Intersubjective Model Research Articles

Adorno on objects: towards an inclusive social theory

ABSTRACT In this paper, we draw insights from Theodor W. Adorno’s views on objects to demonstrate the relevance of the subject-object-oriented model of social theory. By engaging closely with his notion of the ‘preponderance of the object’, we develop an account of social ontology that blends substantial, relational, and processual ontologies. We highlight the usefulness of Adorno’s social ontology in addressing contemporary social concerns related to subject-object relations, such as the reification of consciousness and objectification. Furthermore, we emphasize its significance in light of distorted subject-object relations. Considering these aspects, we argue that subject-object oriented social theory is more inclusive and relevant than the intersubjective model that emerged after the linguistic turn in Critical Theory.

PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain–computer interface

Motor imagery brain–computer interfaces (MI–BCIs) that use electroencephalogram (EEG) signals are essential for motor rehabilitation. One of the most important problems in MI–BCIs is intersubject variability, as brain signals can vary significantly among individuals due to differences in cognitive processing and mental states. To address this issue, some studies have proposed intrasubject learning models that train and evaluate within specific individuals, while others have suggested intersubject learning models that aim to generalize across subjects. However, insufficient data are available for intrasubject learning, so they cannot use a deep learning model, and still, the problem of intersubject variability is unsolved in intersubject learning. To tackle these challenges, we developed a new framework based on deep metric learning called the Personal grouping method for Reducing InterSubject variability using deep Metric learning (PRISM). PRISM aims to reduce intersubject variability by calculating the similarity between subjects based on their EEG signals and generating clusters. Based on these clusters, the model can train by reducing intersubject variability while maintaining the advantage of intersubject learning. Our thorough evaluation indicates that PRISM is poised to redefine the standards in MI–BCIs, significantly alleviating the challenges of intersubject variability. We compared the performance of PRISM and the existing learning paradigm, and the performance of PRISM was similar to that of the intrasubject learning model (p > 0.4), which is the best existing model, and it performed better than the intersubject learning model (**p < 0.001). Our results indicate that the PRISM framework outperforms intersubject learning and closely matches intrasubject learning models. This suggests that PRISM combines the specificity of intrasubject learning with intersubject model generalizability, thereby enhancing performance.

Transferable non-invasive modal fusion-transformer (NIMFT) for end-to-end hand gesture recognition.

Objective.Recent studies have shown that integrating inertial measurement unit (IMU) signals with surface electromyographic (sEMG) can greatly improve hand gesture recognition (HGR) performance in applications such as prosthetic control and rehabilitation training. However, current deep learning models for multimodal HGR encounter difficulties in invasive modal fusion, complex feature extraction from heterogeneous signals, and limited inter-subject model generalization. To address these challenges, this study aims to develop an end-to-end and inter-subject transferable model that utilizes non-invasively fused sEMG and acceleration (ACC) data.Approach.The proposed non-invasive modal fusion-transformer (NIMFT) model utilizes 1D-convolutional neural networks-based patch embedding for local information extraction and employs a multi-head cross-attention (MCA) mechanism to non-invasively integrate sEMG and ACC signals, stabilizing the variability induced by sEMG. The proposed architecture undergoes detailed ablation studies after hyperparameter tuning. Transfer learning is employed by fine-tuning a pre-trained model on new subject and a comparative analysis is performed between the fine-tuning and subject-specific model. Additionally, the performance of NIMFT is compared to state-of-the-art fusion models.Main results.The NIMFT model achieved recognition accuracies of 93.91%, 91.02%, and 95.56% on the three action sets in the Ninapro DB2 dataset. The proposed embedding method and MCA outperformed the traditional invasive modal fusion transformer by 2.01% (embedding) and 1.23% (fusion), respectively. In comparison to subject-specific models, the fine-tuning model exhibited the highest average accuracy improvement of 2.26%, achieving a final accuracy of 96.13%. Moreover, the NIMFT model demonstrated superiority in terms of accuracy, recall, precision, and F1-score compared to the latest modal fusion models with similar model scale.Significance.The NIMFT is a novel end-to-end HGR model, utilizes a non-invasive MCA mechanism to integrate long-range intermodal information effectively. Compared to recent modal fusion models, it demonstrates superior performance in inter-subject experiments and offers higher training efficiency and accuracy levels through transfer learning than subject-specific approaches.

Combining general and personal models for epilepsy detection with hyperdimensional computing

Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on patients’ daily lives. Despite this there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is a promising method for epilepsy detection via wearable devices, characterized by a simpler learning process and lower memory requirements compared to other methods. In this work, we demonstrate additional avenues in which HD computing and the manner in which its models are built and stored can be used to better understand, compare and create more advanced machine learning models for epilepsy detection. These possibilities are not feasible with other state-of-the-art models, such as random forests or neural networks. We compare inter-subject model similarity of different classes (seizure and non-seizure), study the process of creating general models from personal ones, and finally posit a method of combining personal and general models to create hybrid models. This results in an improved epilepsy detection performance. We also tested knowledge transfer between models trained on two different datasets. The attained insights are highly interesting not only from an engineering perspective, to create better models for wearables, but also from a neurological perspective, to better understand individual epilepsy patterns.

GMAC-A Simple Measure to Quantify Upper Limb Use From Wrist-Worn Accelerometers.

Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use - thresholded activity counts (TAC) and the gross movement (GM) score suffer from high sensitivity and low specificity, and vice versa. We previously proposed a hybrid version of these two measures - the GMAC - that showed better overall detection performance than TAC and GM. In this paper, we answer two critical questions to improve the GMAC measure's usefulness: (a) can it be implemented using only the accelerometer data? (b) what are its optimal parameter values? Here, we propose a modified GMAC using only the accelerometer data and optimize its parameters to develop: (a) a generic measure that is both limb- and subject-independent, and (b) limb-specific measures that were only subject-independent. The optimized GMAC showed better detection performance than the previous GMAC and surprisingly had comparable performance to the best-performing machine learning-based measure (random forest inter-subject model). In hemiparetic data, its performance was similar to the previous GMAC and the random forest inter-subject model; the limb-specific GMAC measure, however, had a better performance than the generic measure. The optimized limb-specific GMAC is a simple, interpretable alternative to a machine learning-based inter-subject model. The optimized GMAC can be a valuable measure for offline or real-time detection and feedback of upper limb use. The preliminary results of this study, based on a small dataset, need to be validated on a larger dataset to evaluate its generalizability.

Reading fiction together to support reflective practice and recovery in serious mental illness: The value of book club.

To illustrate the value of reading fiction as a group supervision practice focused on supporting recovery-oriented psychotherapy for individuals experiencing serious mental illness (SMI). Detailed practical description of the practice and conceptual analysis of the proposed value of using fiction as a group supervision method. Authors suggest that the supervisory practice is a novel component of clinical supervision, and offers value in its ability to promote reflective practice, to establish intersubjectivity among peers and supervisory relationships, and may serve for some to counter risks of clinician burnout. Participating in a book club that reads fiction as an adjunctive experience to supervision can promote recovery-oriented psychotherapy in four key ways: including its promotion of reflection on self, patients, and the experience of therapy for psychosis, its promotion of reflection on recovery and barriers to recovery, its ability to offer an experiential model of intersubjectivity, and its value in supporting peer consultation and helping offset clinician burnout. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Relational clinical practice: A hermeneutic, enactive, intersubjective model of osteopathy

This commentary proposes a framework for considering the philosophical foundations that support osteopathic practice, using the metaphor of ‘tectonic plates’. It is argued that reflective osteopathic practitioners, and the organisations they make up, would benefit from engaging with the differing ontological and epistemological traditions that underpin healthcare theory, evidence and practice. It is proposed that having more insight into the philosophical traditions of reality (ontologies) and forms of knowledge (epistemologies), that underpin medicine and healthcare, will support ethical, reflective practice, informed by the concepts of ontic integrity and epistemic humility. We focus particularly on phenomenological ontology and epistemology, which we propose underpins the relational and embodied concept of osteopathy as hermeneutic, enactive, intersubjective healthcare, locating it within the sphere of phenomenological and enactivist theory and research.

Language and social minds: The semantics and pragmatics of intersubjectivity

This book provides a gradient model of intersubjectivity and social cognition in language. Through an interdisciplinary synthesis, the author proposes three levels of linguistic acts: those aimed at the speaker’s benefit alone, those expressing concern for an interlocutor, and those indicating concern for societal reactions. There is a continuum of egocentric to extended intersubjectivity, which includes co-actionality, immediate intersubjectivity, and collective constructions. The model is applied to study children’s mastery of intersubjective polysemy and verbal organization, and to determine “social mind” capabilities in autism spectrum disorders. While non-linguistic topics and further empirical validation are outside the scope, this innovative interdisciplinary approach meaningfully extends understanding of the intertwined nature of language, cognition, and social engagement.

Unsupervised multi-source variational domain adaptation for inter-subject SSVEP-based BCIs

Steady-State Visual Evoked Potential-based Brain–Computer Interfaces (SSVEP-based BCIs) are widely used to decode electroencephalography (EEG) data, often employing subject-specific models. However, these methods demand a substantial volume of labeled data, which tends to induce visual fatigue in subjects. To address this challenge, an efficient approach is to transfer known subject knowledge through inter-subject methods using unlabeled data. However, the previous state-of-the-art unsupervised inter-subject model is limited by the feature extraction capability of spatial filter methods. To overcome this limitation, we propose a novel Multi-source Variational Domain Adaptation (MVDA) method, which incorporates a Filter-bank Dynamic Graph Convolutional Network (FBDGCN). The FBDGCN aims to extract distinctive graph features under different harmonics from offline labeled source and unlabeled target EEG data. Subsequently, the source and target graph features are jointly trained using MVDA for domain adaptation. With proposed variational inference mechanism, MVDA can constrain the distribution manifold and minimize the domain discrepancy. Since the performance of knowledge transfer heavily relies on the similarity between source and target domains, we also propose an unsupervised subject correlation analysis method to select the source subjects from the dataset. Our experimental results demonstrate that the MVDA method yields a higher information transfer rate (ITR) (189.87 ± 24.98 bits/min) and mean accuracy (92.11 ± 2.85%). The proposed model can be calibrated only by unlabeled target subject data to obtain a higher recognition accuracy, which is more applicable to real-world scenarios.

The Evolving Culture Concept in Psychiatric Cultural Formulation: Implications for Anthropological Theory and Psychiatric Practice.

For thirty years, psychiatrists and anthropologists have collaborated to improve the validity of psychiatric diagnosis. This collaboration has produced the DSM-IV Outline for Cultural Formulation (OCF) and the DSM-5 Cultural Formulation Interview (CFI). Nonetheless, some anthropologists have critiqued the concept of culture in DSM-5 as too focused on patient meanings and not on clinician practices. This article traces the evolution of the culture concept from DSM-IV through DSM-5-TR by analyzing publications from the American Psychiatric Association on the OCF and CFI alongside scholarship in psychiatry and anthropology. DSM-IV relied on a culture concept of coherent ethnic communities sharing coherent cultures, primarily for minoritized ethnoracial individuals in the United States. Changing demographics and newer immigration patterns around the world deminoritized the culture concept for DSM-5. After George Floyd's death and demands for social justice, the culture concept in DSM-5-TR emphasized social structures. The article proposes an intersubjective model of culture through which patients and clinicians work through similarities and differences. It recommends a revised formulation that attends to clinician practices such as communicating, diagnosing, recommending treatments, and documenting, beyond collecting patient meanings. It also raises the question of whether an intersubjective model of culture prompts reconsiderations of culture-related text in other sections of the DSM. The social sciences can redirect attention to the clinician's culture of biomedicine to close patient health disparities.

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning.

In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features: 1) experimental measurements - muscle signals and joint angles, and 2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra- and inter-subject tests, and compared to the baseline models (NMS and standard-ANN). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.

Two modes of being together: The levels of intersubjectivity and human relatedness in neuroscience and psychoanalytic thinking.

The notion of intersubjectivity has achieved a primary status in contemporary psychoanalytic debate, stimulating new theoretical proposals as well as controversies. This paper presents an overview of the main contributions on inter-subjectivity in the field of neurosciences. In humans as well as—probably—in other species, the ability for emotional resonance is guaranteed early in development. Based on this capacity, a primary sense of connectedness is established that can be defined inter-subjective in that it entails sharing affective states and intentions with caregivers. We propose to define such a form of inter-subjectivity as contingent, since the infant’s early abilities for resonance do not imply the more generalized capacity to permanently conceive of the relationship outside the realm of current interactions and the infant-caregiver’s mutual correspondence of internal states. This form of connection, hence, results in a self-referential, bodily, and affectively codified, context- and time dependent, like-me experience of interactions. The gradual maturation of brain structures and processes as well as interactive experiences allow proper intersubjectivity exchanges, grounded on new intentional and representational capacities, to evolve. In this more mature form of intersubjectivity, the individual is allowed to conceive of her own psychic space both as distinct and as possibly connected with the other’s contents and experience, even in the absence of current behavioral indicators of such correspondence. This multi-layered model of intersubjectivity, which is embraced by current neuroscience research, seems to allow for new interpretations of psychoanalytic models of human relatedness based upon classic clinical observations.

Flexible coding scheme for robotic arm control driven by motor imagery decoding

Objective. Brain computer interface (BCI) technology is an innovative way of information exchange, which can effectively convert physiological signals into control instructions of machines. Due to its spontaneity and device independence, the motor imagery (MI) electroencephalography (EEG) signal is used as a common BCI signal source to achieve direct control of external devices. Several online MI EEG-based systems have shown potential for rehabilitation. However, the generalization ability of the current classification model of MI tasks is still limited and the real-time prototype is far from widespread in practice. Approach. To solve these problems, this paper proposes an optimized neural network architecture based on our previous work. Firstly, the artifact components in the MI-EEG signal are removed by using the threshold and threshold function related to the artifact removal evaluation index, and then the data is augmented by the empirical mode decomposition (EMD) algorithm. Furthermore, the ensemble learning (EL) method and fine-tuning strategy in transfer learning (TL) are used to optimize the classification model. Finally, combined with the flexible binary encoding strategy, the EEG signal recognition results are mapped to the control commands of the robotic arm, which realizes multiple degrees of freedom control of the robotic arm. Main results. The results show that EMD has an obvious data amount enhancement effect on a small dataset, and the EL and TL can improve intra-subject and inter-subject model evaluation performance, respectively. The use of a binary coding method realizes the expansion of control instructions, i.e. four kinds of MI-EEG signals are used to complete the control of 7 degrees of freedom of the robotic arm. Significance. Our work not only improves the classification accuracy of the subject and the generality of the classification model while also extending the BCI control instruction set.

Intersubjectivity as an antidote to stress: Using dyadic active inference model of intersubjectivity to predict the efficacy of parenting interventions in reducing stress-through the lens of dependent origination in Buddhist Madhyamaka philosophy.

Intersubjectivity refers to one person’s awareness in relation to another person’s awareness. It is key to well-being and human development. From infancy to adulthood, human interactions ceaselessly contribute to the flourishing or impairment of intersubjectivity. In this work, we first describe intersubjectivity as a hallmark of quality dyadic processes. Then, using parent-child relationship as an example, we propose a dyadic active inference model to elucidate an inverse relation between stress and intersubjectivity. We postulate that impaired intersubjectivity is a manifestation of underlying problems of deficient relational benevolence, misattributing another person’s intentions (over-mentalizing), and neglecting the effects of one’s own actions on the other person (under-coupling). These problems can exacerbate stress due to excessive variational free energy in a person’s active inference engine when that person feels threatened and holds on to his/her invalid (mis)beliefs. In support of this dyadic model, we briefly describe relevant neuroimaging literature to elucidate brain networks underlying the effects of an intersubjectivity-oriented parenting intervention on parenting stress. Using the active inference dyadic model, we identified critical interventional strategies necessary to rectify these problems and hereby developed a coding system in reference to these strategies. In a theory-guided quantitative review, we used this coding system to code 35 clinical trials of parenting interventions published between 2016 and 2020, based on PubMed database, to predict their efficacy for reducing parenting stress. The results of this theory-guided analysis corroborated our hypothesis that parenting intervention can effectively reduce parenting stress if the intervention is designed to mitigate the problems of deficient relational benevolence, under-coupling, and over-mentalizing. We integrated our work with several dyadic concepts identified in the literature. Finally, inspired by Arya Nagarjuna’s Buddhist Madhyamaka Philosophy, we described abstract expressions of Dependent Origination as a relational worldview to reflect on the normality, impairment, and rehabilitation of intersubjectivity.

Digitalization and Uncertainty in the University: Coherence and Collegiality Through a Metacurriculum

Recent initiatives to promote ‘digitalization’ in education exhorting increased digital literacy and ‘computational thinking’ have invited implementation research methods to transform curricula, teaching, and learning. While conceived as a movement from the present of education to an imagined future where envisaged curricula embrace data-oriented and computational practices across subjects, we ask: whose present? Whose future? We revisit the concept of a metacurriculum (first conceived in the 1990s) as a way of addressing this question, while not avoiding the challenges inherent in the adaptation of education to an increasingly complex postdigital environment. We argue that the principal challenge facing institutional and individual adaptation is increasing environmental uncertainty produced by technology, not deficiency in individual skills. Using the uncertainty concept, we present a practical co-designed and dialogical approach supporting the student and teacher journeys towards the transdisciplinary opportunities opened out by technology, based on a cybernetic model of intersubjectivity. We discuss the explanatory power of uncertainty in this context, focusing on the ways it can encourage dialogue, collegiality, and experimentation. Evidence for this is presented in a case study from a Russian University Business School where a large group of teachers co-designed and delivered a dialogical module on digitalization and interdisciplinarity over a period of 4 years—a collaboration ended by recent geopolitical events. In analyzing data from one of the central activities of this course, we focus on the teacher collegiality and the students’ mechanisms of selection in navigating the transdisciplinary space, and how these mechanisms may provide deeper insight into the dialogical underpinnings of education.

Dual Head and Dual Attention in Deep Learning for End-to-End EEG Motor Imagery Classification

Event-Related Desynchronization (ERD) or Electroencephalogram (EEG) wavelet is essential for motor imagery (MI) classification and BMI (Brain–Machine Interface) application. However, it is difficult to recognize multiple tasks for non-trained subjects that are indispensable for the complexities of the task or the uncertainties in the environment. The subject-independent scenario, where an inter-subject trained model can be directly applied to new users without precalibration, is particularly desired. Therefore, this paper focuses on an effective attention mechanism which can be applied to a subject-independent set to learn EEG motor imagery features. Firstly, a custom form of sequence inputs with spatial and temporal dimensions is adopted for dual headed attention via deep convolution net (DHDANet). Secondly, DHDANet simultaneously learns temporal and spacial features. The features of spacial attention on each input head are divided into two parts for spatial attentional learning subsequently. The proposed model is validated based on the EEG-MI signals collected from 54 subjects in two sessions with 200 trials in each sessions. The classification of left and right hand motor imagery in this paper achieves an average accuracy of 75.52%, a significant improvement compared to state-of-the-art methods. In addition, the visualization of the frequency analysis method demonstrates that the temporal-convolution and spectral-attention is capable of identifying the ERD for EEG-MI. The proposed machine learning structure enables cross-session and cross-subject classification and makes significant progress in the BMI transfer learning problem.

My Sister Haunts me: Sororal Spectres and the “Otherness” of Female Sexual Desire in the Mid-Nineteenth Century Ghost Story

ABSTRACT The ghost, in its very essence, is “Other”. It sits on the boundaries between life and death, reminding the living of the actuality of their existence and challenging their perceptions of the permanence of self. Applying Hegel’s model of intersubjectivity to depictions of feminine phantoms, this essay explores women’s ghost stories as representations of, and commentary on, the “otherness” of women’s sexuality in the mid-nineteenth century. Focusing on the centrality of the sororal relationship in supernatural fiction by Elizabeth Gaskell and Eliza Lynn Linton, the paper considers sorority as a narrative device through which texts explore, reflect and possibly embed ideologies surrounding female sexual desire.

UNDERSTANDING PANCASILA IN THE MAQASHID-BASED FRAMEWORK AND ITS RELEVANCE TO INDONESIAN RELIGIOUS HARMONY: A DIALOGUE APPROACH

Studies on maqashid al-sharia or the aims of Islamic law often seek to understand the philosophical meaning of public interest (maslahah), such as objective to preserve faith, life, reason, offspring, and wealth. This article develops the idea of the maqashid-based frame work for contextualization of Pancasila, especially in religious life. This article used the dialogue approach by Martin Buber (w. 1965), which are the theory of I-It, I-Thou, and I of The I-it and the idea of totality initiated by Emanuel Lévinas (w. 1995). The purpose of this article provides a new way of interpreting to the three pillars of maslahah; enhancements (tahsiniyat), desirable (hajiyat), and essential (daruriyat). I argue that mutual respect is the central idea of the dialogue approach in the realization of maslahah. Furthermore, the nexus that needs to be developed between maslahah and Pancasila is intersubjective model. On this basis, the mutual respect is relevant to reinforce and be integrated with Pancasila values for strengthening the religious harmony in Indonesia.

Transition Trauma Metaphor in Transgender Narrative

This study combines methodological tools of conceptual metaphor theory and narrative psychology with theoretical assumptions of the intersubjective psycholinguistic approach to meaning to explore instantiations of transition narrative metaphors in 16 TED talks given by transgender people and posted on the TED platform within the period between January 2013 and July 2020. The speakers are aged from 20 to 70; 8 males and 8 females; 2 black and 9 white Americans, 2 Filipinos, 1 black South-African, 1 Puerto Rican, and 1 white Australian. The study offers a new interpretation of narrative metaphor based on the intersubjective model of meaning. Within this model, narrative metaphor is conceived as extended conceptual metaphor instantiated in a number of multimodal metaphoric expressions made coherent by the textual, social, cultural, and historical context of the narrative, but primarily by its interactive situational context, which includes the audience into the narrative through empathy and gives them power to change the narrative. The research reveals that all the 16 analysed narratives rest on the TRANSITION IS CONTEST narrative metaphor that represents a conflict between positive self-evaluation of transition by a transgender individual and its negative evaluation / unacceptance by the society, which makes transition a traumatic experience. The density of words and phrases instantiating the CONTEST metaphor in the 16 narratives varies from 2,5 to 3 % which means that they are key linguistic expressions of the narratives. The CONTEST metaphor provides a deeper insight into transgender transition compared to the JOURNEY/TRAVEL metaphor found to represent transition experience in existing cognitive linguistic and transgender studies. The JOURNEY/TRAVEL metaphor fails to grasp the intersubjective and, consequently, traumatic nature of transition experience. The results of the research suggest that narrative as well as narrative metaphor can be given a more accurate interpretation if they are approached from the intersubjective perspective, which reflects their true nature as socially and culturally shaped interactive phenomena.

The Interpersonal Neurobiology of Intersubjectivity.

In 1975, Colwyn Trevarthen first presented his groundbreaking explorations into the early origins of human intersubjectivity. His influential model dictates that, during intimate and playful spontaneous face-to-face protoconversations, the emotions of both the 2–3-month-old infant and mother are nonverbally communicated, perceived, mutually regulated, and intersubjectively shared. This primordial basic interpersonal interaction is expressed in synchronized rhythmic-turn-taking transactions that promote the intercoordination and awareness of positive brain states in both. In this work, I offer an interpersonal neurobiological model of Trevarthen’s intersubjective protoconversations as rapid, reciprocal, bidirectional visual-facial, auditory-prosodic, and tactile-gestural right brain-to-right brain implicit nonverbal communications between the psychobiologically attuned mother and the developing infant. These co-constructed positive emotional interactions facilitate the experience-dependent maturation of the infant’s right brain, which is in an early critical period of growth. I then address the central role of interpersonal synchrony in intersubjectivity, expressed in a mutual alignment or coupling between the minds and bodies of the mother and infant in face-to-face protoconversations, as well as how these right brain-to-right brain emotional transmissions generate bioenergetic positively charged interbrain synchrony within the dyad. Following this, I offer recent brain laterality research on the essential functions of the right temporoparietal junction, a central node of the social brain, in face-to-face nonverbal communications. In the next section, I describe the ongoing development of the protoconversation over the 1st year and beyond, and the co-creation of a fundamental energy-dependent, growth-promoting social emotional matrix that facilitates the emergence of the highly adaptive human functions of mutual play and mutual love. In the final section, I discuss the clinical applications of this interpersonal neurobiological model of intersubjectivity, which has a long history in the psychotherapy literature. Toward that end, I offer very recent paradigm-shifting hyperscanning research that simultaneously measures both the patient and therapist during a psychotherapeutic interaction. Using the Trevarthen’s two-person intersubjective model, this research demonstrates changes in both brains of the therapeutic dyad and the critical role of nonverbal communications in an emotionally-focused psychotherapy session. These studies specifically document interbrain synchronization between the right temporoparietal junction of the patient and the right temporoparietal junction of the clinician, a right brain-to-right brain nonverbal communication system in the co-constructed therapeutic alliance. Lastly, I discuss the relationship between the affect communicating functions of the intersubjective motivational system and the affect regulating functions of the attachment motivational system.