The Late Positive Potentials Evoked by Cigarette-Related and Emotional Images Show no Gender Differences in Smokers

Feedback processing is enhanced following exploration in continuous environments

Decision-making is typically studied by presenting participants with a small set of options. However, real-world behaviour, like foraging, often occurs in continuous environments. The degree to which human decision-making in discrete tasks generalizes to continuous tasks is questionable. For example, successful foraging comprises both exploration (learning about the environment) and exploitation (taking advantage of what is known). Although progress has been made in understanding the neural processes related to this trade-off in discrete tasks, it is currently unclear how, or whether, the same processes are involved in continuous tasks. To address this, we recorded electroencephalographic data while participants "dug for gold" by selecting locations on a map. Participants were cued beforehand that the map contained either a single patch of gold, or many patches of gold. We then used a computational model to classify participant responses as either exploitations, which were driven by previous reward locations and amounts, or explorations. Our participants were able to adjust their strategy based on reward distribution, exploring more in multi-patch environments and less in single-patch environments. We observed an enhancement of the feedback-locked P300, a neural signal previously linked to exploration in discrete tasks, which suggests the presence of a general neural system for managing the explore-exploit trade-off. Furthermore, the P300 was accompanied by an exploration-related enhancement of the late positive potential that was greatest in the multi-patch environment, suggesting a role for motivational processes during exploration.

Estimating statistical power for event-related potential studies using the late positive potential

The late positive potential (LPP) is a common measurement used to study emotional processes of subjects in ERP paradigms. Despite its extensive use in affective neuroscience, there is presently no gold standard for how to appropriately power ERP studies using the LPP. The present study investigates how the number of trials, number of subjects, and magnitude of the effect size affect statistical power in analyses of the LPP. Using Monte Carlo simulations of ERP experiments with varying numbers of trials, subjects, and synthetic effects of known magnitude, we measured the probability of obtaining a statistically significant effect in 1,489 experiments repeated 1,000 times each. Predictably, our results showed that statistical power increases with increasing numbers of trials and subjects and at larger effect sizes. We also found that higher levels of statistical power can be achieved with lower numbers of subjects and trials and at lower effect sizes in within-subject than in between-subjects designs. Furthermore, we found that, as subjects are added to an experiment, the slope of the relationship between effect size and statistical power increased and shifted to the left until the power asymptoted to nearly 100% at higher effect sizes. This suggests that adding more subjects greatly increases statistical power at lower effect sizes (<1 µV) compared with more robust (>1.5 µV) effect sizes. We confirmed the results from the simulations based on the synthetic effects by running a new series of simulated experiments based on real data collected while participants looked at emotional images.