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Turn-taking

Turn-taking is the rule-governed process by which conversational participants systematically allocate and transition speaking opportunities, minimizing simultaneous talk and silences through projected units of utterance completion.^[1] This organization operates via a set of local rules applied at points where a speaker's turn may end, enabling either continuation by the current speaker or selection of a next speaker.^[1] The foundational model emerged from conversation analysis, a method grounded in detailed transcription and examination of unscripted, naturally occurring interactions.^[2] Harvey Sacks, Emanuel Schegloff, and Gail Jefferson outlined it in their 1974 paper "A Simplest Systematics for the Organization of Turn-Taking for Conversation," identifying turn-constructional units—such as sentences or questions—as the basic segments whose syntactic, prosodic, and pragmatic completion signals transition-relevance places.^[1]^[3] At these junctures, rules prioritize current speaker selection of the next (e.g., via adjacency pairs like questions), followed by self-selection if none occurs, or continuation otherwise, yielding efficient speaker change without centralized control.^[1] Empirical observations from audio recordings demonstrate that this system achieves near-exclusive one-party-at-a-time talk across diverse settings, underscoring conversation's collaborative structure rather than individualistic improvisation.^[1] Extensions reveal variations in institutional contexts, such as courtrooms or meetings, where turn allocation adapts via pre-allocation or restrictions, yet retains core principles.^[4] The framework's robustness has informed applications in designing interactive systems, though debates persist on cultural universals versus context-specific adaptations.^[5]

Theoretical Foundations

Origins in Conversation Analysis

Conversation Analysis (CA), the primary framework for investigating turn-taking, originated in the mid-1960s through the work of sociologist Harvey Sacks at the University of California. Sacks initiated his research by analyzing audio recordings of unscripted telephone interactions, including over 200 calls to a Los Angeles suicide prevention hotline collected between 1963 and 1964, to identify recurrent patterns in how participants organized their talk without relying on preconceived categories or experimental setups. This data-driven method prioritized verbatim transcripts of natural speech, treating conversation as a structured, rule-governed activity accountable to its participants. Sacks collaborated with Emanuel A. Schegloff, who had earlier examined conversation openings in emergency calls, and Gail Jefferson, who refined transcription conventions to capture prosodic and non-verbal features like pauses, overlaps, and intonation. Their joint efforts culminated in the foundational 1974 paper "A Simplest Systematics for the Organization of Turn-Taking for Conversation," published in the journal Language. Drawing from thousands of hours of recorded interactions, the paper outlined turn-taking as operating via turn-construction units (TCUs)—complete utterance components such as sentences or questions—and transition-relevance places (TRPs) where a current speaker's turn naturally completes, signaling potential speaker change.^[6] The proposed systematics included three ordered rules at each TRP: (1) the current speaker selects the next via adjacency pairs or gaze; (2) if no selection occurs, any other participant may self-select; (3) if no one self-selects, the current speaker may continue. This model, derived inductively from empirical observations, minimized simultaneous talk (overlaps averaged 0.3% of conversation time in analyzed data) and gaps (averaging 0.0-0.2 seconds), while accommodating variable participant numbers through locally negotiated adjustments rather than fixed protocols.^[7] The approach rejected top-down psychological or sociological explanations, instead demonstrating turn-taking as an emergent, interactional order accountable in the sequential environment of talk itself.^[1]

Core Concepts and Systematics

Turn-taking in conversation is organized through a systematic set of practices that allocate speaking rights among participants, ensuring orderly exchanges with minimal gaps or overlaps.^[3] The foundational model, developed through empirical analysis of naturally occurring talk, posits a "simplest systematics" comprising turn-constructional units (TCUs) as the building blocks of turns and transition-relevance places (TRPs) as endpoints where speaker change becomes relevant.^[1] This system operates locally at each TRP via a hierarchy of rules: the current speaker may select the next (e.g., via direct address or interrogative form); absent such selection, any participant may self-select by starting to speak; if neither occurs, the current speaker may continue.^[6] TCUs are the minimal units that construct complete turns, varying in form to include sentential, clausal, phrasal, or non-lexical elements such as greetings or lexical items that project their completion through syntactic, prosodic, or pragmatic cues.^[8] Their boundaries are discernible to participants through recognizable patterns of projection, allowing anticipation of completion and thus enabling precise timing of turn transitions. TRPs emerge at these projected completion points, where the relevance of speaker change is interactionally enforced, though not obligatory, permitting extensions if no transition occurs.^[9] The system's efficacy lies in its achievement of "one party talks at a time," with overlaps and gaps treated as accountable deviations rather than normative features, as evidenced in recordings where participants collaboratively minimize simultaneity through mutual monitoring of emerging talk.^[3] This organization is party-administered and context-free in its basic form, applying across informal conversations without pre-allocation of turns, though adaptations occur in structured formats like meetings.^[1] Empirical observations confirm that the rules prioritize reciprocity and reciprocity projection, fostering coherence without centralized control.^[6]

Organizational Mechanisms

Transition-Relevance Places and Timing

Transition-relevance places (TRPs) are specific points within a speaker's turn where a transition to a next speaker becomes relevant and possible. These places are projected through the completion of turn-constructional units (TCUs), which serve as the fundamental building blocks of turns and are formed using syntactic, prosodic, and pragmatic cues that signal possible completion.^[10] TCUs vary in form, ranging from single words or phrases to full clauses or sentences, and their boundaries are recognizable to participants as points where the current turn could end without incompleteness.^[9] At a TRP, turn allocation operates through mechanisms such as current speaker selecting a next speaker via address terms or gaze, or allowing self-selection by any participant if no selection occurs. If no transition happens, the current speaker may continue with another TCU, extending the turn. This system ensures orderly transitions without pre-assigned turns, relying on participants' mutual projection of TRPs during ongoing speech. Empirical analysis of natural conversations reveals that TRPs are finely tuned to local contingencies, with continuations or shifts managed in real-time to maintain conversational flow.^[10] Timing of transitions at TRPs is characterized by minimal delays, with next speakers initiating turns shortly after TRP completion to avoid prolonged gaps. Studies across multiple languages report median inter-turn latencies of approximately 200-300 milliseconds, demonstrating high precision in everyday conversation.^[11] ^[12] This tight timing suggests anticipatory processing, where listeners project upcoming TRPs and prepare responses in advance, resulting in overlaps in about 5-10% of transitions—typically brief and non-disruptive—while gaps exceeding 600 milliseconds are rare and often treated as noticeable absences requiring repair.^[13] Such patterns hold empirically from corpus data, underscoring turn-taking's efficiency in minimizing both silence and intrusion.^[14]

Overlaps, Interruptions, and Repairs

In conversation analysis, overlaps occur when two or more speakers produce talk simultaneously, typically arising from the tight timing constraints at transition-relevance places (TRPs), where turn transitions are projected and anticipated within gaps of approximately 100-300 milliseconds.^[15] These overlaps are often brief—averaging under 200 milliseconds in duration across languages—and serve as a byproduct of the turn-taking system's preference for minimizing silence while avoiding prolonged simultaneity, rather than as inherent disruptions.^[12] Empirical analyses of natural conversations, such as those in English and other languages, reveal that overlaps frequently resolve through speaker withdrawal or adjustment, preserving the one-speaker-at-a-time rule without necessitating repair.^[16] Interruptions, by contrast, involve one speaker initiating talk in encroachment on another's ongoing turn, often outside projected TRPs, which can extend overlaps into competitive or uncoordinated simultaneity.^[17] Foundational work distinguishes interruptions from benign overlaps by their potential to violate turn-taking norms, such as when a second speaker persists despite cues like continued prosody or syntax from the current speaker; however, corpus-based studies indicate that many apparent interruptions function cooperatively, as in collaborative completions or repair initiations, rather than as aggressive dominance.^[18] For instance, in multi-party interactions, interruptions may allocate turns efficiently amid competing claims, with resolution depending on contextual relevance rather than strict hierarchy.^[19] Cross-linguistic data confirm low tolerance for extended interruptions, with overlaps exceeding 500 milliseconds prompting repair or cessation in over 90% of cases observed in diverse corpora.^[12] Repairs intersect with overlaps and interruptions by providing mechanisms to address "troubles" in speaking, hearing, or understanding that arise during turn transitions, often initiated within the same turn (self-repair) or the immediate next turn (other-repair).^[20] Self-repairs, such as cut-offs or reformulations, allow a speaker to correct errors mid-turn without yielding, typically within 200-500 milliseconds of the trouble source, minimizing overlap escalation.^[21] Other-initiations of repair, like "eh?" or partial repeats, exploit post-turn gaps or brief overlaps to signal issues, with timing constrained to avoid new turn competition; delays beyond 1 second reduce repair success rates by up to 40% in empirical recordings.^[15] This organization prioritizes speaker autonomy in correction while enabling collaborative resolution, as evidenced in analyses of everyday talk where 70-80% of repairs are self-initiated, reflecting a preference structure that aligns with turn-taking's efficiency.^[22]

Multimodal Cues

In face-to-face conversations, turn-taking is coordinated through multimodal nonverbal cues that supplement verbal and prosodic signals, enabling precise timing of transitions at transition-relevance places (TRPs). These cues include gaze direction, manual gestures, head movements, and body posture, which collectively facilitate turn yielding, prevent overlaps, and repair disruptions. Empirical analyses of video-recorded interactions demonstrate that such visual signals operate in real-time, often preceding or coinciding with verbal completions to project turn ends.^[23] ^[24] Gaze serves as a primary regulator, with mutual gaze or directed eye contact signaling readiness to yield or take a turn, while gaze aversion at potential TRPs inhibits transitions and extends speaker turns. For instance, speakers who avert their gaze during syntactic completion points delay recipient uptake, allowing time for further elaboration, as observed in corpus-based studies of dyadic and multiparty talk. This mechanism supports speech monitoring and breakdown prevention, with gaze shifts toward listeners often aligning with turn-final intonation to cue handover. Gestures, particularly beat and iconic hand movements, contribute by marking phrase boundaries; incomplete gestures at TRPs hold the floor, whereas gesture completions synchronize with verbal ends to invite responses.^[23] ^[24] ^[25] Head movements and nods provide additional layers of coordination, with forward leans or nods functioning as backchannel cues that encourage continuation or signal impending uptake. Research on embodied interaction reveals that head orientations toward interlocutors at TRPs enhance prediction accuracy in multiparty settings, while postural shifts like leaning back can demarcate turn boundaries. The interplay of these cues exhibits a multimodal facilitation effect, where combined signals reduce transition latencies compared to unimodal inputs, as evidenced in experimental paradigms tracking response times in controlled dialogues. These patterns hold across contexts but are modulated by factors like distance and visibility, underscoring their causal role in efficient conversational flow.^[26] ^[27] ^[28]

Contextual Variations

Cultural Universals and Specific Differences

Cross-cultural research on turn-taking in everyday conversation has identified robust universals, including a strong preference for one speaker at a time, minimization of gaps between turns (typically averaging around 200 milliseconds from the end of a transition-relevance place, or TRP), and avoidance of substantial overlaps (with overlaps exceeding 100 milliseconds occurring in fewer than 2% of turns across languages).^[29] These patterns hold in a diverse sample of 10 languages spanning unrelated families and geographic regions, such as English, Japanese, Italian, German, and Tzeltal (a Mayan language spoken in Mexico), as well as Lao, Cha'palaa (Ecuador), Murrinh-patha (Australia), Siwu (Ghana), and Russian.^[12] The consistency challenges earlier anthropological assertions of radical cultural divergences in conversational timing, demonstrating instead that turn-taking operates under shared structural constraints tied to the projectability of turn ends and real-time processing limits.^[29] While universals predominate, subtle variations exist in the precise timing of responses and tolerance for brief overlaps, often linked to linguistic structure rather than broad cultural norms. For instance, response latencies after TRPs range from about 84 milliseconds in Danish to around 300 milliseconds in Tzeltal, reflecting differences in how turn completions are projected (e.g., via syntax in English versus prosody or particles in Japanese).^[29] In high-context languages like Japanese, turns may anticipate completions more collaboratively, allowing marginally higher rates of short overlaps (under 100 milliseconds) without disruption, compared to low-context languages like English where syntactic boundaries enforce stricter minimal gaps.^[12] Similarly, comparative analyses of English and Spanish conversations reveal slightly elevated overlap frequencies in Spanish (often cooperative and non-competitive), attributed to cultural emphases on relational harmony, though these remain rare and do not violate the one-at-a-time principle.^[30] In specific cultural contexts, such as Saudi Arabic interactions, turn-taking incorporates politeness strategies like extended greetings or formulaic backchannels that elongate acceptable silences, yet still adheres to universal organization for coherence.^[31] Political discourse shows more pronounced differences, with languages like Italian exhibiting shorter gaps and higher overlap tolerance than Finnish, potentially reflecting societal norms around assertiveness versus restraint.^[32] Overall, these variations operate within narrow bounds (e.g., latencies varying by no more than 250 milliseconds across the sampled languages), underscoring that cultural specifics modulate rather than overhaul the foundational mechanics of turn-taking.^[29]

Gender Differences and Power Influences

In conversational turn-taking, empirical evidence from meta-analyses reveals a small but statistically significant tendency for males to interrupt more frequently than females, with an effect size of d = 0.15 across 43 studies examining adult interactions.^[33] ^[34] This pattern emerges primarily in mixed-sex dyads, where males accounted for approximately 75% of interruptions in early observational data from unscripted dialogues.^[35] However, the distinction often blurs with overlap speech, as females may engage in concurrent talk to signal rapport and involvement rather than disruption, contrasting with male-typical assertive incursions.^[36] Critiques of foundational claims, such as those by Zimmerman and West, highlight methodological limitations like small sample sizes (n=31 conversations) and potential confounds with contextual power dynamics, with replications showing variability by setting and participant familiarity.^[37] Power and status exert a stronger, more consistent influence on turn-taking than biological sex alone, enabling higher-status individuals to dominate transitions through strategic timing, reduced pauses, and interruption tolerance.^[38] In group settings, dominant speakers claim longer turns and initiate overlaps to maintain control, often signaled by nonverbal cues like increased head movement and vocal loudness, which correlate with perceived interpersonal influence.^[39] For instance, in hierarchical interactions such as professional discussions or meetings, subordinates exhibit higher rates of turn-yielding at transition-relevance places, while superiors preempt others via minimal gap responses, reinforcing asymmetric speech allocation.^[40] Experimental models of conversational dominance further quantify this, linking trait-level power assertion to elevated turn initiation rates and reduced repair sequences from challengers.^[41] Gender differences in turn-taking may partially mediate through status perceptions, as males historically occupy positions affording greater conversational latitude, amplifying interruption asymmetries in unequal contexts.^[42] Yet, when controlling for occupational or social hierarchy, sex-based effects diminish, suggesting power as the proximal causal mechanism over innate predispositions.^[37] In same-sex groups, females demonstrate collaborative turn extensions via supportive overlaps, while males favor competitive seizures, patterns that intensify under status disparities regardless of dyad composition.^[43] These dynamics underscore turn-taking as a microcosm of broader social hierarchies, where empirical deviations from egalitarian norms reflect enforceable asymmetries rather than mere stylistic variance.

Developmental and Pathological Variations

Turn-taking emerges in infancy through proto-conversations, where caregivers and infants aged 2-5 months alternate vocalizations and gazes, mimicking adult conversational structure with minimal overlaps and pauses averaging 200-600 milliseconds.^[44] These early exchanges foster neural synchrony between mother and infant, correlating with greater infant brain maturity at 12 months and larger vocabulary sizes at 18-24 months.^[45] By 8-21 weeks, infants demonstrate contingent responsiveness, responding to caregiver pauses with vocal or gestural turns, laying the foundation for reciprocal interaction.^[44] In early childhood, turn-taking proficiency develops longitudinally, with increased adult-child conversational turns predicting vocabulary growth from 18 to 30 months and enhanced executive function by age 4.^[46] ^[47] Children refine timing and prediction skills, shifting from reactive responses to anticipating transition-relevance places by ages 3-7, integrating linguistic, motor, and socio-cognitive factors.^[48] ^[49] Disruptions in early turn-taking, such as fewer exchanges, link to delayed social-emotional competencies by preschool age.^[50] Pathologically, autism spectrum disorder (ASD) features atypical turn-taking from toddlerhood, with reduced social initiations and responses in dyadic interactions, impairing joint attention and pragmatic reciprocity.^[51] ^[52] Children with ASD exhibit fewer person-centered exchanges compared to peers, necessitating targeted interventions like parent-mediated social turn-taking programs to build preverbal reciprocity.^[53] In schizophrenia, adults display aberrant turn-taking patterns, including prolonged pauses and disrupted entrainment, tied to pragmatic deficits and self-disorders that hinder intersubjective timing.^[54] ^[55] Aphasia, particularly post-stroke variants, alters turn-taking dynamics, with affected individuals relying more on multimodal cues (e.g., gestures) to signal turns while facing barriers like extended silences or partner-initiated repairs.^[56] Conversation therapy in aphasia emphasizes repairing these asymmetries, reducing test questions from partners that interrupt flow.^[56] Across disorders, deficits often stem from underlying impairments in timing prediction or social cue processing, rather than isolated conversational mechanics.^[57]

Applications and Extensions

In Artificial Intelligence and Dialogue Systems

Turn-taking mechanisms in artificial intelligence dialogue systems simulate the orderly exchange of speaker roles observed in human conversations, primarily through computational models that predict transition points, manage interruptions, and generate verbal or non-verbal cues. These systems, integral to voice assistants and chatbots, rely on end-of-turn detection algorithms to identify when a user has completed their utterance, often using acoustic features like prosody and pause duration rather than simplistic silence thresholds. Early implementations, such as those in Amazon Alexa or Apple Siri, predominantly employed voice activity detection (VAD), which triggers system responses after detecting speech cessation but frequently results in unnatural delays or premature interruptions, averaging 600-1000 milliseconds in latency compared to human gaps of about 200 milliseconds.^[58]^[59] Advanced models address these limitations by incorporating machine learning techniques, including recurrent neural networks and transformer-based architectures like TurnGPT, which forecast turn shifts based on multimodal inputs such as speech intonation, lexical cues, and contextual history. For instance, Voice Activity Projection (VAP) models predict the projected end of a turn from its onset, enabling proactive response planning and reducing overlap errors in real-time interactions. In spoken dialogue systems, turn-taking realism is enhanced by handling barge-ins—user interruptions—via incremental prediction, where the system pauses or yields mid-response, though current commercial assistants like Siri often require full utterance repetition for incomplete turns, leading to user frustration in 20-30% of complex queries.^[60]^[61]^[62] Evaluation benchmarks for these models emphasize metrics like turn-taking accuracy, latency, and naturalness, with recent protocols using supervised judges to assess audio foundation models on dynamics such as hold vs. shift instances. Despite progress, challenges persist in multi-party scenarios and noisy environments, where false positives from background sounds degrade performance by up to 15%, underscoring the need for robust, data-driven training on diverse corpora. Applications extend to customer service bots and virtual agents, where improved turn-taking correlates with higher user satisfaction scores, as measured in studies showing 25% reductions in perceived robotic stiffness.^[63]^[64]^[65]

In Human-Robot and Multi-Party Interactions

In human-robot interaction (HRI), turn-taking relies on computational models for detecting transition-relevance places, such as syntactic completion or prosodic cues like pitch fall, to minimize gaps and overlaps, which average 200 milliseconds in human dyads but often exceed this in robotic systems due to processing latencies.^[64] Empirical studies demonstrate that robots equipped with end-of-turn prediction algorithms, trained on human demonstration data, achieve more fluid exchanges by anticipating user yields, though they frequently misinterpret backchannels or hesitations as full turn completions, leading to premature interruptions.^[66] A 2023 analysis of 15 video-recorded HRI sessions revealed that human participants project and anticipate robot turn designs based on early syntactic cues, adapting their overlaps to robotic predictability, but perceived interactions as less collaborative when robots failed to repair interruptions multimodally.^[67] Handling interruptions poses additional challenges in HRI, where robots must balance yielding to human overrides—occurring in about 10-15% of human turns—with maintaining conversational flow, as delays in cue generation (e.g., gaze aversion or gestural retraction) disrupt causal sequences.^[68] Recent applications of general turn-taking models, such as TurnGPT for probabilistic projection and Voice Activity Projection for latency reduction, to conversational HRI show improved response times under 600 milliseconds, yet struggle with distinguishing turn-holding from yielding in noisy environments, resulting in higher overlap rates compared to human benchmarks.^[60] These models incorporate multimodal inputs like head nods and eye contact, but empirical evaluations indicate that without real-time adaptation, robots elicit more user frustration, as measured by post-interaction surveys rating naturalness 20-30% lower than human-human baselines.^[60] In multi-party interactions involving robots, turn-taking extends beyond dyadic rules to floor management, where self-selection competes with robot-initiated nominations via gaze or pointing, influencing participation equity; studies report robots directing gaze to specific humans reduce random overlaps by 25% but can bias quieter participants out of turns.^[64] A 2023 computational framework for multimodal turn prediction in group settings analyzes gaze, gesture, and prosody to forecast turn-keeping or shifts, achieving 75% accuracy in human multi-party data and adaptable to HRI scenarios with dynamic group sizes.^[69] In repeated multi-party HRI experiments with the EMYS robot, participants developed emergent norms over sessions, with robots learning to insert via brief overlaps resolving in 80% of cases, though challenges persist in handling side sequences or collective repairs amid varying group familiarity.^[70] Disney Research's 2023 work on dynamic groups emphasizes decision-theoretic balancing of wait times against proactive bids, enabling robots to sustain engagement in fluctuating multi-party contexts without dominating the floor.^[71]

Criticisms and Debates

Methodological and Interpretive Critiques

Critiques of the methodological foundations of turn-taking research, particularly the seminal model proposed by Sacks, Schegloff, and Jefferson in 1974, center on its heavy reliance on naturally occurring audio recordings from limited English-language corpora, which constrains generalizability and overlooks multimodal elements essential to interaction.^[72] Early analyses prioritized verbal transcripts, systematically omitting nonverbal cues such as gaze aversion or increased volume, which empirical studies demonstrate influence turn transitions by signaling intent or yielding floors.^[72] ^[24] This audio-centric approach, while valuing ecological validity, introduces limitations by conflating transcript artifacts with interactional realities, as transcription conventions impose interpretive layers without standardized validation across observers.^[73] Further methodological concerns involve the absence of quantitative metrics or experimental manipulations to test causal mechanisms, with claims of systematicity derived from descriptive patterns in small, non-representative samples rather than probabilistic modeling or cross-corpus comparisons.^[73] For instance, the model's rules for transition-relevance places lack explicit criteria beyond prosody and syntax, rendering them vulnerable to subjective application and failing to account for variability in repair sequences where utterances like apologies for interruption are misaligned with turn boundaries.^[72] Such inductive methods, rooted in ethnomethodology, prioritize sequential organization but resist falsification, as deviations are often reframed as rule adherence rather than evidence against universality.^[74] Interpretive critiques highlight the model's idealized portrayal of turn-taking as a locally managed, party-administered system that presumes compulsion in speaker selection, yet counterexamples abound where selected parties decline without disrupting order, suggesting overlooked negotiation dynamics.^[72] This framework interprets overlaps and gaps as minimal by design, but anthropological data reveal systematic cultural variations in timing—such as longer latencies in some non-Western societies—challenging ethnocentric assumptions drawn primarily from Anglo-American dyads and implying that the "simplest systematics" reflects context-specific norms rather than human universals.^[12] Moreover, by emphasizing interactional accountability over cognitive or intentional states, interpretations downplay power asymmetries, treating self-selection as egalitarian while empirical instances show dominant parties overriding first bidders through nonverbal or contextual leverage.^[72] These issues underscore a potential circularity: sequential evidence is both premise and conclusion, insulating the model from broader causal explanations like predictive processing constraints on rapid turns (often under 200 ms).^[13]

Alternative Explanations from Cognition and Evolution

Turn-taking in conversation has been proposed to arise from evolutionary pressures favoring efficient signaling and coordination in social species. Observations of vocal exchanges in nonhuman primates, such as chimpanzees, reveal structured turn-taking with minimal overlap and short response latencies akin to human dialogue, suggesting that these patterns predate language evolution and may stem from ancestral adaptations for cooperative communication or conflict avoidance in group-living primates.^[75]^[76] Similar duetting behaviors across primate clades, including marmosets and gibbons, indicate convergent evolution driven by needs for pair-bonding, territory defense, or predator deterrence, where alternating signals reduce acoustic interference and enhance mutual intelligibility.^[77] These animal analogs challenge purely cultural accounts by implying a biological substrate, potentially refined in humans through selection for rapid, reciprocal exchanges that support alliance formation and information sharing in large social networks.^[78] From a cognitive perspective, turn-taking emerges as a byproduct of processing constraints in language production and comprehension, where simultaneous speaking and listening overloads neural resources, necessitating alternation to minimize delays. Empirical data show median inter-turn gaps of approximately 200-300 milliseconds in diverse languages, a timing too precise for deliberate rules alone and indicative of predictive mechanisms that allow listeners to forecast turn ends via prosodic cues, syntax, and semantics during incremental parsing.^[11]^[13] Probabilistic models formalize this as Bayesian inference over action predictions, where speakers signal intent through gaze aversion or gesture completion, and listeners inhibit responses until projected boundaries, optimizing coordination under cognitive load without invoking top-down social norms.^[79] Neuroimaging supports involvement of mirror neuron systems and prefrontal inhibition, linking turn-taking to domain-general abilities in action anticipation rather than language-specific faculties.^[80] These explanations integrate evolution and cognition by positing that selection acted on pre-existing perceptual-motor loops, yielding turn-taking as an adaptive solution to the dual-task interference of vocalizing while decoding signals, evident in both primate vocalizations and human speech timing universals.^[81] Unlike interactional models emphasizing emergent rules, this view grounds the phenomenon in mechanistic realism, where overlaps disrupt processing efficiency and smooth transitions enhance survival-relevant outcomes like threat coordination.^[77] Empirical cross-species comparisons, however, reveal variability—such as longer latencies in some nonhuman exchanges—highlighting that human precision may reflect amplified cognitive demands from syntactic complexity, not mere inheritance.^[82]

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[PDF] Sex Roles, Interruptions and Silences in Conversation
SEX ROLES, INTERRUPTIONS AND SILENCES. IN CONVERSATION*. Don H. Zimmerman and Candace West. INTRODUCTION. Power and dominance constitute significant aspects of ...Missing: replication | Show results with:replication
[36]
[PDF] Women, Men, and Interruptions: A Critical Review - Stanford University
Some evidence is then provided that women are more likely than men to use simultaneous talk to show involvement and rapport, a fact that would be consistent ...
[37]
How Do We “Do Gender”? Permeation as Over-Talking and Talking ...
Jun 10, 2019 · The authors provide a new examination of conversational overlaps and interruptions. They show how negotiated conversational roles lead a status distinction ( ...
[38]
[PDF] Modeling Dominance in Group Conversations using Nonverbal ...
Abstract. Dominance - a behavioral expression of power - is a fundamental mechanism of social interaction, expressed and perceived in conversations through ...
[39]
Nonverbal Behaviors “Speak” Relational Messages of Dominance ...
Results indicate that perceptions of dominance are associated with a louder voice, more expressive facial behavior, more head movement, and more and longer ...
[40]
Evaluating Power and Status in Turn-taking Mechanism in Insight ...
Aug 7, 2025 · Second, the phenomenon of turn-taking mechanism is influenced by power and status, where status affects participants in dominating turn-taking.
[41]
Predicting and Understanding Turn-Taking Behavior in Open-Ended ...
Jul 3, 2024 · For example, the association between trait dominance – measured as expressed control and prosocial interpersonal power and influence – and ...<|separator|>
[42]
Meta-analyses of gender effects on conversational interruption: Who ...
Meta-analyses of 43 published studies comparing adult women's and men's interruptions during conversations were conducted.
[43]
[PDF] Power-oriented and Rapport-oriented Interruptions Among ...
The purpose of this study is to examine if women in the professional field interrupt each other in casual conversations with the intention to show power or to ...
[44]
Early development of turn-taking in vocal interaction between ... - NIH
We present new evidence of turn-taking interaction in infants aged between 8 and 21 weeks based on the analysis of 176 samples of naturalistic face-to-face ...
[45]
Your turn, my turn. Neural synchrony in mother–infant proto ...
Mar 6, 2023 · Turn-taking was positively associated with infant brain maturity and later vocabulary size, but not with later attachment security.
[46]
The Longitudinal Relationship Between Conversational Turn ...
Feb 6, 2021 · This paper reports on a longitudinal study investigating the developmental relationship between conversational turn-taking and vocabulary growth ...
[47]
Conversation disruptions in early childhood predict executive ... - NIH
May 24, 2023 · Adult-child turn-taking may contribute to positive development of self-regulation, both directly and indirectly. Few studies on adult-child turn ...
[48]
The development of children's ability to track and predict turn ...
Aug 9, 2025 · Children begin developing turn-taking skills in infancy but take several years to fluidly integrate their growing knowledge of language into ...<|separator|>
[49]
The Development of Turn‐Taking Skills in Typical Development and ...
Jul 7, 2025 · (2022) identified four potential driving forces underlying the development of turn-taking abilities: socio-cognitive, linguistic, and motor ...
[50]
The Link Between Conversational Turns and Social Development
Jun 21, 2023 · The number of conversational turns a child experiences at 18 months of age predicts social development and emotional health competencies at 30 months of age.
[51]
Association of turn-taking functions with joint attention in toddlers ...
Aug 18, 2021 · We found a significant relationship between social turn taking and joint attention, but not between nonsocial turn taking and joint attention.<|separator|>
[52]
A Social Turn-Taking, Parent Mediated Learning Intervention for a ...
Mar 30, 2023 · Research also shows that children with autism often have difficulty with turn-taking when the exchange is socially defined and person-centered, ...
[53]
Defining turn taking in intervention for young children with autism
Jul 16, 2021 · Turn taking is a form of preverbal, dyadic, reciprocal communication that may support key areas of development, such as language and joint attention.
[54]
turn-taking patterns in conversations involving individuals ... - PubMed
Our findings suggest that individuals with schizophrenia display peculiar turn-taking behaviour, thereby enhancing our understanding of interactional ...
[55]
Language in interaction: turn-taking patterns in conversations ...
High-quality stereo recordings were obtained and used to quantify turn-taking patterns. We investigated psychopathological dimensions and subjective experiences ...
[56]
Conversation Therapy with People with Aphasia and ... - Frontiers
We explored changes in: (i) conversation facilitators (such as multi-modal turns by people with aphasia); and (ii) conversation barriers (such as use of test ...
[57]
A systematic review and Bayesian meta-analysis of the development ...
Mar 19, 2022 · Several studies point toward atypicalities in the development of turn-taking in a variety of different populations—both traditionally defined ...
[58]
VAD vs. Turn-Taking Endpoints in Conversational AI | Retell AI
Dec 27, 2024 · VAD identifies speech presence, while turn-taking models determine when to respond or allow another to speak, managing dialogue flow.
[59]
Is It My Turn Yet? Teaching a Voice Assistant When to Speak
Oct 10, 2022 · Predicting initiation points based on voice intonation instead of silence detection could lead to the next generation of voice assistant technology.
[60]
Applying General Turn-taking Models to Conversational Human ...
Jan 15, 2025 · This paper applies TurnGPT and Voice Activity Projection (VAP) models to improve turn-taking in HRI, reducing pauses and interruptions.
[61]
[PDF] A Survey of Recent Advances on Turn-taking Modeling in Spoken ...
May 27, 2025 · Turn-taking modeling in dialogue systems aims to determine when a system should take a turn to speak, and is a key trait of realism.
[62]
You have interrupted me again!: making voice assistants more ...
These incomplete turns cannot currently be handled by today's voice assistants without full repetition of the entire utterance.
[63]
Benchmarking Audio Foundation Models on Turn-Taking Dynamics
We propose a novel evaluation protocol that can assess spoken dialog system's turn-taking capabilities using a supervised model as a judge.
[64]
Turn-taking in Conversational Systems and Human-Robot Interaction
We also provide an extensive review of multi-modal cues (including verbal cues, prosody, breathing, gaze and gestures) that have been found to facilitate the ...
[65]
The Complete Guide To AI Turn-Taking | 2025 - Tavus
Mar 12, 2025 · AI turn-taking powers the back-and-forth flow in conversations with artificial intelligence by determining the perfect moments for speaking and listening.
[66]
[PDF] Learning Turn-Taking Behavior from Human Demonstrations for ...
Abstract—Turn-taking is a fundamental behavior during human interactions and robots must be capable of turn-taking to interact with humans.
[67]
Managing Turn-Taking in Human-Robot Interactions - Tidsskrift.dk
Jun 12, 2023 · This study deals with turn-taking in human-robot interactions (HRI). Based on 15 sessions of video-recorded interactions between pairs of ...
[68]
[PDF] Turn-taking cue delays in human-robot communication - CEUR-WS
Abstract. Fluent communication between a human and a robot relies on the use of effective turn-taking cues. In human speech staying silent.Missing: papers | Show results with:papers<|separator|>
[69]
Multimodal Turn Analysis and Prediction for Multi-party Conversations
Oct 9, 2023 · This paper presents a computational study to analyze and predict turns (ie, turn-taking and turn-keeping) in multiparty conversations.<|separator|>
[70]
Multi-party Turn-Taking in Repeated Human–Robot Interactions
Nov 8, 2019 · This paper presents the methodology, setup and results for experiment involving the social robot EMYS participating in multi-party interaction.
[71]
Improving a Robot's Turn-Taking Behavior in Dynamic Multiparty ...
Mar 12, 2023 · In this paper, we present ongoing work to develop a robust and natural turn-taking behavior for a social agent to engage a dynamically changing group in a ...Missing: papers | Show results with:papers
[72]
https://doi.org/10.1515/semi.1986.60.1-2.1
[73]
METHODOLOGICAL ISSUES IN CONVERSATION ANALYSIS
Conversation Analysis (CA), a research tradition that grew out of ethnomethodology, has some unique methodological features.
[74]
Turn-taking: A critical analysis of the research tradition - Academia.edu
This critical analysis scrutinizes the research tradition surrounding turn-taking in conversations. It challenges prescriptive ideals regarding ...
[75]
Vocal turn-taking in a non-human primate is learned during ontogeny
May 22, 2015 · Conversational turn-taking is an integral part of language ... The evolutionary origins of language have been a source of conjecture ...
[76]
Chimps share humans' 'snappy' conversational style - BBC
Jul 22, 2024 · Like humans, wild chimpanzees engage in snappy, turn-taking conversations ... As part of investigating the evolutionary origins of ...
[77]
Turn-taking in Human Communication – Origins and Implications for ...
Proposition 1. Turn-taking among humans is universal, although languages are culture-specific. Proposition 2. Turn-taking is at the limits of human performance ...<|separator|>
[78]
Taking turns: bridging the gap between human and animal ...
Jun 6, 2018 · Outside of the primate order, vocal turn-taking has been studied in four distantly related mammalian groups, cetaceans, bats, elephants and mole ...
[79]
You cannot speak and listen at the same time: a probabilistic model ...
Mar 6, 2017 · Our results provide a computational-level characterization of turn-taking in terms of cognitive mechanisms of action prediction and signaling ...
[80]
Predicting One's Turn With Both Body and Mind - Frontiers
Jul 12, 2021 · ... cognitive mechanisms. We will argue that understanding inter-speaker ... Universals and cultural variation in turn-taking in conversation.
[81]
Evolution of Turn-Taking: A Bio-Cognitive Perspective - J-Stage
Bi-directional communication occurs in a variety of unrelated taxa, suggesting multi- ple independent evolutionary origins.
[82]
Whose turn is it? The question is at the heart of language and ...
Jul 15, 2025 · Chimpanzees' turn-taking shows skills that may have supported the evolution of human conversation.