Do Speaker's emotions influence their language production? Studying the influence of disgust and amusement on alignment in interactive reference

Out, Charlotte; Goudbeek, Martijn; Krahmer, Emiel

doi:10.1016/j.langsci.2019.101255

Cited by 12 publications

(7 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, recent studies demonstrate successful implementation of similar methods for web‐based interaction experiments, with results suggesting that a simulated partner was sufficiently convincing for participants to believe in the authenticity of the interaction (Buz, Jaeger, & Tanenhaus, 2014; Duran & Dale, 2011). Notably, Out, Goudbeek, and Krahmer (2020) also recently replicated (Goudbeek & Krahmer, 2012) Experiment 1, finding that speakers aligned with their hypothetical partner's choice of modifier (color vs. orientation) in more a naturalistic dailog setting with a real interlocutor. This demonstrates that alignment effects are comparable across real and simulated interaction, at least with a speaker's choice of modifier encoding.…”

Section: Discussionmentioning

confidence: 78%

Speakers Align With Their Partner's Overspecification During Interaction

Loy

Smith

2021

Cognitive Science

View full text Add to dashboard Cite

Speakers often overspecify by encoding more information than is necessary when referring to an object (e.g., “the blue mug” for the only mug in a group of objects). We investigated the role of a partner's linguistic behavior (whether or not they overspecify) on a speaker's own tendency to overspecify. We used a director–matcher task in which speakers interacted with a partner who either consistently overspecified or minimally specified in the color/size dimension (Experiments 1, 2, and 3), as well as with a partner who switched behaviors midway through interaction (Experiments 4 and 5). We found that speakers aligned with their partner's linguistic behavior to produce overspecific or minimally specific descriptions, and we saw little evidence that the alignment was enhanced by lexical or semantic repetition across prime and target trials. Time‐course analyses showed that alignment increased over the course of the interaction, and speakers appeared to track a change in the partner's linguistic behavior, altering their reference strategy to continue matching that of their partner's. These results demonstrate the persistent influence of a partner's behavior on speakers across the duration of an interaction.

show abstract

Section: Discussionmentioning

confidence: 78%

Speakers Align With Their Partner's Overspecification During Interaction

Loy

Smith

2021

Cognitive Science

View full text Add to dashboard Cite

show abstract

“…The person describing their illness can modify their emotions if they feel distressed describing their ailment to the medical professional, which is being interpreted due to a language barrier (Out et al, 2020;Valero-Garcés, 2016). Sociology: Karamibekr and Ghorbani (2012) discuss the use of SA for social issues.…”

Section: Applications To Other Research Areasmentioning

confidence: 99%

Applications and Challenges of Sentiment Analysis in Real-life Scenarios

Kanojia¹,

Joshi²

2023

Preprint

View full text Add to dashboard Cite

Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a driving force for research in SA. This chapter describes some of these applications and related challenges in real-life scenarios. In this chapter, we focus on five applications of SA: health, social policy, e-commerce, digital humanities and other areas of NLP. This chapter is intended to equip an NLP researcher with the 'what', 'why' and 'how' of applications of SA: what is the application about, why it is important and challenging and how current research in SA deals with the application. We note that, while the use of deep learning techniques is a popular paradigm that spans these applications, challenges around privacy and selection bias of datasets is a recurring theme across several applications.

show abstract

“…Growing scholarly attention has been devoted to a potentially reciprocal relationship between mood and language. The moodlanguage interactions have been explored in various linguistic domains, including syntactic processing (Vissers et al, 2010;Jiménez-Ortega et al, 2012;Van Berkum et al, 2013;Verhees et al, 2015;Liu et al, 2018;Yano et al, 2018), language production (Isen et al, 1985;Beukeboom and Semin, 2006;Kharkhurin and Altarriba, 2016;Hinojosa et al, 2017;Braun et al, 2019;Forgas and Matovic, 2020;Out et al, 2020), communicative interactions (Forgas, 1999;Koch et al, 2013;Matovic and Forgas, 2018), reading patterns (Bohn-Gettler and Rapp, 2011;Scrimin and Mason, 2015;Mills et al, 2019), and emotional word processing (e.g., Kiefer et al, 2007;Pratt and Kelly, 2008;Egidi and Nusbaum, 2012;Kissler and Bromberek-Dyzman, 2021;Naranowicz et al, 2022a). Arguably, semantic processing (i.e., the cognitive mechanisms engaged in language comprehension) has attracted a particularly keen interest among mood researchers, who have employed a variety of behavioural (e.g., Storbeck and Clore, 2008;Sakaki et al, 2011;Matovic et al, 2014) and electrophysiological measures (e.g., Goertz et al, 2017;Ogawa and Nittono, 2019a,b;Naranowicz et al, 2022b) to understand the principles guiding the relationship between our current affective state and how we understand language.…”

Section: Introductionmentioning

confidence: 99%

Mood effects on semantic processes: Behavioural and electrophysiological evidence

Naranowicz

2022

Front. Psychol.

View full text Add to dashboard Cite

Mood (i.e., our current background affective state) often unobtrusively yet pervasively affects how we think and behave. Typically, theoretical frameworks position it as an embodied source of information (i.e., a biomarker), activating thinking patterns that tune our attention, perception, motivation, and exploration tendencies in a context-dependent manner. Growing behavioural and electrophysiological research has been exploring the mood–language interactions, employing numerous semantics-oriented experimental paradigms (e.g., manipulating semantic associations, congruity, relatedness, etc.) along with mood elicitation techniques (e.g., affectively evocative film clips, music, pictures, etc.). Available behavioural and electrophysiological evidence has suggested that positive and negative moods differently regulate the dynamics of language comprehension, mostly due to the activation of mood-dependent cognitive strategies. Namely, a positive mood has been argued to activate global and heuristics-based processing and a negative mood – local and detail-oriented processing during language comprehension. Future research on mood–language interactions could benefit greatly from (i) a theoretical framework for mood effects on semantic memory, (ii) measuring mood changes multi-dimensionally, (iii) addressing discrepancies in empirical findings, (iv) a replication-oriented approach, and (v) research practices counteracting publication biases.

show abstract

Do Speaker's emotions influence their language production? Studying the influence of disgust and amusement on alignment in interactive reference

Cited by 12 publications

References 73 publications

Speakers Align With Their Partner's Overspecification During Interaction

Speakers Align With Their Partner's Overspecification During Interaction

Applications and Challenges of Sentiment Analysis in Real-life Scenarios

Mood effects on semantic processes: Behavioural and electrophysiological evidence

Contact Info

Product

Resources

About