Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA.
Front Psychol. 2013 Sep 23;4:623. doi: 10.3389/fpsyg.2013.00623. eCollection 2013.
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.
无监督类别学习的理论模型假设人类“发明”类别来适应新的模式，但往往会将刺激物分为少数几个类别。这一“奥卡姆剃刀”原则是由统计推断的规范规则所驱动的。如果类别影响感知，那么人们应该发现类别发明对简单感知估计的影响。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检验这一预测，圆圈的数量来自特定颜色的分布。当与每种颜色相关联的分布重叠很大时，参与者的估计值偏向两个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物归入一个感知类别。这些数据表明，人类更喜欢对感官输入进行更简单的解释。相比之下，当与每种颜色相关联的分布重叠最小化时，偏差会减小（即，每种颜色的估计值更接近真实平均值），这表明更复杂解释的感官证据可以覆盖简单性偏差。我们对我们的任务进行了理性分析，展示了这些定性模式如何从贝叶斯计算中产生。
