2026-10-14T18:07:27Z https://www.scidb.cn/oai

10.57760/sciencedb.45044 2026-08-04T17:28:46Z

2026-08-04 MSC-SCD and LoCoMo-SCD datasets doi:10.57760/sciencedb.45044 en The datasets is part of our paper “Human–AI Social Cognitive Dynamics: A Formal Theory for Long-Term Social Intelligence”. If you use this dataset in your research, please cite both our paper and the accompanying dataset.The original MSC and LoCoMo datasets provide long-term conversational interactions but do not contain the continuous cognitive variables required by HASCD. Therefore, both datasets were extended through a continuous social cognitive annotation process, resulting in two new benchmark datasets:MSC-SCD (Multi-Session Chat for Social Cognitive Dynamics) LoCoMo-SCD (Long Context Memory for Social Cognitive Dynamics) The MSC-SCD and LoCoMo-SCD datasets share an identical schema to facilitate cross-dataset analysis of long-term social cognitive dynamics. Each record corresponds to a single dialogue utterance annotated with personality traits, emotion distributions, social cognitive states, memory representations, and relationship strength.Table 1. Description of the MSC-SCD and LoCoMo-SCD dataset fields.Field Descriptiondataset Source dataset identifier (MSC-SCD or LoCoMo-SCD).conversation_key Unique identifier of a conversation trajectory.conversation_id Original conversation index inherited from the source dataset.session Session index within a multi-session conversation.turn Utterance order within the current session.utterance_id Globally unique utterance identifier.role Dialogue role of the current speaker (user or assistant).speaker Speaker name or identifier in the original dataset.datetime Timestamp of the utterance (available for LoCoMo; empty for MSC).text Raw dialogue utterance.EI Continuous Extraversion–Introversion personality score in the range [0,1]. Larger values indicate stronger Extraversion tendencies.SN Continuous Sensing–Intuition personality score in the range [0,1]. Larger values indicate stronger Intuition tendencies.TF Continuous Thinking–Feeling personality score in the range [0,1]. Larger values indicate stronger Thinking tendencies.JP Continuous Judging–Perceiving personality score in the range [0,1]. Larger values indicate stronger Judging tendencies.no_emotion Estimated probability of a neutral emotional state.anger Continuous intensity of anger.disgust Continuous intensity of disgust.fear Continuous intensity of fear.happiness Continuous intensity of happiness.sadness Continuous intensity of sadness.surprise Continuous intensity of surprise.E0–E6 Seven-dimensional emotion state vector corresponding to (no_emotion, anger, disgust, fear, happiness, sadness, surprise). These values are provided explicitly to facilitate direct computational modeling.P0–P3 Four-dimensional continuous personality state vector corresponding to (EI, SN, TF, JP).M0–M10 Eleven-dimensional social memory representation encoding the accumulated conversational memory state at the current dialogue turn. The memory state is updated incrementally over the interaction trajectory and serves as the memory component of the HACD-H framework.Relationship Continuous relationship strength between interlocutors, normalized to the range [0,1], where larger values indicate a stronger inferred social relationship.All personality, emotion, memory, and relationship variables are represented as continuous values to support temporal modeling and quantitative analysis of long-term human–AI social cognitive dynamics. The vector fields (E0–E6, P0–P3, and M0–M10) are provided for computational convenience and are consistent with their corresponding scalar annotations. Human–AI interaction; Social cognitive dynamics; Long-term social intelligence; Social cognition; Human–AI coevolution; Computational social intelligence Zhou Jingyi PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset Science Data Bank