Effects of 7.5% Carbon Dioxide and Nicotine Administration on Latent Inhibition

Granger, Kiri; Ferrar, Jennifer; Caswell, Sheryl; Haselgrove, Mark; Moran, Paula M.; Attwood, Angela; Barnett, Jennifer H.

doi:10.3389/fpsyt.2021.582745

Cited by 1 publication

(1 citation statement)

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“…is very rapid learning about a reinforcer which has very limited effects on behavior in terms of behavioral choice or other measures of incentive value (i.e., high alpha, low lambda). Alternatively, if predictive cues are not very discriminable, or have undergone pre-exposure manipulations (e.g., Granger et al, 2021), it is possible to imagine slowed learning about highly efficacious reinforcers (i.e., low alpha, high lambda).…”

Section: Qamentioning

confidence: 99%

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

Chase

2023

Front. Psychol.

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IntroductionComputational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses.MethodsSimulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses.ResultsInitial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise.ConclusionOverall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.

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Section: Qamentioning

confidence: 99%

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

Chase

2023

Front. Psychol.

View full text Add to dashboard Cite

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Effects of 7.5% Carbon Dioxide and Nicotine Administration on Latent Inhibition

Cited by 1 publication

References 52 publications

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

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