 Medford N, Critchley HD (2010) Conjoint activity of anterior insular and anterior cingulate cortex: awareness and response. Brain Struct Funct 214(5–6):535–549. 10.1007/S00429-010-0265-X Holroyd CB, Nieuwenhuis S, Yeung N et al (2004) Dorsal anterior cingulate cortex shows fMRI response to internal and external error signals. Nat Neurosci 7(5):497–498. 10.1038/NN1238 Rabbitt PM (1966) Errors and error correction in choice-response tasks. J Exp Psychol 71(2):264–272. 10.1037/H0022853 Ridderinkhof KR, Ullsperger M, Crone EA, Nieuwenhuis S (2004) The role of the medial frontal cortex in cognitive control. Sci (80-) 306(5695):443–447. 10.1126/SCIENCE.1100301/SUPPL_FILE . /RIDDERINKHOF.SOM.PDF Cavanagh JF, Meyer A, Hajcak G (2017) Error-Specific Cognitive Control Alterations in Generalized Anxiety Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging 2(5):413–420. 10.1016/j.bpsc.2017.01.004 Proudfit GH (2015) The reward positivity: from basic research on reward to a biomarker for depression. Psychophysiology 52(4):449–459. 10.1111/PSYP.12370 Thorndike E. Animal Intelligence: Experimental Studies .; 1911. Accessed June 12, 2023. https://books.google.com/books?hl=en&lr=&id=Go8XozILUJYC&oi=fnd&pg=PR7&dq=thorndike+1911&ots=-oetGhp0xF&sig=1b1gm9mSxedQ2PuZ42w-Fr8wMCo Sutton RS, Barto AG (1998) Reinforcement Learning: An Introduction. IEEE Trans Neural Networks 9(5):1054–1054. 10.1109/TNN.1998.712192 Johnson R (1986) For Distinguished Early Career Contribution to Psychophysiology: Award Address, 1985: A Triarchic Model of P300 Amplitude. Psychophysiology 23(4):367–384. 10.1111/J.1469-8986.1986.TB00649.X Hauser TU, Iannaccone R, Stämpfli P et al (2014) The feedback-related negativity (FRN) revisited: New insights into the localization, meaning and network organization. NeuroImage 84:159–168. 10.1016/j.neuroimage.2013.08.028 Asgarinejad M, Saviz M, Sadjadi SM et al (2024) Repetitive transcranial magnetic stimulation (rTMS) as a tool for cognitive enhancement in healthy adults: a review study. Med Biol Eng Comput 62(3):653–673. 10.1007/s11517-023-02968-y Ebrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H (2024) Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Research: Neuroimaging 337:111764. 10.1016/j.pscychresns.2023.111764 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5754204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396998654,"identity":"72a6d6c9-40f1-46b4-81f1-36a0a2d68b1b","order_by":0,"name":"Amin Mohammad","email":"","orcid":"","institution":"Mohammadi","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"","lastName":"Mohammad","suffix":""},{"id":396998463,"identity":"d2bcc5ea-8d39-41d5-8a1e-df15d84301c6","order_by":1,"name":"Elias","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYDACdsYGEJXAwMDDcADIYGyQYD4AYvDg1MKMqYUtgYAWCJUAUwPUwmOA1138zMyNH38w2OSZt589eOjGHzvZfumej4cLcxhkzBuwa5FsZmyW5mFIK5Y5k5dwOIcn2XjmnLMbDs/cxsAjcwC7FoPDjA3SDAyHE2cw5BgczpFgTtxwI3fDYV6gFgkcDrM/zNj88wfD/8QZ/G+AWgzqE/ffyHmAV4sBM2ObBDCsEmdIgGxJOJy4QSKHAa8WicOMbdY8BsnFEhIgWw4cN55xI80A6BcJnFr429sf3/xRYZcnwZ9j/DnnT7Vs/4zkx58Lt9nY49ICdR4aHxhZ+DVgAmYS1Y+CUTAKRsHwBgA7y1pgCrZXaAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8682-936X","institution":"Ebrahimzadeh","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Elias","suffix":""},{"id":396998655,"identity":"587a163b-7959-4abf-ba41-c2d35d6c43c8","order_by":2,"name":"Narjes","email":"","orcid":"","institution":"Amin","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Narjes","suffix":""},{"id":396998821,"identity":"8b0bfd82-42d1-4046-9ad8-3ea81d4bfc7c","order_by":3,"name":"Hamid","email":"","orcid":"https://orcid.org/0000-0002-7302-6856","institution":"Soltanian-Zadeh","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Hamid","suffix":""}],"badges":[],"createdAt":"2025-01-02 22:37:01","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5754204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5754204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73199562,"identity":"f8da90d9-457c-4141-8c95-e611b7dacedf","added_by":"auto","created_at":"2025-01-07 16:04:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA schematic representation of the task undertaken by the subjects. The visual illustrates the sequential process: participants commence the task with an initial account amount. Through 100 trials, they are presented with two randomly generated options, and participants are required to make a choice in each trial. Subsequently, the selected option is transformed into a gain or loss, altering their account’s value accordingly. Each display persists for a fixed duration of 2 seconds.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/543c04b93973ac6e61d78d94.png"},{"id":73199561,"identity":"950ef011-3c0a-4abd-8ff5-9c9c59286269","added_by":"auto","created_at":"2025-01-07 16:04:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA demonstration of the three different modes for calculating FRN. As outlined, these methods include Single Trial FRN, Moving Average FRN, and Grand Average FRN. It is important to note that, for obtaining a Single Trial FRN, a 40 ms period centered around the occurrence time of the FRN in the Moving Average mode was used.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/6b94de30105826e1befa9a36.png"},{"id":73200976,"identity":"d9fff132-de1d-480c-849d-47122b8338be","added_by":"auto","created_at":"2025-01-07 16:12:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140340,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn example of the obtained FRN in the MA mode from subject 10. It clearly showcases that in this case the FRN occurred at 292 ms after stimulus. This FRN occurrence time is calculated for each individual manually in the MA mode.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/a78a68aa35110d9da45e49de.png"},{"id":73199556,"identity":"911e15d8-fe08-4bc5-a979-24fe3e25293c","added_by":"auto","created_at":"2025-01-07 16:04:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":737074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe FRN's two-dimensional and three-dimensional topography at various time-points post-stimulus were computed and presented in the Grand Average mode. Notably, within the initial 300 milliseconds after the stimulus, the FRN becomes apparent in the central region of the head. The developmental sequence of the FRN and its association with the P3 wave are clearly observable during this period.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/cdc5b6c51cf04e6d14655435.png"},{"id":73199570,"identity":"fd93ab20-0b84-4dd4-82b4-591774fd15f0","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA schematic representation of the comparison of winning and losing groups and its statistical charts. Diagram (A) illustrates the FRN amplitudes for the win and loss trials across each subject. As apparent in the visualization, the amplitudes for win and loss are distinctly separated for each individual. It is important to note that in reality the amplitudes for loss are not inherently negative. Instead, due to a decrease in the offset or overall average, they appear as negative values. Diagram (B) effectively presents the median, first and third quartiles, as well as the minimum and maximum amplitudes for both win and loss trials for all participants. Picture (C) is a bar plot depicting the mean and standard error of the mean for both groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/16e2c82c8a4650112ec0f0eb.png"},{"id":73199572,"identity":"e0217dcf-66c4-46b4-8c19-99ef7de5e1b2","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":133843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA schematic representation of the comparison of low and high win trials and its statistical charts.\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;Diagram (A) illustrates the FRN amplitudes for low and high win trials across each subject. As apparent in the visualization, the amplitudes for these two senarios are different for each individual. Diagram (B) presents the median, first and third quartiles, as well as the minimum and maximum amplitudes for low and high wins. Picture (C) is a bar plot depicting the mean and standard error of the mean for the two scenarios\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/8161b8e63312d10c6fdfe8d3.png"},{"id":73199568,"identity":"e4edf6f2-fb35-489a-beca-f321bdfbb858","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA schematic representation of the comparison of low and high loss trials and its statistical charts. Diagram (A) illustrates the FRN amplitudes for low and high loss trails across each subject. As apparent in the visualization, the amplitudes for low and high loss are different for each individual. Diagram (B) presents the median, first and third quartiles, as well as the minimum and maximum amplitudes for the low and high loss scenarios for all participants. Picture (C) is a bar plot depicting the mean and standard error of the mean for the two scenarios\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/708845536e0dfd078e1e4c7e.png"},{"id":73199573,"identity":"0920ba7d-7ef4-46dc-b336-b8cc448b23f9","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":128963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of all the sub-categories; Diagram (A) illustrates the FRN amplitudes for all four sub-categories across each subject. As apparent in the visualization, the amplitudes for these two sub-categories are mostly different for each individual. Diagram (B) effectively presents the median, first and third quartiles, as well as the minimum and maximum amplitudes for high and low losses and high and low wins for all participants. Picture (C) is a bar plot depicting the mean and standard error of the mean for all sub-categories.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/c73e77de1fe21b944a3beeae.png"},{"id":73199577,"identity":"8ad8423b-988a-4c3c-83dd-beb36b30761d","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":329435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDipole fitting results for the FRN source. Using dipole modeling on ST mode data (40 ms period within each trial), the analysis identified the medial frontal cortex, particularly in or near the anterior cingulate cortex (ACC), as the most likely generator of the FRN observed at the scalp.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/a3ef1e5d1e2e9e36b2eaefcb.png"},{"id":73199581,"identity":"fdf41c17-4dca-4e3d-bf09-12af0b82125b","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":98977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributed source localization results for FRN. Utilizing distributed source localization techniques, eLORETA, to map the brain regions of Interests contributing to the observed FRN. The analysis, using ST mode data (40 ms period within each trial), confirmed significant neural activity in the medial frontal cortex, particularly in or near the dorsal anterior cingulate cortex (dACC), corroborating the dipole modeling findings.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/fa473752d6169601a4ff2848.png"},{"id":73199563,"identity":"d82c47e7-064b-4f3e-aa55-72524b74a17c","added_by":"auto","created_at":"2025-01-07 16:04:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":266836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLoss-related whole-brain connectivity matrix, illustrating the coherence (COH) between brain regions based on source activities obtained from ST mode data across all subjects. The color gradient represents the strength of connectivity, with warmer colors indicating higher connectivity levels.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/775adb8335e075b0ae0df3cd.png"},{"id":73199580,"identity":"535f51e8-bc22-463a-9499-d9949b821dfe","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":266181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGain-related whole-brain connectivity matrix, depicting the coherence (COH) between brain regions based on source activities derived from ST mode data across all subjects. The color intensity reflects the strength of connectivity, with warmer colors indicating stronger connections.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/9ccaa1573925a910aa56b967.png"},{"id":73199558,"identity":"1e410793-e1af-4cce-b0cb-118c728aa1b6","added_by":"auto","created_at":"2025-01-07 16:04:50","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":266386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhole-brain connectivity matrix for all trials combined, showing the coherence (COH) between brain regions based on source activities derived from ST mode data across all subjects. The color gradient represents the strength of connectivity, with warmer colors indicating stronger connections.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/6c82e515e343503998500b44.png"},{"id":73199559,"identity":"1129df83-c3f2-499b-9049-99193dd2f3e7","added_by":"auto","created_at":"2025-01-07 16:04:50","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":118964,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain network connectivity visualization showing significant connections (threshold ≥ 0.24) among various brain regions. The graph highlights strong connectivity between the dorsal anterior cingulate cortex, frontal midline cortex, hippocampus, and parietal lobe; between the dorsolateral prefrontal cortex and insular and visual cortex; and between the posterior cingulate cortex and intraparietal sulcus region. Node sizes correspond to label length for readability, while edge thickness and color represent the strength of the connectivity, as indicated by the color bar on the right.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/b286265a24df44b943be0455.png"},{"id":73199590,"identity":"576e32de-80ba-4de6-8728-744a28a43ba5","added_by":"auto","created_at":"2025-01-07 16:04:51","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":145542,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn illustration of the relative amplitude difference of the FRN compared to the baseline across the four subgroups; high and low-value loss and high and low-value win groups, displaying changes in the FRN in response to the magnitude of feedback in the time scale of FRN.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/9184b9b1381beef6006a0099.png"},{"id":73202653,"identity":"46899307-4332-4a61-a70b-6406678065d5","added_by":"auto","created_at":"2025-01-07 16:36:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5420436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5754204/v1/93a3f751-88ff-4332-9642-b3b9caa7c4d8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentifying Cognitive Resources in a Decision-Making during Gambling\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eVast studies indicate that human, as an adaptive biological system in balance with the environment, desires to minimize their non-compliance with the prevailing environment\u0026rsquo;s order and cope with the progressive disorganization of the surrounding system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Our understanding of how human life is shaped by both small and significant decisions has given rise to a significant subfield within decision-making, the study of its neurophysiology, and the neural basis of brain functioning\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnderstanding the neural basis of brain functioning requires knowledge about the temporal and spatial aspects of information processing. Electrophysiology, specifically electroencephalography (EEG), constitutes to be a major tool for studying the neural basis of brain functioning. Contrary to imaging techniques, EEG provides high temporal resolution and records the electrical activity of the brain in the order of milliseconds, using electrodes positioned on the scalp. EEG signal processing methods are applied to determine the quantitative parameters on the spectrum of frequencies, amplitudes, and coherence\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMRI, on the other hand, represents a morphological view of the brain with a significant spatial resolution\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. It provides a multiparametric evaluation of the brain tissue with respect to both its structural and functional properties. In this context, similar to EEG but at different temporal scales (seconds vs. milliseconds), functional MRI (fMRI)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e offers the possibility for examining the brain functional activation non-invasively both during resting state and task execution, expanding the panel of parameters obtainable by MRI [e.g., structural connectivity evaluated by diffusion tensor imaging (DTI), metabolites concentrations evaluated by magnetic resonance spectroscopy (MRS), and perfusion evaluated by arterial spin labeling (ASL)]\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComplementarity of information is highlighted in multimodal recording methods that are designed to overcome the single modality shortcomings and to enhance the patient\u0026rsquo;s treatment experience. These methods show promising potential in providing a more thorough understanding of the underlying mechanisms of neural activities, addressing brain function and dysfunction\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecording EEG and fMRI simultaneously combines the optimal spatial and temporal data of the two modalities and overcomes the restrictions of single modalities. This method used to be mostly used to identify epileptic foci\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, but today it is used to identify cognitive foci in various tasks\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Especially, when the goal is to identify active areas of the brain in a cognitive task, these tools together constitute one of the most efficient methods\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Therefore, simultaneous acquisition of EEG-fMRI can be useful in locating cognitive foci in gambling-based tasks, decision- making tasks and, reward processing stages\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmploying the two modalities together enhances our ability to study complex brain activities in depth. One such intricate brain activity that remains a matter of debate in the literature and requires further investigation is the learning substage of decision-making. In this context, a negative event-related potential (ERP) component, Feedback Related Negativity (FRN) has been observed which is usually sensitive to individual reliance on external feedback to determine whether the responses are correct.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e This component is well-observed when the feedback stimuli revolved around monetary gains or losses \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe FRN is believed to reflect the influence of dopamine signals on the anterior cingulate cortex. A decrease in dopamine activity disinhibits anterior cingulate neurons, leading to a more negative FRN. Conversely, an increase in dopamine activity inhibits anterior cingulate neurons, resulting in a more positive FRN. Nevertheless, understanding the behavior of the mesencephalic dopaminergic system has given rise to the principle of several Reinforcement Learning (RL) algorithms such as the RL theory of Error-Related Negativity (RL-ERN).\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe RL algorithms are based on the idea that the dopamine system evaluates outcomes to assess whether they align with expectations. When there is a positive prediction error, dopamine firing rates increase, and when there is a negative prediction error, dopamine firing rates decrease. The Substantia Nigra pars compacta (SNc) and Ventral Tegmental Area (VTA) transmit prediction errors to the basal ganglia for expectation adjustment. The VTA also communicates prediction errors to the anterior cingulate, where they are used to link reward information with action selection.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStudies have revealed a noteworthy correlation between the phasic responses of dopamine neurons and the differences between the real outcome and the expected outcome. This critical link underscores that the anticipation of outcomes is pivotal in understanding these neural responses. Furthermore, the determination of this expected outcome relies on two key factors: the probability and the magnitude of rewards.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWith regards to the initial factor, reward probability, it indicates that the FRN tends to be more prominent when dealing with unlikely outcomes as opposed to likely ones. A multitude of research studies has consistently revealed a negative correlation between the amplitude of the FRN and the probability of an outcome. RL-ERN takes it a step further by anticipating that these event-related potentials (ERPs) will exhibit a more positive amplitude following improbable wins compared to probable ones, while they will display a more negative amplitude following improbable losses as opposed to likely losses.\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe second factor, the magnitude of reward, although according to RL-ERN must show the same interaction with FRN amplitudes, the few studies which have been done to resolve this matter were unable to obtain this interaction. The RL-ERN posits that the amplitude difference in FRN between significant losses and wins will be greater than the amplitude difference between minor losses and wins. From a neurobiological standpoint, changes in the magnitude of wins and losses, as well as their valence, should be detectable within the time frame of the FRN occurrence. Logically, more substantial negative feedback must lead to a larger absolute value of FRN amplitude than lower-magnitude negative feedback; the same is true about the positive feedback. More substantial positive feedback should result in a more pronounced positive ERP amplitude within the temporal window of FRN compared to lower-magnitude positive feedback. This alignment with RL-ERN is due to the fact that the potential sizes of wins or losses must be reflected in phasic variations in dopaminergic signals.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe concept that an interaction between the feedback magnitude and amplitude of FRN should be observable within the FRN time frame is derived from a 2015 meta-analysis conducted by Sambrook and Goslin, providing supportive evidence for this relationship. Specifically, the authors noted in their studies that the disparity between positive and negative feedback was more pronounced when the feedback was of greater magnitude than when it was of smaller magnitude.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn some studies, such as Kamarajan's work,\u003csup\u003e \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e \u003c/sup\u003e the interaction between the feedback magnitude and the FRN amplitude was observed solely in male subjects, with no specific relationship found in female subjects. The inability of these experiments to establish this connection, specifically the impact of reward magnitude on the amplitude of the FRN, likely stems from the way they designed their tasks. This is because the feedback presented to participants in these experiments lacked information about the magnitude of wins or losses. For example, they only displayed a win of (+ \u003cspan\u003e$\u003c/span\u003e0.1) or a loss of (- \u003cspan\u003e$\u003c/span\u003e0.5) as output, which repeated and the effect of magnitude becomes weakened. Therefore, the results could have been more insightful if they could change the magnitude of feedback or show the differences between two choices as feedback.\u003c/p\u003e \u003cp\u003eIn this regard, we conducted an experiment aimed at assessing the relationship or correlation between the deflection of ERP signal amplitude in the FRN time window and feedback magnitude to assess FRN behavior regarding RL-ERN predictions and also to evaluate that FRN inherits its generator, the mesencephalic dopamine system behavior. We focused on the Feedback-Related Negativity (FRN), so we primarily concentrated on the EEG component of the simultaneous EEG-fMRI dataset that we recorded.\u003c/p\u003e \u003cp\u003eWe recruited 24 participants and recorded 64-channel HD EEG signals with simultaneous fMRI, while they engaged in a modified task designed to control expectations and avoid its interference. The task introduced random variations in both wins and losses states, with our primary focus being the isolation of the interplay between feedback magnitude (win and loss sizes) and the FRN amplitude.\u003c/p\u003e \u003cp\u003eBy focusing on EEG, we leveraged its high temporal resolution to capture the rapid neural dynamics associated with FRN during cognitive tasks. This approach allowed us to precisely monitor the timing and direct neural responses to feedback, which is essential for understanding the decision-making processes underlying FRN. While fMRI provided the structural and broader functional context simultaneously, the analysis and results of fMRI were not the main focus of this paper. Instead, we concentrated on the detailed temporal insights provided by EEG to explore the cognitive foci involved in a gambling-based task, specifically targeting the neural mechanisms underlying FRN.\u003c/p\u003e \u003cp\u003eWe assessed the EEG data on a single trial basis and categorized the feedback magnitude into four distinct levels. Through this approach, we were able to observe and analyze the interaction between these two variables. Our results show a positive correlation between the win magnitude and the positivity of ERP signal amplitude within the FRN time window and a direct correlation between the negativity of ERP signal amplitude within the FRN time scope and the loss magnitude.\u003c/p\u003e \u003cp\u003eMoreover, using dipole fitting and distributed source localization, the source of FRN in both loss and win conditions was identified in the Medial Frontal Cortex. Our findings show robust connections among brain regions associated with error monitoring, memory, attention, and visual processing, with the dorsal Anterior Cingulate Cortex acting as a central hub. The lack of significant differences between win- and loss-related FRNs indicates that similar neural processes are involved in both types of feedback, regardless of their valence.\u003c/p\u003e \u003cp\u003eIn the behavior analysis, our results showcase that the participants displayed different risk-taking behaviors throughout the trials, initially favoring higher-risk options and gradually shifting to more cautious decisions as the experiment progressed. The analysis also showed that the participants took more risks following losses than after gains, emphasizing the impact of context on decision-making.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003eThe experiment conducted, as mentioned, was a monetary gambling task. While gambling involves a significant element of chance and sometimes lacks enticements, verbal inquiries at the end of the study asked participants about their level of arousal during the experiment, proving that participant engagement in the tasks was substantial, possibly due to the enticing and provocative nature of the monetary stakes involved in the task. Further details of the materials and methods are described in the following subsections, where additional information are provided on the gambling task and its execution, how the data was facilitated, including participant selection, methods, equipment used for data collection, and data acquisition procedures.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eA total of twenty-four individuals were recruited. Before acquiring simultaneous EEG-fMRI data, the individuals filled out two-page questionnaires for evaluation and exclusion from participation in the experiment. In fact, the questionnaires and participants were examined by a neurologist to be admitted to or excluded from the experiment in case of any background major illness or psychiatric diseases. All participants had normal or corrected-to-normal visions and had no history of psychiatric, medical, or neurologic illness. In addition, they denied regular use of substances that might affect the central nervous system. The participants consist of eleven females and thirteen males with the age range of 21\u0026ndash;43 years (mean 30) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants provided written informed consent, and ethical approval was received from the local ethics committee of the Iran University of Medical Sciences, Tehran, Iran.\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\u003eDemographic data of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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\" 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;1\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u0026ndash;1\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;1\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;2\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026ndash;3\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;4\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;5\u0026nbsp;-\u0026nbsp;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e30.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e11 F\u0026thinsp;+\u0026thinsp;13 M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStandard Deviation:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.4\u003c/b\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 \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental Setup\u003c/h2\u003e \u003cp\u003eThe experiment utilized a gambling task that underwent optimization based on several previous experimental tasks \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The task was implemented using a new format designed in the MATLAB environment with Psychtoolbox-3 \u003csup\u003e34\u003c/sup\u003e. In this task, participants were initially provided with a real budget, displayed as their account at the beginning of the experiment and after each trial.\u003c/p\u003e \u003cp\u003eDuring each trial, participants were presented with two options, integer numbers ranging from\u003c/p\u003e \u003cp\u003e-100 to 100, on a screen for a fixed duration of 2 seconds. This is where there is a pseudo control over the outcomes of wins or losses for the participant, which helps to sustain the participant's engagement in the task. After choosing an option, the selected number was revealed as a reward or punishment within the next 2 seconds. In this regard, the selected number displayed again was highlighted in green or red to indicate a win or a loss. This display lasted for a fixed duration of 2 seconds. This part of the trial is where the participant's reward system becomes highly engaged with the task. Moreover, to increase the motivational properties of the monetary incentives, cash in the amount of the cumulative total was kept on the table as their account balance and incremented or decremented after each block of trials. Therefore, the amount of money won or lost was added or subtracted from the participant's account. Subsequently, their account balance which was updated was shown for a fixed duration of 2 seconds. For example, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, when the first trial block resulted in a loss (-\u003cspan\u003e$\u003c/span\u003e25), that total was subtracted from the take-home amount delivered at the end of the trial. Conversely, when the second trial block resulted in a net gain (+\u003cspan\u003e$\u003c/span\u003e90), the total was added to the take-home amount delivered at the end of the trial. These procedures (the presence of the cash and the steadily increasing or decreasing amount of money in the account) were used to increase the participants' motivation to attend to the task. Next, a fixation cue (+) was represented for 2 seconds, as a break after displaying the participant's account (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is important to highlight that the entire task comprised 100 trials, and the outcomes of winning or losing were entirely determined by chance with equal probability. In the event of a missed trial (resulting from the participant not providing an answer), no amount was credited to their account; there was neither an increase nor a decrease. Such these trials were disregarded and categorized as ignored instances in the experimental procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Electrophysiological Recording and Preprocessing\u003c/h2\u003e \u003cp\u003eEEG was acquired using the custom-built electrode system, Brainproduct cap (BrainProducts GmbH, Gilching, Germany) equipped with 66 sintered Ag/AgCl Gel-based passive electrodes and multitrodes that are compatible with the Magnetic Resonance Imaging systems. The device operated at a sampling rate of 5000 Hz and EEG signals were amplified with a resolution of 0.5 \u0026micro;V and online highpass (cutoff 0.01 Hz) and low-pass (cutoff 500 Hz) filtered using the BrainVision Recorder 2.0 (Brain Products). The electrode array included AF3/4/7/8, Afz, Fp1/2, FPz, F1/2/3/4/5/6/7/8, Fz, FC1/2/3/4/5/6, FCz, FT7/8/9/10, C1/2/3/4/5/6, Cz, T7/8, TP7/8/9/10, CP1/2/3/4/5/6, CPz, PO3/4/7/8, POz, P1/2/3/4/5/6/7/8, Pz, O1/2, Oz, and ECG.\u003c/p\u003e \u003cp\u003eIn addition to the 63 EEG channels, three supplementary channels were included: one FCz channel as a reference (Ref), one Afz channel as ground (Grnd) and one ECG channel placed on the back. The electrode names and their corresponding locations on the head adhere to the 10/10 system. Additionally, throughout the experiment and across all subjects, the impedance of all electrodes was kept below 5 kΩ. The ribbon cable, which connects the electrode wires to the amplifiers, was fixed using sandbags on foam cushions. This setup was designed to minimize artifacts resulting from the scanner's vibrations.\u003c/p\u003e \u003cp\u003eEEG preprocessing was conducted in the MATLAB environment using the EEGLAB Toolbox\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInitially, the gradient artifact was mitigated using the FMRI Artifact Slice Template Removal (FASTR)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e method, facilitated by the FMRIB plug-in from the University of Oxford Centre for Functional MRI of the Brain (FMRIB)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, at EEGLAB in MATLAB. This comprehensive approach involved several critical steps: aligning slice-timing triggers, subtracting local slice artifact templates, removing residual gradient artifacts using Optimal Basis Sets (OBS) and Adaptive Noise Cancellation (ANC)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Then, the data were low-pass filtered using a finite impulse response (FIR) filter with a 70 Hz cutoff in EEGLAB. Again, we used fMRIB plug-in to reduce the ballistocardiographic (BCG) artifact\u0026mdash;caused by potentials induced by blood movement in the magnetic field during the cardiac cycle and related head and scalp movements\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe QRS peaks in the ECG channel were identified using an algorithm in the FMRIB plug-in and verified visually. When automatic detection failed to achieve satisfactory accuracy, QRS events were semi-automatically set in BrainVision Analyzer. Once QRS detection was finalized, the data were downsampled to 500 Hz. For correction, EEG data from each channel were aligned to the BCG artifact based on the detected QRS peaks, and principal component analysis was performed. The first four principal components were used as an Optimal Basis Set (OBS) to describe the artifact, which was then fitted to and subtracted from each QRS event segment\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, a low-pass finite impulse response (FIR) filter ranging from 1 to 40 Hz was applied to the data. This filter aimed to maintain the data's phase and general characteristics within the specified range (1\u0026ndash;40 Hz) while attenuating frequencies outside this band, which were not the focus of this study.\u003c/p\u003e \u003cp\u003eIn the subsequent step, identification and visual rejection of noise and artifacts were carried out. They could result from activities such as swallowing saliva, head movement, and muscle contractions. Such non-stereotyped artifacts may introduce a variety of unique scalp patterns into the EEG data, which may in turn confound and compromise ICA decompositions. Therefore, it is important to identify and discard them from the data before running ICA, as was done here.\u003c/p\u003e \u003cp\u003eFollowing this, channels with improper data or significant prolonged noise, whose time series did not align with the EEG data, were manually excluded. The criteria for designating a channel as 'bad' involved evaluating its time series and, if deemed necessary, inspecting the frequency spectrum. If the frequency spectrum did not exhibit a 1/f pattern, the corresponding channel was labeled as 'bad' and removed.\u003c/p\u003e \u003cp\u003eSubsequently, manual ICA application in EEGLAB was performed to identify and remove eye-related components and heartbeat residuals. Once the data were devoid of artifacts and noise, the next step involved reconstructing the removed bad channels based on their positions on the electrode channel location. This reconstruction involves using data from other channels through a weighted sum of the surrounding channels' data in a circular radius manner.\u003c/p\u003e \u003cp\u003eAfter channel restoration, the data were segmented, isolating EEG data from half a second before to one second after the stimulus trigger (-0.5 to 1 second). This ensured that the relevant stimulus-related information was organized systematically and accurately.\u003c/p\u003e \u003cp\u003eIn the following step, segments that still exhibited residual artifacts or noise, despite previous steps, were discarded. The data were then re-referenced to the average of all channels, a method where the average signal across all EEG electrodes for each time point is subtracted from the EEG signal in each channel. This method, known as the average reference, assumes that the head can be approximated as a sphere and leverages the idea that the sum of all potentials recorded across the entire sphere is zero, providing electrically a neutral reference for all channels.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data Analysis\u003c/h2\u003e \u003cp\u003eFor obtaining the FRN, the data were segmented into epochs time-locked to the onset of win or loss feedback presentation. Separate EEG epochs of 1,200 ms were baseline-corrected by subtracting the average activity of that channel during the \u0026minus;\u0026thinsp;200 to 0 ms baseline period from each sample. In regard of obtaining a comprehensive understanding of FRN, the FRNs were calculated in three modes: Grand Average (GA) mode, Moving Average (MA) mode, and Single Trial (ST) mode.\u003c/p\u003e \u003cp\u003eGrand Average (GA) mode denotes a scenario in which all trials of all subjects are averaged. The significance of this approach lies in gaining a comprehensive understanding of FRN properties as a collective, and subsequently, the time window/interval for FRN occurrence determined through this method. By calculating this interval, we were able to fit bilateral dipoles. The dipole fitting process enables the localization of the neural generators responsible for the FRN. This localization is crucial for understanding the underlying neural mechanisms and can contribute to further research on the functional significance of the FRN.\u003c/p\u003e \u003cp\u003eMoving Average (MA) mode refers to calculating an average across all trials of each participant. This mode provides a more detailed analysis compared to the GA mode by preserving individual variations and nuances in the data, allowing for a more granular examination of the FRN properties within each subject. In the MA mode, each participant's data is averaged separately. This approach can highlight individual differences and potentially identify unique patterns that might be obscured in the GA mode, where the data is averaged across all subjects.\u003c/p\u003e \u003cp\u003eAs we know, the FRN is a fronto-central ERP component elicited between 200\u0026ndash;350 ms following feedback onset and has time variability within participants \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. By focusing on individual averages, we were able to better understand how FRN manifests differently across participants and explore the variability in neural responses. Armed with this knowledge, the occurrence time of the FRN was manually and separately obtained for each subject by identifying the exact negative peak preceding the P300 using these MAs. These FRN occurrence times were useful for obtaining the Single-Trial (ST) mode, in which the FRN in each trial was extracted.\u003c/p\u003e \u003cp\u003eTo obtain more in-depth information, the FRNs in the ST mode were computed, through isolating an interval of 40 milliseconds centered around the occurrence time of the FRN in the MA mode. It is important to note that, following the calculation of the FRNs in the ST mode, the overall average of the amplitudes within the trials in each EEG channel (e.g., 107.1 mV) was subtracted from the amplitudes of the FRNs in the ST mode. This shift ensures that the amplitudes do not retain a DC or offset value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, the average amplitude of the 40-millisecond interval was considered as the FRN amplitude in that trial. Subsequently, we categorized these amplitudes into two groups based on their association with win or loss states. Then, in both win and loss scenarios, we further divided the amplitudes into low-value feedbacks (ranging from 1 to 19) and high-value feedbacks (ranging from 81 to 99). This process resulted in the creation of four distinct classes: low-value win, high-value win, low-value loss, and high-value loss, for each subject.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Experimental Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Moving Average mode\u003c/h2\u003e \u003cp\u003eThe EEG data across all trials within each subject, spanning from \u0026minus;\u0026thinsp;200 to 1000 milliseconds after the feedback stimulus, were consolidated. Then, by calculating the average of all trials from each subject, the Moving Average mode was derived. By this approach, we were able to smooth out anomalies, noise, and artifact fluctuations, resulting in a more reliable and accurate depiction of the FRN. The occurrence time of FRN in Moving Average mode for each individual was then manually calculated from the respective Moving Averages (MAs) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2lists the FRN occurrence times for each subject extracted from the Moving Average method. These obtained times will be utilized to compute the single trials of FRNs. Nevertheless, it is important to mention that as it is demonstrated in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the FRN peak occurred on average around 293.4 milliseconds after the stimulus.\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\u003eThe occurrence time of the FRN observed for each participant in the MA mode.\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\u003eSUB01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337 ms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256 ms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e355 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e264 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e319 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUB12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUB24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e293.4 ms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSTD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e42.85 ms\u003c/b\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 \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Grand Average mode\u003c/h2\u003e \u003cp\u003eNext, the segmented data from all subjects were normalizedand averaged (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It is important to note that this averaged data, referred to as the Grand Average, consistently revealed the occurrence of the FRN at a specific time, which closely aligned with the average FRN peak observed in the MA mode. In the GA mode, the FRN amplitude peaked at 298 milliseconds after the stimulus. The topography and latency of Grand average are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Single-trial FRN\u003c/h2\u003e \u003cp\u003eThe Single Trial FRN comprises a 40 ms period of the data within each trial, centered around the manually calculated occurrence time of the FRN in the MA mode. Subsequently, we categorized each Single Trial FRN into two groups based on its feedback valence: win and loss. The average of the trials within the win group was then compared to the average of the trials within the loss group, individually and for all subjects. This comparative analysis is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUpon comparing the amplitudes of all trials linked to the win feedback with those associated with the loss states, a significant result was observed. The paired t-test indicated a noteworthy difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), demonstrating that the amplitudes for the loss trials were lower than those for the win trails. The distinction between the two groups is readily apparent in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C and the impact of valence on FRN amplitudes is discernible and comprehensible from the information conveyed by these illustrations.\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\u003eThe outcomes of the comparison among the four modes: high win, low win, high loss, and low loss, using paired t-tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWin \u0026ndash; Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Value Win \u0026ndash; Low Value Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Value Loss \u0026ndash; High Value Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Value Win \u0026ndash; High Value Win\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003eIn the next step, we categorized the loss and win trials further based on the quantity of feedback received. Feedback scores ranging from 1 to 19 were considered low value, while scores from 81 to 99 were regarded as high value. We segregated the trials corresponding to these categories. Consequently, for each subject experiencing both winning and losing scenarios, four classes were formed: low-value loss, high-value loss, low-value win, and high-value win. Utilizing paired t-tests, we identified a notable distinction between high-value losses and low-value losses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that larger losses correlate with a more pronounced drop or a great negativity in the FRN time window compared to baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, a significant contrast was observed between high-value wins and low-value wins (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that greater winnings result in a less deflection/drop and deviation or a broader positivity range compared to the baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the following, for a more comprehensive view, these four subgroups (low-value loss, high-value loss, low-value win, and high-value win) were visualized together in one plot. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e provides a detailed depiction of the FRN amplitudes and their distribution across all subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Source of FRN\u003c/h2\u003e \u003cp\u003eA) Dipole fitting: For revisiting the source of FRN and as there are a debate regarding the generator of FRN, we used dipole modeling with these ST mode data (40 ms period of data within each trial) to identify which cortical region was most likely to generate the FRN observed at the scalp. It worth mentioning The results of the modeling were consistent with a source in the medial frontal cortex, in or near the anterior cingulate cortex (ACC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eB) Distributed Source Localization: To further investigate and to confirm the source of FRN, we also employed distributed source localization techniques in addition to dipole fitting. This method allows for a more comprehensive mapping of the brain regions contributing to the observed FRN by estimating the distribution of neural activity across the cortex. Using the same ST mode data (40 ms period of data within each trial), we applied this advanced technique, \u0026lsquo;exact\u0026rsquo; low-resolution electromagnetic tomography eLORETA, which was embedded in the ROI connect\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e a plug-in in EEGLAB to identify potential sources of FRN. The eLORETA has been shown to outperform other linear solutions in localization precision\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The results corroborated the dipole modeling findings, highlighting significant activity in the medial frontal cortex, particularly in or near the anterior cingulate cortex (ACC). This complementary approach provides a more detailed and robust confirmation of the cortical origins of FRN (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Brain Network associated with FRN\u003c/h2\u003e \u003cp\u003eUsing the source activity data derived from the ST mode, we calculated undirected Functional Connectivity (FC), Coherency (COH), among the sources as clusters. These calculations were performed using the ROIconnect\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e plug-in in EEGLAB. FC was analyzed separately for each group (loss and win) as well as for all trials combined. A paired T-test revealed no significant differences between the groups, suggesting that the neural networks responsible for generating the loss-related FRN and win-related FRN are likely the same. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e the connectivity matrices were performed among 15 clusters or brain regions: Anterior inferior parietal lobe, Angular gyrus, Dorsal anterior cingulate cortex, Dorsal anterior prefrontal cortex, Dorsal lateral prefrontal cortex, Frontal eye field, Frontal midline cortex, Frontal operculum, Hippocampus, Insular cortex, Intraparietal sulcus region, Parietal lobe, Post cingulate cortex, Posterior parietal cortex, Prefrontal cortex, Thalamus, Visual cortex.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the following analysis, we visualized brain network connectivity across all trials combined (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). A graph was created where nodes represent brain regions and edges represent the connections between them, retaining only significant connections above a specified threshold (threshold\u0026thinsp;\u0026ge;\u0026thinsp;0.24). Node sizes were adjusted based on label length to enhance readability, while edge thickness and color were scaled according to the strength of connectivity, visually indicating the relative importance of each connection. The positions of the nodes in the graph schematically represent the regions' spatial locations in a top-down view of their 3D brain coordinates, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eCoordinates of Brain Regions, This table shows the coordinates of various brain regions in this study in both the left and right hemispheres, as well as the average coordinates for each region.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL_X\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL_Y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL_Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR_X\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR_Y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR_Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAvg_X\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAvg_Y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAvg_Z\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior inferior parietal lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-49.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-51.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-50.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngular gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-66.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-65.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-66.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDorsal anterior cingulate cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDorsal anterior prefrontal cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-23.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDorsal lateral prefrontal cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal eye field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal midline cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal operculum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-63.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-24.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-23.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-12.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsular cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-38.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraparietal sulcus region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-63.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-63.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-63.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-35.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-54.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-53.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-54.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost cingulate cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-49.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-49.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosterior parietal cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-35.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-56.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-55.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefrontal cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-23.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-80.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-78.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-79.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.55\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\u003eThis thresholding revealed strong connections between the dorsal anterior cingulate cortex, frontal midline cortex, hippocampus, and parietal lobe; between the dorsolateral prefrontal cortex and insular and visual cortex; and between the posterior cingulate cortex and intraparietal sulcus region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Impact of previous outcomes on subsequent choices\u003c/h2\u003e \u003cp\u003eOur observation showcases that the FRN amplitude relates to the feedback valence and its value, we observed that the monetary prize or the monetary loss both could be seen in the amount of the drop or deviation in the signal or the amount of negative peak preceding the P300, (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e) and this is completely aligned with RL theories we discussed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our task, there was no explicit reward probability; therefore, participants did not have specific expectations of winning or losing, and as a result, there was no clear prediction or expectation of future outcomes. This indicates that participants could not learn consistent rules to guarantee rewards. Thus, the primary determinant of the FRN was the direction and value of the outcome (whether it was a loss or a gain and its size). Specifically, the valence of the feedback significantly influenced the FRN amplitude (P\u0026thinsp;=\u0026thinsp;0.00068).\u003c/p\u003e \u003cp\u003eFurthermore, participants exhibited distinct patterns of risk-taking and risk-avoiding behaviors, encountering varying degrees of risk across different trials. In trials where there were significant differences between the presented options, participants faced choices with higher and lower risk levels. Risk-taking was defined as selecting the option with the greater value when the difference between options exceeded 40, indicating a higher potential reward but also greater risk. For example, choosing 80 over 30 represented a risky decision. Conversely, if the difference was greater than 40 and the participant selected the smaller option, this choice was considered a cautious decision, reflecting a preference for minimizing potential loss despite the larger potential gain.\u003c/p\u003e \u003cp\u003eIn contrast, when the difference between options did not exceed 40, these trials were excluded from our analysis. This was to ensure that our focus remained on trials with clear and substantial risk differentials, allowing for a more precise examination of risk-related decision-making behavior.\u003c/p\u003e \u003cp\u003eUpon closer examination of participants\u0026rsquo; behaviours, it became evident that a predominant strategy involved initiating each block of 100 trials with riskier choices, gradually transitioning towards more cautious decisions. In the initial quarter of the block, the mean proportion of risky choices was 0.59, decreasing to 0.43 by the final quarter (P\u0026thinsp;=\u0026thinsp;0.027). This strategic approach aimed to safeguard gains obtained early in the block.\u003c/p\u003e \u003cp\u003eFurther analysis unveiled additional factors influencing participants' risk preferences. Studies on decision-making have indicated that contexts emphasizing losses tend to promote risk-seeking behaviour. To explore this, we compared choices following losses versus gains. Results showed a higher proportion of risky choices after a loss compared to after a gain (P\u0026thinsp;=\u0026thinsp;0.00083).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this experiment, we reported our observation of neural processing that occurs within 293 milliseconds after feedback stimuli, which illustrate early human perception of gains and losses in a gambling task. A negative-polarity event-related brain potential, known as Feedback-Related Negativity (FRN), likely emanates from dopaminergic neurons within a part of the mesencephalic dopaminergic system called the medial-frontal region in or near the anterior cingulate cortex. This potential showed greater deflection in amplitude when a participant's choice between two alternatives resulted in a loss compared to when it resulted in a gain.\u003c/p\u003e \u003cp\u003eFurthermore, we observed distinct differences in FRN amplitude not only between wins and losses but also within more detailed classes of feedback. We discerned specific variations in FRN amplitude among four classes of feedback\u0026mdash;high-value losses, low-value losses, high-value wins, and low-value wins\u0026mdash;based on feedback value. Specifically, larger losses correlated with a more pronounced negativity/drop in the FRN time window compared to baseline. Conversely, larger winnings resulted in less deflection, drop or deviation, and often a broader positivity amplitude compared to the baseline.\u003c/p\u003e \u003cp\u003eThis is in line with RL theories, which predict that the FRN is influenced by the expected outcome, comprising the probability and magnitude of rewards. In this experiment, the probability is effectively zero due to the absence of rules or previous expectations regarding winning or losing. Thus, the primary factors influencing the amplitude of the FRN are the magnitude of the reward and its valence. We propose that this magnitude remains in working memory until the results are revealed, indicating a close interaction between the network generating the FRN and working memory. This is supported by our connectivity analyses showing that regions like the dorsal Anterior Cingulate Cortex, dorsolateral Prefrontal Cortex, insula, and parietal lobe, which are implicated in working memory\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, also participate in the FRN-related network. According to the literature, the dopaminergic system becomes activated or deactivated upon winning or losing, with the degree of this activation or deactivation depending on the value and valence of the chosen option\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our suggestion is in our experiment, this activation or deactivation excites or inhibits dopaminergic neurons in the ACC the source of FRN, leading to variations in FRN amplitude.\u003c/p\u003e \u003cp\u003eWith a detailed analysis of participants\u0026rsquo; behavior, the findings demonstrate that participants are more inclined to choose riskier options following losses and exhibit greater caution after previous wins. This helps to underscore how medial-frontal computations may influence mental states involved in higher-level decision-making, including economic choices.\u003c/p\u003e \u003cp\u003eThese findings have several theoretical implications. Normative theories of judgment and decision making posit that the context in which a choice occurs\u0026mdash;such as the sequence of recent gains and losses or the aspirations of a decision maker\u0026mdash;should not affect the choice. A great deal of evidence, however, suggests that individuals deviate from normative behaviour, making decisions that depend on the status quo or other nonnormative reference points. A critical issue for psychological theories of choice behaviour is how cognitive and affective processing drive behaviours in nonnormative ways. Our data suggest that a rapid assessment of the motivational impact of an event participates in the evaluation of outcomes. In decision-making behaviour, such processing could affect nonnormative decision making by mediating the role that outcome events play in choices. In particular, the processing represented by the FRN could contribute to the experience that Kahneman refers to as \u0026ldquo;instant utility,\u0026rdquo; which is the momentary mental state resulting from the continuous evaluation of events along a good-bad dimension \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Such a computation can contribute to decision making by influencing the emotional state that individuals anticipate will occur upon making a choice \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, or it may affect the emotional state that drives behaviour at the moment of the choice itself \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn previous studies, researchers have utilized EEG signal source localization techniques to identify the source of the FRN. The findings from these studies indicate that the origin of FRN lies within the anterior cingulate cortex (ACC)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan additionalcitationids=\"CR53 CR54 CR55\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. However, other studies using these localization methods, also have introduced alternative neural sources for FRN, in addition to the ACC. Some studies suggest that FRN is generated in the posterior cingulate \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, .some research indicates that both the anterior and posterior cingulate cortex contribute jointly to FRN generation. Given the reciprocal connectivity between the anterior and posterior cingulate cortex, this finding is plausible, and studies have shown that the posterior cingulate cortex also exhibits reward properties\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e and response error characteristics\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Interestingly, there are other studies suggest that the FRN signal originates in the ventral striatum and posterior insula\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Our results suggest that the anterior cingulate cortex (ACC) likely plays a role in the neural circuitry underlying the generation of FRN. The consistent medial-frontal scalp distribution of the FRN aligns with an ACC origin, as supported by dipole localization modelling and distributed source localization method, eLORETA. Furthermore, the processes associated with losses and wins that lead to the FRN, as well as the relationship between FRN and risky behaviour, reflect a close functional association between the affective and behavioural control functions of the ACC\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Specifically, there is evidence indicating that ACC activity is sensitive to reductions in reward or penalties\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, we mentioned that the phasic responses of dopamine neurons scale with the difference between actual and expected outcomes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and the primary claim of the RL-ERN theory is that the range of FRN depends on the difference between the actual and expected outcome values. Expected value, in turn, depends on probability and reward magnitude. We previously stated that the relationship between reward probability and FRN amplitude has been thoroughly investigated in studies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. As we know, observations by researchers have indicated that the FRN amplitude is greater for improbable outcomes compared to probable outcomes. Many other studies have also demonstrated an inverse relationship between the range of FRN and the probability of the outcome being probable\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, besides the impact of reward probability on FRN ranges, the RL-ERN theory suggests that the range of rewards and losses affects FRN amplitudes. It should be noted that many experiments were unable to observe this relationship\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. We pointed out that inappropriate task design and a lack of comprehensive and sufficient study with modern High-Density EEG (HD-EEG) which provides suitable temporal resolution for examining the nature of ERP quality and good spatial resolution for evaluating the neurobiology of activity pathways, have been effective reasons for this observation (the lack of observation of the effect of the feedback magnitudes on FRN amplitude). In this experiment, with a HD-EEG and with the appropriate design of a gambling task, a significant difference was found in the amplitude of FRNs in four levels of feedbacks (in the form of reward or punishment). Eventually, our observation demonstrates that the magnitude of wins and losses has a significant effect on FRN as a single measure.\u003c/p\u003e \u003cp\u003eFurthermore, the Feedback-Related Negativity (FRN) is produced by the combined work of different brain networks, primarily the salience network (SN) and the cognitive control network. The salience network is crucial in detecting behaviorally important events\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e and initiating cognitive control, maintaining and implementing task sets, and coordinating behavioral responses\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. The SN consists of three main cortical areas: the dorsal anterior cingulate cortex (dACC), the left and right anterior insula (aRI), and the adjacent inferior frontal gyri\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Increased SN activity is observed when it is important to change behavior, such as after errors and change monitoring\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, which often signal the need for behavioral adaptation\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. The FRN can help define the actual cognitive operation performed by this monitoring system, whether it is monitoring for explicit behavioral errors, the outcomes of choices, or a more general monitoring function.\u003c/p\u003e \u003cp\u003eIn addition to the dACC, other brain areas like the supplementary motor area (SMA), dorsolateral PFC (dlPFC), ventrolateral PFC (vlPFC), inferior parietal lobule (IPL), and the amygdala are involved in error processing or monitoring. These regions form an error-monitoring network where the dACC acts as a central hub, monitoring conflicts and signaling the need for increased cognitive control\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The dACC also interacts with lateral prefrontal structures to implement behavioral changes\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings of strong connections between the dorsal anterior cingulate cortex, frontal midline cortex, hippocampus, and parietal lobe, as well as between the dorsolateral prefrontal cortex and the insular and visual cortex, and between the posterior cingulate cortex and intraparietal sulcus region, provide additional insights into how these networks interact during the occurrence of FRN. These strong connections suggest that the dACC not only serves as a hub for error monitoring but also integrates information from memory-related regions like the hippocampus and regions involved in spatial attention and visual processing, such as the parietal lobe and visual cortex. The connections between the dlPFC, insular cortex, and visual cortex further emphasize the role of cognitive control and error processing in guiding visual and attentional processes. The link between the posterior cingulate cortex and the intraparietal sulcus underscores the involvement of these regions in orienting attention and processing feedback, contributing to the adaptive changes in behavior following feedbacks.\u003c/p\u003e \u003cp\u003eIn addition, our findings emphasize that there are no significant differences between the neural activity and the networks that generate win-related FRN (sometimes referred to as Reward Positivity, or RewP\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e) and loss-related FRN. This helps clarify ongoing debates in previous studies and suggests that similar neural processes may be involved in feedback monitoring, potentially guiding behavioral responses in the same way. Together, these findings enhance our understanding of the complex neural networks involved in producing the FRN and their role in monitoring and adapting behavior based on feedback.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003ePeople gain knowledge from the consequences of their actions. Thorndike's law of effect, developed in 1911, explains that actions leading to satisfaction tend to be repeated, while those resulting in unfavorable outcomes are less likely to recur. This principle of reinforcement learning has been expanded upon in artificial intelligence, giving rise to algorithms that train autonomous systems to operate independently in complex and uncertain environments \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe FRN, or Feedback-Related Negativity, is a dip, curvature, or negativity observed in the ERP signal occurring typically between 200 to 350 milliseconds after the stimulus. It's important to note that while the term \"negativity\" is used, the signal does not actually drop below zero; rather, within this timeframe, the ERP wave appears relatively more negative compared to its surrounding peaks. This negativity \"rides\" on a larger, more positive wave known as the P300, which is evoked by stimulus processing\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies show a significant correlation between the phasic responses of dopamine neurons the generator of FRN and the difference between actual and expected outcomes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, emphasizing the importance of outcome anticipation in neural responses. The expected outcome is influenced by two key factors: reward probability and reward magnitude\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor reward probability, the FRN is more pronounced with unlikely outcomes, showing a negative correlation with outcome probability. According to the RL-ERN model, ERPs are expected to be more positive after improbable wins and more negative after improbable losses. The second factor, reward magnitude, should interact with FRN amplitudes similarly, according to RL-ERN. Despite numerous experiments failing to establish this relationship\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e (the failure to demonstrate the effect of win and loss amounts on FRN amplitude), this experiment aimed to address this through the design of a suitable task and the implementation of a controlled experiment. Finally, a significant distinction was obtained in the magnitude of the FRN amplitudes across four modes: low win value feedback, high win value feedback, low loss value feedback, and high loss value feedback. It was observed that both the valence and the magnitude of wins and losses exert an effect on FRN as a singular measure.\u003c/p\u003e \u003cp\u003eWe observed strong connections between the dACC, frontal midline cortex, hippocampus, and parietal lobe, as well as between the dorsolateral prefrontal cortex, insular cortex, and visual cortex, and between the posterior cingulate cortex and intraparietal sulcus during the monetary feedback task. Notably, no significant differences were found between the neural activity generating win- and loss-related FRN, suggesting similar processes in feedback monitoring for both types of feedback. This finding helps clarify ongoing debates in previous studies and indicates a consistent network for monitoring and adapting behavior regardless of the feedback's valence.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Future Directions\u003c/h2\u003e \u003cp\u003eFrom a neurobiological perspective, alterations in the quantity and magnitude of wins and losses are expected to manifest within the time frame of the FRN. Specifically, it is reasonable to anticipate that a greater magnitude of negative feedback would elicit a more pronounced negativity, resulting in a larger FRN, compared to a lesser degree of negative feedback. Similarly, positive feedback with a greater magnitude should yield a more prominent positivity, leading to a smaller FRN, than positive feedback with a smaller magnitude. This concept aligns entirely with RL theories and is attributed to the scaling of reward potential with phasic changes in dopaminergic signaling \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA thorough investigation, utilizing cutting-edge equipment like simultaneous EEG-fMRI, is essential for examining the neurobiological pathways of activities in this manner with comparable hypotheses and tasks, also assessing the brain networks and neurobiological pathways in all these four states (low-value loss, high-value loss, low-value win, and high-value win) could provide valuable insight regarding the fact that there is a great debate on FRN sources. Nevertheless, this cutting-edge equipment provides suitable temporal resolution for analyzing ERP characteristics simultaneously with an excellent spatial resolution for assessing neurobiological sources and networks. It is worth mentioning that however, several studies \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e employing this equipment, solely focused on investigating the reward probability aspect of RL theories, with differing objectives.\u003c/p\u003e \u003cp\u003ePresently, the precise determinants influencing the delay of FRN and its associated properties remain incompletely understood. It is imperative for future research to delve into the underlying causes of this delay and explore its relationship with various other factors impacting FRN properties. Expanding our understanding in this area will not only enhance our comprehension of FRN dynamics but also contribute to broader insights into cognition and decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors report no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo specific grant was given for this research by public, private, or nonprofit funding organizations.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eThe work was conceptualized and designed with AMM, EE, and NA. The manuscript was prepared by AMM, EE and NA. Important intellectual content was revised extensively by EE and HSZ. HSZ gave permission for the content to be published, and all authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eWe extend our sincere appreciation to the National Brain Mapping Laboratory for their invaluable assistance in data acquisition through simultaneous EEG-fMRI, which significantly contributed to the completion of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFriston K (2010) The free-energy principle: A unified brain theory? 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Psychiatry Research: Neuroimaging 337:111764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pscychresns.2023.111764\u003c/span\u003e\u003cspan address=\"10.1016/j.pscychresns.2023.111764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institute for Research in Fundamental Sciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Event-Related Potential (ERP), EEG, Feedback-Related Negativity (FRN), Gambling, Reinforcement Learning","lastPublishedDoi":"10.21203/rs.3.rs-5754204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5754204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the learning stage of reward processing, the presence of an event-related potential (ERP) denoted as the Feedback-Related Negativity (FRN) is vastly mentioned which is elicited 200\u0026ndash;350 milliseconds after feedback onset. Previous studies have confirmed Reinforcement Learning theory's prediction that a significant correlation exists between dopamine neuron responses (which generate the FRN) and the disparity between actual and expected outcomes where the expected outcome is determined by the probability and magnitude of rewards. Although previous studies have extensively illustrated the impact of reward probability on the FRN, the demonstration of the impact of reward magnitude on the FRN has not been established conclusively and still remains a matter of debate. Here in this study, we wanted to assess the effects that reward magnitude has on the FRN and its generator(s) as well in an isolated context. We recruited 24 participants and recorded 65-channel High-Density EEG signals with simultaneous fMRI, while they engaged in a modified task designed to control reward probability and evaluate the effects of reward magnitude. In our findings, firstly, a substantial positive correlation is observed between the ERP amplitude within the temporal window of FRN and the magnitude of outcomes, and through dipole fitting and distributed source localization, the source of FRN, regardless of magnitude, was located in the Medial Frontal Cortex. Our findings reveal strong connections among brain regions involved in error monitoring, memory, attention, and visual processing, with the dorsal Anterior Cingulate Cortex serving as a central hub. No significant differences were found between connectivity of win- and loss-related FRN\u0026rsquo;s brain sources. Additionally, participants demonstrated varying risk-taking behaviors across trials, favoring higher-risk options and transitioning towards more cautious decisions over consecutive trials during the experiment. The analysis also revealed increased risk-taking following losses compared to gains, highlighting contextual influences on decision-making.\u003c/p\u003e","manuscriptTitle":"Identifying Cognitive Resources in a Decision-Making during Gambling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-07 16:04:44","doi":"10.21203/rs.3.rs-5754204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d45f9ce-fb8c-4a89-8fb8-a9b01a8e799d","owner":[],"postedDate":"January 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42296399,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2025-01-07T16:04:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-07 16:04:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5754204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5754204","identity":"rs-5754204","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}