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, anti-phase modulation increases decision accuracy, confidence and speed. Increasing oscillation frequency reverses these effects. Changing oscillatory phase difference gradually modulates decision behaviour, with decision confidence affected nonlinearly. These effects decouple decision accuracy, speed and confidence, challenge standard speed-accuracy trade-off, and can be explained via state-space trajectories’ momentum swinging with respect to network steady states and decision uncertainty manifold. Our work provides mechanistic insights into how neurobiological diversity shapes decision-making processes in the presence of ubiquitous neural oscillations.