Department of Psychology, University of Florida, Gainesville, FL 32611, USA.
Department of Psychology, University of Florida, Gainesville, FL 32611, USA; Department of Psychology, University of Bremen, Bremen 28359, Germany.
J Neurosci Methods. 2025 Jan;413:110303. doi: 10.1016/j.jneumeth.2024.110303. Epub 2024 Oct 19.
Experience changes visuo-cortical tuning. In humans, re-tuning has been studied during aversive generalization learning, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. Previous work utilized pre-defined tuning shapes (generalization and sharpening patterns). This approach may constrain the ways in which re-tuning can be characterized since the tuning patterns may not match the prototypical functions.
The present study proposes a flexible and data-driven method for precisely quantifying changes in tuning based on the Ricker wavelet function and the Bayesian bootstrap. This method was applied to EEG and psychophysics data from an aversive generalization learning paradigm.
The Ricker wavelet model fitted the steady-state visual event potentials (ssVEP), alpha-band power, and detection accuracy data well. A Morlet wavelet function was used for comparison and fit the data better in some situations, but was more challenging to interpret. The pattern of re-tuning in the EEG data, predicted by the Ricker model, resembled the shapes of the best fitting a-priori patterns.
Although the re-tuning shape modeled by the Ricker function resembled the pre-defined shapes, the Ricker approach led to greater Bayes factors and more interpretable results compared to a-priori models. The Ricker approach was more easily fit and led to more interpretable results than a Morlet wavelet model.
This work highlights the promise of the current method for capturing the precise nature of visuo-cortical tuning, unconstrained by the implementation of a-priori models.
经验会改变视皮层的调谐。在人类中，重新调谐已在厌恶泛化学习中进行了研究，其中使用泛化刺激（GS）与条件威胁线索（CS+）的相似性来量化调谐函数。以前的工作利用了预定义的调谐形状（泛化和锐化模式）。这种方法可能会限制重新调谐的特征方式，因为调谐模式可能与原型功能不匹配。
本研究提出了一种灵活的数据驱动方法，用于基于 Ricker 小波函数和贝叶斯引导来精确量化调谐变化。该方法应用于厌恶泛化学习范式的 EEG 和心理物理学数据。
Ricker 小波模型很好地拟合了稳态视觉事件电位（ssVEP）、α 波段功率和检测准确性数据。Morlet 小波函数用于比较，在某些情况下拟合数据更好，但更难以解释。Ricker 模型预测的 EEG 数据中的重新调谐模式，与最佳拟合先验模式的形状相似。
尽管由 Ricker 函数建模的重新调谐形状类似于预定义的形状，但与先验模型相比，Ricker 方法导致更大的贝叶斯因子和更具解释性的结果。与 Morlet 小波模型相比，Ricker 方法更容易拟合并且导致更具解释性的结果。
这项工作强调了当前方法在捕捉视皮层调谐的精确性质方面的潜力，不受先验模型实现的限制。
