RESUMO
The electrophysiological response to rewards recorded during laboratory tasks has been well documented, yet little is known about the neural response patterns in a more naturalistic setting. Here, we combined a mobile-EEG system with an augmented reality headset to record event-related brain potentials (ERP) while participants engaged in a naturalistic operant task to find rewards. Twenty-five participants were asked to navigate towards a west or east goal location marked by floating orbs, and once participants reached the goal location, the orb would then signify a reward (5 cents) or no-reward (0 cents) outcome. Following the outcome, participants returned to a start location marked by floating purple rings, and once standing in the middle, a 3 second counter would signal the start of the next trial, for a total of 200 trials. Consistent with previous research, reward feedback evoked the reward positivity, an ERP component believed to index the sensitivity of the anterior cingulate cortex to reward prediction error signals. The reward positivity peaked around 230 ms post-feedback with a maximal at channel FCz (M = -0.695µV, ± .23) and was significantly different than zero (p< 0.01). Participants took approximately 3.38 seconds to reach the goal-location and exhibited a general lose-shift (68.3% ±3.5) response strategy and post-error slowing. Overall, these novel findings provide support for the idea that combining mobile-EEG with augmented reality technology is a feasible solution to enhance the ecological validity of human electrophysiological studies of goal-directed behavior and a step towards a new era of human cognitive neuroscience research that blurs the line between laboratory and reality.Significant Statement Building on decades of experimental, computational, and theoretical analyses of reinforcement learning in animal and humans, the present study reveals for the first time that scalp-recorded electrophysiological signals associated with the anterior cingulate cortex sensitivity to reward prediction error signals is dynamically modulated by rewards in humans freely navigating a more realistic environment, and that participants performed the task in accordance with reinforcement learning theory.
