Ernst Marc O
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
J Vis. 2007 Jun 25;7(5):7.1-14. doi: 10.1167/7.5.7.
When different perceptual signals of the same physical property are integrated, for example, an objects' size, which can be seen and felt, they form a more reliable sensory estimate (e.g., M. O. Ernst & M. S. Banks, 2002). This, however, implies that the sensory system already knows which signals belong together and how they relate. In other words, the system has to know the mapping between the signals. In a Bayesian model of cue integration, this prior knowledge can be made explicit. Here, we ask whether such a mapping between two arbitrary sensory signals from vision and touch can be learned from their statistical co-occurrence such that they become integrated. In the Bayesian framework, this means changing the belief about the distribution of the stimuli. To this end, we trained subjects with stimuli that are usually unrelated in the world--the luminance of an object (visual signal) and its stiffness (haptic signal). In the training phase, we then presented subjects with combinations of these two signals, which were artificially correlated, and thus, we introduced a new mapping between them. For example, the stiffer the object, the brighter it was. We measured the influence of learning by comparing discrimination performance before and after training. The prediction is that integration makes discrimination worse for stimuli, which are incongruent with the newly learned mapping, because integration would cause this incongruency to disappear perceptually. The more certain subjects are about the new mapping, the stronger should the influence be on discrimination performance. Thus, learning in this context is about acquiring beliefs. We found a significant change in discrimination performance before and after training when comparing trials with congruent and incongruent stimuli. After training, discrimination thresholds for the incongruent stimuli are increased relative to thresholds for congruent stimuli, suggesting that subjects learned effectively to integrate the two formerly unrelated signals.
当同一物理属性的不同感知信号被整合时，例如一个物体的大小，既可以看到也可以触摸到，它们会形成一个更可靠的感官估计（例如，M. O. 恩斯特和M. S. 班克斯，2002年）。然而，这意味着感官系统已经知道哪些信号属于一起以及它们之间的关系。换句话说，系统必须知道信号之间的映射。在贝叶斯线索整合模型中，这种先验知识可以被明确表示。在这里，我们要问的是，来自视觉和触觉的两个任意感官信号之间的这种映射是否可以从它们的统计共现中学习到，从而使它们被整合。在贝叶斯框架中，这意味着改变对刺激分布的信念。为此，我们用通常在现实世界中不相关的刺激——物体的亮度（视觉信号）和它的硬度（触觉信号）来训练受试者。在训练阶段，我们向受试者呈现这两个信号的组合，这些组合是人为关联的，因此，我们在它们之间引入了一种新的映射。例如，物体越硬，它就越亮。我们通过比较训练前后的辨别性能来测量学习的影响。预测是，对于与新学习的映射不一致的刺激，整合会使辨别变差，因为整合会使这种不一致在感知上消失。受试者对新映射越确定，对辨别性能的影响就应该越强。因此，在这种情况下的学习是关于获取信念。当比较一致和不一致刺激的试验时，我们发现训练前后辨别性能有显著变化。训练后，不一致刺激的辨别阈值相对于一致刺激的阈值增加，这表明受试者有效地学会了整合这两个以前不相关的信号。
