Statistical decision theory to relate neurons to behavior in the study of covert visual attention
Statistical decision theory to relate neurons to behavior in the study of covert visual attention
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DOI：
10.1016/j.visres.2008.12.008
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发表时间：
2009-06-02
期刊：
影响因子：
1.8
通讯作者：
Droll, Jason A.
中科院分区：
文献类型：
--
作者：
Eckstein, Miguel P.;Peterson, Matthew F.;Droll, Jason A.
仔细研究视觉注意的众多生理学和成像研究表明，将神经科学的结果与基于行为工作的视觉注意的经典理论相结合并非易事。不同的子领域提出了不同的问题，使用了不同的实验范式，发展了不同的模式。本综述的目的是利用统计决策理论和计算模型将心理学研究中的经典注意理论与神经可观测性联系起来，如平均放电率或功能成像大胆反应、调谐函数、Fano因子、神经元可探测性指数和受试者工作特征下的面积(ROC)。我们专注于线索实验，并试图区分注意研究中的两个主要理论：有限资源模型/提高敏感度与选择/差异加权。我们使用贝叶斯理想观测器(BIO)建模，其中预测线索或先验知识改变感觉信息的差分权重(先验)，以基于高斯响应变量和泊松过程神经模型来生成对行为和神经可观测的预测。理想观察者模型可以被修改为代表许多经典的视觉注意心理学理论，通过包括假想的人类注意有限资源，以同样的方式使用序贯理想观察者分析来包括人类空间视觉的生理加工成分(Geisler，W.S.(1989))。视觉辨别的序贯理想观察者分析。《心理评论》96,267-314。特别是，我们比较了BID和变种模型在有限资源下的新的生物学上可信的实现。我们发现，在人类心理物理学领域发展的模型所预测的线索的行为效应与它们基于神经元的类似物之间存在着密切的关系。关键的是，我们证明了线索对实验观测的影响，如平均神经活动、方差、Fano因子和神经元可检测性指数，可以与两个主要的注意理论模型一致，这取决于神经元是被假设为计算似然、对数似然还是直接对泊松变量进行操作的简单模型。神经元调谐功能的变化也可以与这两种理论一致，这取决于调谐的变化是沿着实验提示的维度还是不同的维度。我们表明，使用接收操作特征曲线下的面积适当地测量神经元的灵敏度可以用于区分这两种理论，并且对于决策变量的许多变换是健壮的。我们提供了一个汇总表，希望它可以为解释过去的结果以及规划未来的研究提供一些指导。(C)2009爱思唯尔有限公司。保留所有权利。
Scrutiny of the numerous physiology and imaging studies of visual attention reveal that integration of results from neuroscience with the classic theories of visual attention based on behavioral work is not simple. The different subfields have pursued different questions, used distinct experimental paradigms and developed diverse models. The purpose of this review is to use statistical decision theory and computational modeling to relate classic theories of attention in psychological research to neural observables such as mean firing rate or functional imaging BOLD response, tuning functions, Fano factor, neuronal index of detectability and area under the receiver operating characteristic (ROC). We focus on cueing experiments and attempt to distinguish two major leading theories in the study of attention: limited resources model/increased sensitivity vs. selection/differential weighting. We use Bayesian ideal observer (BIO) modeling, in which predictive cues or prior knowledge change the differential weighting (prior) of sensory information to generate predictions of behavioral and neural observables based on Gaussian response variables and Poisson process neural based models. The ideal observer model can be modified to represent a number of classic psychological theories of visual attention by including hypothesized human attentional limited resources in the same way sequential ideal observer analysis has been used to include physiological processing components of human spatial vision (Geisler, W. S. (1989). Sequential ideal-observer analysis of visual discrimination. Psychological Review 96, 267-314.). In particular we compare new biologically plausible implementations of the BID and variant models with limited resources. We find a close relationship between the behavioral effects of cues predicted by the models developed in the field of human psychophysics and their neuron-based analogs. Critically, we show that cue effects on experimental observables such as mean neural activity, variance, Fano factor and neuronal index of detectability can be consistent with the two major theoretical models of attention depending on whether the neuron is assumed to be computing likelihoods, log-likelihoods or a simple model operating directly on the Poisson variable. Change in neuronal tuning functions can also be consistent with both theories depending on whether the change in tuning is along the dimension being experimentally cued or a different dimension. We show that a neuron's sensitivity appropriately measured using the area under the Receive Operating Characteristic curve can be used to distinguish across both theories and is robust to the many transformations of the decision variable. We provide a summary table with the hope that it might provide some guidance in interpreting past results as well as planning future studies. (C) 2009 Elsevier Ltd. All rights reserved.