On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization
On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization
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DOI：
10.3758/bf03194378
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发表时间：
2003-03-01
影响因子：
2.4
通讯作者：
Maddox, WT
中科院分区：
文献类型：
--
作者：
Bohil, CJ;Maddox, WT
有偏的类别回报矩阵产生单独的奖励和准确性最大化的决策标准。虽然指示最大化奖励，观察员使用次优决策标准，更强调准确性比最优。在这项研究中，客观分类反馈（客观正确的反应）进行了比较，最佳分类反馈（最佳分类器的反应）在两个级别的类别区分时，零或负成本伴随着两个回报矩阵乘法因子的不正确的反应。性能是上级的最佳分类器反馈相对于客观的分类器反馈为零和负成本的条件下，特别是当类别的可辨别性低，但最佳分类器的优势的幅度是近似相等的零和负成本的条件。最佳分类器反馈性能优势不与回报矩阵乘法因子相互作用。基于模型的分析表明，放置在准确性上的权重降低了最佳分类器反馈相对于客观分类器反馈和高类别的区分度相对于低类别的区分度。此外，当反馈基于最佳分类器时，准确性的权重随着训练而下降，而当反馈基于客观分类器时，准确性的权重保持相对稳定。这些结果表明，基于最佳分类器的反馈导致在广泛的实验条件下的上级决策标准学习。
Biased category payoff matrices engender separate reward- and accuracy-maximizing decision criteria. Although instructed to maximize reward, observers use suboptimal decision criteria that place greater emphasis on accuracy than is optimal. In this study, objective classifier feedback (the objectively correct response) was compared with optimal classifier feedback (the optimal classifier's response) at two levels of category discriminability when zero or negative costs accompanied incorrect responses for two payoff matrix multiplication factors. Performance was superior for optimal classifier feedback relative to objective classifier feedback for both zero- and negative-cost conditions, especially when category discriminability was low, but the magnitude of the optimal classifier advantage was approximately equal for zero- and negative-cost conditions. The optimal classifier feedback performance advantage did not interact with the payoff matrix multiplication factor. Model-based analyses suggested that the weight placed on accuracy was reduced for optimal classifier feedback relative to objective classifier feedback and for high category discriminability relative to low category discriminability. In addition, the weight placed on accuracy declined with training when feedback was based on the optimal classifier and remained relatively stable when feedback was based on the objective classifier. These results suggest that feedback based on the optimal classifier leads to superior decision criterion learning across a wide range of experimental conditions.