PDF(Pubmed)
Abstract：
To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model of object recognition which provides an integrated account of how visual and semantic information emerge over time; therefore, it remains unknown how and when semantic representations are evoked from visual inputs. Here, we test whether a model of individual objects--based on combining the HMax computational model of vision with semantic-feature information--can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms. These results provide important insights into the functional properties of visual processing across time.
摘要：
为了适当地响应对象，我们必须快速处理视觉输入并赋予它们意义。这涉及高度动态，信息积累和认知操作的交互式神经过程在多个时间尺度上得到解决。然而,目前还没有对象识别的模型，它提供了视觉和语义信息如何随着时间的推移而出现的综合说明；因此，它仍然不知道如何以及何时从视觉输入中唤起语义表示。这里,我们测试了基于视觉的HMax计算模型与语义特征信息相结合的单个对象模型是否可以解释和预测脑磁图记录的时变神经活动。我们表明，与单独的HMax相比，结合HMax和语义属性可以更好地说明神经对象表示，通过模型拟合和分类性能。我们的结果表明，通过添加超过200毫秒的语义特征信息，可以显着改善单个对象的建模和分类。这些结果为视觉处理的功能特性提供了重要的见解。
