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相继领域表象记忆任务中集中趋势回忆和时间整合的直接比较。

A direct comparison of central tendency recall and temporal integration in the successive field iconic memory task.

机构信息

University of South Florida, Tampa, FL, USA.

University of Trento, Trento, Italy.

出版信息

Atten Percept Psychophys. 2021 Apr;83(3):1337-1356. doi: 10.3758/s13414-020-02187-9. Epub 2021 Jan 3.

Abstract

The ensemble coding literature suggests the existence of a fast, automatic formation of some ensemble codes. Can statistical representations, such as memory for the central tendency along a particular visual feature dimension, be extracted from information held in the sensory register? Furthermore, can knowledge of early, iconic memory processes be used to determine how central tendency is extracted? We focused on the potential role of visible persistence mechanisms that support temporal integration. We tested whether mean orientation could be accurately recalled from brief visual displays using the successive field task. On separate blocks of trials, participants were asked to report the location of a split element (requiring differentiation of frames), a missing element (requiring integration across frames), and the average orientation of elements pooled across both frames (central tendency recall). Results replicate the expected tradeoff between differentiation and integration performance across inter-frame interval (IFI). In contrast, precision of mean estimates was high and invariant across IFIs. A manipulation of within-frame distributional similarity coupled with simulations using 12 models supported 2-item subsampling. The results argue against the "strategic" interpretation of subsampling since 2-item readout was predicted by information theoretic estimates of STM encoding rate: the 2 items were not from a superset in STM. Most crucially, the results argue against the various early "preattentive/parallel/global pooling" accounts and instead suggest that non-selective readout of information from iconic memory supplies a relatively small amount of item information to STM, and it is only at this point that the computation of ensemble averages begins.

摘要

集合编码文献表明,一些集合代码会快速自动形成。是否可以从感官寄存器中存储的信息中提取出统计表示形式,例如沿特定视觉特征维度的中心趋势的记忆?此外,是否可以利用早期的直觉记忆过程的知识来确定如何提取中心趋势?我们专注于支持时间整合的可见持久性机制的潜在作用。我们使用连续场任务测试了从短暂的视觉显示中是否可以准确回忆出平均方向。在单独的试验块中,要求参与者报告分裂元素的位置(需要区分帧),缺失元素的位置(需要跨帧整合)以及跨两个帧的元素的平均方向(中心趋势回忆)。结果复制了跨帧间间隔(IFI)的分化和整合性能之间的预期权衡。相比之下,平均估计值的精度在 IFI 上是高且不变的。在一个帧内分布相似性的操作与使用 12 个模型的模拟相结合,支持 2 项抽样。结果反对了抽样的“策略”解释,因为 2 项读取是由 STM 编码率的信息论估计预测的:2 项不是来自 STM 的超集。最重要的是,结果反对各种早期的“无选择/平行/全局池化”解释,而是表明非选择性地从直觉记忆中读取信息会向 STM 提供相对较少的项目信息,并且只有在此时,集合平均值的计算才开始。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba8c/7778862/41471606eccf/13414_2020_2187_Fig1_HTML.jpg

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