在统计学学习之后,预测刺激方向的增强感知选择
Elizabeth A Lawler1,2, Michael A Silver1,3,4
1Vision Science Graduate Group and Herbert Wertheim School of Optometry & Vision Science, University of California, Berkeley, Berkeley, CA, USA.
Journal of vision
|July 6, 2023
概括
统计学学习塑造了视觉感知. 暴露于可预测的视觉模式会增加感知预期刺激的可能性,而不是意想不到的刺激,从而影响感知选择.
科学领域:
- 认知神经科学 认知神经科学
- 视觉感知 视觉感知 视觉感知
- 统计学学习 统计学学习
背景情况:
- 感知是由从过去的经验和感官模式中得出的预测来调节的.
- 预测性处理可以增强对预期刺激的感知,也可以抑制它们,以支持新奇.
研究的目的:
- 研究视觉模式的统计学习如何影响感知选择.
- 为了确定暴露于一致的刺激序列是否会影响预期与意想不到的视觉信息的感知.
主要方法:
- 利用统计学习来训练参与者对定向网格的序列.
- 通过双眼对抗评估视觉感知选择.
- 向不同的眼睛呈现学习的序列和意想不到的直角刺激,以测量感知主导地位.
主要成果:
- 参与者更有可能感知与统计学习建立的预测上下文相匹配的视觉刺激.
- 这项研究发现,观察者在双眼竞争任务中主要感知到预期的刺激而不是意想不到的刺激.
结论:
- 暴露于可预测的视觉模式可以增强对预期刺激的感知.
- 研究结果表明,预测驱动的感知偏好预期而不是意想不到的信息,这与之前的一些报告相反.
- 与其他研究的差异可能源于视觉处理水平的差异,其中感知竞争得到解决.
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