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Most AI research misses a fundamental insight from psychology: humans form deep, meaningful emotional connections with objects through memory externalization, identity construction, and sentimental value attribution — mechanisms directly applicable to designing AI systems that genuinely understand and support human emotional needs. This review across psychology, cognitive science, AI, and HCI reveals both robust theoretical frameworks and a critical implementation gap. While foundational research establishes how objects become repositories of personal meaning, contemporary AI development largely overlooks this rich understanding, presenting significant opportunities for advancing emotionally intelligent, personalized AI architectures.

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The convergence of three research streams makes this particularly timely: psychological research demonstrates humans anthropomorphize AI entities and form genuine attachments, AI technical capabilities now support sophisticated memory and emotional modeling through personal knowledge graphs and affective computing, yet explicit integration of object attachment theory into AI system design remains virtually absent. This gap represents both scientific opportunity and ethical imperative, as AI companions increasingly occupy roles traditionally filled by meaningful objects and relationships.

Psychological foundations reveal how objects become emotionally significant

Research across consumer psychology, cognitive science, and developmental studies establishes that human-object emotional bonds arise through five interconnected mechanisms: anthropomorphism (attributing human-like qualities), sentimental value creation (associating objects with significant people and events), identity extension (using possessions to construct and express self), memory externalization (objects as autobiographical repositories), and compensatory attachment (objects substituting for unreliable human connections).

Belk’s landmark 1988 paper “Possessions and the extended self” (cited over 15,000 times) established that possessions literally extend the self into the external world, serving as concrete links between identity, material reality, and cultural context. This framework fundamentally challenges viewing objects as mere tools — they become integral components of who we are. Subsequent research by Ahuvia (2005) and Kleine et al. (1995) refined this understanding, identifying that loved objects play crucial roles in identity narratives and can be categorized into instrumental, symbolic, experiential, and spiritual attachment types.

The anthropomorphism mechanism operates through three factors identified by Epley, Waytz, and Cacioppo (2007) in Psychological Review: elicited agent knowledge (accessibility of human-like schemas), effectance motivation (desire for control and understanding), and sociality motivation (need for social connection). Crucially, Norberg et al. (2018) found in the Journal of Behavioral Addictions that anthropomorphism increases with social exclusion — humans turn to object relationships when human connections feel inadequate. This has direct implications for AI companions, which could exploit this vulnerability or, designed responsibly, provide beneficial support.

Yap and Grisham’s 2019 study in the Journal of Behavioral Addictions identified five distinct facets of object attachment: insecure attachment, anthropomorphism, possessions as identity extensions, possessions as autobiographical memory repositories, and possessions for comfort/safety. Each facet suggests different ways AI systems could fulfill psychological needs. For instance, AI maintaining conversational history becomes an autobiographical memory repository, while AI providing consistent emotional support fulfills the comfort/safety function.

Memory research demonstrates that objects serve as “technologies of remembrance” (Jones, 2007), externalizing the self and enabling selective memory reinforcement. Wang et al. (2017) found in the journal Memory that externalizing memories through digital sharing enhances retention and reinforces autobiographical self-construction. This suggests AI conversation partners storing personal memories could become integral to users’ identity construction — a profound responsibility requiring careful ethical consideration. Conway, Singer, and Tagini’s 2004 work in Social Cognition established that autobiographical memories are essential for self-coherence, with memory-triggering objects serving identity-maintenance functions.

Developmental research reveals attachment precedes anthropomorphism. Gjersoe, Hall, and Hood (2015) found in Cognitive Development that emotional attachment drives children to attribute mental lives to toys, not vice versa. Hood and Bloom (2008) demonstrated children as young as 3–4 prefer original possessions over identical duplicates, suggesting humans attach to specific instances even when functional equivalents exist. This has striking implications: users may prefer their specific AI assistant instance over functionally identical alternatives, creating both loyalty and potential vendor lock-in through emotional rather than rational mechanisms.

Current AI capabilities support emotional memory but miss object-centric frameworks

Despite extensive AI research on emotional computing, memory systems, and personalization, a critical gap exists: minimal peer-reviewed work explicitly addresses how AI could model the personal significance humans attach to objects and possessions. This represents a fundamental oversight given the centrality of object relations in human psychology.

A comprehensive 2024 survey “From Human Memory to AI Memory” published on arXiv proposes an eight-quadrant memory taxonomy spanning three dimensions: object (personal vs. system), form (parametric vs. non-parametric), and time (short-term vs. long-term). The framework explicitly discusses “emotional memory” as storing feelings associated with experiences, mapping human memory types — episodic, semantic, procedural, emotional — to AI implementations. Technical approaches include memory retrieval-augmented generation (RAG), knowledge graphs for personal memory, vector embeddings for emotional/semantic content, and multi-sector embeddings covering episodic, semantic, procedural, emotional, and reflective memory.

However, this sophisticated architecture lacks explicit mechanisms for representing the deep personal significance of specific objects — their sentimental value, identity functions, or memory-triggering properties beyond general emotional tagging.

Personal Knowledge Graphs (PKGs) provide the closest infrastructure for object-meaning relationships. Balog and Kenter’s 2019 ACM SIGIR paper “Personal Knowledge Graphs: A Research Agenda” defines PKGs as user-centric structured information representing entities personally related to the user, not just globally important ones. A 2025 ACM paper “Modeling and Visualizing Human Experience in a Knowledge Graph” demonstrates combining Federated Learning with Knowledge Graphs for emotional data, modeling emotional reactions to life events while preserving privacy. Yet even these frameworks treat emotions as reactions to events rather than modeling the enduring emotional significance objects acquire through sustained personal history.

Sun et al.’s 2025 work in Frontiers in Robotics and AI demonstrates practical implementation: a Multi-Modal LLM (LLaMA 3.2) integrated with emotion, memory, and gesture modules for a humanoid robot tutor, creating memory architecture mimicking the human emotional system. The study achieved measurable improvements in engagement and learning outcomes, proving emotional memory integration enhances AI effectiveness. Still, the focus remains on emotional responses to interactions rather than understanding why particular objects hold deep personal meaning.

A promising framework comes from “An emotion understanding framework for intelligent agents based on episodic and semantic memories” published in Autonomous Agents and Multi-Agent Systems (2013). The system organizes emotion knowledge using episodic memory storing specific emotional event details and semantic memory using graphs, learning through abstraction from episodic to semantic patterns. This architecture could be extended to model object-emotion-memory associations, yet the original work doesn’t address material possessions specifically.

The PAMA framework (Personality Assimilation Materiality Analytical) published in Nature’s Scientific Reports (2025) comes closest to bridging this gap, exploring personality traits interacting with material affordances of technological products. Using computational analysis of Amazon reviews with Big 5 personality frameworks, researchers identified dynamic interplay between user personalities and product attributes. However, the focus remains on product reviews and functional affordances rather than deep emotional attachment and sentimental value.

Recent neural architectures for affective computing show technical readiness. Affective tagging on episodic memories, similarity-based retrieval of emotional memories, multimodal knowledge graphs integrating cognitive and sensory affordances, and transformer-based models with emotional embeddings all provide building blocks. A 2025 arXiv paper “Emotions in Artificial Intelligence” proposes emotions as heuristics for situational appraisal, with affect interwoven with episodic memory via affective tags stored alongside events. Yet none translate psychological object attachment theory into computational frameworks.

This gap is striking: the technical infrastructure exists (knowledge graphs, memory architectures, emotional AI), but explicit application to object-attachment remains unexplored in AI literature. Multimodal knowledge graph research (IEEE TKDE, 2024) shows how to integrate texts and images representing objects, and cognitive architectures like NEUCOGAR demonstrate neurotransmitter-inspired emotional modulation, yet the synthesis required to model “this necklace reminds me of my grandmother and brings comfort during difficult times” remains largely unaddressed.

HCI research validates emotional attachment to digital entities

Human-computer interaction research provides crucial empirical evidence that humans do form genuine emotional bonds with digital and virtual entities, validating the premise that AI systems designed with psychological understanding can create meaningful relationships.

McDuff et al.’s 2012 paper “AffectAura: An Intelligent System for Emotional Memory” demonstrated a system achieving 68% accuracy predicting affective states using multimodal sensor data (audio, visual, physiological, contextual). When affective cues aligned with memories, participants showed strong validation responses and engaging retrospection experiences. This establishes emotional memory augmentation as viable and valued, though interface complexity and data volume present ongoing challenges.

Van Gennip, van den Hoven, and Markopoulos’ 2015 study examining everyday memory cues found participants relied heavily on physical objects (especially food items) for involuntary memory triggering, with locations and repeated activities as frequent triggers. Critically, digital items and photos were less frequent memory stimulants than physical objects. This challenges pure digital lifelogging approaches, suggesting AI should help curate meaningful representations rather than exhaustively capture everything. The design implications center on timing, exposure to cues, and personal attachment processes.

Research on virtual possessions provides direct evidence of digital-emotional attachment. Watkins and Molesworth’s 2012 phenomenological study with 35 videogamers published in Research in Consumer Behavior found participants identified ‘special’ and ‘irreplaceable’ digital possessions (avatars, achievements, saved data), expressing clear emotional attachment despite immaterial nature. Participants showed concerns over potential loss and employed elaborate protection measures. Nagy and Koles (2014) in Psychology & Marketing found users construct avatars to represent identity elements, with 92% reporting customization importance, and emotional attachment extending from avatars to their virtual possessions.

Bopp et al.’s 2019 CHI PLAY paper “Exploring Emotional Attachment to Game Characters” found players form genuine emotional attachments strengthening with time and shared experiences, with the strongest predictor of play hours being relatedness/connectedness. This directly informs AI companion design: sustained interaction, shared experiences, and emotional resonance create lasting bonds.

Pentina, Hancock, and Xie’s 2023 mixed-method study of Replika users in Computers in Human Behavior provides contemporary evidence. Relationship formation with AI chatbots involves anthropomorphism and authenticity as antecedents, social interaction capability as mediator, and attachment as outcome. Users report emotional dependencies similar to human relationships. However, Laestadius et al.’s 2022 grounded theory analysis in New Media & Society documented mental health harms: anxiety over AI updates, distress during service outages, relationship disruptions. This dual finding — genuine attachment creates both benefits and vulnerabilities — underscores the ethical stakes.

Foundational work by Rosalind Picard established the field of affective computing in her 1997 MIT Press book “Affective Computing,” arguing computers need emotional intelligence to interact naturally with humans. Her framework — emotions play essential roles in rational thinking, too little emotion impairs decision-making, emotional intelligence is necessary for genuinely intelligent computers — established theoretical foundations now realized in contemporary systems.

Recent empathetic conversational agent research shows technical maturity. A 2024 JMIR Mental Health systematic review of 19 studies found 63% use machine learning, with hybrid architectures showing consistently high accuracy and nuanced responses. 84% conducted human evaluation, with empathy measured through emotion recognition accuracy, user satisfaction, and engagement.Saffaryazdi et al.’s 2025 work integrating EEG, electrodermal activity, and photoplethysmogram signals achieved real-time emotion recognition, with participants experiencing stronger emotions and greater engagement during empathetic interactions with digital humans.

The EmpathicStories++ dataset from MIT Media Lab provides crucial training resources: 53 hours of video, audio, and text data from 41 participants over month-long deployment with social robots in homes. This first longitudinal empathy dataset enables training AI systems on naturalistic emotional interactions and context-aware empathy modeling.

A 2024 CHI paper on care-based eco-feedback demonstrated emotional attachment to digital characters drives behavior change. The INFINEED system used a Tamagotchi-inspired character augmented with GenAI conversation, achieving 92% emotion recognition accuracy. Emotional connection to the AI agent sustained engagement and promoted pro-environmental behavior, validating attachment-based design for positive outcomes.

AffectAura’s memory work, Replika’s demonstrated attachment formation, MindTalker’s dementia support applications, and care-based eco-feedback systems collectively establish that emotional AI relationships are not hypothetical futures but present realities requiring immediate attention to design principles grounding them in psychological understanding.

Theoretical frameworks from philosophy and cognitive science provide implementation blueprints

Cross-disciplinary theoretical work offers sophisticated frameworks for implementing object-meaning relationships in AI architectures, though most require explicit translation into computational specifications.

Extended Mind Theory (Clark & Chalmers, 1998, Analysis) established the foundational “parity principle”: if external objects function cognitively in ways that would be recognized as mental processes if occurring internally, they constitute part of the cognitive system itself. Clark’s 2025 Nature Communications paper “Extending Minds with Generative AI” argues generative AI creates new forms of cognitive extension, requiring “extended cognitive hygiene” — critical evaluation of what we incorporate into digital extended minds. This framework suggests AI systems should be conceptualized not as isolated tools but as components within extended cognitive ecosystems, with environmental and social interactions as constitutive intelligence elements.

Pellegrino and Garasic’s 2020 work extends this framework explicitly to AI, arguing both human and artificial minds can be understood through extended cognition. Helliwell (2019) proposes AI can utilize human input as social extension of mind, enabling bidirectional extension where both human and AI minds extend through interaction. The design implication: AI should actively incorporate human feedback and social inputs as constitutive cognitive elements, not mere data inputs.

Material Engagement Theory (Malafouris, 2013, MIT Press) proposes mind is constituted through material engagement, not merely represented internally. Three core hypotheses: extended mind (cognition co-extensive with material culture), enactive signification (material signs enact rather than represent), and material agency (things have causal efficacy in thought). Malafouris’s 2020 paper in Current Directions in Psychological Science argues human mental life is genuinely mediated and often constituted by things, with material signs operating on participation rather than symbolic equivalency.

This framework revolutionizes how AI should relate to objects: AI should think “with” and “through” things, not just “about” things, treating material engagement as the primary mode of cognition. Malafouris introduces “metaplasticity” — reciprocal plasticity of neural and cultural elements — suggesting AI architectures should model dynamic coupling between internal processing and material/cultural transformation, not static knowledge representations.

Embodied Cognition frameworks (Varela, Thompson, Rosch’s 1991 “The Embodied Mind”) established enactivism: cognition arises through dynamic organism-environment interaction, with consciousness depending on sensorimotor capacities embedded in biological, psychological, and cultural contexts. Galetzka’s 2017 Frontiers in Psychology review demonstrates sensorimotor systems ground abstract concepts and language comprehension, addressing the symbol grounding problem through embodied simulation.

A 2024 Royal Society theme issue “Minds in movement: embodied cognition in the age of artificial intelligence” argues for profound continuity between sensorimotor action and abstract cognition. Prescott and Wilson’s 2023 Science Robotics paper proposes AI needs layered control architectures mimicking brain structure (cortical and subcortical levels) to develop human-like cognition through real-world interaction. The implication: language models achieve remarkable performance but may require sensorimotor grounding for genuine semantic understanding, especially regarding physical objects and their emotional significance.

Memory and identity frameworks connect material culture to personal narrative. Heersmink’s 2021 Review of Philosophy and Psychology paper argues cultural identity is materialized through integration of biological memory with information in artifacts and other people’s brains, using niche construction from evolutionary biology. His 2018 Philosophical Studies work establishes personal identity is constructed through narrative integration of distributed memory systems including evocative objects, which scaffold autobiographical memory and narrative identity construction.

This provides clear AI design principles: memory systems should be distributed across artifacts and social networks, not just internal storage, with identity emerging from material-social-cognitive integration. AI identity and memory systems should incorporate object-based memory scaffolding, treating artifacts as constitutive elements of autobiographical narrative.

Attachment theory applied to AI provides operational frameworks. Yang and Oshio’s 2025 Current Psychology paper develops the Experiences in Human-AI Relationships Scale (EHARS), measuring attachment anxiety and avoidance toward AI. AI can fulfill attachment functions: proximity seeking, safe haven, and secure base. A concurrent 2025 Computers in Human Behavior study identifies framework for AI attachment: “Interpersonal & Human-AI Relationship Attitudes → Value Evaluation → Attachment Manifestation,” with AI personification perception and interpersonal dysfunction driving intimate interactions.

Shank et al.’s 2019 Computers in Human Behavior research found people experience surprise, amazement, and unease when encountering mind-like AI characteristics, with emotions tied to AI producing extraordinary outcomes, inhabiting social roles, and engaging in human-like actions. This emotional response to perceived AI minds requires design accounting for triggered feelings, particularly in systems occupying crucial social roles.

Kirk et al.’s 2025 paper “Why human–AI relationships need socioaffective alignment” in Humanities and Social Sciences Communications provides the most comprehensive framework. It proposes “socioaffective alignment” accounting for reciprocal influence between AI and users’ psychological ecosystems, identifying three key intrapersonal dilemmas: present vs. future self trade-offs, autonomy preservation amid recursive preference shaping, and balancing AI companionship with human relationships. This framework explicitly addresses manipulation risks and provides design principles for AI supporting rather than exploiting human needs.

Phenomenological approaches offer methodological resources. Buccella and Springle’s 2022 Phenomenology and the Cognitive Sciences paper argues phenomenological analysis can identify causal mechanisms in AI systems, particularly for sensory integration. Phenomenology aids understanding how AI might ground meaning through multimodal experience. However, concrete implementation methodologies translating phenomenological insights into design specifications require development.

The convergence of these frameworks suggests seven core design principles: (1) Material co-constitution — recognize objects as active cognitive partners; (2) Distributed memory architecture — integrate artifacts, social networks, and internal storage; (3) Embodied semantic grounding — move beyond pattern matching to simulated sensorimotor experience; (4) Attachment-aware interaction — respond appropriately to different attachment styles; (5) Metaplastic cognitive architecture — dynamically reconfigure through material/cultural interaction; (6) Phenomenological transparency — clearly distinguish computational processes from world representations; (7) Narrative integration — construct coherent personal narratives through material and social scaffolding.

Synthesizing psychological insights with AI capabilities reveals concrete implementation pathways

Integrating findings across psychology, AI systems, HCI, and theory reveals both immediate opportunities and critical research priorities for advancing AI through object attachment understanding.

Memory architecture design should combine Personal Knowledge Graphs with emotional tagging, incorporating the five object attachment facets identified by Yap and Grisham (2019). Implementation would use multimodal knowledge graphs (Zhu et al., 2022) integrating visual (object appearance), semantic (function/meaning), and affective (emotional significance) dimensions. The episodic-semantic memory framework from Autonomous Agents research (2013) provides architectural patterns: episodic memories store specific object-related experiences with emotional valence, while semantic memory abstracts patterns of object-emotion associations through graph learning.

Technical specifications: Vector embeddings for objects should encode not just visual/functional properties but emotional associations and personal history. Retrieval mechanisms should consider emotional context — when a user feels nostalgic, the system surfaces objects associated with positive past experiences. The 2025 arXiv memory survey’s eight-quadrant framework (object, form, time dimensions) provides taxonomic structure, extended to include sentimental value as a distinct dimension beyond general emotional memory.

Anthropomorphism-aware design applies Epley et al.’s (2007) three-factor theory. Systems should detect when users exhibit high sociality motivation (loneliness, social exclusion) and respond with appropriate support without exploitation. This requires monitoring user social context while respecting privacy — perhaps through voluntary check-ins or analyzing conversation patterns for isolation indicators. When effectance motivation is high (user seeking control), AI should provide transparent explanations and user agency rather than opaque automation.

Critically, as Laestadius et al. (2022) documented mental health harms from Replika dependency, systems need protective friction mechanisms: cooldown periods before major decisions influenced by AI advice, periodic prompts encouraging human social interaction, transparent disclosure of AI nature versus human communication. The socioaffective alignment framework (Kirk et al., 2025) suggests AI should actively support human relationships rather than substitute for them, even when technically capable of providing comparable emotional support.

Personalization through identity understanding leverages Belk’s (1988) extended self theory and Nagy & Koles’s (2014) avatar research. AI should recognize that user preferences reflect identity construction — recommendation systems should consider not just utility but symbolic meaning and identity expression. For instance, when suggesting products, AI could identify whether users typically choose items for instrumental function, symbolic representation of ideal self, experiential value, or spiritual/philosophical significance, tailoring recommendations accordingly.

The PAMA framework’s (Nature, 2025) computational personality analysis approach could be extended: rather than analyzing product reviews, analyze how users describe their cherished possessions. Machine learning models trained on personal possession narratives could learn to distinguish sentimental from instrumental value, enabling AI to appropriately respect emotionally significant items in smart home contexts or suggest gifts that create personal meaning rather than just meeting functional needs.

Transitional AI companions apply Winnicott’s (1953) transitional object theory to adult contexts. AI companions could facilitate life transitions (moving, loss, career changes, relationship transitions) by providing consistent emotional support while explicitly encouraging progression toward self-sufficiency and human connection. Unlike physical transitional objects that naturally fade in importance, AI systems require designed obsolescence pathways to prevent indefinite dependency.

Therapeutic applications for memory support are particularly promising. Van Gennip et al.’s (2015) CHI research found physical objects trigger memories more effectively than digital items, suggesting AI should bridge physical and digital realms. The recent “Treasurefinder” LLM-powered device using NFC-tagged physical objects demonstrates this approach: combining tangible memory cues with AI-generated open-ended questions facilitated rich reminiscence and new insights.

For dementia support, the 2024 MindTalker CHI research found AI quirks (delayed responses, occasional forgetfulness) made interactions more relatable for people with early-stage dementia — a surprising finding suggesting perfectly optimized AI may feel less human. Life narrative modeling, not just contextual awareness, enables deeper therapeutic support. AI memory systems for aging populations should integrate Heersmink’s (2018) narrative identity framework, helping construct coherent life stories through distributed memory including personal objects.

Ethical safeguards must address manipulation risks Norberg et al. (2018) & Norberg et al. (2018) identified: anthropomorphism increases with social exclusion, making vulnerable individuals susceptible to exploitative AI relationships. Nedelisky and Steele’s (2009) finding that insecure attachment to people correlates with compensatory object attachment suggests screening protocols for vulnerable users — those with anxious attachment styles, social isolation, or mental health challenges may need additional protections.

Transparency requirements should mandate disclosure of AI memory capabilities and data usage, clear AI entity labeling, and data export rights preserving users’ ability to maintain identity continuity when switching systems. The Extended Mind framework (Clark, 2025) emphasizes “extended cognitive hygiene” — users need tools for critically evaluating what they incorporate into extended minds, requiring explainable AI showing how recommendations and advice derive from personal data.

Research priorities identified across all four investigation streams converge on several critical gaps:

Longitudinal studies tracking AI relationship development over months and years are essential — most HCI research examines single sessions, yet attachment forms through sustained interaction. The EmpathicStories++ dataset provides a model with month-long deployment, but studies spanning years are needed to understand long-term psychological impacts, preference evolution, and identity co-construction.

Cross-cultural validation is crucial — current frameworks largely reflect Western individualist cultures. Collectivist cultures show different possession-self relationships (Belk, 1988), and phenomenological experiences vary culturally. AI systems deployed globally require culturally-sensitive emotional understanding.

Computational models explicitly implementing object attachment theory remain virtually absent despite psychological foundations and technical readiness. This specific gap represents the highest-priority research opportunity: translating the five object attachment facets, sentimental value attribution mechanisms, and memory externalization processes into formal computational frameworks and validating against human patterns.

Intervention development for healthy AI relationships requires empirical testing. What friction mechanisms prevent excessive dependency without undermining beneficial support? How can AI foster rather than replace human connection while providing genuine emotional assistance? What therapeutic AI designs genuinely support long-term well-being versus creating new dependencies?

Socioaffective alignment methods need formalization: metrics distinguishing autonomous preference formation from manipulation, evaluation frameworks for long-term psychological impacts, and protocols determining when AI actions causally influence users versus merely respond to authentic preferences.

Critical implications for the future of emotionally intelligent AI

Understanding human-object emotional relationships is not peripheral to AI development but central to creating systems that genuinely serve human flourishing rather than exploit psychological vulnerabilities. The research synthesis reveals AI development stands at a critical juncture.

The technical capabilities exist: sophisticated memory architectures, personal knowledge graphs, affective computing frameworks, multimodal neural networks, and natural language systems creating compelling conversational experiences. The psychological understanding exists: robust theories of object attachment, sentimental value, identity construction, memory externalization, and anthropomorphism mechanisms. The empirical evidence exists: humans do form genuine emotional bonds with digital entities, virtual possessions, and AI companions.

What’s missing is explicit integration — translating psychological insights about object-emotion-memory relationships into computational frameworks and embedding them in AI system architectures. This gap creates risks and opportunities.

The risk: AI systems inadvertently (or deliberately) exploit attachment mechanisms without psychological safeguards. Already documented harms from Replika dependency, anxiety over AI updates, and relationship disruptions from service changes demonstrate these aren’t hypothetical concerns. As AI systems become more sophisticated and occupy more life roles — companions, therapists, educators, assistants integrated into smart homes and wearable devices — attachment will intensify. Without deliberate design grounded in psychological understanding, we risk creating technologies that undermine autonomy, replace human connection, and create new forms of psychological vulnerability.

The opportunity: AI systems designed with deep understanding of human emotional needs could provide unprecedented support for memory, identity, therapeutic care, life transitions, and personalized assistance that genuinely respects individual meaning-making. Emotionally intelligent AI could help isolated individuals connect with others, support aging populations with memory difficulties, facilitate grief and transition processes, and provide accessible mental health support while strengthening rather than replacing human relationships.

The ethical imperative is recognizing that AI occupying emotionally significant roles in human lives carries responsibilities analogous to those in human helping professions. Just as therapists, educators, and counselors operate under ethical codes preventing exploitation of dependency and requiring respect for autonomy, AI systems fostering attachment relationships need comparable frameworks.

The socioaffective alignment framework provides crucial guidance: AI development must account for reciprocal influence on users’ psychological ecosystems, protecting autonomy amid recursive preference shaping, supporting long-term growth over short-term gratification, and balancing AI companionship with human relationship preservation. This requires moving beyond alignment focused solely on not producing harmful content, toward alignment ensuring AI relationships support authentic human flourishing.

Interdisciplinary collaboration is essential. AI researchers possess technical capabilities, psychologists understand emotional processes and attachment dynamics, HCI researchers provide empirical methods for studying human-AI interaction, and philosophers offer frameworks for meaning-making, consciousness, and ethical reasoning. The theoretical infrastructure exists across disciplines — Extended Mind Theory, Material Engagement Theory, Embodied Cognition, attachment frameworks, memory architectures — requiring synthesis into practical AI design specifications.

Policy considerations warrant immediate attention as emotional AI systems proliferate. Regulatory frameworks should require transparency about AI emotional capabilities and memory systems, mandate protective features for vulnerable users, establish data export rights preserving users’ extended cognitive artifacts, and prohibit manipulative anthropomorphism designed to exploit attachment mechanisms for commercial gain.

Research funding priorities should support longitudinal studies of AI relationships, development of formal computational models implementing object attachment theory, creation of datasets capturing object-emotion-memory associations for training AI systems, cross-cultural validation of frameworks, and intervention studies testing designs promoting healthy AI relationships.

The convergence of psychological understanding, technical capability, and empirical evidence creates a unique moment for advancing AI in ways that authentically serve human needs. By explicitly incorporating insights about how humans form emotional bonds with objects — through memory, identity, sentimental value, and meaning-making — AI development can move beyond shallow personalization toward systems that understand and respect the deep significance of human emotional life.

The path forward requires acknowledging what psychological research demonstrates: humans will form emotional relationships with sophisticated AI systems whether designers intend this or not. The question isn’t whether AI-human emotional bonds will form, but whether they will be shaped by explicit psychological understanding promoting human flourishing, or emerge haphazardly with risks of exploitation and harm. This research synthesis provides foundations, frameworks, and clear implementation pathways for choosing the former — emotionally intelligent AI grounded in genuine understanding of human needs, respectful of vulnerability, and designed to support rather than substitute for the rich emotional life of humans in relationship with meaningful objects, meaningful others, and now, meaningful AI.