Models of Quantitative Estimations: Rule-Based and Exemplar-Based Processes Compared

被引:34
|
作者
von Helversen, Bettina [1 ]
Rieskamp, Joerg [1 ]
机构
[1] Univ Basel, Dept Psychol, CH-4055 Basel, Switzerland
关键词
decision making; simple heuristics; multiple cue judgments; quantitative estimation; MULTIPLE-CUE JUDGMENT; DECISION-MAKING; INFORMATION INTEGRATION; SELECTIVE ATTENTION; PRIOR KNOWLEDGE; CATEGORY SIZE; CLASSIFICATION; MEMORY; CATEGORIZATION; RECOGNITION;
D O I
10.1037/a0015501
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model-the mapping model-that outperformed the exemplar model in a task thought to promote exemplar-based processing. This raised questions about the assumptions of rule-based versus exemplar-based models that underlie the notion of task contingency of cognitive processes. Rule-based models, such as the mapping model, assume the abstraction of explicit task knowledge. In contrast, exemplar models should profit if storage and activation of the exemplars is facilitated. Two studies tested the importance of the two models' assumptions. When knowledge about cues existed, the rule-based mapping model predicted quantitative estimations best. In contrast, when knowledge about the cues was difficult to gain, participants' estimations were best described by an exemplar model. The results emphasize the task contingency of cognitive processes.
引用
收藏
页码:867 / 889
页数:23
相关论文
共 50 条
  • [31] Exemplar-based logo and trademark recognition
    Farajzadeh, Nacer
    MACHINE VISION AND APPLICATIONS, 2015, 26 (06) : 791 - 805
  • [32] Exemplar-based human contour tracking
    Xiang, SM
    Nie, FP
    Zhang, CS
    COMPUTER VISION - ACCV 2006, PT I, 2006, 3851 : 338 - 347
  • [33] NESTED HYPERRECTANGLES FOR EXEMPLAR-BASED LEARNING
    SALZBERG, S
    ANALOGICAL AND INDUCTIVE INFERENCE /, 1989, 397 : 184 - 201
  • [34] Exemplar-based facial expression recognition
    Farajzadeh, Nacer
    Hashemzadeh, Mandi
    INFORMATION SCIENCES, 2018, 460 : 318 - 330
  • [35] SPATIALLY CONSISTENT EXEMPLAR-BASED CLUSTERING
    Zheng, Yun
    Chen, Pei
    He, Yuan
    Sun, Jun
    Hu, Haifeng
    2013 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME 2013), 2013,
  • [36] Deep Exemplar-based Video Colorization
    Zhang, Bo
    He, Mingming
    Liao, Jing
    Sander, Pedro V.
    Yuan, Lu
    Bermak, Amine
    Chen, Dong
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 8044 - 8053
  • [37] Exemplar-Based Emotive Speech Synthesis
    Wu, Xixin
    Cao, Yuewen
    Lu, Hui
    Liu, Songxiang
    Kang, Shiyin
    Wu, Zhiyong
    Liu, Xunying
    Meng, Helen
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2021, 29 : 874 - 886
  • [38] Exemplar-based logo and trademark recognition
    Nacer Farajzadeh
    Machine Vision and Applications, 2015, 26 : 791 - 805
  • [39] The role of prototypicality in exemplar-based learning
    Biberman, Y
    MACHINE LEARNING: ECML-95, 1995, 912 : 77 - 91
  • [40] Exemplar-Based Processing for Speech Recognition
    Sainath, Tara N.
    Ramabhadran, Bhuvana
    Nahamoo, David
    Kanevsky, Dimitri
    Van Compernolle, Dirk
    Demuynck, Kris
    Gemmeke, Jort Florent
    Bellegarda, Jerome R.
    Sundaram, Shiva
    IEEE SIGNAL PROCESSING MAGAZINE, 2012, 29 (06) : 98 - 113