Likelihood Model Research Articles

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets.

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

Soibean: High-Resolution Taxonomic Identification of Ancient Environmental DNA Using Mitochondrial Pangenome Graphs.

Ancient environmental DNA (aeDNA) is becoming a powerful tool to gain insights about past ecosystems, overcoming the limitations of conventional fossil records. However, several methodological challenges remain, particularly for classifying the DNA to species level and conducting phylogenetic analysis. Current methods, primarily tailored for modern datasets, fail to capture several idiosyncrasies of aeDNA, including species mixtures from closely related species and ancestral divergence. We introduce soibean, a novel tool that utilizes mitochondrial pangenomic graphs for identifying species from aeDNA reads. It outperforms existing methods in accurately identifying species from multiple closely related sources within a sample, enhancing phylogenetic analysis for aeDNA. soibean employs a damage-aware likelihood model for precise identification at low coverage with a high damage rate. Additionally, we reconstructed ancestral sequences for soibean's database to handle aeDNA that is highly diverged from modern references. soibean demonstrates effectiveness through simulated data tests and empirical validation. Notably, our method uncovered new empirical results in published datasets, including using porpoise whales as food in a Mesolithic community in Sweden, demonstrating its potential to reveal previously unrecognized findings in aeDNA studies.

The power of electronic Word of Mouth in inducing adoption of emerging technologies

This study examines the influence of electronic Word-of-Mouth (eWOM) on consumer expectations and intentions to adopt emerging technologies, specifically focusing on cryptocurrency payment methods. Employing the Elaboration Likelihood Model (ELM), the research utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (PLS-MGA) to analyze data from a diverse sample of 505 respondents sourced from MTurk. The findings reveal that the quality, consistency, and volume of eWOM significantly shape consumer expectations. Notably, the two-sidedness of online reviews does not have any substantial impact on both expectations and adoption behaviors toward cryptocurrency payment methods. Furthermore, factors such as the time spent online, and the frequency of online shopping were found to partially moderate the effects of eWOM on adoption behavior. This research contributes pioneering insights into the role of eWOM in influencing consumer attitudes towards cutting-edge technologies, extending existing knowledge beyond traditional consumer decisions to include technological adoption, particularly in digital finance. This offers valuable implications for technology firms and digital marketers aiming to harness eWOM to promote new technological solutions.

Preoperative Radiographic Features Independently Predict High Blood Loss During Intracranial Meningioma Resection: A Case-Control Study

Surgical resection of intracranial meningioma carries the risk of several complications, including intraoperative blood loss. The objective of this study was to investigate preoperative clinical and radiographic factors predictive of intraoperative estimated blood loss (EBL). This case-control study evaluated EBL for all adults who underwent intracranial meningioma resection from January, 2010 to December, 2021 at our institution. Fifty cases of high EBL (i.e., ≥500mL) and 75 instances of low EBL (i.e., <500mL) were randomly selected. Patients were excluded if they had a recurrent meningioma, preoperative embolization, or lack of imaging data. A multivariable logistic regression model of high EBL likelihood was created. A total of 92 patients met eligibility criteria, with 48 (52%) cases of high EBL. Bivariable analyses identified maximal tumor diameter, intratumoral flow voids, skull base location, and dural venous sinus invasion as potential predictors of high EBL. Multivariable regression found intratumoral flow voids (adjusted odds ratio [aOR]= 5.68 [1.52-21.23], P= 0.009), maximal tumor diameter (aOR= 1.58 [1.11-2.25] per 1-cm increase, P= 0.01), and skull base location (aOR= 3.35 [1.19-9.41], P= 0.02) to be independent predictors of high EBL. Intratumoral flow voids, larger maximal tumor diameter, and skull base location were independently predictive of EBL ≥500mL. Intratumoral flow void presence was the strongest predictor, with 5.68 times the odds of high EBL. Each 1-cm increase in tumor diameter had 58% greater odds of high EBL. Skull base location was associated with 3.35 times the odds of high EBL. These results can inform preoperative patient counseling and blood management preparation.

What You Perceive Is What You Get: Enhancing Rumor-Combating Effectiveness on Social Media Based on Elaboration Likelihood Model

Rumors spread on social media overshadow the truth and trigger public panic. One effective countermeasure to address this issue is online rumor-combating. However, its effectiveness on social media has not been fully verified. In this study, drawing on construal level theory, we use temporal distance—the time interval between a rumor-combating post being released and receiving responses from social media users—to measure the effectiveness of rumor-combating. We also adopt elaboration likelihood model to explore the factors that could enhance this effectiveness. The empirical results show that perceptible (central route) factors, including the author’s authoritative combating methods, media richness, and positive emotions, are negatively related to temporal distance and are more effective for enhancing rumor-combating effectiveness than imperceptible (peripheral route) factors, such as the author’s influence and activeness. In addition, media richness exerts positive moderating effects on the relationship between perceptible route factors and rumor-combating effectiveness, implying that with the help of images or videos, rumor-combating effectiveness improves. This study addresses the need to enhance the effectiveness of rumor-combating and has practical implications for combating rumors in the social media.

Persuasive Effects of Crisis Communication during Public Health Emergency Outbreaks in China.

Major global public health emergencies face unprecedented challenges, such as an infodemic and scientific disputes, and governments especially need to implement fast and effective crisis communication. Firstly, this paper takes the Elaboration Likelihood Model as a framework and constructs a crisis communication persuasion effect evaluation method with emotion analysis. Secondly, this paper takes the crisis communication at the beginning of the COVID-19 outbreak in China as an example and examines the persuasive effects of the peripheral route, represented by medical experts, and the central route, represented by mainstream media. This study finds that the peripheral route of persuasive communication can quickly establish communication trust and quickly change the public's peripheral attitude, but the persuasive effect is unstable. The central route of persuasive communication demonstrates a significantly positive, stable, and anti-interference persuasive effect. Dual-subject persuasion is an important strategy for controlling an outbreak by rapidly establishing communication trust, combating an infodemic, boosting public confidence, and popularizing medical knowledge. This study evaluates the persuasive effects of crisis communication in the early stages of the COVID-19 outbreak in China, hoping to provide valuable practical references for crisis communication during the outbreak in future global public health emergencies.

Toward Better Customer Engagement: The Mediating Role of Cognitive and Behavioral Trust in Search and Social Media Platforms

ABSTRACT The increasing diversity in advertising options warrants more research on user engagement with advertisements across different digital platforms. Drawing on the elaboration likelihood model and consumer skepticism theories, we examine the central role of the three-dimensional construct of trust—cognitive, emotional, and behavioral—in overcoming advertisement skepticism (AS) and improving advertisement engagement (AE). Using survey data from a large sample in the United States (N = 841), we compare the effect of skepticism on engagement as mediated by trust, on social (Facebook) versus search (Google) platforms. In a multistage analysis, initial results indicated that trust is a significant mediator for both Facebook and Google. Subsequent testing revealed that for Facebook, the behavioral dimension of trust fully mediated the skepticism and engagement link, whereas for Google, all three dimensions of trust only partially mediated the skepticism and engagement link. This suggests that on social media platforms, behavioral trust is crucial for overcoming skepticism to achieve better engagement. Furthermore, when comparing all pathways in the model, we found that on search platforms, cognitive trust was significantly more influential in overcoming skepticism for better engagement.

When Low Social Trust Undermines Social Marketing Campaigns: The Impact of Social Trust on the Effectiveness of COVID-19 Vaccination Campaigns

Background Although COVID-19 social marketing vaccination campaigns have been developed and vigorously promoted to encourage citizens to vaccinate against COVID-19, many people have not responded to vaccination campaigns. Very little is known about the extent to which vaccination campaigns are effective at overcoming people’s resistance to getting the vaccine. Focus of the Article Integrating social trust into the elaboration likelihood model, the current study proposes a model to analyse the effectiveness of COVID-19 social marketing vaccination campaigns and to examine the extent to which social trust plays a role in influencing people’s perceptions of the effectiveness of COVID-19 social media vaccination campaigns. Research Hypotheses The study proposes hypotheses regarding the influence of argument quality and source credibility in COVID-19 vaccination campaigns on individuals’ perceptions of campaign usefulness. In addition, it suggests that source credibility and perceived usefulness of these campaigns play a role in shaping individuals’ attitudes toward COVID-19 vaccination. Furthermore, the study posits that both attitude and perceived usefulness contribute to individuals’ intentions to take the COVID-19 vaccine. Lastly, the study hypothesises that perceived social trust moderates the impact of perceived usefulness on vaccination intentions. Importance to the Social Marketing Field The study holds significant importance in the field of social marketing for several reasons. Firstly, it contributes to a deeper understanding of the dynamics influencing individuals’ perceptions, attitudes, and intentions related to COVID-19 vaccination campaigns. Secondly, by examining the impact of argument quality, source credibility, and social trust, the study provides valuable insights into the factors that can enhance or hinder the success of vaccination campaigns. The inclusion of perceived social trust as a moderating factor in the study further enhances its importance in the field of social marketing. Understanding the role of social trust in influencing the relationship between perceived usefulness and vaccination intentions is crucial for designing context-specific strategies that account for the broader societal dynamics. Methods Online survey data were obtained from 413 adult South Africans. The partial least squares structural equation modelling technique was used to examine the model and test the hypotheses. Results The study found that the quality of arguments in vaccination campaigns and the credibility of the message sources affected how people viewed the usefulness of the messages and their overall attitudes. Surprisingly, although attitudes influenced individuals’ intentions to get the COVID-19 vaccine, the perceived usefulness of the campaign had no direct impact on vaccination intentions. Notably, the results revealed that low social trust played a role in making the perceived usefulness of the COVID-19 vaccination campaign message insignificant in predicting vaccination intentions. Recommendations for Research/Practice Social marketers aiming to improve vaccination attitudes and intentions should focus on two key strategies: emphasising the usefulness of vaccination by highlighting its benefits and leveraging credible sources like healthcare professionals and community leaders to enhance message trust. In addition, this study emphasises the crucial role of social trust in shaping individual responses to vaccination campaigns. Building trust in governing institutions, healthcare systems, and science can significantly impact vaccination acceptance and promotion. Limitations The limitations include potential social desirability bias influencing responses, the need for longitudinal data to establish causality, and the possibility of other influential factors beyond the study’s scope, such as culture and health literacy.

THE INFLUENCE OF MESSAGES IN SOCIAL MEDIA ON TAXPAYER COMPLIANCE

This study integrates the Theory of Planned Behavior (TPB) and the Elaboration Likelihood Model (ELM) to assess and explain taxpayers' compliance with the Twitter account @DitjenPajakRI. This is a quantitative study with an explanatory approach. A purposive sample strategy was used with a questionnaire to collect data for this research. A total of 200 people filled out the research questionnaire. According to the study’s findings, Source Credibility influences Attitudes, and the two TPB components, Attitudes and Perceived Behavioral Control, influence Taxpayer Compliance. As for the relationship between Argument Quality and Source Credibility on Attitudes, there is no moderating effect of Involvement; instead, the relationship between Source Credibility and Taxpayer Compliance through Attitudes is a full mediation relationship. The findings of this study can assist the tax authority in developing strategies and policies for efficiently and effectively communicating tax information to taxpayers as they make decisions about how to fulfill their tax rights and obligations through messages from central route, peripheral route, normative, and non-volitional processes.

Genetic variability and diversity analysis for some agronomic traits of a sweet potato (Ipomoea batatas L.) collection: Insights for breeding superior genotypes

A study was conducted during the 2017 cropping season to assess the genetic variability of selected agronomic traits in sweet potato. A total of 355 sweet potato genotypes (351 from different countries of the world and four check varieties) were evaluated at Tuber Crops Research Sub Centre, Bangladesh Agricultural Research Institution, Bogura, Bangladesh. The experiment was conducted using an augmented randomized complete block design. Findings revealed positive correlations among five agronomic traits, namely the weight of marketable storage root per plant (MRW), weight of storage root per plant (RW), fresh weight of foliage per plant (FW), number of marketable storage root per plant (MRN) and number of storage root per plant (RN) suggesting their suitability for phenotypic selection. There was a positive skewness in most studied traits (FW, MRW, MRW and RW), indicating the presence of a few genotypes with exceptionally high values for those traits. By employing principal component analysis, the most influential traits in sweet potato genotypes were identified for first two components (Dim) showing significant contributions to Dim-1 and Dim-2 collectively accounting for 84.7 % of the total variance. Through cluster analysis, two main clusters (Cluster I and Cluster II) were identified among the studied genotypes, each characterized by distinct trait patterns. Cluster I demonstrated superior performance in all traits, while Cluster II exhibited lower values for all traits. Further sub-clustering within Cluster II (sub-clusters IIA and IIB) revealed additional variations of the collection. The Euclidean distance for inter-cluster (ranging from 7.94 to 11.39) and intra-cluster (ranging from 3.17 to 10.38) were determined utilizing the complete distance method. In restricted maximum likelihood (REML) model, all the traits were significantly influenced by genetic effects, with broad-sense heritability ranging from 32 % to 70 %. The REML displayed high selection accuracy, with values exceeding 91 % for studied traits. The Multi-Trait Genotype-Ideotype Distance Index (MGIDI) identified 18 genotypes, with Moz1.15 emerging as the best-performing genotype under a 5 % selection pressure. Among the selected genotypes, trait uniqueness was highest for FW (54 %), followed by RN (50 %), MRN (18 %), MRW (14 %), and RW (5 %). The selection gain for these top-ranked genotypes varied between 23.2 % and 69.2 %. Findings established the existence of useful genetic variability for selected agronomic traits of sweet potato that could be exploited for breeding, genetics study and conservation.

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

AbstractAt the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

Model Komunikasi Persuasif Pada Pengikut Akun Instagram @infogarut Dalam Pemberitaan Citizen Journalism

Social media as an interactive platform for citizen journalism is increasingly important in the growing internet era. The focus of the research is the Instagram account @infogarut, as a citizen journalism platform in Garut. The purpose of this research is to find out how persuasive communication on @infogarut account followers related to citizen journalism news in the central route and peripheral route. This research method uses descriptive method with qualitative approach. The theory used is persuasive communication theory with the Elaboration Likelihood Model (ELM) model. Data collection techniques were carried out through interviews, observation of literature study and documentation. The results showed that the central route creates respondents who are involved and check facts and analyze information in depth before forming an opinion or taking action. The peripheral route in turn creates respondents who are more passive readers and only receive information without detailed analysis. The @infogarut account is known for providing the latest facts and information about Garut. This study provides insight into how citizen journalism on social media, especially Instagram, can influence the way people interact and react to information.

A new perspective on denoising based on optimal transport

Abstract In the standard formulation of the classical denoising problem, one is given a probabilistic model relating a latent variable $\varTheta \in \varOmega \subset{\mathbb{R}}^{m} \; (m\ge 1)$ and an observation $Z \in{\mathbb{R}}^{d}$ according to $Z \mid \varTheta \sim p(\cdot \mid \varTheta )$ and $\varTheta \sim G^{*}$, and the goal is to construct a map to recover the latent variable from the observation. The posterior mean, a natural candidate for estimating $\varTheta $ from $Z$, attains the minimum Bayes risk (under the squared error loss) but at the expense of over-shrinking the $Z$, and in general may fail to capture the geometric features of the prior distribution $G^{*}$ (e.g. low dimensionality, discreteness, sparsity). To rectify these drawbacks, in this paper we take a new perspective on this denoising problem that is inspired by optimal transport (OT) theory and use it to study a different, OT-based, denoiser at the population level setting. We rigorously prove that, under general assumptions on the model, this OT-based denoiser is mathematically well-defined and unique, and is closely connected to the solution to a Monge OT problem. We then prove that, under appropriate identifiability assumptions on the model, the OT-based denoiser can be recovered solely from information of the marginal distribution of $Z$ and the posterior mean of the model, after solving a linear relaxation problem over a suitable space of couplings that is reminiscent of standard multimarginal OT problems. In particular, due to Tweedie’s formula, when the likelihood model $\{ p(\cdot \mid \theta ) \}_{\theta \in \varOmega }$ is an exponential family of distributions, the OT-based denoiser can be recovered solely from the marginal distribution of $Z$. In general, our family of OT-like relaxations is of interest in its own right and for the denoising problem suggests alternative numerical methods inspired by the rich literature on computational OT.

Zero-shot transfer of protein sequence likelihood models to thermostability prediction

Zero-shot transfer of protein sequence likelihood models to thermostability prediction

The effect of involvement and perceived usefulness on attitudinal and behavioral loyalty in online bookings

Purpose This study aims to examine involvement and perceived usefulness (PU) as the drivers of loyalty for online booking websites, revealing findings of theoretical and practical relevance. The proposed conceptual model is drawn on elaboration likelihood model (ELM) along with the consideration of expectation confirmation model for loyalty development. Design/methodology/approach A two-stage structural equation modeling was applied to test the reliability and validity of the constructs and the strength of the hypothesized relationships. Findings Attitudinal loyalty (AL) is found to have a significant role, resulting in behavioral loyalty (BL) toward online booking. The results of this study supported the ELM framework, where involvement and PU resulted in BL through AL. PU and AL also served as mediating mechanisms underlying the impact of consumer involvement on BL. Originality/value This study applies the ELM to online ticketing mechanisms and nuances amid AL and BL with their drivers.

CANival: A multimodal approach to intrusion detection on the vehicle CAN bus

Vehicles of today are composed of over 100 electronic embedded devices known as Electronic Control Units (ECU), each of which controls a different component of the vehicle and communicates via the Controller Area Network (CAN) bus. However, unlike other network protocols, the CAN bus communication protocol lacks security features, which is a growing concern as more vehicles become connected to the Internet. To enable the detection of intrusions on the CAN bus, numerous intrusion detection systems (IDS) have been proposed. Although some are able to achieve high accuracy in detecting specific attacks, no IDS has been able to accurately detect all types of attacks against the CAN bus. To overcome the aforementioned issues, we propose a multimodal analysis framework named CANival, which consists of time interval-based and signal-based analyzers developed by designing a novel Time Interval Likelihood (TIL) model and optimizing an existing model CANet. Experimental results show that our multimodal IDS outperforms the base models and enhances the detection performance testing on two recent datasets, X-CANIDS Dataset and SynCAN, achieving average true positive rates of 0.960 and 0.912, and true negative rates of 0.997 and 0.996, respectively.

Fixational eye movements enhance the precision of visual information transmitted by the primate retina

Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

How CSR communication and value co-creation shape consumer well-being and brand love in the post-COVID-19 pandemic: evidence from the UK

PurposeDrawing upon the service-dominant logic (S-D logic) and elaboration likelihood model (ELM), this paper provides an integrative model to investigate how argument quality and source credibility of CSR communication affects customer value co-creation behavior, resulting in increased brand trust. Additionally, it unveils how brand trust and the perception of COVID-19 risk influence both brand love and subjective well-being.Design/methodology/approachThe data of this study were collected through survey questionnaire from 304 coffee shop customers using convenience sampling and tested using the partial least squares structural equation modeling (PLS-SEM) technique to validate its model.FindingsThe research findings confirm the positive association between source credibility and customer value co-creation (i.e. customer participation and citizenship behavior). By contrast, argument quality is revealed to have a significantly positive effect on customer citizenship behavior (CCB) but a non-significant effect on customer participation behavior (CPB). Additionally, the study identifies that customer value co-creation behavior significantly promotes brand trust. Finally, findings indicate that brand trust and the perception of COVID-19 risk significantly influence brand love and subjective well-being.Originality/valueThis study contributes to the literature on CSR communication and S-D logic and provides new insights for marketers and advertisers to manage brands in the post-pandemic scenario through CSR communications. Furthermore, this study theoretically extends the ELM model to the CSR communication research. Finally, this study expands the relevant literature by clarifying the relationships between the perception of COVID-19 risk, brand love and subjective well-being.

Unveiling the influence of persuasion strategies on cognitive engagement: an ERPs study on attentional search.

This study aimed to investigate how central versus peripheral persuasion methods, delivered through rational and emotional persuasion strategies, influence cognitive engagement and information processing during visual search tasks. Participants were allocated into four groups based on the media type (video vs. text) and the persuasion route (central vs. peripheral). The early and late stages of attentional processing were examined through the N1, P2, and P3 ERP components. The results demonstrated a pronounced N1 amplitude in response to text-based peripheral persuasion, indicating enhanced early attentional engagement. Additionally, parallel search tasks revealed a larger P3 amplitude for central versus peripheral routes, suggesting significant cognitive resource allocation during tasks requiring higher attention. These findings underscore the nuanced role of persuasive strategies in modulating attentional resources and cognitive processing. The study offers insights into designing more effective communication messages and highlights the potential for tailored persuasion approaches to influence audience engagement and information processing, with implications for public health campaigns and beyond.