Digital environments impose an attention economy characterized by information overabundance, continuous stimulation, and the multiplication of competing stimuli [12][13]. In such contexts, individuals’ attentional resources become highly fragmented, profoundly altering information-processing mechanisms and fostering forms of rapid, discontinuous, and often heuristic cognition [14].

Recommendation and ranking algorithms actively participate in the selection and prioritization of content by taking over part of the cognitive operations traditionally performed by individuals, such as information filtering, prioritization, external memory storage, and, in some cases, decision anticipation [2][15]. This algorithmic mediation transforms the relationship to knowledge and information by directing cognitive pathways and reducing the visibility of certain interpretive alternatives.

Digital cognition is thus characterized, first, by fragmented and intermittent attention, subject to frequent interruptions and constant competition among content streams [16]. Second, it is accompanied by chronic cognitive overload resulting from excessive information and notifications that exceed the limited capacity of working memory [17][18]. Finally, it manifests itself through an increasing delegation of cognitive functions to technical devices, including search engines, digital platforms, and algorithmic assistants, illustrating what Clark and Chalmers [19] and later Clark [20] describe as a form of extended cognition.

These transformations are not merely functional adjustments; they contribute to a lasting reconfiguration of the ways individuals think, learn, and make decisions in digital environments. They highlight the need for an integrated psychological approach to digital communication that takes into account the complex interactions between human cognition, technical devices, and algorithmic mediation.

Digital platforms promote an intensification and increased visibility of affects by transforming emotions into public, quantifiable, and comparable signals. Interaction mechanisms—likes, reactions, comments, and shares—produce an emotional formatting of communication in which affects are not only experienced but also displayed, measured, and valorized [6]. This emotional quantification modifies social recognition dynamics and influences users’ communicational behaviors.

Within these environments, emotionally charged content benefits from greater visibility because it captures attention more effectively and generates higher levels of engagement. Recent studies show that emotions—particularly anger, indignation, and enthusiasm—increase the likelihood of message diffusion and contribute to their virality [21]. Recommendation algorithms reinforce these dynamics by prioritizing content that elicits intense emotional reactions, thereby contributing to discourse polarization and the formation of affective echo chambers [22].

Digital affectivity is characterized by several structural features. First, it manifests itself through emotional amplification resulting from the constant staging of affects and their algorithmic valorization. Second, it is marked by immediate affective reactivity, fostered by the accelerated temporality of digital platforms, which reduces opportunities for emotional regulation and critical reflection [14]. Finally, it is accompanied by phenomena of emotional polarization and contagion, whereby emotions spread rapidly through social networks, reinforcing divisions and attitudinal homogenization within groups [23][24].

These dynamics confirm that digital affectivity is not merely an extension of offline emotions but rather a specific reconfiguration of emotional processes shaped by technical architectures, visibility logics, and algorithmic mediation. Their analysis therefore requires an integrated psychological approach to digital communication that considers the interactions among emotions, technical systems, and social dynamics.

Digital sociality is based on interactions mediated by technical interfaces and regulated by algorithmic architectures that structure the visibility, circulation, and prioritization of exchanges. Unlike face-to-face interactions, digital relationships are filtered through sociotechnical devices that intervene in mutual recognition processes, the distribution of attention, and the formation of collectives [2][25].

Within these environments, social norms no longer result solely from implicit interactional conventions but are partially redefined and imposed by platform rules, including participation formats, moderation policies, reporting mechanisms, and visibility criteria. These algorithmized norms shape communicational behavior by defining what can be published, made visible, or sanctioned, thereby transforming the modalities of social regulation [26].

Moreover, power relations are increasingly reconfigured around visibility and moderation mechanisms. Influence depends not only on actors’ social status or symbolic legitimacy but also on their ability to align with algorithmic logics of recommendation and amplification. Platforms thus become central actors in the structuring of the digital public sphere by modulating audience access and the circulation of discourse [27].

From this perspective, digital communication introduces several major transformations of sociality:

the emergence of new forms of social recognition based on quantified indicators (likes, followers, shares);the establishment of algorithmized interactional norms that shape participation and speaking practices;the reconfiguration of influence dynamics, in which algorithmic visibility becomes a central lever of communicational power.

These developments demonstrate that digital sociality is not merely a transposition of offline social relations but a specific reconfiguration of mechanisms of recognition, regulation, and influence, inseparable from the technical and algorithmic architectures that mediate contemporary communication.

Finally, cognition enables the processing, interpretation, and organization of information; affectivity directs attention, motivation, and engagement; and sociality embeds exchanges within relational, normative, and interactional frameworks that make the co-construction of meaning possible. These three dimensions—digital cognition, digital affectivity, and digital sociality—must be understood as interdependent. Platforms influence the content to which individuals pay attention, the emotions they experience, and the interactions in which they engage. In turn, users’ affective and social responses feed algorithmic systems that reorganize content visibility. Digital communication thus appears as a dynamic psycho-communicational configuration in which human and technical processes mutually shape one another.

Building on the preceding analyses, this article proposes an integrated model of digital communication aimed at explaining how digital environments jointly reconfigure the cognitive, affective, and social processes of communication. The model is based on the idea that these dimensions are neither autonomous nor sequential, but are simultaneously mobilized and mutually dependent in any communication situation mediated by platforms and algorithms.

The first dimension concerns digital cognition, understood as the set of information-processing processes reconfigured through technical mediation. It includes the orientation and fragmentation of attention, the partial externalization of memory, and decision-making assisted by algorithmic recommendation and ranking systems. Cognition is no longer located solely within the individual but is distributed between humans and technical devices, in accordance with the logic of extended cognition [19][20].

The second dimension refers to digital affectivity, characterized by emotions that are mediated, made visible, measurable, and amplified by platform architectures. Dynamics of engagement, virality, and emotional polarization are embedded in systems that privilege highly affective content, thereby altering the psychological conditions of persuasion, influence, and opinion formation [6][21].

The third dimension concerns digital sociality, which is based on interactions mediated by interfaces and regulated by algorithmic norms. Social relations, recognition mechanisms, and influence dynamics are reconfigured through visibility, moderation, and recommendation systems that redistribute forms of communicational power within the digital public sphere [2][26].

By articulating these three dimensions, the model makes it possible to analyze digital communication as a hybrid psychological system involving humans, technical devices, and algorithms in the co-production of meaning, emotions, and social relations. It therefore provides an integrative theoretical framework capable of overcoming fragmented approaches while capturing the psychological complexity of contemporary communicational practices and preparing the development of analytical tools adapted to digital and algorithmic environments.

The present contribution is essentially conceptual and theoretical in nature. It is based on a narrative and integrative synthesis of the literature devoted to the digital transformation of communication. The objective is not to conduct a systematic review or statistical meta-analysis, but rather to identify the main theoretical convergences capable of illuminating the psychological reconfigurations induced by digital platforms and algorithmic systems [28][29].

The literature was selected from research in Information and Communication Sciences, cognitive psychology, emotion psychology, social psychology, platform studies, extended cognition theory, and recent work on algorithmic mediation. Particular attention was given to academic publications that have contributed to understanding the effects of digital environments on perception, attention, emotional engagement, social influence, and meaning-making processes [2][3][20][30].

To structure the analysis, the selected studies were grouped around three psychological dimensions considered fundamental to contemporary digital communication. The first concerns digital cognition, encompassing research on attention, information processing, memory, decision-making, and cognitive delegation to digital devices [10][14][31]. The second concerns digital affectivity, bringing together studies on online emotions, affective engagement, emotional virality, and audience mobilization mechanisms [6][7][11]. The third concerns digital sociality, referring to research on social influence, digital norms, recognition, participation, and platform-mediated relational dynamics [8][9][32].

The comparative examination of these three bodies of literature seeks to identify the common psychological mechanisms that structure communication mediated by platforms and algorithms. This approach makes it possible to propose an integrated theoretical framework linking digital cognition, digital affectivity, and digital sociality in order to jointly analyze the psychological dimensions of communication in contemporary digital environments [3][8][10].

Beyond its theoretical scope, the proposed model can be used as an empirical framework for analyzing the psychological reconfigurations of digital communication. Each of the model’s three dimensions—digital cognition, digital affectivity, and digital sociality—can be operationalized through observable indicators, identifiable data sources, and units of analysis adapted to digital environments (See Table 1).

Digital cognition may be illustrated through the study of a personalized news

Table 1. Operational framework for the three dimensions of the model.

feed on a social media platform. The analysis may focus on the content actually viewed by users, their ability to retain exposed information, and their dependence on algorithmic recommendations to access content.

Digital affectivity can be observed through the diffusion of a message that generates strong emotional reactions. Relevant indicators may include the number of reactions, the emotional tone of comments, the level of engagement generated, and the propensity to share the content.

Digital sociality may be examined through interactions within an online community. Mechanisms of influence, social validation, cooperation, and polarization can be analyzed through interaction networks, participation rates, and indicators of relational centrality.

This operationalization provides a methodological foundation for the future development of communicometric indices such as a Digital Cognition Index (DCI), a Digital Affectivity Index (DAI), and a Digital Sociality Index (DSI). These instruments would make it possible to comparatively measure the psychological effects of digital platforms and assess contemporary transformations in algorithm-mediated communication.

The proposed model builds upon several theoretical frameworks that have contributed to the analysis of the digital transformation of communication while distinguishing itself through its explicitly psychological grounding.

Mediatization theories primarily emphasize the ways in which media and communication infrastructures progressively reconfigure social practices and institutions [3][33]. Affordance-based approaches focus on the possibilities for action enabled or constrained by technological devices and digital platforms [34][35]. Sociomaterial approaches, in turn, highlight the co-production of social phenomena by human actors and technological artifacts within hybrid assemblages [36][37].

Without challenging the relevance of these perspectives, the present model shifts the analytical focus toward the psychological mechanisms through which these transformations are concretely experienced by individuals. Its originality lies in integrating digital cognition, digital affectivity, and digital sociality within a single explanatory framework, making it possible to analyze how platforms and algorithms simultaneously influence information-processing processes, emotional dynamics, and social interactions.

The model therefore appears as a psycho-communicational complement to mediatization, affordance-based, and sociomaterial approaches by placing at the forefront the psychological transformations underlying contemporary digital practices.

Despite its heuristic value and integrative ambition, the proposed model presents several limitations that should be acknowledged.

The first concerns its level of generalization. In seeking to account for the psychological reconfigurations of digital communication through the dimensions of digital cognition, digital affectivity, and digital sociality, the model emphasizes psychological mechanisms common to a wide variety of digital environments. Yet social networking platforms, messaging applications, information platforms, collaborative workspaces, and AI-based conversational systems often operate according to very different logics [2][3]. Consequently, the observed effects may vary significantly across contexts.

The second limitation relates to the analytical simplification implied by distinguishing between digital cognition, digital affectivity, and digital sociality. In real-world situations, these dimensions constantly interact. Attentional mechanisms influence emotional reactions; emotions shape social interactions; and relational dynamics, in turn, modify cognitive processes. The boundaries between these dimensions remain highly porous and should therefore be understood as analytical categories rather than independent realities [8][9].

A third limitation concerns the status attributed to algorithmic systems. The model considers platforms and algorithms as active mediators of communication. However, this formulation should not be interpreted as attributing intentionality to technical systems. The notions of “co-actor” and “algorithmic agent” are employed from a sociotechnical perspective to emphasize their capacity to shape information flows, emotions, and social interactions, without granting them autonomy comparable to that of human actors [10][26].

Finally, the model remains primarily conceptual at this stage. Its relevance must be assessed through empirical research aimed at operationalizing the three proposed dimensions and measuring their interactions across different digital contexts. Recent developments in generative artificial intelligence, algorithmic personalization, and automated conversational systems further underscore the need for continual reassessment of the theoretical framework in order to account for the rapid evolution of contemporary digital environments [38][39].

These limitations do not invalidate the relevance of the model. On the contrary, they highlight its evolving nature and open new avenues for research aimed at refining, enriching, and empirically validating the analysis of the psychological reconfigurations of communication in the age of platforms and algorithms.

From a theoretical perspective, this framework contributes first to a clearer understanding of the psychological effects of digital technologies. By highlighting cognitive reconfigurations (attention, memory, decision-making), affective transformations (mediated emotions, engagement, polarization), and social dynamics (algorithmic norms, visibility, influence), it moves beyond analyses focused exclusively on technological devices or patterns of use. Digital communication is thus conceptualized as a structured psychological process embedded within active technical architectures.

Second, this approach promotes the integration of algorithms as co-actors in communication. Recommendation, ranking, and moderation systems are no longer viewed as neutral infrastructures but as entities that participate in the production of meaning, the modulation of emotions, and the structuring of social relations. This theoretical recognition contributes to a renewed understanding of communicational power in the platform era.

Finally, the proposed model strengthens interdisciplinary dialogue between Information and Communication Sciences, psychology, and digital studies. It provides a common conceptual framework capable of articulating insights from cognitive and affective sciences with critical analyses of platforms and algorithms, thereby enhancing theoretical coherence and scientific cumulativeness.

From a methodological perspective, this approach opens the way for the development of specific indices of digital cognition and digital affectivity designed to empirically measure phenomena such as attentional fragmentation, cognitive overload, emotional engagement, and affective polarization. These indices constitute essential tools for operationalizing the psychological transformations induced by digital environments.

The framework also encourages a stronger integration of psychological analysis with platform-generated data. The combined use of behavioral data, interaction indicators, and psychological measures makes it possible to analyze digital communication practices from both subjective and structural perspectives.

Finally, this approach supports a greater reliance on mixed and computational methods, combining surveys, interviews, discourse analysis, statistical techniques, and algorithmic analysis. Such methodological hybridization appears essential for capturing the complexity of digital communication ecosystems and for developing rigorous diagnostic tools adapted to contemporary challenges in Information and Communication Sciences.

The proposed model primarily targets digital environments in which platforms and algorithms actively intervene in the organization of information, interactions, and communicational behaviors. Its scope of application focuses mainly on four categories of digital environments.

The first category includes social networking platforms such as Facebook, X (formerly Twitter), Instagram, TikTok, LinkedIn, and YouTube. These platforms constitute privileged spaces for observing processes of digital cognition, digital affectivity, and digital sociality, since recommendation algorithms directly influence content visibility, emotional engagement, and users’ relational dynamics [2][10].

The second category concerns messaging and interpersonal communication applications, including WhatsApp, Messenger, Telegram, Signal, and WeChat. Within these environments, interactions occur within more restricted relational networks, yet mechanisms of social influence, emotional circulation, and meaning construction remain highly significant. Analysis therefore focuses more specifically on conversational dynamics, interpersonal trust, and processes of social coordination.

The third category encompasses information and search platforms, such as Google News, news aggregators, search engines, and online news platforms. These systems play a major role in access to knowledge, information prioritization, and the orientation of users’ cognitive pathways. They therefore provide a particularly relevant context for studying digital cognition and the algorithmic mediation of information [3][26].

Finally, the model can also be applied to other specific digital environments, including educational platforms (Moodle, Google Classroom), professional collaborative spaces (Microsoft Teams, Slack), e-commerce platforms, virtual communities, immersive environments, and conversational systems based on generative artificial intelligence. In these contexts, platforms intervene not only in the circulation of information but also in the structuring of interactions, the support of decision-making, and the production of meaning.

However, the model is not intended to treat all digital environments as homogeneous. The cognitive, affective, and social mechanisms it identifies may vary according to technical architectures, communicational objectives, platform governance models, and usage contexts. It should therefore be understood as a general analytical framework that must be adapted to the specific characteristics of each digital environment under investigation.

Upon opening the application, the student does not access all the information available about the election. Instead, the algorithm selects specific content based on his previous searches, interests, and past interactions.

The first posts displayed mainly concern candidates he has already viewed. His attention is therefore directed toward a limited set of information. Moreover, rather than actively seeking diverse sources, he relies on the platform’s automatic recommendations. Part of the work of selecting and prioritizing information is thus delegated to the algorithm.

This situation illustrates attentional fragmentation, information overload, and cognitive delegation to digital systems.

Among the suggested content is a video denouncing a political scandal. The comments are numerous, and emotional reactions are abundant: anger, indignation, fear, and enthusiasm. These emotions attract the student’s attention more effectively than nuanced analyses or technical documents.

Encouraged by visible reactions (likes, shares, and emojis), he experiences a strong emotional response and decides to share the post. The algorithm detects this engagement and further increases the visibility of similar content. This process illustrates emotional amplification, affective contagion, and the gradual polarization of opinions.

The student then examines the reactions of his friends and the groups to which he belongs. Most members of the group support the same political position. Contradictory viewpoints rarely appear in his news feed.

Gradually, he develops the impression that this opinion is widely shared. Algorithmic visibility mechanisms therefore reinforce group norms and influence the formation of his judgments.

This situation illustrates the formation of echo chambers, social recognition through likes and shares, and the redistribution of communicational influence through algorithms.

In this example, digital communication results from the continuous interaction among three processes (See Table 2).

Table 2. Manifestations of the operational framework of the three dimensions of the model.

The experience of this student demonstrates that contemporary digital communication is no longer merely an exchange between individuals. It constitutes a hybrid psychological system in which platforms and algorithms actively participate in the production of meaning, the modulation of emotions, and the structuring of social relations. Cognitive, affective, and social processes are thus co-constructed by human actors and the digital architectures that mediate their interactions.

This illustration highlights the relevance of conceiving digital communication as a hybrid human-algorithm psychological system, which constitutes the central thesis of this article.