Reciprocal Competition Between Cognitive Tasks and Emotional Processing Revealed by EEG and Eye Tracking

Reciprocal Competition Between Cognitive Tasks and Emotional Processing Revealed by EEG and Eye Tracking | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 November 2024 V1 Latest version Share on Reciprocal Competition Between Cognitive Tasks and Emotional Processing Revealed by EEG and Eye Tracking Authors : Jose Fernando Mora-Quiroga , Juan Pablo Abril-Ronderos , and Marisol R. Lamprea 0000-0001-6348-3730 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173254169.97879902/v1 Published International Journal of Psychophysiology Version of record Peer review timeline 233 views 133 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Processing of sensory stimuli triggers changes in brain electrical activity characterized by increases in the centroparietal Late Positive Potential (LPP) and larger posterior alpha frequency desynchronization. Additionally, it has been showed that visual inspection parameters change during the presentation of emotional high arousing stimuli, suggesting orienting and attention allocation, in accordance with recent approaches suggesting that the processing of emotional stimuli requires attentional resources. Interestingly, indicators of visual scanning have been few used to determine engagement in tasks used as distractors during the processing of emotional images. This work aimed to analyze the effects of the attentional competence between the performance of a cognitive task and the processing of an emotionally arousing picture using EEG and Eye Tracking. Results showed that the superimposition of a cognitive task in a very reduced space of the visual field (1,2%) had an early attractor effect causing a reduction in the LPP amplitude, suggesting that the allocation of attentional resources is necessary for the processing of arousing images. Unexpectedly, the background image exploration was reassumed after participants responded to the task, particularly the unpleasant pictures. This effect can be attributed to the intrinsic significance of the emotional images. Reciprocal Competition Between Cognitive Tasks and Emotional Processing Revealed by EEG and Eye Tracking Jose Fernando Mora-Quiroga a , Juan Pablo Abril-Ronderos a , Marisol R. Lamprea a a Laboratorio de Neurociencias, Departamento de Psicología, Facultad de Ciencias Humanas, Universidad Nacional de Colombia, Bogotá, Colombia Correspondence Author: Marisol R. Lamprea, Laboratorio de Neurociencias, Departamento de Psicología, Facultad de Ciencias Humanas, Universidad Nacional de Colombia. Carrera 30 # 45-03, Bogotá, Colombia. ZIP code 111321. E-mail address: [email protected] Co-authors email: [email protected] ; [email protected] Conflict of Interest: None Abstract Processing of sensory stimuli triggers changes in brain electrical activity characterized by increases in the centroparietal Late Positive Potential (LPP) and larger posterior alpha frequency desynchronization. Additionally, it has been showed that visual inspection parameters change during the presentation of emotional high arousing stimuli, suggesting orienting and attention allocation, in accordance with recent approaches suggesting that the processing of emotional stimuli requires attentional resources. Interestingly, indicators of visual scanning have been few used to determine engagement in tasks used as distractors during the processing of emotional images. This work aimed to analyze the effects of the attentional competence between the performance of a cognitive task and the processing of an emotionally arousing picture using EEG and Eye Tracking. Results showed that the superimposition of a cognitive task in a very reduced space of the visual field (1,2%) had an early attractor effect causing a reduction in the LPP amplitude, suggesting that the allocation of attentional resources is necessary for the processing of arousing images. Unexpectedly, the background image exploration was reassumed after participants responded to the task, particularly the unpleasant pictures. This effect can be attributed to the intrinsic significance of the emotional images. Keyowords: Emotion, Attention, perceptual competition, EEG, Eye tracker. Introduction Processing of sensory stimuli triggers changes in brain electrical activity. The Late Positive Potential (LPP) is an ERP component usually associated with centroparietal electrodes peaking at approximately 300 ms following stimulus onset. This LPP has shown to be modulated by arousal in emotional image processing (Dunning & Hajcak, 2009; Farkas et al., 2021; Pastor et al., 2008); suggesting activation of motivational systems related to appetitive and defensive behavioral patterns (Schupp et al., 2000). Research that has attempted to find the neural generators of increases in LPP has suggested that the main contributing regions are the occipital cortex and the posterior region of the parietal lobe (Keil et al., 2002; Liu et al., 2012). In addition to the modulation of the LPP, several investigations have shown that processing of arousing stimuli elicits a larger desynchronization of power in the alpha band (a-ERD) on the posterior sensors (Ferrari et al., 2015). It has been proposed that the enhanced a-ERD might reflect a basic mechanism of engagement of motivational systems, which is mandatory in the processing of stimulus significance (Codispoti et al., 2023). There is debate about the role of attention in the processing of emotional stimuli. The automatic view proposes that the processing of emotional information occurs is not limited by the availability of attentional resources. The competition model of selective attention proposes that the processing of emotional stimuli requires attentional resources (Shafer et al., 2012). In this regard, it is interesting to establish how using a cognitive task as a distractor can alter the processing of emotional stimuli. For example, Thiruchselvam et al. (2011) showed that when subjects were asked to feel neutral in response to the unpleasant image (by generating thoughts unrelated to the image presented on screen), LPP reductions were observed. Similar results were obtained by Li et al. (2017), showing that a cognitive task (mathematical equation) superimposed on unpleasant images resulted in a reduction of the early window of the LPP. Even though EEG activity is a good index for central processing of emotional activity, peripheral measures can be equally informative. It has been showed that visual inspection parameters change during the presentation of emotional high arousing stimuli. For example, Calvo & Lang (2004) showed that, when emotional and non-emotional stimuli are presented simultaneously, the gaze was directed to the emotional ones suggesting orienting and attention allocation. In the same vein, Simola et al. (2013) described a faster first saccade and larger LPP amplitude to the emotional images when compared to the neutral ones. Interestingly these authors also showed that it is possible to time-lock the ERPs to the first eye entry on an unpleasant image, suggesting that both measures can be informative of emotional processing. When distraction is introduced, this pattern of visual inspection changes. Strauss et al. (2016) showed that when subjects were asked to generate thoughts unrelated to the unpleasant images presented, reductions in total dwell time to arousing interest areas were observed during the entire stimulus presentation. To our knowledge, these indicators of visual scanning have not been used to determine engagement in a task superimposed on a high-arousal image. This work aimed to analyze the effects of the attentional competence between the simultaneous processing of both a cognitive task and an emotionally arousing image using EEG and Eye Tracking. Methodology 2.1 Participants Twenty-nine volunteer university students participated in the investigation. Due to the high noise level and artifact recording, data from four participants were excluded from the analysis. The final analyzed sample included 25 participants (21.8 ± 2.3), 16 females. All participants reported no psychiatric or neurological disorders and no alcohol or psychoactive drug use. All participants had normal or corrected-to-normal visual acuity. The experiment was approved by the Ethics Committee of the Faculty of Human Sciences of the National University of Colombia. The protocols used followed the Helsinki Agreements and the Manual of the American Psychological Association. All participants signed an informed consent before the beginning of the experiments. 2.2 Stimuli A total of 360 color images were selected from the International Affective Picture System-IAPS (Lang, P et al., 2005) stimulus battery (180 unpleasant, 180 neutral) based on valence and arousal ratings provided in the database. The same 360 images were scrambled in Photoshop to use them as a baseline. Average luminance values between the scrambled and experimental images did not show statistical differences (p>0.05). 2.3 Experimental task The experimental task used is an adapted version of Kanske et al. (2011) and Schönfelder et al. (2014). After instruction about the task, participants were seated in an adjustable chair in front of the computer screen at an approximate distance of 60 cm at eye level. Nine trials with feedback served as training to confirm that participants correctly understood the task instructions. The experiment was presented in nine blocks of 40 trials. Every trial began with a scrambled image (1000 ms) intended to orient the gaze to the center of the screen. Then, the experimental image was presented (2000 ms). In Nt (neutral image) and Un (unpleasant image) conditions, participants were asked to observe the image attentively and to respond naturally to the content presented without changing the emotion that might be elicited. On the other hand, in the Nt+D (neutral image with distractor) and Un+D (unpleasant image with distractor), the distractor consisted of a two-digit sum (e.g., 12+ 5) centered and superimposed on a translucent gray box occupying 1.2% of the image. Participants were asked to solve the operation and indicate whether the result was odd or even using a keyboard. Reaction time (RT) and number of correct responses were used as measures of behavioral performance. The complete experiment lasted about 60 minutes, Figure 1 illustrates the course of the experiment and the time course of two trials (one with the task). (Figure 1) 2.4 Electroencephalogram EEG recording was performed from 32 active Ag/AgCl electrodes using the Brain Vision Actichamp system and Lab Streaming Layer software. The signal was digitized at a sampling rate of 500 Hz, the recording was reference-free, and the signal from each electrode was measured against the signal from the ground electrode located in this study at FCz. Electrode impedances were set below 5KΩ and positions were based on the International System 10-20 (Fp1/2, F3/4, F7/8, FT9/10, FC1/2, FC5/6, C3/4, CP1/2, CP5/6, T7/8, TP9/10, P3/4, P7/8, O1/2, Oz, Pz, Cz, Fz). 2.4.1 Decoding analysis In addition to the time-frequency and ERP analyses, a decoding analysis was performed using the function pop_decoding in EEGLAB. For this, all data were converted to BEST format after the preprocessing explained above. Once with these files, the function was used to make two decodings, one to decode between Un stimuli against Un+D and another to decode Nt stimuli against Nt+D. At the time of running the analysis, decoding was done with all 32 channels, trials were matched for the conditions, the decoding algorithm was the support vector machine (SVM) and a one-versus-all method was done with a three-fold cross-validation and the hall process was iterated for 100 times. This analysis was applied for all subjects and the statistical analysis was done on the average performance of all iterations. To test for statistical significance, cluster-based permutation testing was used in the same way as described in Bae (2021). First, a surrogate distribution was simulated with a Monte Carlo-based approach and tested against chance level (0.5 % accuracy) 10.000 times. For each iteration, the highest t-value was calculated and stored to generate the null distribution. After that, we used the t-value of the 95% of the null distribution as our significance threshold. Lastly, the original data was compared against the chance level and all t-values above the threshold were marked as statistically significant. To compare both decodings, the labels were permuted between participants, and both conditions were tested for 10.000 times. For each iteration, the highest t-value was stored and with those t-values , we generated the null distribution. After that, we set the threshold at 95% of the null distribution, then the original data was compared and t-values were marked as significant if the value was above the threshold 2.4.2 ERPs analysis The files were exported to MATLAB and analyzed using the EEGLAB (Delorme & Makeig, 2004) and ERPLAB (Lopez-Calderon & Luck, 2014) toolboxes. EEG data were downsampled to 256 Hz, and a bandpass filter was applied from 0.1 Hz to 31 Hz (IIR Butterworth, order 2, half-amplitude cutoff of 80 dB/oct). Artifacts were identified and removed if the voltage signal had an amplitude> 100 μV. In addition, Independent Component Analysis (ICA) was applied to find and remove components with ocular artifacts. To preserve the number of channels, defective channels were interpolated with the EEGLAB spherical algorithm, except those that would be used in subsequent statistical analyses. Data from ERP trials were extracted using a 200 ms window before unscrambled image onset to the end of the 2000 ms stimulus presentation. We analyzed two components, the N1, recorded in an occipital cluster (O1/2, Oz) in the averaged time window of 150 ms and 250 ms. The LPP was recorded in a centroparietal cluster (CP1/2, CP5/6, and Pz) averaging the time window between 400 ms and 1200 ms. 2.4.3 Time-frequency analysis The preprocessing steps for the time-frequency decomposition were the same as those used for the ERP analysis. For these analyses, a baseline correction of -500 ms to -200 ms before image appearance was used. Then a time-frequency decomposition was implemented using the Morlet Wavelet approximation. Wave frequencies ranged from 1 to 31 Hz in 40 linearly spaced steps. The full width at half maximum (FWHM) ranged from 300 ms to 600 ms and increased linearly with the maximum wavelet frequency. Finally, we analyzed the theta power averaging the Fz electrode data between 4 and 8 Hz in a time window from 0 to 2000 ms and the alpha desynchronization in the parieto-occipital cluster (P3/4, P7/8, Pz, O1/2, and Oz) in the LPP time window. 2.5 Eye tracker Visual tracking data were acquired from the left eye with the SR Research Eyelink 1000 plus system (sampling rate 500 Hz, resolution 0.01°, mean accuracy 0.25). Images were presented using Experiment Builder software at a viewing distance of approximately 60 cm, on a 24-inch TN computer screen with a resolution of 1920x1080 pixels and a refresh rate of 144 Hz. A nine-point calibration was performed to ensure that the device performed correct eye tracking. All participants used head support to maximize the accuracy of the recording throughout the experiment. 2.5.1 Eye Movement Analysis For each trial, the number of visual fixations during image viewing was determined. A fixation was defined as the amount of time the eye remained within 1 degree of visual angle for at least 100 ms. Dwell time was calculated as time spent in the distractor (region of interest - ROI) when superimposed on neutral and unpleasant images. 2.6 Statistical analysis Reaction times (RT), number of correct responses, and dwell time in both Nt+D and Un+D conditions were compared with a two-tailed t-test. N1 and LPP amplitudes, alpha desynchronization, theta power, and fixations analysis were performed using two-way repeated measures ANOVA with the factors: image type (neutral, unpleasant) and distractor (no distractor, distractor). All Statistical analyses were performed in JASP (JASP Team, 2024); in cases of violation of sphericity, the Greenhouse-Geisser correction was applied. Post-hoc comparisons were performed using Holm’s adjustments for p-values. Results 3.1 Task performance Participants obtained a higher percentage of correct responses in the Nt+D condition ( M =76.76 ± 13.04) compared to Un+D condition ( M =73.91 ± 13.53) t (24) = 2.73, p = 0.01, d = 0.22. Comparison between reaction times for the stimuli showed no significant difference between Nt+D ( M =1398.96 ± 296.17) and Un+D ( M =1319.28 ± 351.21) t (24) = 0.85, p = 0.40, d = 0.25. The results are summarized in Figure 5 A 3.2 EEG 3.2.1 Decoding As shown in Figure 2 decoding accuracy for both neutral and unpleasant pictures (as compared against the images with the distractor superimposed) was significantly above chance between the 200 and 1800 milliseconds after the images onset. Additionally, only for the 1200 - 1300 ms time window a significant difference was observed in the comparison of both decodings. (Figure 2) 3.2.2 ERPs Repeated measures ANOVA of N1 amplitudes showed a significant effect for distractor use ( F [1,24] = 285.17, p < 0.001, ηp 2 = 0.92) and for the interaction between stimulus type and distractor use ( F [1,24] = 8.16, p = 0.009, ηp 2 = 0.25). The stimulus type factor showed no significant effect ( F [1,24] = 1.01, p = 0.32, ηp 2 = 0.04). Post-hoc analysis by distractor use indicated greater amplitudes when images were projected with distractor t = 16.89, p < 0.001. Analysis of the interaction between factors showed that the amplitudes of Nt stimuli presented smaller amplitudes when compared with Nt+D t = 14.04, p < 0.001, and with Un+D t = 12.68, p < 0.001. Similarly, it was found that the Un condition showed lower amplitudes when compared with the Nt condition t = 13.95, p < 0.001, and the aNt+D condition t = 16.49, p 0.05). The waveform is presented in Figure 3 A. Repeated measures ANOVA of LPP amplitudes showed a significant effect by stimulus type ( F [1,24] = 26.99, p < 0.001, ηp 2 = 0.53) and for the interaction between stimulus type and distractor use ( F [1,24] = 46.32, p < 0.001, ηp 2 = 0.66), no effect was found for the distractor factor ( F [1,24] = 1.00, p = 0.33, ηp 2 = 0.04). Post-hoc analysis by stimulus type indicated greater amplitudes when Un images were projected t = -5.20, p < 0.001. Finally, post-hoc analysis of the interaction indicated that Un stimuli produced greater amplitudes when compared to those produced by Nt t = -8.19, p = condition t = 3.23, p = 0.01. The rest of the comparisons did not reach the significance level (all p > 0.05). The waveform is presented in Figure 3 B. (Figure 3) 3.2.3 Time-Frequency analysis The repeated measures ANOVA of the alpha band desynchronization amplitudes showed a significant effect on the distractor factor ( F [1,24] = 189.37, p < 0.001, ηp 2 = 0. 43). In the interaction between stimulus type and distractor ( F [1,24] = 12.48, p = 0.002, ηp 2 = 0.34), no effect was found for stimulus type ( F [1,24] = 0.15, p = 0.70, ηp 2 = 0.006). Post-hoc analysis of distractor use indicated greater alpha desynchronization when distractors were superimposed on the images t = -4.29, p < 0.001. Finally, post-hoc analysis of the interaction indicated that Nt+D images presented greater alpha desynchronization when compared to Nt t = -5.35, p = -2.58, p = 0. 04; images in Un+D condition showed larger alpha desynchronization when compared to Nt t = -3.62, p = 0.004 and Un t = -2.50, p = 0.04. A trend was found when comparing Nt and Un stimuli t = 1.98 p = 0.053. The results are summarized in Figure 4 A. The repeated measures ANOVA for theta power showed a significant effect on the distractor factor ( F [1,24] = 15.01, p < 0.001, ηp 2 = 0. 39), no effect was found for stimulus type ( F [1,24] = 0.99, p = 0.33, ηp 2 = 0.04) and the interaction between factors ( F [1,24] = 0.11, p = 0.36, ηp 2 = 0.04). Post-hoc analysis of distractor use indicated greater theta power when the stimuli had the operation superimposed t = -3.88, p < 0.001. The results are summarized in Figure 4 B. (Figure 4) 3.3 Visual response The repeated measures ANOVA for the number of fixations showed a significant effect for stimulus type ( F [1,24] = 30.29, p <0.001, ηp 2 = 0.56), no effect was found for the distractor factor ( F [1,24] = 0.07, p = 0.79, ηp 2 = 0.003), and for the interaction between stimulus type and strategy ( F [1,24] = 3.09, p = 0.09, ηp 2 = 0.114). Post-hoc analysis indicated that neutral stimuli presented fewer fixations compared to unpleasant stimuli t = -5.50, p < 0.001. Although the interaction yielded no significant effect we ran planned t-tests to identify whether the distractor influenced the number of fixations in the two types of stimuli, we found that there were fewer fixations in the Nt stimuli when compared to the Un t = -3.69 p = 0.003 and when compared to the Un+D t = -2.69 p = 0.04, furthermore, the Nt+D stimuli had fewer fixations when compared to the Un+D t = -5.59 p < 0.001. The comparison for dwell time indicated that participants kept their gaze more in the region of interest for the Nt+D condition compared to the Un+D condition t (24) = 3.02, p = 0.006, d = 0.61. The results are summarized in Figure 5 B. Finally, we performed some additional analyses. We ran a repeated measures ANOVA every 200 milliseconds for the percentage of time spent in the ROI in the two conditions with the distractor superimposed. A significant effect in the interaction between window and type of stimuli was found ( F [1,24] = 8.76, p < 0.001, ηp 2 = 0.26). The post-hoc analysis showed that from 1200 ms to the end of the window, participants spent more time in the ROI when the background stimulus was neutral (all p values <0.05). The results are summarized in Figure 5 C. (Figure 5) 4 Discussion This work aimed to analyze the effects of the attentional competence between the simultaneous processing of both a cognitive task and an emotional arousing image through EEG and Eye Tracking data. Consistent with the literature, we found an emotional effect indexed by larger LPP amplitudes and a greater number of fixations when unpleasant stimuli were presented. In the same vein, we also found an independent processing of the distraction regardless of image arousal, our results show a consistent difference in early processing as indicated by multivariate, univariate, and time-frequency methods. Finally, our results suggest a reciprocal competition between emotional image processing and cognitive task processing. On the emotion processing side, there was a LPP reduction (lower emotional response) when the task was superimposed in the Un+D. On the cognitive task processing side, the dwell time in the ROI was also reduced in the Un+D condition, possibly resulting in a weaker aERD and a deteriorated performance in the task. 4.1 Emotional effect As we expected, unpleasant stimuli generated significant increases in LPP amplitude compared to the response generated by neutral images. These results align with research in which participants passively observe unpleasant stimuli (De Cesarei & Codispoti, 2011; Farkas et al., 2021; Liu et al., 2012). It has been described that the increase in the amplitude of this waveform generated by both pleasant and unpleasant stimuli can be attributed to the activation of motivational systems, as well as to the allocation of visuospatial resources to emotional relevant signals. (Hajcak & Foti, 2020; Schupp et al., 2000). Furthermore, our findings indicate that the observation of unpleasant stimuli, which elicit a higher level of arousal, leads to a greater number of fixations compared to neutral images, a result that replicates previous findings by Bradley et al. (2011). These authors argue that fixations represent a sensitive measure of information capture, suggesting that unpleasant stimuli trigger a greater search for information related to cues considered emotionally relevant. On the other hand, we find a larger aERD produced by the unpleasant images as compared to the neutral ones. Our result is in line with research that has described a larger aERD during the presentation of arousing stimuli compared to neutral ones (Codispoti et al., 2023; Cui et al., 2013; Schubring & Schupp, 2021). These studies suggest that larger values of aERD are indicators of motivational engagement when salient stimuli are detected. It is important to note that although in our study we observed a timing overlapping of aERD and LPP (a result previously observed by De Cesarei & Codispoti, 2011) these indicators seem to reflect different processes. Increases in LPP amplitude seem to reflect motivational significance (Hajcak & Foti, 2020) whereas aERD could be considered an indicator of the engagement of the motivational systems required for the processing of stimulus significance (Codispoti et al., 2023). 4.2 Distractor processing Global brain activity analyzed through multivariate EEG decoding showed above-chance decoding between the Un and Un+D conditions, as well as between Nt and Nt+D, around 180 ms after the image onset. In line with these multivariate results, N1 in the occipital region generated larger amplitudes when both types of images were presented with the distractor superimposed. This component has been linked to a voluntary discriminative process that occurs following perceptual focus on a specific target (Hopf et al., 2002; Vogel & Luck, 2000). It should be noted that the decoding reaches an accuracy peak in the same time window where differences were found in the N1 component, suggesting a relationship between these indicators. This has been previously proposed by Carrasco et al. (2024) who performed both types of analysis and showed that the two indicators were closely related. However, they concluded that the multivariate analysis was more favorable due to the effect sizes were larger. In addition to the differences found in the time domain, we also found effects in the frequency domain. Neutral images superimposed with the distractor produced larger aERD as compared to neutral without the distractor. These results support what was proposed in the review of Jensen (2024) who proposes that the perceptual load of a task modulates the response of this frequency band. Other authors have also proposed that the a-ERD acts as a facilitating mechanism for the selection of relevant elements in the visual field (Bacigalupo & Luck, 2022; Codispoti et al., 2023). In this way, our results suggest that operation processing is predominant against image processing. In the same vein, the observed increase in theta synchronization in the frontal region in both neutral and unpleasant images superimposed with the distractor may be associated with that described in working memory tasks, computation, and even in the effort to regulate emotion (Ertl et al., 2013; Itthipuripat et al., 2013; Sammer et al., 2007). For example, Sammer et al. (2007) reported an increase in power in the range of 3.5 Hz to 7.5 Hz at frontal electrodes when participants were engaged in the mental performance of an arithmetic task. On the other hand, increases have been described in theta power over the frontal cortex during WM maintenance and manipulation (Itthipuripat et al., 2013). Finally, Ertl et al. (2013) found an increase in theta oscillations at electrode Fz during the use of reappraisal to decrease or increase emotions induced by negative pictures. Thus, it has been proposed that the increase in the power of theta does not seem to be related only to arithmetic processes, but to the coordination of relevant information that involves the participation of the prefrontal cortex (Cavanagh et al., 2012). 4.3 Competition between emotional processing and distractor processing In our task, a decrease in LPP amplitude was observed during the presentation of unpleasant stimuli with the superimposed operation compared to those presented without the operation. These results are similar to those reported in research that implements regulation strategies during the early stages of emotional processing. For example, Schönfelder et al. (2014) observed a strong and early attenuation of the LPP magnitude for negative pictures using an arithmetic distractor. In the same vein, Thiruchselvam et al. (2011) described a LPP amplitude reduction during distraction, as well as an increase in this indicator during the exposure to negative images with a distraction history. These authors propose that distraction is acting prior to the evaluative processing of the emotional stimulus, in the stage of attentional deployment proposed in the modal model of Gross (2015). As mentioned above, there are two approaches to the role of attention in the processing of emotional information (Shafer et al., 2012): The first one emphasizes rapid and automatic processing, not limited by the availability of attentional resources(Anderson et al., 2003) and another that proposes that the processing of emotional stimuli requires attentional resources (Pessoa et al., 2002). The reduction in LPP amplitude observed in our study suggests that the solving of the arithmetic task competed with the attentional resource required for image processing, in agreement with that proposed by the top-down control hypothesis. Our results showed a smaller aERD for the Un+D condition as compared to the Nt+D one. This result was unexpected because the same distractor was superposed over both neutral and unpleasant images. As previously mentioned, alpha desynchronization enhances the selection of relevant elements in the visual field (Bacigalupo & Luck, 2022). It appears that the prioritization of the arithmetic task was lower in the Un+D condition, probably due to the high salience of unpleasant images. his result is consistent with the improved performance observed in the Nt+D condition compared to the Un+D condition, a result similar to that described by Zhozhikashvili et al. (2022) who found that a weak aERD increased the likelihood of error during the execution of a working memory task. To our knowledge, no previous studies have shown the effect of an arousing image on performance on a task used as a distractor and aERD. In accordance with the aERD results, we observed that dwell time in the ROI was shorter in the Un+D in contrast to the Nt+D condition, mainly during the last second of image presentation, suggesting that emotional stimuli acted as competitors, probably after participants solved the task. This effect has been related to the named negativity bias, the tendency for negativity to have strong effects on attention, perception, memory, physiology, affect, behavior, motivation, and decision-making (Norris, 2021). The shorter time in ROI observed in the Un+D condition can also be related to the arousal produced by the unpleasant background images. In this sense, Müller et al. (2008) showed neutral and arousing images (pleasant and unpleasant) in the background of a target detection task. These authors found that the emotionally arousing background pictures consumed processing resources from the superimposed task producing reductions in target detection rates. 5 Conclusion Our study showed that it is necessary the allocation of attentional resources to the processing of arousing images and that the superimposition of a cognitive task in a very reduced space of the visual field (1,2%) had an early attractor effect causing a reduction in the LPP amplitude. Nonetheless, when participants responded to the task, they tended to increase background image exploration, particularly in the Un+D condition. This effect can be attributed to the intrinsic significance of the emotional images. Author contributions Jose Fernando Mora Quiroga Conceptualization; data curation; formal analysis;investigation; methodology; project administration; writing – original draft; writing – review and editing. Juan Pablo Abril-Ronderos Conceptualization; data curation; formal analysis; investigation; methodology; project administration; writing – original draft; writing – review and editing. Marisol R. Lamprea Funding acquisition; conceptualization; methodology; project administration; writing – original draft; writing – review and editing. Conflict of interest The authors do not have any conflicts of interest to report in connection with this manuscript. Funding This research was financed by the Universidad Nacional de Colombia. Grant No. 57594. Acknowledgments We would like to thank Milena Lamprea, Ferney Galindo, and Carolina Suarez for their help with data collection. Data availability Data will be made available on request. Orcid Jose Fernando Mora Quiroga https://orcid.org/0000-0002-5757-1213 Juan Pablo Abril-Ronderos https://orcid.org/0000-0002-3759-5998 Marisol R. Lamprea https://orcid.org/0000-0001-6348-3730 Bibliography Anderson, A. K., Christoff, K., Panitz, D., De Rosa, E., & Gabrieli, J. D. E. (2003). Neural Correlates of the Automatic Processing of Threat Facial Signals. The Journal of Neuroscience , 23 (13), 5627-5633. https://doi.org/10.1523/JNEUROSCI.23-13-05627.2003 Bacigalupo, F., & Luck, S. J. (2022). Alpha-band EEG suppression as a neural marker of sustained attentional engagement to conditioned threat stimuli. Social Cognitive and Affective Neuroscience , 17 (12), 1101-1117. https://doi.org/10.1093/scan/nsac029 Bae, G.-Y. (2021). The Time Course of Face Representations during Perception and Working Memory Maintenance. Cerebral Cortex Communications , 2 (1), tgaa093. https://doi.org/10.1093/texcom/tgaa093 Bradley, M. M., Houbova, P., Miccoli, L., Costa, V. D., & Lang, P. J. (2011). Scan patterns when viewing natural scenes: Emotion, complexity, and repetition. Psychophysiology , 48 (11), 1544-1553. https://doi.org/10.1111/j.1469-8986.2011.01223.x Calvo, M. G., & Lang, P. J. (2004). Gaze Patterns When Looking at Emotional Pictures: Motivationally Biased Attention. Motivation and Emotion , 28 (3), 221-243. https://doi.org/10.1023/B:MOEM.0000040153.26156.ed Carrasco, C. D., Bahle, B., Simmons, A. M., & Luck, S. J. (2024). Using multivariate pattern analysis to increase effect sizes for event‐related potential analyses. Psychophysiology , 61 (7), e14570. https://doi.org/10.1111/psyp.14570 Cavanagh, J. F., Zambrano‐Vazquez, L., & Allen, J. J. B. (2012). Theta lingua franca: A common mid‐frontal substrate for action monitoring processes. Psychophysiology , 49 (2), 220-238. https://doi.org/10.1111/j.1469-8986.2011.01293.x Codispoti, M., De Cesarei, A., & Ferrari, V. (2023). Alpha‐band oscillations and emotion: A review of studies on picture perception. Psychophysiology , 60 (12), e14438. https://doi.org/10.1111/psyp.14438 Cui, Y., Versace, F., Engelmann, J. M., Minnix, J. A., Robinson, J. D., Lam, C. Y., Karam-Hage, M., Brown, V. L., Wetter, D. W., Dani, J. A., Kosten, T. R., & Cinciripini, P. M. (2013). Alpha Oscillations in Response to Affective and Cigarette-Related Stimuli in Smokers. Nicotine & Tobacco Research , 15 (5), 917-924. https://doi.org/10.1093/ntr/nts209 De Cesarei, A., & Codispoti, M. (2011). Affective modulation of the LPP and α‐ERD during picture viewing. Psychophysiology , 48 (10), 1397-1404. https://doi.org/10.1111/j.1469-8986.2011.01204.x Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods , 134 (1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Dunning, J. P., & Hajcak, G. (2009). See no evil: Directing visual attention within unpleasant images modulates the electrocortical response. Psychophysiology , 46 (1), 28-33. https://doi.org/10.1111/j.1469-8986.2008.00723.x Ertl, M., Hildebrandt, M., Ourina, K., Leicht, G., & Mulert, C. (2013). Emotion regulation by cognitive reappraisal—The role of frontal theta oscillations. NeuroImage , 81 , 412-421. https://doi.org/10.1016/j.neuroimage.2013.05.044 Farkas, A. H., Wanger, T. J., & Sabatinelli, D. (2021). Do rare emotional scenes enhance LPP modulation? Biological Psychology , 166 , 108204. https://doi.org/10.1016/j.biopsycho.2021.108204 Ferrari, V., Bradley, M. M., Codispoti, M., & Lang, P. J. (2015). Massed and distributed repetition of natural scenes: Brain potentials and oscillatory activity. Psychophysiology , 52 (7), 865-872. https://doi.org/10.1111/psyp.12424 Gross, J. J. (2015). Emotion Regulation: Current Status and Future Prospects. Psychological Inquiry , 26 (1), 1-26. https://doi.org/10.1080/1047840X.2014.940781 Hajcak, G., & Foti, D. (2020). Significance?… Significance! Empirical, methodological, and theoretical connections between the late positive potential and P300 as neural responses to stimulus significance: An integrative review. Psychophysiology , 57 (7), e13570. https://doi.org/10.1111/psyp.13570 Hopf, J.-M., Vogel, E., Woodman, G., Heinze, H.-J., & Luck, S. J. (2002). Localizing Visual Discrimination Processes in Time and Space. Journal of Neurophysiology , 88 (4), 2088-2095. https://doi.org/10.1152/jn.2002.88.4.2088 Itthipuripat, S., Wessel, J. R., & Aron, A. R. (2013). Frontal theta is a signature of successful working memory manipulation. Experimental Brain Research , 224 (2), 255-262. https://doi.org/10.1007/s00221-012-3305-3 JASP Team. (2024). JASP (Versión 0.18.3) [Software]. Jensen, O. (2024). Distractor inhibition by alpha oscillations is controlled by an indirect mechanism governed by goal-relevant information. Communications Psychology , 2 (1), 36. https://doi.org/10.1038/s44271-024-00081-w Kanske, P., Heissler, J., Schönfelder, S., Bongers, A., & Wessa, M. (2011). How to Regulate Emotion? Neural Networks for Reappraisal and Distraction. Cerebral Cortex , 21 (6), 1379-1388. https://doi.org/10.1093/cercor/bhq216 Keil, A., Bradley, M. M., Hauk, O., Rockstroh, B., Elbert, T., & Lang, P. J. (2002). Large‐scale neural correlates of affective picture processing. Psychophysiology , 39 (5), 641-649. https://doi.org/10.1111/1469-8986.3950641 Lang, P, Bradley, M. M, & Cuthbert, B. N. (2005). International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual . Technical Report A-6, Gainesville, FL: University of Florida. Li, P., Wang, W., Fan, C., Zhu, C., Li, S., Zhang, Z., Qi, Z., & Luo, W. (2017). Distraction and Expressive Suppression Strategies in Regulation of High- and Low-Intensity Negative Emotions. Scientific Reports , 7 (1), 13062. https://doi.org/10.1038/s41598-017-12983-3 Liu, Y., Huang, H., McGinnis-Deweese, M., Keil, A., & Ding, M. (2012). Neural Substrate of the Late Positive Potential in Emotional Processing. The Journal of Neuroscience , 32 (42), 14563-14572. https://doi.org/10.1523/JNEUROSCI.3109-12.2012 Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: An open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience , 8 . https://doi.org/10.3389/fnhum.2014.00213 Muller, M. M., Andersen, S. K., & Keil, A. (2008). Time Course of Competition for Visual Processing Resources between Emotional Pictures and Foreground Task. Cerebral Cortex , 18 (8), 1892-1899. https://doi.org/10.1093/cercor/bhm215 Norris, C. J. (2021). The negativity bias, revisited: Evidence from neuroscience measures and an individual differences approach. Social Neuroscience , 16 (1), 68-82. https://doi.org/10.1080/17470919.2019.1696225 Pastor, M. C., Bradley, M. M., Löw, A., Versace, F., Moltó, J., & Lang, P. J. (2008). Affective picture perception: Emotion, context, and the late positive potential. Brain Research , 1189 , 145-151. https://doi.org/10.1016/j.brainres.2007.10.072 Pessoa, L., McKenna, M., Gutierrez, E., & Ungerleider, L. G. (2002). Neural processing of emotional faces requires attention. Proceedings of the National Academy of Sciences , 99 (17), 11458-11463. https://doi.org/10.1073/pnas.172403899 Sammer, G., Blecker, C., Gebhardt, H., Bischoff, M., Stark, R., Morgen, K., & Vaitl, D. (2007). Relationship between regional hemodynamic activity and simultaneously recorded EEG‐theta associated with mental arithmetic‐induced workload. Human Brain Mapping , 28 (8), 793-803. https://doi.org/10.1002/hbm.20309 Schönfelder, S., Kanske, P., Heissler, J., & Wessa, M. (2014). Time course of emotion-related responding during distraction and reappraisal. Social Cognitive and Affective Neuroscience , 9 (9), 1310-1319. https://doi.org/10.1093/scan/nst116 Schubring, D., & Schupp, H. T. (2021). Emotion and Brain Oscillations: High Arousal is Associated with Decreases in Alpha- and Lower Beta-Band Power. Cerebral Cortex , 31 (3), 1597-1608. https://doi.org/10.1093/cercor/bhaa312 Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by motivational relevance. Psychophysiology , 37 (2), 257-261. https://doi.org/10.1111/1469-8986.3720257 Shafer, A. T., Matveychuk, D., Penney, T., O’Hare, A. J., Stokes, J., & Dolcos, F. (2012). Processing of Emotional Distraction Is Both Automatic and Modulated by Attention: Evidence from an Event-related fMRI Investigation. Journal of Cognitive Neuroscience , 24 (5), 1233-1252. https://doi.org/10.1162/jocn_a_00206 Simola, J., Torniainen, J., Moisala, M., Kivikangas, M., & Krause, C. M. (2013). Eye movement related brain responses to emotional scenes during free viewing. Frontiers in Systems Neuroscience , 7 . https://doi.org/10.3389/fnsys.2013.00041 Strauss, G. P., Ossenfort, K. L., & Whearty, K. M. (2016). Reappraisal and Distraction Emotion Regulation Strategies Are Associated with Distinct Patterns of Visual Attention and Differing Levels of Cognitive Demand. PLOS ONE , 11 (11), e0162290. https://doi.org/10.1371/journal.pone.0162290 Thiruchselvam, R., Blechert, J., Sheppes, G., Rydstrom, A., & Gross, J. J. (2011). The temporal dynamics of emotion regulation: An EEG study of distraction and reappraisal. Biological Psychology , 87 (1), 84-92. https://doi.org/10.1016/j.biopsycho.2011.02.009 Vogel, E. K., & Luck, S. J. (2000). The visual N1 component as an index of a discrimination process. Psychophysiology , 37 (2), 190-203. https://doi.org/10.1111/1469-8986.3720190 Zhozhikashvili, N., Zakharov, I., Ismatullina, V., Feklicheva, I., Malykh, S., & Arsalidou, M. (2022). Parietal Alpha Oscillations: Cognitive Load and Mental Toughness. Brain Sciences , 12 (9), 1135. https://doi.org/10.3390/brainsci12091135 Figure 1. Experimental task. The task was divided into nine blocks, with approximately one-minute resting intervals between each block. Before starting a new block, a drift correction (DC) or calibration (CAL) was performed. Images used are representative to illustrate the task. Figure 2. Decoding analysis. Dots indicate differences against chance for each valence (top dots) and differences between neutral and unpleasant conditions (bottom dots) Figure 3. ERPs analyses. ( A ) N1 in the occipital cluster. Barplot shows the mean power by conditions. (°): Differences against conditions without distractor superimposed. ( B ) Late positive potential in the centro-parietal cluster. Barplot shows the mean power by conditions. (*): Difference against Nt; (°): Difference against Un. Figure 4. Time-frequency analyses. ( A ) Theta synchronization in the mid-frontal electrode of the contrast [Nt]–[Nt+D] and [Un-Un+D]. (*): Difference against no distractor condition. ( B ) Alpha desynchronization in the parieto-occipital cluster of the contrast [Nt]–[Nt+D] and [Un-Un+D]. (°): Difference against no distractor conditions; (&): Difference against Nt+D; (.05): Trend against Nt Figure 5. ( A )Performance in the arithmetic task. (*): Difference against Nt+D condition. ( B ) Number of fixations by condition. (*): Difference against neutral images. ( C ) Time in the ROI in windows of 200ms. (*): Difference against Nt+D condition. Information & Authors Information Version history V1 Version 1 25 November 2024 Peer review timeline Published International Journal of Psychophysiology Version of Record 1 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Jose Fernando Mora-Quiroga Universidad Nacional de Colombia Facultad de Ciencias Humanas View all articles by this author Juan Pablo Abril-Ronderos Universidad Nacional de Colombia Facultad de Ciencias Humanas View all articles by this author Marisol R. Lamprea 0000-0001-6348-3730 [email protected] Universidad Nacional de Colombia Facultad de Ciencias Humanas View all articles by this author Metrics & Citations Metrics Article Usage 233 views 133 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jose Fernando Mora-Quiroga, Juan Pablo Abril-Ronderos, Marisol R. Lamprea. Reciprocal Competition Between Cognitive Tasks and Emotional Processing Revealed by EEG and Eye Tracking. Authorea . 25 November 2024. DOI: https://doi.org/10.22541/au.173254169.97879902/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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