Classifying Emotions: Prospects for a Psychoevolutionary Approach

被引:4
|
作者
Starkey, Charles [1 ]
机构
[1] Clemson Univ, Dept Philosophy & Relig, Clemson, SC 29634 USA
关键词
Classification; Emotion; Kinds; Griffiths;
D O I
10.1080/09515080802513300
中图分类号
B82 [伦理学(道德学)];
学科分类号
摘要
One of the most significant developments in the area of emotion theory in recent years is the revival of the psychoevolutionary approach to classification. This essay appraises the prospects for such an approach. The first contention is that the supposed advantages of psychoevolutionary classification over functional classification in scientific psychological research is less than presumed, particularly with respect to the utility of the classification, which is the basis of the argument for the superiority of psychoevolutionary classification. The second and central contention is that classification in terms of mechanisms proposed by empirical psychology and neuroscience has better prospects than psychoevolutionary classification with respect to both the utility for psychological research and the ability to carve psychological systems at their joints, that is, to produce natural kind divisions that distinguish emotions from other psychological traits.
引用
收藏
页码:759 / 777
页数:19
相关论文
共 50 条
  • [21] Classifying Emotions in Twitter Messages Using a Deep Neural Network
    da Silva, Isabela R. R.
    Lima, Ana C. E. S.
    Pasti, Rodrigo
    de Castro, Leandro N.
    DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 2019, 801 : 283 - 290
  • [22] Emotion-specific Features for Classifying Emotions in Story Text
    Harikrishna, D. M.
    Rao, K. Sreenivasa
    2016 TWENTY SECOND NATIONAL CONFERENCE ON COMMUNICATION (NCC), 2016,
  • [23] Classifying quarries vis-a-vis prospects of profitability
    Dvoracek, J.
    Sousedikova, R.
    Sterba, J.
    Bartak, P.
    Zapletalova, R.
    JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY, 2012, 112 (03) : 251 - 255
  • [24] History of Emotions and 'Affective Turn': Prospects of a Dialogue
    Nickolai, Feodor
    Khazina, Anna
    DIALOG SO VREMENEM-DIALOGUE WITH TIME, 2015, (50): : 97 - 115
  • [25] Model for Effectively Extracting Mixed Features and Classifying Emotions from Electroencephalograms
    Zhang, Shijing
    Ruan, Qunsheng
    Huang, Lixia
    Wu, Qingfeng
    SENSORS AND MATERIALS, 2023, 35 (07) : 2337 - 2354
  • [26] Comparison between five classification techniques for classifying emotions in human speech
    Pathak, Bageshree, V
    Patil, Deepti R.
    More, Shweta D.
    Mhetre, Nikita R.
    PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICCS), 2019, : 201 - 207
  • [27] Classifying the precancers: A metadata approach
    Jules J Berman
    Donald E Henson
    BMC Medical Informatics and Decision Making, 3 (1)
  • [28] Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets
    Koufakou, Anna
    Garciga, Jairo
    Paul, Adam
    Morelli, Joseph
    Frank, Christopher
    2022 IEEE 34TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI, 2022, : 342 - 346
  • [29] Classifying Group Emotions for Socially-Aware Autonomous Vehicle Navigation
    Bera, Aniket
    Randhavane, Tanmay
    Wang, Austin
    Manocha, Dinesh
    Kubin, Emily
    Gray, Kurt
    PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2018, : 1152 - 1160
  • [30] Acoustic Methodologies for Classifying Gender and Emotions using Machine Learning Algorithms
    Aggarwal, Gaurav
    Vig, Rekha
    PROCEEDINGS 2019 AMITY INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AICAI), 2019, : 672 - 677