A meta-analysis of executive components of working memory.

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.

Impaired error-likelihood prediction in medial prefrontal cortex in schizophrenia.

The cognitive impairment in individuals with schizophrenia includes deficits of working memory in dorsolateral prefrontal cortex and deficits of performance monitoring in medial prefrontal cortex (MPFC). Recent work suggests a more general role for MPFC in predicting the outcome of actions and then evaluating those predictions. Here we investigate, in individuals with schizophrenia, two specific effects associated with this role: the error likelihood effect (occurring on trials with correct performance, but features that predict a high probability of errors), and the error unexpectedness effect (occurring on trials with an error, but features that predict errors are of low probability). In a rapid event-related fMRI design with a modified version of the change-signal task, a cue incidentally predicting error likelihood was encoded into working memory by participants in order to perform a secondary delayed match-to-sample task. There were four key findings: (1) individuals with schizophrenia exhibited poorer working memory performance and reduced error signals in MPFC; (2) even in control and schizophrenia subgroups matched on working memory performance, the schizophrenia subgroup showed a deficit in error-likelihood prediction in MPFC at the time of the predictive cue; (3) the schizophrenia subgroup also showed a deficit in evaluative error-unexpectedness activity when errors were committed; and (4) a mediation analysis indicated that error-likelihood predictions successfully explained error-unexpectedness evaluations in both controls and patients. Collectively, these findings suggest that individuals with schizophrenia have a disturbance in the evaluation of outcomes that is the result of a primary deficit in the prediction of error likelihood in MPFC.

Anterior insula activity predicts the influence of positively framed messages on decision making.

The neural mechanisms underlying the influence of persuasive messages on decision making are largely unknown. We address this issue using event-related fMRI to investigate how informative messages alter risk appraisal during choice. Participants performed the Iowa Gambling Task while viewing a positively framed, negatively framed, or control message about the options. The right anterior insula correlated with improvement in choice behavior due to the positively framed but not the negatively framed message. With the positively framed message, there was increased activation proportional to message effectiveness when less-preferred options were chosen, consistent with a role in the prediction of adverse outcomes. In addition, the dorsomedial and the left dorsolateral prefrontal cortex correlated with overall decision quality, regardless of message type. The dorsomedial region mediated the relationship between the right anterior insula and decision quality with the positively framed messages. These findings suggest a network of frontal brain regions that integrate informative messages into the evaluation of options during decision making. Supplemental procedures and results for this article may be downloaded from http://cabn.psychonomic-journals.org/content/supplemental.

The impact of subjective cognitive decline on Iowa Gambling Task performance.

OBJECTIVE: To ascertain whether the Iowa Gambling Task (IGT) could be used to detect and identify measurable cognitive differences between older adults with subjective cognitive decline (SCD) as compared with healthy older controls (HC). METHOD: Older adults with self-identified SCD and age-matched controls completed a comprehensive neuropsychological assessment battery including the clinical version of the IGT, as well as self-report measures of mood and personality. RESULTS: The groups did not differ on clinically normed scores on the IGT. However, the groups did differ in the specific decks chosen as they progressed through the task, with the SCD group choosing the advantageous, high loss-frequency deck (Deck C) more often toward the end of the task. Using hierarchical Bayesian parameter estimation, we show that the prospect valence learning (PVL) model outperforms the expectancy valence learning (EVL) model in parsimoniously accounting for task performance by both groups. The PVL model explains the difference in deck choices between groups as being because of an underlying difference in their learning rate, with the SCD group emphasizing the current outcome over past outcomes more than the HC group. CONCLUSIONS: 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.

Rightward-biased hemodynamic response of the parahippocampal system during virtual navigation.

Phase reset of parahippocampal electrophysiological oscillations in the theta frequency range is said to contribute to item encoding and retrieval during spatial navigation. Although well-studied in non-human animals, this mechanism is poorly understood in humans. Previously we found that feedback stimuli presented in a virtual maze environment elicited a burst of theta power over right-posterior areas of the human scalp, and that the power and phase angle of these oscillations were greater following right turns compared to left turns in the maze. Here we investigated the source of this effect with functional magnetic resonance imaging. Consistent with our predictions, we found that 1) feedback encountered in the maze task activated right parahippocampal cortex (PHC), 2) right PHC was more activated by rewards following right turns compared to left turns in the maze, and 3) the rightward-biased activation was more pronounced in individuals who displayed good spatial abilities. These findings support our previous electrophysiological findings and highlight, in humans, a role for PHC theta oscillations in encoding salient information for the purpose of spatial navigation.

A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.

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.