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The dorsal medial frontal cortex (dMFC) is highly active during choice behavior. Though many models have been proposed to explain dMFC function, the conflict monitoring model is the most influential. It posits that dMFC is primarily involved in detecting interference between competing responses thus signaling the need for control. It accurately predicts increased neural activity and response time (RT) for incompatible (high-interference) vs. compatible (low-interference) decisions. However, it has been shown that neural activity can increase with time on task, even when no decisions are made. Thus, the greater dMFC activity on incompatible trials may stem from longer RTs rather than response conflict. This study shows that (1) the conflict monitoring model fails to predict the relationship between error likelihood and RT, and (2) the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task.

The behavior of neural circuits is determined largely by the electrophysiological properties of the neurons they contain. Understanding the relationships of these properties requires the ability to first identify and catalog each property. However, information about such properties is largely locked away in decades of closed-access journal articles with heterogeneous conventions for reporting results, making it difficult to utilize the underlying data. We solve this problem through the NeuroElectro project: a Python library, RESTful API, and web application (at http://neuroelectro.org) for the extraction, visualization, and summarization of published data on neurons' electrophysiological properties. Information is organized both by neuron type (using neuron definitions provided by NeuroLex) and by electrophysiological property (using a newly developed ontology). We describe the techniques and challenges associated with the automated extraction of tabular electrophysiological data and methodological metadata from journal articles. We further discuss strategies for how to best combine, normalize and organize data across these heterogeneous sources. NeuroElectro is a valuable resource for experimental physiologists attempting to supplement their own data, for computational modelers looking to constrain their model parameters, and for theoreticians searching for undiscovered relationships among neurons and their properties.