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Visual statistical learning provides scaffolding for emerging object representations

机译:视觉统计学习为新兴对象表示提供了脚手架

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Although an abundance of studies demonstrated human's abilities for visual statistical learning (VSL), much fewer studies focused on the consequences of VSL. Recent papers reported that attention is biased toward detected statistical regularities, but this observation was restricted to spatial locations and provided no functional interpretation of the phenomenon. We tested the idea that statistical regularities identified by VSL constrain subsequent visual processing by coercing further processing to be compatible with those regularities. Our paradigm used the well-documented fact that within-object processing has an advantage over across-object processing. We combined the standard VSL paradigm with a visual search task in order to assess whether participants detect a target better within a statistical chunk than across chunks. Participants (N=11) viewed 4-4 alternating blocks of "observation" and "search" trials. In both blocks, complex multi-shape visual scenes were presented, which unbeknownst to the participants, were built from pairs of abstract shapes without any clear segmentation cues. Thus, the visual chunks (pairs of shapes) generating the scenes could only be extracted by tracking the statistical contingencies of shapes across scenes. During "observation", participants just passively observed the visual scenes, while during "search", they performed a 3-AFC task deciding whether T letters appearing in the middle of the shapes formed a horizontal or vertical pairs. Despite identical distance between the target letters, participants performed significantly better in trials in which targets appeared within a visual chunk than across two chunks or across a chunk and a single shape. These results suggest that similar to object-defined within/between relations, statistical contingencies learned implicitly by VSL facilitate visual processing of elements that belong to the same statistical chunk. This similarity between the effects of true objects and statistical chunks support the notion that VSL has a central role in the emergence of internal object representations.
机译:尽管大量研究证明了人类的视觉统计学习(VSL)能力,但很少有研究关注VSL的后果。最近的论文报道,注意力偏向于检测到的统计规律,但是这种观察仅限于空间位置,没有对该现象进行功能解释。我们测试了这样的想法,即由VSL识别的统计规律性通过强制进一步的处理与那些规律性兼容,从而约束了后续的视觉处理。我们的范例使用了有据可查的事实,即对象内处理比跨对象处理具有优势。我们将标准VSL范式与可视搜索任务结合在一起,以评估参与者在统计块内是否比跨块更好地检测到目标。参与者(N = 11)观察了4-4个“观察”和“搜索”试验的交替区域。在这两个模块中,呈现了参与者不知道的复杂的多形状视觉场景,这些场景是由成对的抽象形状构建而成的,没有任何清晰的分割线索。因此,只能通过跟踪跨场景的形状的统计偶然性来提取生成场景的视觉块(形状对)。在“观察”过程中,参与者只是被动地观察了视觉场景,而在“搜索”过程中,他们执行了3-AFC任务,确定出现在形状中间的T字母形成水平对还是垂直对。尽管目标字母之间的距离相同,但参与者在试验中的表现要好得多,在试验中,目标出现在视觉块中比跨越两个块或跨越一个块和单个形状。这些结果表明,类似于对象定义的关系之间/之间的关系,由VSL隐式学习的统计意外事件有助于对属于同一统计块的元素进行可视化处理。真实对象和统计块效果之间的相似性支持以下观点:VSL在内部对象表示的出现中起着核心作用。

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