Is Model Fitting Necessary for Model-Based fMRI?
Is Model Fitting Necessary for Model-Based fMRI?
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DOI：
10.1371/journal.pcbi.1004237
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发表时间：
2015-06
影响因子：
4.3
通讯作者：
Niv Y
中科院分区：
文献类型：
--
作者：
Wilson RC;Niv Y
基于模型的fMRI数据分析是研究不同脑区计算作用的重要工具。通过这种方法，可以利用行为的理论模型从特定算法中找到潜在变量的大脑结构，例如强化学习中的预测误差。这种方法的一个潜在缺点是模型通常有自由参数，因此分析的结果可能取决于如何设置这些自由参数。在这项工作中，我们询问这种假设的弱点在实践中是否存在问题。我们首先为使用不同回归量的fMRI分析结果之间的关系开发了一般的封闭形式表达式，例如，一个对应于测量数据背后的真实过程，另一个对应于真实生成回归量的模型推导近似。然后，作为一个具体的测试案例，我们在理论上和之前发表的两个数据集中检验了基于模型的fMRI对强化学习中学习率参数的敏感性。我们发现，即使学习率的严重误差也只会导致神经结果的微小变化。因此，我们的研究结果表明，精确的模型拟合对于基于模型的fMRI并不总是必要的。他们还强调了使用功能磁共振成像数据在不同模型或模型参数之间进行仲裁的困难。虽然这些具体的结果只适用于简单强化学习模型中学习率的影响，但我们提供了一个模板来测试其他模型中不同参数的影响。近年来，基于模型的功能磁共振成像已成为心理学和神经科学领域的一项强大技术。通过这种方法，可以利用行为的计算模型来确定不同的算法在大脑中是在哪里、是否以及如何实现的。然而，这种方法似乎有一个致命的弱点，即模型经常有自由参数，设置这些参数的错误可能导致对数据解释的错误。在这里，我们问的是，这个潜在的弱点，在理论上，是否在实践中是一个实际的弱点。特别是，我们测试了在试错强化学习设置中估计参与者学习率的错误是否会对识别学习过程的神经基质产生不利影响。令人惊讶的是，事实证明，即使学习率上的严重错误也只会导致神经结果的微小变化。好消息是，自由参数的精确识别并不总是必要的；由此带来的坏消息是，要确定不同大脑区域的精确计算作用，可能比我们之前所认识到的要困难得多。根据我们的分析结果，我们根据需要提供了最大化或最小化模型参数敏感性的实验设计建议。
Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models. In recent years, model-based fMRI has emerged as a powerful technique in psychology and neuroscience. With this method, computational models of behavior can be leveraged to identify where, whether and how different algorithms are implemented in the brain. Yet this approach seems to have an Achilles heel in that the models frequently have free parameters, and errors in setting these parameters could lead to errors in interpretation of the data. Here we asked whether this potential weakness, in theory, is an actual weakness in practice. In particular, we tested whether errors in estimating participants’ learning rate in a trial-and-error reinforcement learning setting would have adverse effects on identifying the neural substrates of the learning process. Amazingly, it turns out that even gross errors in the learning rate lead to only minute changes in the neural results. The good news is that precise identification of free parameters is not always necessary; the corollary bad news is that it may be harder to identify the precise computational roles of different brain areas than we had previously appreciated. Based on our analytical results, we offer suggestions for designing experiments that maximize or minimize sensitivity to model parameters, as needed.
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影响因子：
5.7
作者：
Bartra, Oscar;McGuire, Joseph T.;Kable, Joseph W.
通讯作者：
Kable, Joseph W.
DOI：
10.1196/annals.1390.022
发表时间：
2007-01-01
期刊：
REWARD AND DECISION MAKING IN CORTICOBASAL GANGLIA NETWORKS
影响因子：
--
作者：
O'Doherty, John P.;Hampton, Alan;Kim, Hackjin
通讯作者：
Kim, Hackjin
影响因子：
64.8
作者：
Daw, Nathaniel D.;O'Doherty, John P.;Dayan, Peter;Seymour, Ben;Dolan, Raymond J.
通讯作者：
Dolan, Raymond J.
影响因子：
5.7
作者：
Abler, Birgit;Walter, Henrik;Spitzer, Manfred
通讯作者：
Spitzer, Manfred
影响因子：
5.3
作者：
Niv, Yael;Edlund, Jeffrey A.;O'Doherty, John P.
通讯作者：
O'Doherty, John P.