About

Gus Cooney is a faculty member in the Operations, Information and Decisions Department at the Wharton School of the University of Pennsylvania. His research focuses on negotiation, examining the art and science of negotiation, conflict resolution, and social interaction. Cooney's work emphasizes understanding negotiation as a complex social process influenced by emotions, personalities, cultures, and situational factors, and explores how variability in these features affects negotiation outcomes. He has contributed to the scholarly understanding of negotiation by reviewing its fundamental features, phases, and the influence of social and psychological dynamics. Cooney actively pursues research on negotiation in real-world settings and its intersection with emerging areas such as artificial intelligence and conversation analysis. Additionally, he teaches courses on negotiations, engaging students through simulations, theoretical models, and practical exercises to improve negotiation skills.

Research topics

• Sociology
• Psychology
• Social psychology
• Communication
• Engineering
• Philosophy
• Epistemology

Selected publications

• The rise and fall of conversations: Dyads first calibrate and then differentiate using linguistic, facial, and acoustic communication channels

  PsyArXiv (OSF Preprints) · 2026-03-29

  preprintOpen access

  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.

  Publisher

• The rise and fall of conversations: Dyads first calibrate and then differentiate using linguistic, facial, and acoustic communication channels

  2026-03-29

  articleOpen access

  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.

  PublisherDOI

• ConversationAlign: Open-source software for analyzing patterns of lexical use and alignment in conversation transcripts

  Behavior Research Methods · 2026-02-20

  articleOpen access

  Much of our scientific understanding of language processing has been informed by controlled experiments divorced from the real-world demands of naturalistic communication. Conversation requires synchronization of rate, amplitude, lexical complexity, affective coloring, shared reference, and countless other verbal and nonverbal dimensions. Conversation is not merely a vector for information transfer but also serves as a mechanism for establishing or maintaining social relationships. This process of language calibration between interlocutors is known as linguistic alignment. We developed an open-source R package, ConversationAlign, capable of computing novel indices of linguistic alignment and main effects of language use between interlocutors by evaluating word choice across numerous semantic, affective, and lexical dimensions (e.g., valence, concreteness, frequency, word length). We describe the operations of ConversationAlign, including its primary functions of cleaning and transforming raw language data into simultaneous time series objects aggregated by interlocutor, turn, and conversation. We then outline mathematical operations involved in computing complementary indices of linguistic alignment that capture both local (synchrony in turn-by-turn scores) and global relations (overall proximity) between interlocutors. We present a use case of ConversationAlign applied to interview transcripts from American radio legend Terry Gross and her many guests spanning 15 years. We identify caveats for use and potential sources of bias (e.g., polysemy, missing data, robustness to brief language samples) and close with a discussion of potential applications to other populations. ConversationAlign (v 0.4.0) is freely available for download and use via CRAN or GitHub. For technical instructions and download, visit https://github.com/Reilly-ConceptsCognitionLab/ConversationAlign .

  PublisherOA PDFDOI

• The role of topic choice in cross-partisan conversations

  SocArXiv (OSF Preprints) · 2026-03-25

  preprintOpen access1st authorCorresponding

  Animosity between Republicans and Democrats has escalated for decades, threatening the health of American democracy. Research on intergroup contact suggests that talking across party lines can reduce this affective polarization, yet studies disagree on whether confronting or avoiding political disagreement is the more effective strategy. We address this debate using a large-scale integrative experiment in which Republicans and Democrats engaged in face-to-face video conversations, with levels of disagreement and political relevance systematically varied across a diverse set of topics. While some topics reduced affective polarization more than others, no measure of a topic’s “politicalness” predicted which conversations went well. Moreover, topic assignment explained just 2% of the outcome variance in our sample, with people assigned to the same topic often having entirely different experiences. What did correlate with conversational success was how individuals experienced the interaction, for example, whether they felt their partner listened well, took their perspective, and made them feel heard. We suggest a shift in focus from choosing the “right” topic to understanding the detailed interactional dynamics that make cross-partisan conversation succeed.

  Publisher

• Publisher Correction: NaturalTurn: a method to segment speech into psychologically meaningful conversational turns

  Scientific Reports · 2025-12-01

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Abstract word dropout and cross-speaker misalignment of word concreteness are features of conversation in aging

  Cortex · 2025-07-23 · 2 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Evaluating the psychological and social nature of actual and perceived liking gaps.

  Journal of Personality and Social Psychology · 2025-02-24 · 4 citations

  articleOpen access

  = 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).

  PublisherOA PDFDOI

• CANDORspeech: A large-scale corpus of phonetically annotated conversational speech from dyadic online conversations with human quality control

  SWISSUbase UZH · 2025-11-04

  datasetOpen access
  PublisherDOI

• CANDORspeech: A large-scale corpus of phonetically annotated conversational speech from dyadic online conversations with human quality control

  SWISSUbase UZH · 2025-11-04

  datasetOpen access
  PublisherDOI

• CABank CANDOR Corpus

  TalkBank · 2025-01-01

  datasetOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Erica J. Boothby

  University of Pennsylvania

  9 shared

• Gillian M. Sandstrom

  7 shared

• Andrew Reece

  Better Education Inc

  5 shared

• Daniel T. Gilbert

  4 shared

• Timothy D. Wilson

  University of Virginia

  4 shared

• Christine H. Chung

  Moffitt Cancer Center

  3 shared

• Adam Mastroianni

  3 shared

• Bryn Dawson

  3 shared

Labs

• Operations, Information and Decisions DepartmentPI

Education

• PhD, Psychology

  Harvard University