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The interaction between chunking and stimulus complexity in infant visual statistical learning

机译:婴儿视觉统计学习中的分块与刺激复杂性之间的相互作用

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Human infants are known to learn statistical regularities of the sensory environment implicitly in various perceptual domains. Visual statistical leaning studies have illustrated that this learning is highly sophisticated and well_approximated by optimal probabilistic chunking of the unfamiliar input. However, the emergence and unitization of such perceptual chunks at an early age and their relation to stimulus complexity have not been investigated before. This study examines how 8_month_old infants can extract statistical relationships within more complex, hierarchically structured visual scenes and how unitization of chunks is linked to familiarity performance. In the first experiment, infants were habituated to quadruplet scenes composed of a triplet of elements always appearing in the same relative spatial arrangement and one noise element connected to the triplet in various ways. After meeting a criterion of habituation, in each of several test trials, infants saw one original triplet and a new triplet containing a rearrangement of familiar elements. Contrary to earlier results obtained with pairs rather than triplets, infants did not show a significant preference for either test stimulus (N = 20, p 0.9). In a second experiment, infants were habituated using the same quadruplet scenes, but during the test, they saw one of the habituation quadruplets, and a second quadruplet in which the associated noise element was switched with a noise element from another triplet. Infants that habituated (N = 13) to the familiar quadruplet looked longer to the novel quadruplets, indicating they can recognize a change of one single element (p = 0.026), whereas non_habituating infants (N = 9) showed no preference (p 0.9). These results suggest that as stimulus complexity increases, infantsa?? ability to learn and unitize chunks becomes limited, even though they are perfectly able to encode the structure of the scene. Apparently, unitization and the ability to use embedded features in more general contexts emerge after encoding itself is already operational.
机译:已知人类婴儿会在各种感知领域中隐式地学习感觉环境的统计规律。视觉统计研究表明,这种学习是非常复杂的,并且可以通过对陌生输入的最佳概率分块进行很好的近似。然而,这种知觉块在幼年时的出现和单元化以及它们与刺激复杂性的关系尚未得到研究。这项研究探讨了8个月大的婴儿如何在更复杂,层次结构化的视觉场景中提取统计关系,以及块的单位化如何与熟悉度联系起来。在第一个实验中,婴儿习惯于四重奏场景,该场景由始终以相同的相对空间排列出现的三元组元素和以各种方式连接到三元组的一个噪声元素组成。满足习惯标准后,在几项测试试验中,婴儿均看到一个原始的三联体和一个新的三联体,其中包含熟悉的元素重排。与成对而非三联体获得的早期结果相反,婴儿对两种测试刺激均未显示出明显的偏爱(N = 20,p> 0.9)。在第二个实验中,使用相同的四联体场景对婴儿进行了习惯化,但是在测试过程中,他们看到了一个习惯性四联体中的一个,以及第二个四联体,其中关联的噪声元素与另一个三联体中的噪声元素进行了切换。习惯于(N = 13)熟悉的四联体的婴儿看起来比新颖的四联体更长,表明他们可以识别一个元素的变化(p = 0.026),而非习惯性的婴儿(N = 9)没有偏好(p> 0.9) )。这些结果表明,随着刺激复杂性的增加,婴儿步步高。即使它们完全能够编码场景的结构,学习和组合块的能力也会受到限制。显然,在编码本身已经可操作之后,出现了单元化和在更一般的上下文中使用嵌入式功能的能力。

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