Maruya Akihito, Ramakrishnan Bhavatharini, Olianezhad Farzaneh, Wang Jingyun, Alonso Jose-Manuel, Zaidi Qasim
Graduate Center for Vision Research, State University of New York, New York, NY.
bioRxiv. 2025 Jun 25:2025.06.24.661078. doi: 10.1101/2025.06.24.661078.
We introduce Generative Phenomenology: making viewable images of people's perceptions (Perceptograms) and generating the images from neural models, as a powerful technique for understanding the neural bases of perception. Amblyopia, a disorder of spatial vision, provides a perfect case because signals from the two eyes go through partly different cortical neurons, and many amblyopes report phantom forms when viewing sinusoidal gratings through their amblyopic eye (AE) but not through the fellow eye (FE). Using a dichoptic display, we acquired high-fidelity perceptograms for 24 gratings shown to AE while sums-of-gratings plaids were shown to FE with contrast, frequency, phase, and orientation of the plaid gratings adjusted to match the two percepts exactly. Plaids provided exact matches to 92.6% of distortions. A formal equation that the signals generated in visual cortex by the test gratings seen through AE match the signals generated by their matched perceptograms seen through FE for each observer, was used to analytically derive cortical filters processing AE signals as linear transforms of standard steerable filters modeling normal V1 neurons for FE. Passing gratings through AE filters accurately generated the measured perceptograms. The filter transformations reflected complex changes in V1 receptive fields and possibly in cross-correlations. The AE filters also explained amblyopic deficits in perceiving sinusoidally modulated circular contours and were consistent with orientation perceptive fields estimated from reverse-correlation experiments. Changes in neuronal receptive fields thus have profound effects on perception, to the extent that observers can see more features than are present in the viewed stimulus.
制作人们感知的可视图像(感知图)并从神经模型生成这些图像,作为理解感知神经基础的一种强大技术。弱视是一种空间视觉障碍,它提供了一个完美的案例,因为来自双眼的信号通过部分不同的皮质神经元传递,并且许多弱视患者在通过弱视眼(AE)而非健眼(FE)观察正弦光栅时会报告幻视形态。使用双眼分视显示器,我们获取了向AE呈现的24个光栅的高保真感知图,同时向FE呈现光栅总和的方格,调整方格光栅的对比度、频率、相位和方向以使其与两种感知精确匹配。方格与92.6%的失真精确匹配。对于每个观察者,通过AE看到的测试光栅在视觉皮层中产生的信号与通过FE看到的其匹配感知图产生的信号相匹配的一个形式方程,被用于分析性地推导处理AE信号的皮质滤波器,将其作为对FE的正常V1神经元进行建模的标准可操纵滤波器的线性变换。让光栅通过AE滤波器能准确地生成测量到的感知图。滤波器变换反映了V1感受野以及可能的互相关的复杂变化。AE滤波器还解释了在感知正弦调制圆形轮廓时的弱视缺陷,并且与从反向相关实验估计的方向感知野一致。因此,神经元感受野的变化对感知有深远影响,以至于观察者能看到比所观察刺激中存在更多的特征。