Design Drives Discovery in Causal Learning

被引:10
|
作者
Walker, Caren M. [1 ]
Rett, Alexandra [1 ]
Bonawitz, Elizabeth [2 ]
机构
[1] Univ Calif San Diego, Dept Psychol, McGill Hall,MC 0109,9500 Gilman Dr, La Jolla, CA 92093 USA
[2] Rutgers State Univ, Dept Psychol, Newark, NJ USA
关键词
causality; cognitive development; reasoning; inference; open data; CHILDREN USE INFORMATION; HYPOTHESIS GENERATION; KNOWLEDGE; BLICKETS; LEARNERS; SEARCH; ADULTS; LEAST;
D O I
10.1177/0956797619898134
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
We assessed whether an artifact's design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults' (N = 102) inference of a distinct conjunctive cause (i.e., that two blocks together activate the machine). Results of both experiments demonstrated that causal inference is sensitive to an artifact's design: Participants in the relational-design conditions were more likely to infer rules that were a priori unlikely. Our findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses but that artifact design aids causal inference. These findings have clear implications for creating intuitive learning environments.
引用
收藏
页码:129 / 138
页数:10
相关论文
共 50 条
  • [41] Reverse engineering for causal discovery based on monotonic characteristic of causal structure
    Ko, Song
    Lim, Hyunki
    Kim, Dae-Won
    PATTERN RECOGNITION LETTERS, 2017, 95 : 91 - 97
  • [42] Causal Discovery with Heterogeneous Observational Data
    Zhou, Fangting
    He, Kejun
    Ni, Yang
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, VOL 180, 2022, 180 : 2383 - 2393
  • [43] Foundations of causal discovery on groups of variables
    Wahl, Jonas
    Ninad, Urmi
    Runge, Jakob
    JOURNAL OF CAUSAL INFERENCE, 2024, 12 (01)
  • [44] Relational Discovery in Category Learning
    Goldwater, Micah B.
    Don, Hilary J.
    Krusche, Moritz J. F.
    Livesey, Evan J.
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL, 2018, 147 (01) : 1 - 35
  • [45] The KDD'23 Workshop on Causal Discovery, Prediction and Decision (CDPD 2023)
    Thuc Duy Le
    PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, 2023, : 5865 - 5866
  • [46] Causal Learning Across Culture and Socioeconomic Status
    Wente, Adrienne O.
    Kimura, Katherine
    Walker, Caren M.
    Banerjee, Nirajana
    Flecha, Maria Fernandez
    MacDonald, Bridget
    Lucas, Christopher
    Gopnik, Alison
    CHILD DEVELOPMENT, 2019, 90 (03) : 859 - 875
  • [47] Causal Structure Learning
    Heinze-Deml, Christina
    Maathuis, Marloes H.
    Meinshausen, Nicolai
    ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 5, 2018, 5 : 371 - 391
  • [48] Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
    Sun, Xiao
    Bahmani, Bahador
    Vlassis, Nikolaos N.
    Sun, WaiChing
    Xu, Yanxun
    GRANULAR MATTER, 2022, 24 (01)
  • [49] Causal Relational Learning
    Salimi, Babak
    Parikh, Harsh
    Kayali, Moe
    Getoor, Lise
    Roy, Sudeepa
    Suciu, Dan
    SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2020, : 241 - 256
  • [50] Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph
    Li, Tingpeng
    Wang, Lei
    Peng, Danhua
    Liao, Jun
    Liu, Li
    Liu, Zhendong
    IEEE ACCESS, 2024, 12 : 136502 - 136514