Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, United Kingdom.
Proc Natl Acad Sci U S A. 2012 Feb 28;109(9):3593-8. doi: 10.1073/pnas.1120118109. Epub 2012 Feb 13.
According to signal detection theoretical analyses, visual signals occurring at a cued location are detected more accurately, whereas frequently occurring ones are reported more often but are not better distinguished from noise. However, conventional analyses that estimate sensitivity and bias by comparing true- and false-positive rates offer limited insights into the mechanisms responsible for these effects. Here, we reassessed the prior influences of signal probability and relevance on visual contrast detection using a reverse-correlation technique that quantifies how signal-like fluctuations in noise predict trial-to-trial variability in choice discarded by conventional analyses. This approach allowed us to estimate separately the sensitivity of true and false positives to parametric changes in signal energy. We found that signal probability and relevance both increased energy sensitivity, but in dissociable ways. Cues predicting the relevant location increased primarily the sensitivity of true positives by suppressing internal noise during signal processing, whereas cues predicting greater signal probability increased both the frequency and the sensitivity of false positives by biasing the baseline activity of signal-selective units. We interpret these findings in light of "predictive-coding" models of perception, which propose separable top-down influences of expectation (probability driven) and attention (relevance driven) on bottom-up sensory processing.
根据信号检测理论分析，在提示位置出现的视觉信号被检测得更准确，而频繁出现的信号被报告的频率更高，但与噪声的区分度没有提高。然而，通过比较真阳性率和假阳性率来估计敏感性和偏差的传统分析方法，对这些效应的机制提供的见解有限。在这里，我们使用反相关技术重新评估了信号概率和相关性对视觉对比检测的先前影响，该技术量化了噪声中类似信号的波动如何预测传统分析中丢弃的试验间选择的可变性。这种方法允许我们分别估计真阳性和假阳性对信号能量参数变化的敏感性。我们发现，信号概率和相关性都增加了能量敏感性，但方式不同。预测相关位置的线索主要通过在信号处理过程中抑制内部噪声来提高真阳性的敏感性，而预测更高信号概率的线索则通过偏置信号选择性单元的基线活动来增加假阳性的频率和敏感性。我们根据感知的“预测编码”模型来解释这些发现，该模型提出了期望（概率驱动）和注意力（相关性驱动）对自上而下的感觉处理的可分离的自上而下的影响。
