It is well established that holding information in working memory (WM) elicits sustained stimulus-specific patterns of neural activity. Nevertheless, here we provide evidence for a distinct class of neural activity that tracks the number of individuated items in working memory, independent of the type of visual features stored. We present two EEG studies of young adults of both sexes that provide robust evidence for a signal tracking the number of individuated representations in working memory, regardless of the specific feature values stored. In Study 1, subjects maintained either colors or orientations across separate blocks in a single session. We found near-perfect generalization of the load signal between these two conditions, despite being able to simultaneously decode which feature had been voluntarily stored. In Study 2, participants attended to two features with very distinct cortical representations: color and motion coherence. We again found evidence for a neural load signal that robustly generalized across these distinct visual features, even though cortically disparate regions process color and motion coherence. Moreover, representational similarity analysis provided converging evidence for a content-independent load signal, while simultaneously showing that unique variance in EEG activity tracked the specific features that were stored. We posit that this load signal reflects a content-independent "pointer" operation that binds objects to the current context while parallel but distinct neural signals represent the features that are stored for each item in memory.

Key event-related potentials (ERPs) of perceptual decision-making such as centroparietal positivity (CPP) elucidate how evidence is accumulated toward a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs different hemifield relative to targets) on perceptual decision-making. We confirmed that responses for perceptual decisions were slower for spatially incongruent versus congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent versus congruent. To evaluate the effects of congruency further, we applied drift diffusion modeling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter nondecision times when considering the effect of congruency. The modeling also suggested that congruency's effect on behavior occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase and then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor.

Estimating the direction of functional connectivity (FC) can help further elucidate complex brain function. However, the estimation of directed FC at the voxel level in fMRI data, and evaluating its performance, has yet to be done. We therefore developed a novel directed seed-based connectivity analysis (SCA) method based on normalized pairwise Granger causality that provides greater detail and accuracy over ROI-based methods. We evaluated its performance against 145 cortical retrograde tracer injections in male and female marmosets that were used as ground truth cellular connectivity on a voxel-by-voxel basis. The receiver operating characteristic (ROC) curve was calculated for each injection, and we achieved area under the ROC curve of 0.95 for undirected and 0.942 for directed SCA in the case of high cell count threshold. This indicates that SCA can reliably estimate the strong cellular connections between voxels in fMRI data. We then used our directed SCA method to analyze the human default mode network (DMN) and found that dlPFC (dorsolateral prefrontal cortex) and temporal lobe were separated from other DMN regions, forming part of the language-network that works together with the core DMN regions. We also found that the cerebellum (Crus I-II) was strongly targeted by the posterior parietal cortices and dlPFC, but reciprocal connections were not observed. Thus, the cerebellum may not be a part of, but instead a target of, the DMN and language-network. Summarily, our novel directed SCA method, visualized with a new functional flat mapping technique, opens a new paradigm for whole-brain functional analysis.

Different people listening to the same story may converge upon a largely shared interpretation while still developing idiosyncratic experiences atop that shared foundation. What linguistic properties support this individualized experience of natural language? Here, we investigate how the "concrete–abstract" axis—the extent to which a word is grounded in sensory experience—relates to within- and across-subject variability in the neural representations of language. Leveraging a dataset of human participants of both sexes who each listened to four auditory stories while undergoing functional magnetic resonance imaging, we demonstrate that neural representations of "concreteness" are both reliable across stories and relatively unique to individuals, while neural representations of "abstractness" are variable both within individuals and across the population. Using natural language processing tools, we show that concrete words exhibit similar neural representations despite spanning larger distances within a high-dimensional semantic space, which potentially reflects an underlying representational signature of sensory experience—namely, imageability—shared by concrete words but absent from abstract words. Our findings situate the concrete–abstract axis as a core dimension that supports both shared and individualized representations of natural language.

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