Extrapolation: the sine qua non for abstraction in function learning.
Extrapolation: the sine qua non for abstraction in function learning.
复制标题
外推法：函数学习中抽象的必要条件。
DOI：
10.1037//0278-7393.23.4.968
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
McDaniel,MA
中科院分区：
文献类型：
--
作者：
DeLosh,EL;Busemeyer,JR;McDaniel,MA
摘要通过考察功能学习任务中的外推行为来研究抽象。在训练过程中，参与者将刺激和反应幅度（以单杠长度的形式）关联起来，这些幅度根据线性、指数或二次函数进行协变。训练后，新的刺激幅度作为外推和内插的测试。参与者的推断远远超出了习得反应的范围，他们的反应捕捉到了分配功能的一般形状，但存在一些系统性偏差。观察到显著的个体差异，特别是在二次条件下。在训练过程中给出的独特刺激-反应对的数量（即密度）也被操纵，但不影响训练或转移性能。两个规则学习模型，联想学习模型，和一个新的混合模型与联想学习和基于规则的响应（外推关联模型[EXAM]）进行了评估相对于传输数据。EXAM最接近外推性能的总体模式。（PsycINFO数据库记录（c）2016阿帕，保留所有权利）
Abstraction was investigated by examining extrapolation behavior in a function-learning task. During training, participants associated stimulus and response magnitudes (in the form of horizontal bar lengths) that covaried according to a linear, exponential, or quadratic function. After training, novel stimulus magnitudes were presented as tests of extrapolation and interpolation. Participants extrapolated well beyond the range of learned responses, and their responses captured the general shape of the assigned functions, with some systematic deviations. Notable individual differences were observed, particularly in the quadratic condition. The number of unique stimulus–response pairs given during training (ie, density) was also manipulated but did not affect training or transfer performance. Two rule-learning models, an associative-learning model, and a new hybrid model with associative learning and rule-based responding (extrapolation–association model [EXAM]) were evaluated with respect to the transfer data. EXAM best approximated the overall pattern of extrapolation performance.(PsycINFO Database Record (c) 2016 APA, all rights reserved)