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Bridging across functional models: The OFC as a value-making neural network.
Behavioral Neuroscience ( IF 1.9 ) Pub Date : 2021-6-2 , DOI: 10.1037/bne0000464
Mathias Pessiglione 1 , Jean Daunizeau 1
Affiliation  

Many functions have been attributed to the orbitofrontal cortex (OFC)-some classical roles, such as signaling the value of action outcomes, being challenged by more recent ones, such as signaling the position of a trial within a task space. In this paper, we propose a unifying neural network architecture, whose function is to generate a value from a set of attributes attached to a particular object. Our model reverses the logic of perceptual choice models, by considering values as outputs of (and not inputs to) the neural network. In doing so, the model explains why univariate value signals have been observed in both likeability rating and economic choice tasks, while the features associated with a particular task trial can be decoded using multivariate analysis. Moreover, simulations show that a globally positive correlation with subjective value at the population level can coexist with a variety of correlation coefficients at the single-unit level, bridging typical observations made in human neuroimaging and monkey electrophysiology studies of OFC activity. To better explain binary choice, we equipped the neural network with recurrent feedback connections that enable simultaneous coding of values associated with currently attended and previously considered objects. Simulations of this augmented model show that virtual lesions produce systematically intransitive preferences, as observed in patients with damage to the OFC. Thus, our neural network model is sufficiently general and flexible to account for a core set of observations and make specific predictions about both OFC activity during value judgment and behavioral consequence of OFC damage. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译:

跨功能模型的桥接:OFC 作为创造价值的神经网络。

许多功能都归因于眶额皮层 (OFC) - 一些经典角色,例如指示行动结果的价值,受到更新的挑战,例如指示任务空间中试验的位置。在本文中,我们提出了一种统一的神经网络架构,其功能是从附加到特定对象的一组属性中生成一个值。我们的模型颠倒了感知选择模型的逻辑,将值视为神经网络的输出(而不是输入)。这样做时,该模型解释了为什么在喜欢程度评级和经济选择任务中都观察到了单变量值信号,而与特定任务试验相关的特征可以使用多变量分析进行解码。而且,模拟表明,在群体水平上与主观价值的全局正相关可以与在单个单位水平上的各种相关系数共存,从而弥合了人类神经影像学和猴子电生理学对 OFC 活动的研究中的典型观察结果。为了更好地解释二元选择,我们为神经网络配备了循环反馈连接,可以同时编码与当前参与和先前考虑的对象相关的值。这种增强模型的模拟表明,正如在 OFC 受损的患者中所观察到的那样,虚拟病变会产生系统性的不及物偏好。因此,我们的神经网络模型具有足够的通用性和灵活性,可以解释一组核心观察结果,并对价值判断期间的 OFC 活动和 OFC 损害的行为后果做出具体预测。(PsycInfo 数据库记录 (c) 2021 APA,保留所有权利)。
更新日期:2021-06-03
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