Neural Oscillation as a Selective Modulatory Mechanism on Decision Confidence, Speed, and Accuracy.

Neural oscillations have been associated with decision-making processes, but their underlying network mechanisms remain unclear. This study investigates how neural oscillations influence decision network models of competing cortical columns with varying intrinsic and emergent timescales. Our findings reveal that decision networks with faster excitatory than inhibitory synapses are more susceptible to oscillatory modulations. Higher in-phase oscillation amplitude reduces decision confidence without affecting accuracy, while decision speed increases. In contrast, antiphase modulation increases decision accuracy, confidence, and speed. Increasing oscillation frequency reverses these effects. Changing oscillatory phase difference gradually modulates decision behavior, with decision confidence affected nonlinearly. Moreover, neural resonance can further enhance modulatory susceptibility for network with faster excitatory than inhibitory synapses. These effects decouple decision accuracy, speed, and confidence, challenging standard speed-accuracy trade-off. These phenomena can be explained by excitatory neural populations contributing more to in-phase modulation, while inhibitory neural populations contribute more to antiphase modulation. State-space trajectories' momentum swinging with respect to network steady states and decision uncertainty manifold further provide insights into the neural circuit mechanisms. Our work provides mechanistic insights into how neurobiological diversity shapes decision-making processes in the presence of ubiquitous neural oscillations.