Biology subjects

McClure, S. M.

Publications and source records attributed to McClure, S. M..

3 recordsLinked to original sources

bioRxiv2022

Midbrain-Hippocampus Structural Integrity Selectively Predicts Motivated Memory Encoding

Motivation is a powerful driver of learning and memory. Functional MRI studies show that interactions between the dopaminergic midbrain (SN/VTA), hippocampus, and nucleus accumbens (NAc) are critical for motivated memory encoding. However, it is not known if these effects are transient and purely functional, or if individual differences in the structure of this circuit underlie motivated memory encoding. To quantify individual differences in structure, diffusion-weighted MRI and probabilistic tractography were used to quantify SN/VTA-striatum and SN/VTA-hippocampus pathways associated with motivated memory encoding in humans. Participants completed a motivated source memory paradigm. During encoding, words were randomly assigned to one of three conditions: reward ($1.00), control ($0.00), or punishment (-$1.00). During retrieval, participants were asked to retrieve item and source information of the previously studied words and were rewarded or penalized according to their performance. Source memory for words assigned to both reward and punishment conditions was greater than control words, while there were no differences in item memory based on value. Anatomically, probabilistic tractography results revealed a heterogeneous, topological arrangement of the SN/VTA. Tract density measures of SN/VTA-hippocampus pathways were positively correlated with individual differences in reward and punishment modulated memory performance, while density of SN/VTA-striatum pathways showed no association. This novel finding suggests that pathways emerging from the human SV/VTA are anatomically separable and functionally heterogeneous. Individual differences in structural connectivity of the dopaminergic hippocampus-VTA loop are selectively associated with motivated memory encoding. Significance StatementFunctional MRI studies show that interactions between the dopaminergic midbrain (SN/VTA), hippocampus, and nucleus accumbens (NAc) are critical for motivated memory encoding. This has led to competing theories that posit either SN/VTA-NAc reward prediction errors or SN/VTA-hippocampus signals underlie motivated memory encoding. Additionally, it is not known if these effects are transient and purely functional, or if individual differences in the structure of these circuits underlie motivated memory encoding. Using diffusion-weighted MRI and probabilistic tractography, we show that tract density measures of SN/VTA-hippocampus pathways are positively correlated with motivated memory performance, while density of SN/VTA-striatum pathways show no association. This finding suggests that anatomical individual differences of the dopaminergic hippocampus-VTA loop are selectively associated with motivated memory encoding.

neuroscience↗

bioRxiv2018

Joint Modeling of Reaction Times and Choice Improves Parameter Identifiability in Reinforcement Learning Models

BackgroundReinforcement learning models provide excellent descriptions of learning in multiple species across a variety of tasks. Many researchers are interested in relating parameters of reinforcement learning models to neural measures, psychological variables or experimental manipulations. We demonstrate that parameter identification is difficult because a range of parameter values provide approximately equal quality fits to data. This identification problem has a large impact on power: we show that a researcher who wants to detect a medium sized correlation (r = .3) with 80% power between a variable and learning rate must collect 60% more subjects than specified by a typical power analysis in order to account for the noise introduced by model fitting.\n\nNew MethodWe derive a Bayesian optimal model fitting technique that takes advantage of information contained in choices and reaction times to constrain parameter estimates.\n\nResultsWe show using simulation and empirical data that this method substantially improves the ability to recover learning rates.\n\nComparison with Existing MethodsWe compare this method against the use of Bayesian priors. We show in simulations that the combined use of Bayesian priors and reaction times confers the highest parameter identifiability. However, in real data where the priors may have been misspecified, the use of Bayesian priors interferes with the ability of reaction time data to improve parameter identifiability.\n\nConclusionsWe present a simple technique that takes advantage of readily available data to substantially improve the quality of inferences that can be drawn from parameters of reinforcement learning models.\n\nHighlights- Parameters of reinforcement learning models are particularly difficult to estimate\n- Incorporating reaction times into model fitting improves parameter identifiability\n- Bayesian weighting of choice and reaction times improves the power of analyses assessing learning rate

neuroscience↗

bioRxiv2018

Hippocampal Pattern Separation Supports Reinforcement Learning

1Animals rely on learned associations to make decisions. Associations can be based on relationships between object features (e.g., the three-leaflets of poison ivy leaves) and outcomes (e.g., rash). More often, outcomes are linked to multidimensional states (e.g., poison ivy is green in summer but red in spring). Feature-based reinforcement learning fails when the values of individual features depend on the other features present. One solution is to assign value to multifeatural conjunctive representations. We tested if the hippocampus formed separable conjunctive representations that enabled learning of response contingencies for stimuli of the form: AB+, B-, AC-, C+. Pattern analyses on functional MRI data showed the hippocampus formed conjunctive representations that were dissociable from feature components and that these representations influenced striatal PEs. Our results establish a novel role for hippocampal pattern separation and conjunctive representation in reinforcement learning.

neuroscience↗