Conversations are dynamic systems of coordinated behavior. Utterances lengthen and shorten, emotions converge and diverge, topics stabilize and vary. Yet the temporal structure of these changes remains uncharacterized. We analyzed 1,656 extended conversations between strangers, using data across linguistic, facial, and acoustic communication channels. Our preregistered analyses tested the temporal trajectories of 27 conversational measures (e.g., utterance length, emotional similarity, topic persistence, acoustic properties). All measures showed quadratic trajectories as predicted, with an initial calibration phase characterized by utterances becoming gradually longer and dyads becoming more semantically and emotionally similar, and these trends subsiding or reversing during a subsequent differentiation phase. This two-phase pattern reliably emerged across multiple communication channels. Conversations with stronger quadratic trajectories in utterance length were associated with greater shared reality between participants. These findings reveal that diverse conversation features may share a common, fundamental temporal structure, part of the hidden choreography underlying human conversation.

Language development is significant for an abstractness shift where the early concrete lexicon of childhood steadily expands to incorporate abstract concepts (e.g., mental states, emotions, ideas). Much remains to be learned about the trajectories of concrete versus abstract words across the span of development, especially within the domain of discourse production. We investigated the prevalence of abstract word use in dyadic conversations between adults of different ages (range 18-66). Our aim was to adjudicate between two opposing trajectories of language use in normal cognitive aging. The vocabulary growth perspective holds that aging is associated with a steady accrual of vocabulary knowledge resulting in greater lexical diversity and a larger pool of abstract words to draw upon in conversation. In contrast, resource pruning predicts the opposite trajectory such that age-associated gains in vocabulary acquisition are offset by diminished executive resources. Since abstract words tend to have higher lexical retrieval demands than concrete words, normal aging will result in an apparent dropout of abstract words. Moreover, this concreteness effect will be amplified within a resource-intensive communication modality such as conversation. We analyzed distributions of abstract and concrete words and cross-speaker alignment within unscripted conversations between adults of different ages (N = 1565 conversations, >8 million words, age range 19-66). Aging was associated with abstract word dropout, and misalignment between conversation partners on concreteness expanded in parallel with their respective age differences. These results add to a growing body of research that implicates executive functioning in controlled lexical retrieval of abstract words. We discuss significance of these results for understanding intergenerational communication and relationships between aging, executive functioning, and lexical retrieval in high-level discourse.

Much of our understanding of language processing has been informed by controlled laboratory experiments abstracted from the real world demands of naturalistic communication. Interactive language use “in the wild” involves synchronizing numerous verbal and non-verbal behaviors between interlocutors. Conversation partners synchronize verbal production through modulations of rate, amplitude, lexical-semantic complexity, affective tone, and many other dimensions. Much remains to be learned about how to measure linguistic alignment in naturalistic interactions. We developed an open-source R package (ConversationAlign) capable of computing novel indices of alignment and main effects of language use between interlocutors across 30 psycholinguistic dimensions (e.g., valence, concreteness, frequency, word length). We describe operations of the ConversationAlign workflow, including its primary functions of cleaning transcripts and transforming raw language data to simultaneous time series objects aggregated by interlocutor, turn, and conversation. We present a use case of ConversationAlign applied to interview transcripts between American radio legend, Terry Gross, and her many guests over the span of 15 years (Fresh Air Archive, 2001). We identify caveats for use and potential sources of bias (e.g., polysemy, missing data, robustness to brief language samples). We close with a discussion of potential applications to better understand lexical alignment. ConversationAlign is freely available for download and use via GitHub at https://github.com/Reilly-ConceptsCognitionLab/ConversationAlign

Our beliefs about how much we are liked tend to be less positive than liking judgments of others, a finding termed the "liking gap." Because much of the past work has studied liking gaps at the sample level, it has overlooked important nuances in how these gaps can be measured and experienced. We introduce a distinction between the actual liking gap (i.e., a between-person discrepancy between how much others actually like us and how much we think others like us) and the perceived liking gap (i.e., a within-person discrepancy between how much we like others and how much we think others like us). Across three large first-impression samples (Ntotal = 2,753), we use condition-based regression analyses to examine (a) who tends to exhibit these gaps, and (b) how people experience social interactions marked by gaps. Our findings suggest that people display two types of gaps, actual and perceived, that are psychologically distinct. Larger negative perceived liking gaps were related to indicators of insecurity (i.e., lower self-esteem, higher social anxiety, and higher neuroticism), whereas actual gaps did not show the same pattern. Neither gap was reliably associated with the quality of people's social interaction. Finally, our approach also allowed us to isolate the unique effect of feeling liked as a robust, consistent correlate of both psychological adjustment and interaction quality. Overall, this research offers new insights into the (mal)adaptiveness of two types of liking gaps. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Conversation is a subject of increasing interest in the social, cognitive, and computational sciences. Yet as conversational datasets continue to increase in size and complexity, researchers lack scalable methods to segment speech-to-text transcripts into conversational "turns"-the basic building blocks of social interaction. We discuss this challenge and then introduce "NaturalTurn," a turn-segmentation algorithm designed to accurately capture the dynamics of conversational exchange. NaturalTurn operates by distinguishing speakers' primary conversational turns from listeners' secondary utterances, such as backchannels, brief interjections, and other forms of parallel speech that characterize human conversation. Using data from a large conversation corpus, we show that NaturalTurn captures conversational turns more accurately than a baseline model. For example, it produces turns with durations and gaps that match empirical literature, reveals stronger linguistic alignment patterns between speakers, and uncovers otherwise hidden relationships between turn-taking and affective outcomes. NaturalTurn thus represents a pragmatic development in machine-generated transcript-processing methods, or "turn models", that will enable researchers to link turn-taking dynamics with important outcomes of social interaction, a central goal of conversation science.

Our earliest acquired words tend to reference concrete objects that can be seen, heard, touched, and felt. One hallmark of language maturation involves an ‘abstractness shift’ where language is used to convey intangible mental states, emotions, and ideas. Much of our understanding of the trajectory of concreteness in language development ends at early adolescence and is informed by studies of comprehension. We investigated concreteness effects in language production across the span of adulthood with a focus on conversation. We tested two competing hypotheses about abstract word use in conversation as a function of normal aging. The first hypothesis is that a steady accrual of vocabulary knowledge over the lifespan will confer a more expansive abstract lexicon and that older adults will in turn produce more abstract words in conversation. An alternative hypothesis is that aging gradually compromises executive resources that impact abstract word retrieval, leading to a concreteness bias in conversation. We analyzed distributions of abstract and concrete words and cross-speaker alignment within unscripted conversations between adults of different ages (N=1565 conversations, >8 million words, age range 19-66) using a novel computational algorithm (ConversationAlign). Aging was associated with abstract word dropout, a finding that is consistent with the hypothesis that high processing demands of conversation (e.g., alignment, topic maintenance) bias lexical retrieval toward concrete word use. We discuss the significance of these results for understanding intergenerational communication and relationships between aging, executive functioning, and lexical retrieval in high-level discourse.

People spend a substantial portion of their lives engaged in conversation, and yet, our scientific understanding of conversation is still in its infancy. Here, we introduce a large, novel, and multimodal corpus of 1656 conversations recorded in spoken English. This 7+ million word, 850-hour corpus totals more than 1 terabyte of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, together with an extensive survey of speakers’ postconversation reflections. By taking advantage of the considerable scope of the corpus, we explore many examples of how this large-scale public dataset may catalyze future research, particularly across disciplinary boundaries, as scholars from a variety of fields appear increasingly interested in the study of conversation.

Much of employees’ professional success and emotional well-being comes from their social interactions in the workplace. Unfortunately, employees sometimes fail to socialize as effectively as they could, reducing their social capital at work and limiting the potential benefits they could gain from building strong social connections in the workplace. This symposium demonstrates four new ways that employees fail to maximize their social value at work, and additionally suggests a reason why they do so: workers have mistaken forecasts regarding their social interactions. In particular, the symposium showcases four distinct contexts of social interactions – talking to dissimilar others, seeking help, gossiping, and being humorous – and suggests methods for improving social capital and consequently career success. Taken together, these symposium presentations shed light on the various pitfalls, mistaken beliefs, and surprising ignorance we have when it comes to optimal workplace socialization. The research findings will encourage people to examine their own assumptions regarding social interactions at work, so that they can create more effective connections and uplifting moments, and achieve greater social capital for themselves in the workplace. A Closer Look at Homophily: Why Do People Avoid Talking to Dissimilar Others? Author: Erica Boothby; The Wharton School, U. of Pennsylvania Author: Gus Cooney; Harvard U. Should I Ask Over Zoom, Phone, Email, or In-Person? Communication Channel and Predicted Compliance Author: Vanessa Bohns; Cornell U. Author: Mahdi Roghanizad; Ted Rogers School of Management, Toronto Metropolitan U. Gossipers Beware: Gossipers Underestimate the Negative Reputational Consequences of Gossiping Author: Andrew Choi; U. of California, Berkeley Author: Sonya Mishra; U. of California, Berkeley Author: Juliana Schroeder; U. of California, Berkeley The First Laugh: It is Easier Than We Think to Attempt Humor with Strangers Author: Elizabeth Jiang; UCLA Author: Sanford Ely DeVoe; UCLA

In this review, we identify emerging trends in negotiation scholarship that embrace complexity, finding moderators of effects that were initially described as monolithic, examining the nuances of social interaction, and studying negotiation as it occurs in the real world. We also identify areas in which research is lacking and call for scholarship that offers practical advice. All told, the existing research highlights negotiation as an exciting context for examining human behavior, characterized by features such as strong emotions, an intriguing blend of cooperation and competition, the presence of fundamental issues such as power and group identity, and outcomes that deeply affect the trajectory of people's personal and professional lives.

People spend a substantial portion of their lives engaged in conversation, and yet our scientific understanding of conversation is still in its infancy. In this report we advance an interdisciplinary science of conversation, with findings from a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections. We leverage the considerable scope of the corpus to (1) extend key findings from the literature, such as the cooperativeness of human turn-taking; (2) define novel algorithmic procedures for the segmentation of speech into conversational turns; (3) apply machine learning insights across various textual, auditory, and visual features to analyze what makes conversations succeed or fail; and (4) explore how conversations are related to well-being across the lifespan. We also report (5) a comprehensive mixed-method report, based on quantitative analysis and qualitative review of each recording, that showcases how individuals from diverse backgrounds alter their communication patterns and find ways to connect. We conclude with a discussion of how this large-scale public dataset may offer new directions for future research, especially across disciplinary boundaries, as scholars from a variety of fields appear increasingly interested in the study of conversation.

After conversations, people continue to think about their conversation partners. They remember their stories, revisit their advice, and replay their criticisms. But do people realize that their conversation partners are doing the same? In eight studies, we explored the possibility that people would systematically underestimate how much their conversation partners think about them following interactions. We found evidence for this thought gap in a variety of contexts, including field conversations in a dining hall (Study 1), "getting acquainted" conversations in the lab (Study 2), intimate conversations among friends (Study 3), and arguments between romantic partners (Study 4). Several additional studies investigated a possible explanation for the thought gap: the asymmetric availability of one's own thoughts compared with others' thoughts. Accordingly, the thought gap increased when conversations became more salient (Study 4) and as people's thoughts had more time to accumulate after a conversation (Study 6); conversely, the thought gap decreased when people were prompted to reflect on their conversation partners' thoughts (Study 5). Consistent with our proposed mechanism, we also found that the thought gap was moderated by trait rumination, or the extent to which people's thoughts come easily and repetitively to mind (Study 7). In a final study, we explored the consequences of the thought gap by comparing the effects of thought frequency to thought valence on the likelihood of reconciliation after an argument (Study 8). Collectively, these studies demonstrate that people remain on their conversation partners' minds more than they know. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Although people derive substantial benefit from social connection, they often refrain from talking to strangers because they have pessimistic expectations about how such conversations will go (e.g., they believe they will be rejected or not know what to say). Previous research has attempted but failed to get people to realize that their concerns about talking to strangers are overblown. To reduce people's fears, we developed an intervention in which participants played a week-long scavenger hunt game that involved repeatedly finding, approaching, and talking to strangers. Compared to controls, this minimal, easily replicable treatment made people less pessimistic about the possibility of rejection and more optimistic about their conversational ability-and these benefits persisted for at least a week after the study ended. Daily reports revealed that people's expectations grew more positive and accurate by the day, emphasizing the importance of repeated experience in improving people's attitudes towards talking with strangers.

Do conversations end when people want them to? Surprisingly, behavioral science provides no answer to this fundamental question about the most ubiquitous of all human social activities. In two studies of 932 conversations, we asked conversants to report when they had wanted a conversation to end and to estimate when their partner (who was an intimate in Study 1 and a stranger in Study 2) had wanted it to end. Results showed that conversations almost never ended when both conversants wanted them to and rarely ended when even one conversant wanted them to and that the average discrepancy between desired and actual durations was roughly half the duration of the conversation. Conversants had little idea when their partners wanted to end and underestimated how discrepant their partners' desires were from their own. These studies suggest that ending conversations is a classic "coordination problem" that humans are unable to solve because doing so requires information that they normally keep from each other. As a result, most conversations appear to end when no one wants them to.

Every relationship begins with a conversation. Past research suggests that after initial conversations, there exists a liking gap: people underestimate how much their partners like them. We extend this finding by providing evidence that it arises in conversations among small groups (Study 1), continues to exist in engineering teams working on a project together (Study 2), and is linked to important consequences for teams’ ability to work together in a sample of working adults (Study 3). Additional evidence suggests that the liking gap is largest for peer relationships and that it is determined in part by the extent to which people focus on negative aspects of the impressions they make on others. Group conversations and team interactions often leave people feeling uncertain about where they stand with others, but our studies suggest that people are liked more than they know.

What causes people to disclose their preferences or withhold them? Declare their love for each other or keep it a secret? Gossip with a coworker or bite one’s tongue? We argue that to understand disclosure, we need to understand a critical and often overlooked aspect of human conversation: group size. Increasing the number of people in a conversation creates systematic challenges for speakers and listeners, a phenomenon we call the many minds problem. Here, we review the substantial implications that group size is likely to have on how much people disclose, what they disclose, and how they feel about it.

Having conversations with new people is an important and rewarding part of social life. Yet conversations can also be intimidating and anxiety provoking, and this makes people wonder and worry about what their conversation partners really think of them. Are people accurate in their estimates? We found that following interactions, people systematically underestimated how much their conversation partners liked them and enjoyed their company, an illusion we call the liking gap. We observed the liking gap as strangers got acquainted in the laboratory, as first-year college students got to know their dorm mates, and as formerly unacquainted members of the general public got to know each other during a personal development workshop. The liking gap persisted in conversations of varying lengths and even lasted for several months, as college dorm mates developed new relationships. Our studies suggest that after people have conversations, they are liked more than they know.