Perceptual estimation obeys Occam's razor
Perceptual estimation obeys Occam's razor
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DOI：
10.3389/fpsyg.2013.00623
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发表时间：
2013-09-23
影响因子：
3.8
通讯作者：
Niv, Yael
中科院分区：
文献类型：
--
作者：
Gershman, Samuel J.;Niv, Yael
无监督类别学习的理论模型假设人类“发明”类别以适应新的模式，但倾向于将刺激分为少数类别。这个“奥卡姆剃刀”原理是由统计推断的规范规则驱动的。如果类别影响知觉，那么我们应该发现类别发明对简单知觉估计的影响。在一系列实验中，我们通过要求参与者估计计算机屏幕上彩色圆圈的数量来测试这一预测，圆圈的数量来自特定颜色的分布。当与每种颜色相关的分布基本上重叠时，参与者的估计偏向于两种平均值之间的中间值，这表明受试者忽略了圆圈的颜色，并将不同颜色的刺激归为一个感知类别。这些数据表明，人类喜欢对感官输入进行更简单的解释。相反，当与每种颜色相关联的分布最小限度地重叠时，偏差减小（即，每种颜色的估计值更接近真实平均值），表明更复杂解释的感官证据可以超越简单性偏见。我们提出了一个合理的分析，我们的任务，显示这些定性模式可以从贝叶斯计算。
Theoretical models of unsupervised category learning postulate that humans "invent" categories to accommodate new patterns, but tend to group stimuli into a small number of categories. This "Occam's razor" principle is motivated by normative rules of statistical inference. If categories influence perception, then one should find effects of category invention on simple perceptual estimation. In a series of experiments, we tested this prediction by asking participants to estimate the number of colored circles on a computer screen, with the number of circles drawn from a color-specific distribution. When the distributions associated with each color overlapped substantially, participants' estimates were biased toward values intermediate between the two means, indicating that subjects ignored the color of the circles and grouped different-colored stimuli into one perceptual category. These data suggest that humans favor simpler explanations of sensory inputs. In contrast, when the distributions associated with each color overlapped minimally, the bias was reduced (i.e., the estimates for each color were closer to the true means), indicating that sensory evidence for more complex explanations can override the simplicity bias. We present a rational analysis of our task, showing how these qualitative patterns can arise from Bayesian computations.