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Statistical learning and memory.
Cognition ( IF 2.8 ) Pub Date : 2020-06-29 , DOI: 10.1016/j.cognition.2020.104346
Ansgar D Endress 1 , Lauren K Slone 2 , Scott P Johnson 3
Affiliation  

Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs). However, it is unclear what the outputs of statistical segmentation mechanisms are, and if learners store these outputs as discrete chunks in memory. We critically review the evidence for the possibility that statistically coherent items are stored in memory and outline difficulties in interpreting past research. We use Slone and Johnson's (2018) experiments as a case study to show that it is difficult to delineate the different mechanisms learners might use to solve a learning problem. Slone and Johnson (2018) reported that 8-month-old infants learned coherent chunks of shapes in visual sequences. Here, we describe an alternate interpretation of their findings based on a multiple-cue integration perspective. First, when multiple cues to statistical structure were available, infants' looking behavior seemed to track with the strength of the strongest one — backward TPs, suggesting that infants process multiple cues simultaneously and select the strongest one. Second, like adults, infants are exquisitely sensitive to chunks, but may require multiple cues to extract them. In Slone and Johnson's (2018) experiments, these cues were provided by immediate chunk repetitions during familiarization. Accordingly, infants showed strongest evidence of chunking following familiarization sequences in which immediate repetitions were more frequent. These interpretations provide a strong argument for infants' processing of multiple cues and the potential importance of multiple cues for chunk recognition in infancy.



中文翻译:

统计学习和记忆。

学习者通常需要识别并记住连续序列中的重复单元,但是其潜在机制尚有争议。一个特别杰出的候选机制依赖于分布统计数据,例如过渡概率(TP)。但是,尚不清楚统计分割机制的输出是什么,以及学习者是否将这些输出作为离散的块存储在内存中。我们批判性地审查了统计上连贯的项目存储在内存中的可能性的证据,并概述了解释过去研究的困难。我们使用Slone和Johnson(2018)的实验作为案例研究,以显示很难描述学习者用来解决学习问题的不同机制。Slone和Johnson(2018)报道8个月大的婴儿在视觉序列中学习了连贯的形状。在这里,我们基于多线索集成的角度描述了他们的发现的另一种解释。首先,当有多个线索可用于统计结构时,婴儿的外表行为似乎与最强的线索(向后TP)的强度一致,这表明婴儿同时处理多个线索并选择最强的线索。其次,像成年人一样,婴儿对块非常敏感,但可能需要多个提示来提取它们。在Slone and Johnson(2018)的实验中,这些提示是在熟悉过程中立即进行块重复来提供的。因此,婴儿表现出最强的循序渐进的熟悉顺序的证据,其中立即重复的频率更高。这些解释为婴儿处理多个线索提供了有力的论据,并为婴儿期识别大块线索提供了多个线索的潜在重要性。

更新日期:2020-06-29
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