Working memory (WM) enables the online maintenance and manipulation of information and is central to intelligent cognitive functioning. Much research has investigated executive processes of WM in order to understand the operations that make WM "work." However, there is yet little consensus regarding how executive processes of WM are organized. Here, we used quantitative meta-analysis to summarize data from 36 experiments that examined executive processes of WM. Experiments were categorized into 4 component functions central to WM: protecting WM from external distraction (distractor resistance), preventing irrelevant memories from intruding into WM (intrusion resistance), shifting attention within WM (shifting), and updating the contents of WM (updating). Data were also sorted by content (verbal, spatial, object). Meta-analytic results suggested that rather than dissociating into distinct functions, 2 separate frontal regions were recruited across diverse executive demands. One region was located dorsally in the caudal superior frontal sulcus and was especially sensitive to spatial content. The other was located laterally in the midlateral prefrontal cortex and showed sensitivity to nonspatial content. We propose that dorsal-"where"/ventral-"what" frameworks that have been applied to WM maintenance also apply to executive processes of WM. Hence, WM can largely be simplified to a dual selection model.

A recent trend in decision neuroscience is the use of model-based fMRI using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences due to the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual differences. Here we demonstrate the application of hierarchical Bayesian parameter estimation to model-based fMRI using the example of decision making in the Iowa Gambling Task. First we use a simulation study to demonstrate that hierarchical Bayesian analysis outperforms conventional (individual- or group-level) maximum likelihood estimation in recovering true parameters. Then we perform model-based fMRI analyses on experimental data to examine how the fMRI results depend upon the estimation method.

A recent trend in decision neuroscience is the use of model-based fMRI using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences due to the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual differences. Here we demonstrate the application of hierarchical Bayesian parameter estimation to model-based fMRI using the example of decision making in the Iowa Gambling Task. First we use a simulation study to demonstrate that hierarchical Bayesian analysis outperforms conventional (individual-or group-level) maximum likelihood estimation in recovering true parameters. Then we perform model-based fMRI analyses on experimental data to examine how the fMRI results depend upon the estimation method.How we make decisions to obtain rewards and avoid punishments is a fundamental topic across research areas including psychology, economics, neuroscience, and computer science. In the last decade, decision neuroscience researchers have begun to approach decision making from an interdisciplinary perspective -integrating quantitative models with neural signals. For example, early pioneering studies demonstrated that the phasic responses of midbrain dopamine neurons can be well described by the temporal-difference reinforcement learning algorithm (Montague, Dayan, & Sejnowski, 1996;Schultz, Dayan, & Montague, 1997). More recently, human functional magnetic resonance imaging (fMRI) studies have shown that blood-oxygen-level-dependent (BOLD) activations in brain regions including the striatum and orbitofrontal cortex correlate with prediction error signals from the temporaldifference learning model (McClure, Berns, & Montague, 2003;O'Doherty, Dayan, Friston, Critchley, & Dolan, 2003). These fMRI studies used the method of "model-based fMRI," in which a mathematical model of behavior provides a framework to study neural mechanisms of reward learning. In model-based fMRI, predictions derived from a mathematical model of choice behavior are correlated with fMRI data to determine brain areas related to postulated decision-making processes. This method is increasingly popular in decision neuroscience because it provides insight into the neural correlates of predicted cognitive processes and can be useful to discriminate competing theories of brain function (see O'Doherty, Hampton, & Kim, 2007 for a review and methodological recipe).The first step in model-based fMRI is to estimate the free parameters in the mathematical model of behavior. Getting accurate parameter estimates is important not only because parameter estimates reflect psychological traits (such as the learning rate or the balance of exploitation and exploration), but also because they affect the results of the subsequent model-based fMRI analysis (e.g., Tanaka et al...

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Behavioral results indicate measureable differences in risky decision making in older adults with SCD as compared with healthy controls. Modeling results allow us to interpret this difference as potentially being because of rapid forgetting of trial-to-trial information. This work furthers our understanding of SCD, while demonstrating the use of computational modeling in the interpretation of neuropsychological data.