Cognitive flexibility forms the core of the extraordinary ability of humans to adapt, but the precise neural mechanisms underlying our ability to nimbly shift between task sets remain poorly understood. Recent functional magnetic resonance imaging (fMRI) studies employing multivoxel pattern analysis (MVPA) have shown that a currently relevant task set can be decoded from activity patterns in the frontoparietal cortex, but whether these regions support the dynamic transformation of task sets from trial to trial is not clear. Here, we combined a cued task-switching protocol with human (both sexes) fMRI, and harnessed representational similarity analysis (RSA) to facilitate a novel assessment of trial-by-trial changes in neural task-set representations. We first used MVPA to define task-sensitive frontoparietal and visual regions and found that neural task-set representations on switch trials are less stably encoded than on repeat trials. We then exploited RSA to show that the neural representational pattern dissimilarity across consecutive trials is greater for switch trials than for repeat trials, and that the degree of this pattern dissimilarity predicts behavior. Moreover, the overall neural pattern of representational dissimilarities followed from the assumption that repeating sets, compared with switching sets, results in stronger neural task representations. Finally, when moving from cue to target phase within a trial, pattern dissimilarities tracked the transformation from previous-trial task representations to the currently relevant set. These results provide neural evidence for the longstanding assumptions of an effortful task-set reconfiguration process hampered by task-set inertia, and they demonstrate that frontoparietal and stimulus processing regions support “dynamic adaptive coding,” flexibly representing changing task sets in a trial-by-trial fashion.SIGNIFICANCE STATEMENTHumans can fluently switch between different tasks, reflecting an ability to dynamically configure “task sets,” rule representations that link stimuli to appropriate responses. Recent studies show that neural signals in frontal and parietal brain regions can tell us which of two tasks a person is currently performing. However, it is not known whether these regions are also involved in dynamically reconfiguring task-set representations when switching between tasks. Here we measured human brain activity during task switching and tracked the similarity of neural task-set representations from trial to trial. We show that frontal and parietal brain regions flexibly recode changing task sets in a trial-by-trial fashion, and that task-set similarity over consecutive trials predicts behavior.

People preferentially attend to external stimuli that are related to themselves compared with others. Whether a similar self-reference bias applies to internal representations, such as those maintained in working memory (WM), is presently unknown. We tested this possibility in four experiments, in which participants were first trained to associate social labels (self, friend, stranger) with arbitrary colors and then performed a delayed match-to-sample spatial WM task on color locations. Participants consistently responded fastest to WM probes at locations of self-associated colors (Experiments 1–4). This self-bias was driven not by differential exogenous attention during encoding or retrieval (Experiments 1 and 2) but by internal attentional prioritization of self-related representations during WM maintenance (Experiment 3). Moreover, self-prioritization in WM was nonstrategic, as this bias persisted even under conditions in which it hurt WM performance. These findings document an automatic prioritization of self-referential items in WM, which may form the basis of some egocentric biases in decision making.

Humans show a pervasive bias for processing self- over other-related information, including in working memory (WM), where people prioritize the maintenance of self- (over other-) associated cues. To elucidate the neural mechanisms underlying this self-bias, we paired a self- versus other-associated spatial WM task with fMRI and transcranial direct current stimulation (tDCS) of human participants of both sexes. Maintaining self- (over other-) associated cues resulted in enhanced activity in classic WM regions (frontoparietal cortex), and in superior multivoxel pattern decoding of the cue locations from visual cortex. Moreover, ventromedial PFC (VMPFC) displayed enhanced functional connectivity with WM regions during maintenance of self-associated cues, which predicted individuals' behavioral self-prioritization effects. In a follow-up tDCS experiment, we targeted VMPFC with excitatory (anodal), inhibitory (cathodal), or sham tDCS. Cathodal tDCS eliminated the self-prioritization effect. These findings provide strong converging evidence for a causal role of VMPFC in driving self-prioritization effects in WM and provide a unique window into the interaction between social, self-referential processing and high-level cognitive control processes.SIGNIFICANCE STATEMENTPeople have a strong tendency to attend to self-related stimuli, such as their names. This self-bias extends to the automatic prioritization of arbitrarily self-associated stimuli held in working memory. Since working memory is central to high-level cognition, this bias could influence how we make decisions. It is therefore important to understand the underlying brain mechanisms. Here, we used neuroimaging and noninvasive neurostimulation techniques to show that the source of self-bias in working memory is the ventromedial PFC, which modulates activity in frontoparietal brain regions to produce prioritized representations of self-associated stimuli in sensory cortex. This work thus reveals a brain circuit underlying the socially motivated (self-referential) biasing of high-level cognitive processing.

Attention network theory suggests that there are three separate neural networks that execute the discrete functions of alerting, orienting, and executive attention. Previous research on the influence of mood on attention has shown subtle and inconsistent results. The attention network theory may aid in clarifying the influence of mood on attention. The present study investigated the influence of mood on attentional networks in a normal population. Participants performed the Attention Network Test (ANT), which provides functional measures of alerting, orienting, and executive attention. Positive or negative mood was induced by listening to music with a positive or negative valence, respectively; neutral mood was induced by reading a collection of basic facts about China. The results revealed that negative mood led to a significantly higher alerting efficiency relative to other moods, while there were no significant mood effects on orienting or executive attention efficiency. According to the algorithm underlying the ANT, the higher alerting efficiency in the negative mood condition can be attributed to relatively greater benefits of cueing effects. The findings are discussed in the context of the noradrenergic system and of evolutionary significance. Specifically, the increase in the alerting function during negative mood states may be due to the modulation effect of negative mood on the noradrenergic system, and/or to the survival benefit resulting from an increase in automatic vigilance towards negative information. The current results suggest that as the influence of negative mood on attention appears to specifically consist in an enhanced alerting function, it may not be found in studies where the three attentional networks are not dissociated.

Self-esteem is an affective, self-evaluation of oneself and has a significant effect on mental and behavioral health. Although research has focused on the neural substrates of self-esteem, little is known about the spontaneous brain activity that is associated with trait self-esteem (TSE) during the resting state. In this study, we used the resting-state functional magnetic resonance imaging (fMRI) signal of the amplitude of low-frequency fluctuations (ALFFs) and resting state functional connectivity (RSFC) to identify TSE-related regions and networks. We found that a higher level of TSE was associated with higher ALFFs in the left ventral medial prefrontal cortex (vmPFC) and lower ALFFs in the left cuneus/lingual gyrus and right lingual gyrus. RSFC analyses revealed that the strengths of functional connectivity between the left vmPFC and bilateral hippocampus were positively correlated with TSE; however, the connections between the left vmPFC and right inferior frontal gyrus and posterior superior temporal sulcus were negatively associated with TSE. Furthermore, the strengths of functional connectivity between the left cuneus/lingual gyrus and right dorsolateral prefrontal cortex and anterior cingulate cortex were positively related to TSE. These findings indicate that TSE is linked to core regions in the default mode network and social cognition network, which is involved in self-referential processing, autobiographical memory and social cognition.