Extrapolation: The sine qua non for abstraction in function learning
Extrapolation: The sine qua non for abstraction in function learning
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DOI：
10.1037/0278-7393.23.4.968
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发表时间：
1997-07-01
影响因子：
2.6
通讯作者：
McDaniel, MA
中科院分区：
文献类型：
--
作者：
DeLosh, EL;Busemeyer, JR;McDaniel, MA
抽象是通过检查函数学习任务中的外推行为来研究的。在训练期间，参与者根据线性、指数或二次函数将刺激和反应大小(以水平条长的形式)关联起来。训练后，提出了新的刺激大小作为外推和内插测试。参与者的推断远远超出了习得的反应范围，他们的反应反映了分配的职能的大致形状，但有一些系统性的偏差。观察到显著的个体差异，特别是在二次条件下。训练期间给出的独特刺激-反应对的数量(即密度)也被操纵，但不影响训练或转移性能。两种规则学习模型，一种联想学习模型，以及一种新的具有联想学习和基于规则的响应的混合模型(Extrapolation-Association Model[Examination])针对传输数据进行了评估。考试最接近外推成绩的总体模式。
'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 (i.e., 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.