Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of Korea.
Department of Psychology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Atten Percept Psychophys. 2021 Apr;83(3):970-981. doi: 10.3758/s13414-020-02139-3. Epub 2020 Oct 8.
Ensemble perception is efficient because it summarizes redundant and complex information. However, it loses the fine details of individual items during the averaging process. Such characteristics of ensemble perception are similar to those of coarse processing. Here, we tested whether extracting an average of a set was similar to coarse processing. To manipulate coarse processing, we used the fast flicker adaptation known as suppressing coarse information processed by the magnocellular pathway. We hypothesized that if computing the average of a set relied on coarse processing, the precision of an averaging task should decrease after adaptation compared to baseline (no-adaptation). Across experiments with various features (orientation in Experiment 1, size in Experiment 2, and facial expression in Experiment 3), we found that suppressing coarse information did not disrupt the performance of the averaging tasks. Rather, adaptation increased the precision of mean representation. The precision of mean representation might have increased because fine information was relatively enhanced after adaptation. Our results suggest that the quality of ensemble representation relies on that of individual items.
集合感知之所以高效，是因为它对冗余且复杂的信息进行了总结。然而，在平均化的过程中，它丢失了各个项目的细节。这种集合感知的特征类似于粗糙处理。在这里，我们测试了对一组数据进行平均化是否类似于粗糙处理。为了操纵粗糙处理，我们使用了快速闪烁适应的方法，即抑制由大细胞通路处理的粗糙信息。我们假设，如果计算一组数据的平均值依赖于粗糙处理，那么与基线（无适应）相比，在适应后，平均任务的精度应该会下降。在具有各种特征的实验中（实验 1 中的方向、实验 2 中的大小和实验 3 中的面部表情），我们发现抑制粗糙信息并没有破坏平均任务的表现。相反，适应提高了平均值的表示精度。平均值的表示精度可能提高了，因为适应后精细信息相对增强。我们的结果表明，集合表示的质量依赖于各个项目的质量。
