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<b><i>Introduction:</i></b> The emergence of Pavlovian-to-instrumental transfer (PIT) research in the human neurobehavioral domain has been met with increased interest over the past two decades. A variety of PIT tasks were developed during this time; while successful in demonstrating transfer phenomena, existing tasks have limitations that should be addressed. Herein, we introduce two PIT paradigms designed to assess outcome-specific and general PIT within the context of addiction. <b><i>Materials and Methods:</i></b> The single-lever PIT task, based on an established paradigm, replaced button presses with joystick motion to better assess avoidance behavior. The full transfer task uses alcohol and nonalcohol rewards associated with Pavlovian cues and instrumental responses, along with other gustatory and monetary rewards. We constructed mixed-effects models with the addition of other statistical analyses as needed to interpret various behavioral measures. <b><i>Results:</i></b> Single-lever PIT: both versions were successful in eliciting a PIT effect (joystick: <i>p</i> &#x3c; 0.001, η_p² = 0.36, button-box: <i>p</i> &#x3c; 0.001, η_p² = 0.30). Full transfer task: it was determined that the alcohol and nonalcoholic reward cues selectively primed their respective reward-associated responses (gustatory version: <i>p</i> &#x3c; 0.001, <i>r</i> = 0.59, and monetary version: <i>p</i> &#x3c; 0.001, <i>r</i> = 0.84). The appetitive/aversive cues resulted in a general transfer effect (gustatory: <i>p</i> &#x3c; 0.001, η_p² = 0.09, and monetary: <i>p</i> &#x3c; 0.001, η_p² = 0.17). <b><i>Discussion/Conclusion:</i></b> Single-lever PIT: PIT was observed in both task versions. We posit that the use of a joystick is more advantageous for the analysis of avoidance behavior. It evenly distributes movement between approach and avoid trials, which is relevant to analyzing fMRI data. Full transfer task: While gustatory conditioning has been used in the past to elicit transfer effects, we present the first paradigm that successfully elicits both specific and general transfers in humans with gustatory alcohol rewards.

In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices.To investigate this, coefficients of a GLM with one regressor per trial were used as features for a support vector machine (SVM) in combination with a searchlight approach for feature selection and cross-validation. We then compare the results to the performance of four different behavioral models.We found that the behavioral models reached mean accuracies of 90% and above, while the fMRI model only reached 54.84% at the best location in the brain with a spatial distribution similar to the well-known value-tracking network. This low, though significant, accuracy is in line with simulations showing that classifying based on signals with realistic correlations with subjective value produces comparable, low accuracies. These results emphasize the limitations of fMRI recordings from single events to predict human choices, especially when compared to conventional behavioral models. Better performance may be obtained with paradigms that allow the construction of miniblocks to improve the available signal-to-noise ratio.

In experimental psychology, subjects are often confronted with computer-based experimental paradigms. Creating such paradigms can require a lot of effort. PyParadigm is a newly developed Python library to ease the development of such paradigms by employing a declarative approach to build user interfaces (UIs). Paradigm specifications in this approach requires much less code and training than in alternative libraries. Although PyParadigm was initially developed for the creation of experimental paradigms, it is generally suited to build UIs that display or interact with 2D objects.