Is a newly learnt sense immediately combined with vision?

Is a newly learnt sense immediately combined with vision?
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新学到的感觉是否会立即与视觉结合?

DOI:
10.1167/16.12.577
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发表时间:
2016
期刊:
影响因子:
1.8
通讯作者:
Nardini M
Nardini M
中科院分区:
医学4区
文献类型:
--
作者:
Nardini M

文献摘要

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在许多感知任务中,观察者通过可靠性加权平均来最小化随机误差(例如Ernst & Banks,Nature 2002)。儿童期这种能力的晚期发展(例如Nardini et al,Curr Biol 2008; Dekker et al,Curr Biol 2015)表明,它需要对特定线索的大量经验或神经系统的成熟。为了研究经验的作用,我们询问成年人学习一种新的感觉是否会立即将其与视觉联合收割机结合起来。10名成年人判断隐藏在虚拟“海洋”中的鱼的距离。在最初的75次试验中,他们被训练使用一种新的感觉,回声定位点击和他们的回声,来自鱼远处的虚拟表面。在随后的175次试验中,他们使用回声或嘈杂的视觉线索(气泡)或两者兼而有之来定位鱼。视觉线索的方差随试验而变化,平均跟踪回声线索的估计方差。每次试验后都要展示鱼。7/10的受试者在使用新线索时高于机会。他们的数据进行了分析,贝叶斯模型比较。对于大多数人(5/7)来说,单线索模型(切换或仅视觉)比线索组合模型(包括那些不按可靠性重新加权或系统性错误加权的模型)更适合(贝叶斯因子> 20)。作者JN和最后一名参与者通过最佳组合模型(BF> 100超过最佳单线索模型)获得最佳拟合。大多数学习新感觉的参与者并没有立即将其与视觉联合收割机结合起来。这表明,对特定线索的扩展经验是人类线索组合能力的基础。然而,在很少或相对较少(作者JN)的经验后,也看到了最佳组合。在未来的研究中,我们将询问单一线索学习的哪些方面(如偏见)预测线索组合,是否有更多的参与者通过练习学会组合联合收割机线索,以及存在哪些中间阶段。
In many perceptual tasks, observers minimise random error by reliability-weighted averaging (eg Ernst & Banks, Nature 2002). Late development of this ability in childhood (eg Nardini et al, Curr Biol 2008; Dekker et al, Curr Biol 2015) suggests that it requires either considerable experience with specific cues or maturation of the nervous system. To study the role of experience, we asked if adults learning a new sense would immediately combine it with vision. Ten adults judged the distance to a fish hidden in a virtual" sea". In 75 initial trials they were trained in using a new sense, echolocation–clicks and their echoes, coming from a virtual surface at the distance of the fish. In 175 subsequent trials they localised the fish using echoes, a noisy visual cue (bubbles), or both. The visual cue's variance varied trial-to-trial, tracking on average the echo cue's estimated variance. The fish was shown after each trial. 7/10 subjects were above chance at using the new cue. Their data were analysed by Bayesian model comparison. For most (5/7), single-cue models (switching or vision-only) were better fits than cue combination models–including those that do not reweight by reliability or that systematically mis-weight–by a large margin (Bayes factors> 20). Author JN and one final participant were best fit by an optimal combination model (BF> 100 over the best single-cue model). Most participants who learned a new sense did not immediately combine it with vision. This suggests that extended experience with specific cues underlies human cue combination abilities. However, optimal combination after very little, or relatively little (author JN) experience was also seen. In future research we will ask which aspects of single-cue learning (eg bias) predict cue combination, whether with practice more participants learn to combine cues, and what intermediate stages exist.