On the evolution of the human self: A data-driven review and reconsideration

被引:15
|
作者
Skowronski, John J. [1 ]
Sedikides, Constantine [2 ]
机构
[1] Northern Illinois Univ, Dept Psychol, De Kalb, IL 60115 USA
[2] Univ Southampton, Ctr Res Self & Ident, Southampton, Hants, England
关键词
Self; self-concept; evolution; EPISODIC-LIKE MEMORY; AUTONOETIC CONSCIOUSNESS; NATURAL-HISTORY; RHESUS-MONKEYS; SOUTH-AFRICA; ORDER; RECOGNITION; INFORMATION; HIPPOCAMPUS; CHIMPANZEES;
D O I
10.1080/15298868.2017.1350601
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
We revisit the thesis we first offered in 1997, namely, that the human capacity called "the self" is the product of evolutionary pressures. A review of the literature accumulated in the 20 years prompted three changes to the original thesis. First, we expanded our 1997 conception of the self. We argue that the self consists of a multiplicity of cognitions, each of which may reflect the action of a different neural system. Second, we revised the timeline for the evolution of the human self. At least some components of the human self were present in hominids earlier than the 100,000 years-old date that we speculated served as the oldest-age boundary for the emergence of the self. Third, we supplemented the evidentiary basis by relying on advances in brain structure, brain function, and the genetic underpinnings of the brain. In comparison to the state of knowledge in 1997, there is more reason to assert in 2017 that humans have the capacity to experience a self because this trait was selected via evolution.
引用
收藏
页码:4 / 21
页数:18
相关论文
共 50 条
  • [41] DATA-DRIVEN
    Lev-Ram, Michal
    FORTUNE, 2016, 174 (05) : 76 - 81
  • [42] The Data-Based Self: Self-Quantification and the Data-Driven (Good) Life
    Schull, Natasha D.
    SOCIAL RESEARCH, 2019, 86 (04): : 909 - 930
  • [43] Designing for Human Data Interaction in Data-Driven Media Experiences
    Sailaja, Neelima
    Jones, Rhianne
    McAuley, Derek
    EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21), 2021,
  • [44] A safe self-evolution algorithm for autonomous driving based on data-driven risk quantification model
    Yang, Shuo
    Li, Shizhen
    Huang, Yanjun
    Chen, Hong
    ACCIDENT ANALYSIS AND PREVENTION, 2025, 214
  • [45] A Review of Data-Driven Methods for Power Flow Analysis
    Akter, Mahmuda
    Nazaripouya, Hamidreza
    2023 NORTH AMERICAN POWER SYMPOSIUM, NAPS, 2023,
  • [46] Data-driven material discovery for photocatalysis:a short review
    Jinbo Pan
    Qimin Yan
    Journal of Semiconductors, 2018, 39 (07) : 6 - 15
  • [47] Just what is data-driven campaigning? A systematic review
    Dommett, Katharine
    Barclay, Andrew
    Gibson, Rachel
    INFORMATION COMMUNICATION & SOCIETY, 2024, 27 (01) : 1 - 22
  • [48] Data-Driven Advancements in Lip Motion Analysis: A Review
    Torrie, Shad
    Sumsion, Andrew
    Lee, Dah-Jye
    Sun, Zheng
    ELECTRONICS, 2023, 12 (22)
  • [49] Data-Driven Design for Metamaterials and Multiscale Systems: A Review
    Lee, Doksoo
    Chen, Wei
    Wang, Liwei
    Chan, Yu-Chin
    Chen, Wei
    ADVANCED MATERIALS, 2024, 36 (08)
  • [50] Data-Driven Distributed Optical Vibration Sensors: A Review
    Shao, Li-Yang
    Liu, Shuaiqi
    Bandyopadhyay, Sankhyabrata
    Yu, Feihong
    Xu, Weijie
    Wang, Chao
    Li, Hengchao
    Vai, Mang I.
    Du, Linlin
    Zhang, Jinsheng
    IEEE SENSORS JOURNAL, 2020, 20 (12) : 6224 - 6239