Predict or classify: The deceptive role of time-locking in brain signal classification

TL;DR

This paper demonstrates that high classification accuracy in brain signal studies can result from time-locking effects rather than true predictive information about decisions, highlighting a potential misinterpretation in prior research.

Contribution

It introduces a stochastic model and information-theoretic analysis showing that time-locking can produce apparent predictive signals without actual decision-related information.

Findings

Classification accuracy can be above chance before decision time due to time-locking.

High accuracy is linked to the relaxation time of the neural process.

Time-locking influences the perceived timing of predictive signals.

Abstract

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.