Shi Rushen, Emond Emeryse
Université du Québec à Montréal, Montreal, QC, Canada.
Front Psychol. 2023 Oct 30;14:1251124. doi: 10.3389/fpsyg.2023.1251124. eCollection 2023.
Most learning theories agree that the productivity of a rule or a pattern relies on regular exemplars being dominant over exceptions; the threshold for productivity is, however, unclear; moreover, gradient productivity levels are assumed for different rules/patterns, regular or irregular. One theory by Yang, the Tolerance Principle (TP), specified a productivity threshold applicable to all rules, calculated by the numbers of total exemplars and exceptions of a rule; furthermore, rules are viewed as quantal, either productive or unproductive, with no gradient levels. We evaluated the threshold and gradience-quantalness questions by investigating infants' generalization. In an implicit learning task, 14-month-olds heard exemplars of an artificial word-order rule and exceptions; their distributions were set closed to the TP-threshold (5.77) on both sides: 11 regular exemplars vs. 5 exceptions in Condition 1 (productiveness predicted), and 10 regular exemplars vs. 6 exceptions in Condition 2 (unproductiveness predicted). These predictions were pitted against those of the statistical majority threshold (50%), a common assumption which would predict generalization in both conditions (68.75, 62.5%). Infants were tested on the trained rule with new exemplars. Results revealed generalization in Condition 1, but not in Condition 2, supporting the TP-threshold, not the statistical majority threshold. Gradience-quantalness was assessed by combined analyses of Conditions 1-2 and previous experiments by Koulaguina and Shi. The training across the conditions contained gradually decreasing regular exemplars (100, 80, 68.75, 62.5, 50%) relative to exceptions. Results of test trials showed evidence for quantalness in infants (productive: 100, 80, 68.75%; unproductive: 62.5, 50%), with no gradient levels of productivity.
大多数学习理论都认为,规则或模式的生成能力取决于常规范例比例外情况占主导地位;然而,生成能力的阈值尚不清楚;此外,不同规则/模式(无论是常规的还是非常规的)都假定存在渐变的生成能力水平。杨提出的一种理论,即容忍原则(TP),规定了适用于所有规则的生成能力阈值,该阈值通过规则的范例总数和例外情况的数量来计算;此外,规则被视为量子化的,要么具有生成能力,要么没有生成能力,不存在渐变水平。我们通过研究婴儿的泛化来评估阈值和渐变 - 量子化问题。在一项隐性学习任务中,14个月大的婴儿听到了一个人工词序规则的范例和例外情况;它们的分布在两侧都设置为接近TP阈值(5.77):条件1中11个常规范例对5个例外情况(预测具有生成能力),条件2中10个常规范例对6个例外情况(预测不具有生成能力)。这些预测与统计多数阈值(50%)的预测进行了对比,统计多数阈值是一个常见的假设,它会预测在两种条件下都会出现泛化(68.75%,62.5%)。用新的范例对婴儿进行了关于训练规则的测试。结果显示在条件1中出现了泛化,但在条件2中没有,这支持了TP阈值,而不是统计多数阈值。通过对条件1 - 2以及库拉吉娜和施之前的实验进行综合分析来评估渐变 - 量子化。跨条件的训练包含相对于例外情况逐渐减少的常规范例(100%、80%、68.75%、62.5%、50%)。测试试验的结果显示婴儿存在量子化的证据(具有生成能力:100%、80%、68.75%;不具有生成能力:62.5%、50%),不存在生成能力的渐变水平。