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Perceptual learning of second order cues for layer decomposition

机译:层分解的二阶线索的感知学习

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摘要

Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition—the process of distinguishing between reflectance and illumination changes—is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250 ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5 days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow.
机译:亮度变化是模棱两可的:它们可以表示表面反射率的变化或照明的变化。层分解(区分反射率和照明变化的过程)由一系列辅助提示(包括颜色和纹理)支持。对于照明的波纹状纹理表面,阴影图案包括亮度(一阶,LM)和局部亮度幅度(二阶,AM)的调制。这两个信号之间的相位关系可进行层分解,预测反射率和照明变化的感知,并已基于早期,快速,前馈视觉处理()建立模型。但是,虽然经验不足的观众会在较长的演示时间欣赏这种选择,但在短的演示时间(250毫秒)中却无法这样做。这可能暗示了较慢的高级机制的作用。在这里,我们考虑训练如何减轻这种延迟,以及由此产生的学习是否发生在感知水平上。我们训练了观察者以区分在5天的时间里混合了同相和反相LM / AM信号的格子刺激的成分。训练后,区分格子组件所需的AM信号强度急剧下降,表明学习。我们测试了使用具有不同空间频率,面内方向和锐角格子的刺激进行的学习转移。我们报告说,当刺激改变时,学习仅部分转移,这表明,受益于调优特定的机制,而不是一般的解释过程。我们建议,使用二阶线索支持层分解的机制相对较早,而并非固有地较慢。

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