The present study investigates the effects of trait anxiety on the neural efficiency of working memory component functions (manipulation vs. maintenance) in the absence of threat-related stimuli. For the manipulation of affectively neutral verbal information held in working memory, high- and low-anxious individuals (N = 46) did not differ in their behavioral performance, yet trait anxiety was positively related to the neural effort expended on task processing, as measured by BOLD signal changes in fMRI. Higher levels of anxiety were associated with stronger activation in two regions implicated in the goal-directed control of attention--that is, right dorsolateral prefrontal cortex (DLPFC) and left inferior frontal sulcus--and with stronger deactivation in a region assigned to the brain's default-mode network--that is, rostral-ventral anterior cingulate cortex. Furthermore, anxiety was associated with a stronger functional coupling of right DLPFC with ventrolateral prefrontal cortex. We interpret our findings as reflecting reduced processing efficiency in high-anxious individuals and point out the need to consider measures of functional integration in addition to measures of regional activation strength when investigating individual differences in neural efficiency. With respect to the functions of working memory, we conclude that anxiety specifically impairs the processing efficiency of (control-demanding) manipulation processes (as opposed to mere maintenance). Notably, this study contributes to an accumulating body of evidence showing that anxiety also affects cognitive processing in the absence of threat-related stimuli.

Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis - a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time - to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding indicates that early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time, inconsistent with predictions of strictly serial models. These results are in accordance with an incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.

Our ability to select relevant information from the environment is limited by the resolution of attention - i.e., the minimum size of the region that can be selected. Neural mechanisms that underlie this limit and its development are not yet understood. Functional magnetic resonance imaging (fMRI) was performed during an object tracking task in 7- and 11-year-old children, and in young adults. Object tracking activated canonical fronto-parietal attention systems and motion-sensitive area MT in children as young as 7 years. Object tracking performance improved with age, together with stronger recruitment of parietal attention areas and a shift from low-level to higher-level visual areas. Increasing the required resolution of spatial attention - which was implemented by varying the distance between target and distractors in the object tracking task - led to activation increases in fronto-insular cortex, medial frontal cortex including anterior cingulate cortex (ACC) and supplementary motor area, superior colliculi, and thalamus. This core circuitry for attentional precision was recruited by all age groups, but ACC showed an age-related activation reduction. Our results suggest that age-related improvements in selective visual attention and in the resolution of attention are characterized by an increased use of more functionally specialized brain regions during the course of development.

Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks. However, these analyses have been limited to static (time-averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting-state data (N = 281) and estimated subject-specific time-varying functional connectivity networks. Modularity optimization was applied to determine individual time-variant module partitions and to assess fluctuations in modularity across time. We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post-hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularity - which are characterized by greater disconnection of task-positive from task-negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.

Cognitive flexibility - the ability to adjust one ´s behavior to changing environmental demands - is crucial for controlled behavior. However, the term 'cognitive flexibility' is used heterogeneously, and associations between cognitive flexibility and other facets of flexible behavior have only rarely been studied systematically. To resolve some of these conceptual uncertainties, we directly compared cognitive flexibility (cue-instructed switching between two affectively neutral tasks), affective flexibility (switching between a neutral and an affective task using emotional stimuli), and feedback-based flexibility (non-cued, feedback-dependent switching between two neutral tasks). Three experimental paradigms were established that share as many procedural features (in terms of stimuli and/or task rules) as possible and administered in a pre-registered study plan (N = 100). Correlation analyses revealed significant associations between the efficiency of cognitive and affective task switching (response time switch costs). Feedback-based flexibility (measured as mean number of errors after rule reversals) did not correlate with task switching efficiency in the other paradigms, but selectively with the effectiveness of affective switching (error rate costs when switching from neutral to emotion task). While preregistered confirmatory factor analysis (CFA) provided no clear evidence for a shared factor underlying the efficiency of switching in all three domains of flexibility, an exploratory CFA suggested commonalities regarding switching effectiveness (accuracy-based switch costs). We propose shared mechanisms controlling the efficiency of cue-dependent task switching across domains, while the relationship to feedback-based flexibility may depend on mechanisms controlling switching effectiveness. Our results call for a more stringent conceptual differentiation between different variants of psychological flexibility.

To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only 8 out of 291 participants scored in the clinical range. Subject-specific brain network graphs were modeled from functional MRI resting-state data and revealed significant associations between (nonclinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.

The P600 Event-Related Brain Potential, elicited by syntactic violations in sentences, is generally interpreted as indicating language-specific structural/combinatorial processing, with far-reaching implications for models of language. P600 effects are also often taken as evidence for language-like grammars in non-linguistic domains like music or arithmetic. An alternative account, however, interprets the P600 as a P3, a domain-general brain response to salience. Using time-generalized multivariate pattern analysis, we demonstrate that P3 EEG patterns, elicited in a visual Oddball experiment, account for the P600 effect elicited in a syntactic violation experiment: P3 pattern-trained MVPA can classify P600 trials just as well as P600-trained ones. A second study replicates and generalizes this finding, and demonstrates its specificity by comparing it to face- and semantic mismatch-associated EEG responses. These results indicate that P3 and P600 share neural patterns to a substantial degree, calling into question the interpretation of P600 as a language-specific brain response and instead strengthening its association with the P3. More generally, our data indicate that observing P600-like brain responses provides no direct evidence for the presence of language-like grammars, in language or elsewhere.

Genetic and pharmacological studies suggest an important role of the dopamine D2 receptor (DRD2) in flexible behavioral adaptation, mostly shown in reward-based learning paradigms. Recent evidence from imaging genetics indicates that also intentional cognitive flexibility, associated with lateral frontal cortex, is affected by variations in DRD2 signaling. In the present functional magnetic resonance imaging (MRI) study, we tested the effects of a direct pharmacological manipulation of DRD2 stimulation on intentional flexibility in a task-switching context, requiring switches between cognitive task rules and between response hands. In a double blind, counterbalanced design, participants received either a low dose of the DRD2 agonist bromocriptine or a placebo in two separate sessions. Bromocriptine modulated the blood-oxygen-level-dependent (BOLD) signal during rule switching: rule-switching-related activity in the left posterior lateral frontal cortex and in the striatum was increased compared to placebo, at comparable performance levels. Fronto-striatal connectivity under bromocriptine was slightly increased for rule switches compared to rule repetitions. Hand-switching-related activity, in contrast, was reduced under bromocriptine in sensorimotor regions. Our results provide converging evidence for an involvement of DRD2 signaling in fronto-striatal mechanisms underlying intentional flexibility, and indicate that the neural mechanisms underlying different types of flexibility (cognitive vs motor) are affected differently by increased dopaminergic stimulation.

The abilities to either flexibly adjust behavior according to changing demands (cognitive flexibility) or to maintain it in the face of potential distractors (cognitive stability) are critical for adaptive behavior in many situations. Recently, a novel human paradigm has found individual differences of cognitive flexibility and stability to be related to common prefrontal networks. The aims of the present study were, first, to translate this paradigm from humans to mice and, second, to test conceptual predictions of a computational model of prefrontal working memory mechanisms, the Dual State Theory, which assumes an antagonistic relation between cognitive flexibility and stability. Mice were trained in a touchscreen-paradigm to discriminate visual cues. The task involved "ongoing" and cued "switch" trials. In addition distractor cues were interspersed to test the ability to resist distraction, and an ambiguous condition assessed the spontaneous switching between two possible responses without explicit cues. While response times did not differ substantially between conditions, error rates (ER) increased from the "ongoing" baseline condition to the most complex condition, where subjects were required to switch between two responses in the presence of a distracting cue. Importantly, subjects switching more often spontaneously were found to be more distractible by task irrelevant cues, but also more flexible in situations, where switching was required. These results support a dichotomy of cognitive flexibility and stability as predicted by the Dual State Theory. Furthermore, they replicate critical aspects of the human paradigm, which indicates the translational potential of the testing procedure and supports the use of touchscreen procedures in preclinical animal research.

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain's modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.

It is widely agreed upon that linguistic predictions are an integral part of language comprehension. Yet, experimental proof of their existence remains challenging. Here, we introduce a new predictive eye gaze reading task combining eye tracking and functional magnetic resonance imaging (fMRI) that allows us to infer the existence and timing of linguistic predictions via anticipatory eye-movements. Participants read different types of word sequences (i.e., regular sentences, meaningless jabberwocky sentences, non-word lists) up to the pre-final word. The final target word was displayed with a temporal delay and its screen position was dependent on the syntactic word category (nouns vs verbs). During the delay, anticipatory eye-movements into the correct target word area were indicative of linguistic predictions. For fMRI analysis, the predictive sentence conditions were contrasted to the non-word condition, with the anticipatory eye-movements specifying differences in timing across conditions. A conjunction analysis of both sentence conditions revealed the neural substrate of word category prediction, namely a distributed network of cortical and subcortical brain regions including language systems, basal ganglia, thalamus, and hippocampus. Direct contrasts between the regular sentence condition and the jabberwocky condition indicate that prediction of word category in meaningless jabberwocky sentences relies on classical left-hemispheric language systems involving Brodman's area 44/45 in the left inferior frontal gyrus, left superior temporal areas, and the dorsal caudate nucleus. Regular sentences, in contrast, allowed for the prediction of specific words. Word-specific predictions were specifically associated with more widely distributed temporal and parietal cortical systems, most prominently in the right hemisphere. Our results support the presence of linguistic predictions during sentence processing and demonstrate the validity of the predictive eye gaze paradigm for measuring syntactic and semantic aspects of linguistic predictions, as well as for investigating their neural substrates.

The neural correlates of developmental dyslexia have been investigated intensively over the last two decades and reliable evidence for a dysfunction of left-hemispheric reading systems in dyslexic readers has been found in functional neuroimaging studies. In addition, structural imaging studies using voxel-based morphometry (VBM) demonstrated grey matter reductions in dyslexics in several brain regions. To objectively assess the consistency of these findings, we performed activation likelihood estimation (ALE) meta-analysis on nine published VBM studies reporting 62 foci of grey matter reduction in dyslexic readers. We found six significant clusters of convergence in bilateral temporo-parietal and left occipito-temporal cortical regions and in the cerebellum bilaterally. To identify possible overlaps between structural and functional deviations in dyslexic readers, we conducted additional ALE meta-analyses of imaging studies reporting functional underactivations (125 foci from 24 studies) or overactivations (95 foci from 11 studies ) in dyslexics. Subsequent conjunction analyses revealed overlaps between the results of the VBM meta-analysis and the meta-analysis of functional underactivations in the fusiform and supramarginal gyri of the left hemisphere. An overlap between VBM results and the meta-analysis of functional overactivations was found in the left cerebellum. The results of our study provide evidence for consistent grey matter variations bilaterally in the dyslexic brain and substantial overlap of these structural variations with functional abnormalities in left hemispheric regions.

A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.

Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations - typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to 'explain away' redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ​ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.

Working memory (WM) performance varies substantially among individuals but the precise contribution of different WM component processes to these functional limits remains unclear. By analyzing different types of responses in a spatial WM task, we recently demonstrated a functional dissociation between confident and not-confident errors reflecting failures of WM encoding and maintenance, respectively. Here, we use event-related brain potentials to further explore this dissociation. Healthy participants performed a delayed orientation-discrimination task and rated their response confidence for each trial. The encoding-related N2pc component was significantly reduced for confident errors compared to confident correct responses, which is indicative of an encoding failure. In contrast, the maintenance-related contra-lateral delay activity was similar for these response types indicating that in confident error trials, WM representations - potentially the wrong ones - were maintained accurately and with stability throughout the delay interval. However, contra-lateral delay activity measured during the early part of the delay period was decreased for not-confident errors, potentially reflecting compromised maintenance processes. These electrophysiological findings contribute to a refined understanding of the encoding and maintenance processes that contribute to limitations in WM performance and capacity.

Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword (PW) familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N = 38 human participants), while experiment 2 assessed behavioral facilitation effects (N = 24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context-based facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in PWs familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context-based facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.

Even though extensively investigated, the nature of working memory (WM) deficits in patients with schizophrenia (PSZ) is not yet fully understood. In particular, the contribution of different WM sub-processes to the severe WM deficit observed in PSZ is a matter of debate. So far, most research has focused on impaired WM maintenance. By analyzing different types of errors in a spatial delayed response task (DRT), we have recently demonstrated that incorrect yet confident responses (which we labeled as false memory errors) rather than incorrect/not-confident responses reflect failures of WM encoding, which was also impaired in PSZ. In the present study, we provide further evidence for a functional dissociation between confident and not-confident errors by manipulating the demands on WM maintenance, i.e., the length over which information has to be maintained in WM. Furthermore, we investigate whether these functionally distinguishable WM processes are impaired in PSZ. Twenty-four PSZ and 24 demographically matched healthy controls (HC) performed a spatial DRT in which the length of the delay period was varied between 1, 2, 4, and 6 s. In each trial, participants also rated their level of response confidence. Across both groups, longer delays led to increased rates of incorrect/not-confident responses, while incorrect/confident responses were not affected by delay length. This functional dissociation provides additional support for our proposal that false memory errors (i.e., confident errors) reflect problems at the level of WM encoding, while not-confident errors reflect failures of WM maintenance. Schizophrenic patients showed increased numbers of both confident and not-confident errors, suggesting that both sub-processes of WM-encoding and maintenance-are impaired in schizophrenia. Combined with the delay length-dependent functional dissociation, we propose that these impairments in schizophrenic patients are functionally distinguishable.

Attributing meaning to diverse visual input is a core feature of human cognition. Violating environmental expectations (e.g., a toothbrush in the fridge) induces a late event-related negativity of the event-related potential/ERP. This N400 ERP has not only been linked to the semantic processing of language, but also to objects and scenes. Inconsistent object-scene relationships are additionally associated with an earlier negative deflection of the EEG signal between 250 and 350 ms. This N300 is hypothesized to reflect pre-semantic perceptual processes. To investigate whether these two components are truly separable or if the early object-scene integration activity (250-350 ms) shares certain levels of processing with the late neural correlates of meaning processing (350-500 ms), we used time-resolved multivariate pattern analysis (MVPA) where a classifier trained at one time point in a trial (e.g., during the N300 time window) is tested at every other time point (i.e., including the N400 time window). Forty participants were presented with semantic inconsistencies, in which an object was inconsistent with a scene's meaning. Replicating previous findings, our manipulation produced significant N300 and N400 deflections. MVPA revealed above chance decoding performance for classifiers trained during time points of the N300 component and tested during later time points of the N400, and vice versa. This provides no evidence for the activation of two separable neurocognitive processes following the violation of context-dependent predictions in visual scene perception. Our data supports the early appearance of high-level, context-sensitive processes in visual cognition.