Recording Neural Reward Signals in a Naturalistic Operant Task Using Mobile-EEG and Augmented Reality.
eNeuro
; 11(8)2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-39013585
ABSTRACT
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 (ERPs) while participants engaged in a naturalistic operant task to find rewards. Twenty-five participants were asked to navigate toward 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 s counter signaled 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 â¼230 ms with a maximal at channel FCz (M = -0.695 µV, ±0.23) and was significantly different than zero (p < 0.01). Participants took â¼3.38 s to reach the goal location and exhibited a general lose-shift (68.3% ±3.5) response strategy and posterror 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 toward a new era of human cognitive neuroscience research that blurs the line between laboratory and reality.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Reward
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Electroencephalography
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Evoked Potentials
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Augmented Reality
Limits:
Adult
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Female
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Humans
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Male
Language:
En
Journal:
ENeuro
Year:
2024
Document type:
Article