Assessing the Impact of Personality on Affective States from Video Game
Communication
- URL: http://arxiv.org/abs/2309.13214v1
- Date: Fri, 22 Sep 2023 23:24:37 GMT
- Title: Assessing the Impact of Personality on Affective States from Video Game
Communication
- Authors: Atieh Kashani, Johannes Pfau, Magy Seif El-Nasr
- Abstract summary: Individual differences in personality determine our preferences, traits and values, which should similarly hold for the way we express ourselves.
In this exploratory work, we investigate the impact of personality on the tendency how players of a team-based collaborative alternate reality game express themselves affectively.
- Score: 17.01727448431269
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Individual differences in personality determine our preferences, traits and
values, which should similarly hold for the way we express ourselves. With
current advancements and transformations of technology and society, text-based
communication has become ordinary and often even surpasses natural voice
conversations -- with distinct challenges and opportunities. In this
exploratory work, we investigate the impact of personality on the tendency how
players of a team-based collaborative alternate reality game express themselves
affectively. We collected chat logs from eleven players over two weeks, labeled
them according to their affective state, and assessed the connection between
them and the five-factor personality domains and facets. After applying
multi-linear regression, we found a series of reasonable correlations between
(combinations of) personality variables and expressed affect -- as increased
confusion could be predicted by lower self-competence (C1), personal annoyance
by vulnerability to stress (N6) and expressing anger occured more often in
players that are prone to anxiety (N1), less humble and modest (A5), think less
carefully before they act (C6) and have higher neuroticism (N). Expanding the
data set, sample size and input modalities in subsequent work, we aim to
confirm these findings and reveal even more interesting connections that could
inform affective computing and games user research equally.
Related papers
- Nonverbal Interaction Detection [83.40522919429337]
This work addresses a new challenge of understanding human nonverbal interaction in social contexts.
We contribute a novel large-scale dataset, called NVI, which is meticulously annotated to include bounding boxes for humans and corresponding social groups.
Second, we establish a new task NVI-DET for nonverbal interaction detection, which is formalized as identifying triplets in the form individual, group, interaction> from images.
Third, we propose a nonverbal interaction detection hypergraph (NVI-DEHR), a new approach that explicitly models high-order nonverbal interactions using hypergraphs.
arXiv Detail & Related papers (2024-07-11T02:14:06Z) - EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection [19.98674724777821]
We propose a new personality detection method called EERPD.
This method introduces the use of emotion regulation, a psychological concept highly correlated with personality, for personality prediction.
Experimental results demonstrate that EERPD significantly enhances the accuracy and robustness of personality detection.
arXiv Detail & Related papers (2024-06-23T11:18:55Z) - Large Language Models Can Infer Personality from Free-Form User Interactions [0.0]
GPT-4 can infer personality with moderate accuracy, outperforming previous approaches.
Results show that the direct focus on personality assessment did not result in a less positive user experience.
Preliminary analyses suggest that the accuracy of personality inferences varies only marginally across different socio-demographic subgroups.
arXiv Detail & Related papers (2024-05-19T20:33:36Z) - Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation [30.820334868031537]
Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content.
We propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC.
arXiv Detail & Related papers (2024-04-03T09:14:24Z) - PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for
Personality Detection [50.66968526809069]
We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner.
Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
arXiv Detail & Related papers (2023-10-31T08:23:33Z) - Editing Personality for Large Language Models [73.59001811199823]
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs)
We construct PersonalityEdit, a new benchmark dataset to address this task.
arXiv Detail & Related papers (2023-10-03T16:02:36Z) - Modeling Player Personality Factors from In-Game Behavior and Affective
Expression [17.01727448431269]
We explore possibilities to predict a series of player personality questionnaire metrics from recorded in-game behavior.
We predict a wide variety of personality metrics from seven established questionnaires across 62 players over 60 minute gameplay of a customized version of the role-playing game Fallout: New Vegas.
arXiv Detail & Related papers (2023-08-27T22:59:08Z) - Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A
Survey [71.43956423427397]
We aim to identify the nonverbal cues and computational methodologies resulting in effective performance.
This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings.
Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
arXiv Detail & Related papers (2022-07-20T13:37:57Z) - Understanding How People Rate Their Conversations [73.17730062864314]
We conduct a study to better understand how people rate their interactions with conversational agents.
We focus on agreeableness and extraversion as variables that may explain variation in ratings.
arXiv Detail & Related papers (2022-06-01T00:45:32Z) - Shades of confusion: Lexical uncertainty modulates ad hoc coordination
in an interactive communication task [8.17947290421835]
We propose a communication task based on color-concept associations.
In Experiment 1, we establish several key properties of the mental representations of these expectations.
In Experiment 2, we examine the downstream consequences of these representations for communication.
arXiv Detail & Related papers (2021-05-13T20:42:28Z) - Towards Persona-Based Empathetic Conversational Models [58.65492299237112]
Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains.
In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy.
We propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
arXiv Detail & Related papers (2020-04-26T08:51:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.