Temporal Predictability Facilitates Causal Learning

被引:47
|
作者
Greville, W. James [1 ]
Buehner, Marc J. [1 ]
机构
[1] Cardiff Univ, Dept Psychol, Cardiff, S Glam, Wales
关键词
causality; predictability; contiguity; time; learning; OUTCOME CONTINGENCY; JUDGMENT; CONTIGUITY; REINFORCEMENT; COVARIATION; INDUCTION; MODELS; VARIABILITY; ATTRIBUTION; DELAY;
D O I
10.1037/a0020976
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Temporal predictability refers to the regularity or consistency of the time interval separating events When encountering repeated instances of causes and effects we also experience multiple cause effect temporal intervals Where this Interval is constant it becomes possible to predict when the effect will follow from the cause In contrast interval variability entails unpredictability Three experiments investigated the extent to which temporal predictability contributes to the inductive processes of human causal learning The authors demonstrated that (a) causal relations with fixed temporal intervals are consistently judged as stronger than those with variable temporal Intervals (b) that causal judgments decline as a function of temporal uncertainty and (c) that this effect remains undiminished with increased learning time The results therefore clearly indicate tint temporal predictability facilitates causal discovery The authors considered the implications of their findings for various theoretical perspectives Including associative learning theory the attribution shift hypothesis and causal structure models
引用
收藏
页码:756 / 771
页数:16
相关论文
共 50 条
  • [1] Temporal Predictability Facilitates Action, Not Perception
    Thomaschke, Roland
    Dreisbach, Gesine
    PSYCHOLOGICAL SCIENCE, 2013, 24 (07) : 1335 - 1340
  • [2] The role of temporal factors in causal learning
    Krynski, Tevye
    Tenenbaum, Joshua
    INTERNATIONAL JOURNAL OF PSYCHOLOGY, 2008, 43 (3-4) : 33 - 33
  • [3] Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions
    Kang, Pyungwon
    Moisa, Marius
    Lindstrom, Bjorn
    Soutschek, Alexander
    Ruff, Christian C.
    Tobler, Philippe N.
    NATURE COMMUNICATIONS, 2024, 15 (01)
  • [4] Learning Conditional Granger Causal Temporal Networks
    Balashankar, Ananth
    Jagabathula, Srikanth
    Subramanian, Lakshminarayanan
    CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 213, 2023, 213 : 692 - 706
  • [5] Temporal and Statistical Information in Causal Structure Learning
    McCormack, Teresa
    Frosch, Caren
    Patrick, Fiona
    Lagnado, David
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION, 2015, 41 (02) : 395 - 416
  • [6] Temporal comparative feedback facilitates motor learning in children
    Chiviacowsky, Suzete
    Harter, Natalia M.
    JOURNAL OF SPORT & EXERCISE PSYCHOLOGY, 2017, 39 : S120 - S120
  • [7] Inferring Temporal Structure from Predictability in Bumblebee Learning Flight
    Meyer, Stefan
    Bertrand, Olivier J. N.
    Egelhaaf, Martin
    Hammer, Barbara
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2018, PT I, 2018, 11314 : 508 - 519
  • [8] Robot Learning with a Spatial, Temporal, and Causal And-Or Graph
    Xiong, Caiming
    Shukla, Nishant
    Xiong, Wenlong
    Zhu, Song-Chun
    2016 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2016, : 2144 - 2151
  • [9] Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention
    Greville, W. James
    Buehner, Marc J.
    QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2016, 69 (04): : 678 - 697
  • [10] Learning the Structure of Causal Models with Relational and Temporal Dependence
    Marazopoulou, Katerina
    Maier, Marc
    Jensen, David
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 2015, : 572 - 581