Language in Brains, Minds, and Machines.

It has long been argued that only humans could produce and understand language. But now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the new purchase LMs are providing on the question of how language is implemented in the brain. We discuss why, a priori, LMs might be expected to share similarities with the human language system. We then summarize evidence that LMs represent linguistic information similarly enough to humans to enable relatively accurate brain encoding and decoding during language processing. Finally, we examine which LM properties-their architecture, task performance, or training-are critical for capturing human neural responses to language and review studies using LMs as in silico model organisms for testing hypotheses about language. These ongoing investigations bring us closer to understanding the representations and processes that underlie our ability to comprehend sentences and express thoughts in language.

Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

The topology and geometry of neural representations.

A central question for neuroscience is how to characterize brain representations of perceptual and cognitive content. An ideal characterization should distinguish different functional regions with robustness to noise and idiosyncrasies of individual brains that do not correspond to computational differences. Previous studies have characterized brain representations by their representational geometry, which is defined by the representational dissimilarity matrix (RDM), a summary statistic that abstracts from the roles of individual neurons (or responses channels) and characterizes the discriminability of stimuli. Here, we explore a further step of abstraction: from the geometry to the topology of brain representations. We propose topological representational similarity analysis, an extension of representational similarity analysis that uses a family of geotopological summary statistics that generalizes the RDM to characterize the topology while de-emphasizing the geometry. We evaluate this family of statistics in terms of the sensitivity and specificity for model selection using both simulations and functional MRI (fMRI) data. In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds). In fMRI, the ground truth is a visual area and the models are the same and other areas measured in different subjects. Results show that topology-sensitive characterizations of population codes are robust to noise and interindividual variability and maintain excellent sensitivity to the unique representational signatures of different neural network layers and brain regions.

Neural Systems Underlying the Implementation of Working Memory Removal Operations.

Recently, multi-voxel pattern analysis has verified that information can be removed from working memory (WM) via three distinct operations replacement, suppression, or clearing compared to information being maintained ( Kim et al., 2020). While univariate analyses and classifier importance maps in Kim et al. (2020) identified brain regions that contribute to these operations, they did not elucidate whether these regions represent the operations similarly or uniquely. Using Leiden-community-detection on a sample of 55 humans (17 male), we identified four brain networks, each of which has a unique configuration of multi-voxel activity patterns by which it represents these WM operations. The visual network (VN) shows similar multi-voxel patterns for maintain and replace, which are highly dissimilar from suppress and clear, suggesting this network differentiates whether an item is held in WM or not. The somatomotor network (SMN) shows a distinct multi-voxel pattern for clear relative to the other operations, indicating the uniqueness of this operation. The default mode network (DMN) has distinct patterns for suppress and clear, but these two operations are more similar to each other than to maintain and replace, a pattern intermediate to that of the VN and SMN. The frontoparietal control network (FPCN) displays distinct multi-voxel patterns for each of the four operations, suggesting that this network likely plays an important role in implementing these WM operations. These results indicate that the operations involved in removing information from WM can be performed in parallel by distinct brain networks, each of which has a particular configuration by which they represent these operations.

The functions of sleep: A cognitive neuroscience perspective.

This Special Feature explores the various purposes served by sleep, describing current attempts to understand how the many functions of sleep are instantiated in neural circuits and cognitive structures. Our feature reflects current experts' opinions about, and insights into, the dynamic processes of sleep. In the last few decades, technological advances have supported the updated view that sleep plays an active role in both cognition and health. However, these roles are far from understood. This collection of articles evaluates the dynamic nature of sleep, how it evolves across the lifespan, becomes a competitive arena for memory systems through the influence of the autonomic system, supports the consolidation and integration of new memories, and how lucid dreams might originate. This set of papers highlights new approaches and insights that will lay the groundwork to eventually understand the full range of functions supported by sleep.

Neither Enhanced Nor Lost: The Unique Role of Attention in Children's Neural Representations.

A defining feature of children's cognition is the especially slow development of their attention. Despite a rich behavioral literature characterizing the development of attention, little is known about how developing attentional abilities modulate neural representations in children. This information is critical to understanding how attentional development shapes the way children process information. One possibility is that attention might be less likely to shape neural representations in children as compared with adults. In particular, representations of attended items may be less likely to be enhanced relative to unattended items. To investigate this possibility, we measured brain activity using fMRI while children (seven to nine years; male and female) and adults (21-31 years; male and female) performed a one-back task in which they were directed to attend to either motion direction or an object in a display where both were present. We used multivoxel pattern analysis to compare decoding accuracy of attended and unattended information. Consistent with attentional enhancement, we found higher decoding accuracy for task-relevant information (i.e., objects in the object-attended condition) than for task-irrelevant information (i.e., motion in the object-attended condition) in adults' visual cortices. However, in children's visual cortices, both task-relevant and task-irrelevant information were decoded equally well. What is more, whole-brain analysis showed that the children represented task-irrelevant information more than adults in multiple regions across the brain, including the prefrontal cortex. These findings show that (1) attention does not modulate neural representations in the child visual cortex, and (2) developing brains can, and do, represent more information than mature brains.SIGNIFICANCE STATEMENT Children have been shown to struggle with maintaining their attention to specific information, and at the same time, can show better learning of "distractors." While these are critical properties of childhood, their underlying neural mechanisms are unknown. To fill in this critical knowledge gap, we explored how attention shapes what is represented in children's and adults' brains using fMRI while both were asked to focus on just one of two things (objects and motion). We found that unlike adults, who prioritize the information they were asked to focus on, children represent both what they were asked to prioritize and what they were asked to ignore. This shows that attention has a fundamentally different impact on children's neural representations.

Is it too loud? Ask your brain!

PURPOSE: In this study, the objectification of the subjective perception of loudness was investigated using electroencephalography (EEG). In particular, the emergence of objective markers in the domain of the acoustic discomfort threshold was examined. METHODS: A cohort of 27 adults with normal hearing, aged between 18 and 30, participated in the study. The participants were presented with 500 ms long noise stimuli via in-ear headphones. The acoustic signals were presented with sound levels of [55, 65, 75, 85, 95 dB]. After each stimulus, the subjects provided their subjective assessment of the perceived loudness using a colored scale on a touchscreen. EEG signals were recorded, and afterward, event-related potentials (ERPs) locked to sound onset were analyzed. RESULTS: Our findings reveal a linear dependency between the N100 component and both the sound level and the subjective loudness categorization of the sound. Additionally, the data demonstrated a nonlinear relationship between the P300 potential and the sound level as well as for the subjective loudness rating. The P300 potential was elicited exclusively when the stimuli had been subjectively rated as "very loud" or "too loud". CONCLUSION: The findings of the present study suggest the possibility of the identification of the subjective uncomfortable loudness level by objective neural parameters.

Neurophysiological measures and correlates of cognitive load in attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations.

Working memory is integral to a range of critical cognitive functions such as reasoning and decision-making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near-infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye-tracking. Notably, eye-related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level-dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye-related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well-being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

Oscillatory Coupling Between Neural and Cardiac Rhythms.

Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health.

Association of hippocampal subfield volumes with prevalence, course and incidence of depressive symptoms: The Maastricht Study.

BACKGROUND: Late-life depression has been associated with volume changes of the hippocampus. However, little is known about its association with specific hippocampal subfields over time. AIMS: We investigated whether hippocampal subfield volumes were associated with prevalence, course and incidence of depressive symptoms. METHOD: We extracted 12 hippocampal subfield volumes per hemisphere with FreeSurfer v6.0 using T1-weighted and fluid-attenuated inversion recovery 3T magnetic resonance images. Depressive symptoms were assessed at baseline and annually over 7 years of follow-up (9-item Patient Health Questionnaire). We used negative binominal, logistic, and Cox regression analyses, corrected for multiple comparisons, and adjusted for demographic, cardiovascular and lifestyle factors. RESULTS: A total of n = 4174 participants were included (mean age 60.0 years, s.d. = 8.6, 51.8% female). Larger right hippocampal fissure volume was associated with prevalent depressive symptoms (odds ratio (OR) = 1.26, 95% CI 1.08-1.48). Larger bilateral hippocampal fissure (OR = 1.37-1.40, 95% CI 1.14-1.71), larger right molecular layer (OR = 1.51, 95% CI 1.14-2.00) and smaller right cornu ammonis (CA)3 volumes (OR = 0.61, 95% CI 0.48-0.79) were associated with prevalent depressive symptoms with a chronic course. No associations of hippocampal subfield volumes with incident depressive symptoms were found. Yet, lower left hippocampal amygdala transition area (HATA) volume was associated with incident depressive symptoms with chronic course (hazard ratio = 0.70, 95% CI 0.55-0.89). CONCLUSIONS: Differences in hippocampal fissure, molecular layer and CA volumes might co-occur or follow the onset of depressive symptoms, in particular with a chronic course. Smaller HATA was associated with an increased risk of incident (chronic) depression. Our results could capture a biological foundation for the development of chronic depressive symptoms, and stresses the need to discriminate subtypes of depression to unravel its biological underpinnings.

Associations of an individual's need for cognition with structural brain damage and cognitive functioning/impairment: cross-sectional population-based study.

BACKGROUND: High cognitive activity possibly reduces the risk of cognitive decline and dementia. AIMS: To investigate associations between an individual's need to engage in cognitively stimulating activities (need for cognition, NFC) and structural brain damage and cognitive functioning in the Dutch general population with and without existing cognitive impairment. METHOD: Cross-sectional data were used from the population-based cohort of the Maastricht Study. NFC was measured using the Need For Cognition Scale. Cognitive functioning was tested in three domains: verbal memory, information processing speed, and executive functioning and attention. Values 1.5 s.d. below the mean were defined as cognitive impairment. Standardised volumes of white matter hyperintensities (WMH), cerebrospinal fluid (CSF) and presence of cerebral small vessel disease (CSVD) were derived from 3T magnetic resonance imaging. Multiple linear and binary logistic regression analyses were used adjusted for demographic, somatic and lifestyle factors. RESULTS: Participants (n = 4209; mean age 59.06 years, s.d. = 8.58; 50.1% women) with higher NFC scores had higher overall cognition scores (B = 0.21, 95% CI 0.17-0.26, P < 0.001) and lower odds for CSVD (OR = 0.74, 95% CI 0.60-0.91, P = 0.005) and cognitive impairment (OR = 0.60, 95% CI 0.48-0.76, P < 0.001) after adjustment for demographic, somatic and lifestyle factors. The association between NFC score and cognitive functioning was similar for individuals with and without prevalent cognitive impairment. We found no significant association between NFC and WMH or CSF volumes. CONCLUSIONS: A high need to engage in cognitively stimulating activities is associated with better cognitive functioning and less presence of CSVD and cognitive impairment. This suggests that, in middle-aged individuals, motivation to engage in cognitively stimulating activities may be an opportunity to improve brain health.

Change in food choice during acute treatment and the effect on longer-term outcome in patients with anorexia nervosa.

BACKGROUND: Restriction of food intake is a central pathological feature of anorexia nervosa (AN). Maladaptive eating behavior and, specifically, limited intake of calorie-dense foods are resistant to change and contribute to poor long-term outcomes. This study is a preliminary examination of whether change in food choices during inpatient treatment is related to longer-term clinical course. METHODS: Individuals with AN completed a computerized Food Choice Task at the beginning and end of inpatient treatment to determine changes in high-fat and self-controlled food choices. Linear regression and longitudinal analyses tested whether change in task behavior predicted short-term outcome (body mass index [BMI] at discharge) and longer-term outcome (BMI and eating disorder psychopathology). RESULTS: Among 88 patients with AN, BMI improved significantly with hospital treatment (p < 0.001), but Food Choice Task outcomes did not change significantly. Change in high-fat and self-controlled choices was not associated with BMI at discharge (r = 0.13, p = 0.22 and r = 0.10, p = 0.39, respectively). An increase in the proportion of high-fat foods selected (ß = 0.91, p = 0.02) and a decrease in the use of self-control (ß = -1.50, p = 0.001) predicted less decline in BMI over 3 years after discharge. CONCLUSIONS: Short-term treatment is associated with improvement in BMI but with no significant change, on average, in choices made in a task known to predict actual eating. However, the degree to which individuals increased high-fat choices during treatment and decreased the use of self-control over food choice were associated with reduced weight loss over the following 3 years, underscoring the need to focus on changing eating behavior in treatment of AN.

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology.

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity.

Evaluating the explanatory power of the Conscious Turing Machine.

The recent "Conscious Turing Machine" (CTM) proposal offered by Manuel and Lenore Blum aims to define and explore consciousness, contribute to the solution of the hard problem, and demonstrate the value of theoretical computer science with respect to the study of consciousness. Surprisingly, given the ambitiousness and novelty of the proposal (and the prominence of its creators), CTM has received relatively little attention. We here seek to remedy this by offering an exhaustive evaluation of CTM. Our evaluation considers the explanatory power of CTM in three different domains of interdisciplinary consciousness studies: the philosophy of mind, cognitive neuroscience, and computation. Based on our evaluation in each of the target domains, at present, any claim that CTM constitutes progress is premature. Nevertheless, the model has potential, and we highlight several possible avenues of future research which proponents of the model may pursue in its development.

Super-optimality and relative distance coding in location memory.

The prevailing model of landmark integration in location memory is Maximum Likelihood Estimation, which assumes that each landmark implies a target location distribution that is narrower for more reliable landmarks. This model assumes weighted linear combination of landmarks and predicts that, given optimal integration, the reliability with multiple landmarks is the sum of the reliabilities with the individual landmarks. Super-optimality is reliability with multiple landmarks exceeding optimal reliability given the reliability with each landmark alone; this is shown when performance exceeds predicted optimal performance, found by aggregating reliability values with single landmarks. Past studies claiming super-optimality have provided arguably impure measures of performance with single landmarks given that multiple landmarks were presented at study in conditions with a single landmark at test, disrupting encoding specificity and thereby leading to underestimation in predicted optimal performance. This study, unlike those prior studies, only presented a single landmark at study and the same landmark at test in single landmark trials, showing super-optimality conclusively. Given that super-optimal information integration occurs, emergent information, that is, information only available with multiple landmarks, must be used. With the target and landmarks all in a line, as throughout this study, relative distance is the only emergent information available. Use of relative distance was confirmed here by finding that, when both landmarks are left of the target at study, the target is remembered further right of its true location the further left the left landmark is moved from study to test.

Decomposing the multiple encoding benefit in visual long-term memory: Primary contributions by the number of encoding opportunities.

Although access to the seemingly infinite capacity of our visual long-term memory (VLTM) can be restricted by visual working memory (VWM) capacity at encoding and retrieval, access can be improved with repeated encoding. This leads to the multiple encoding benefit (MEB), the finding that VLTM performance improves as the number of opportunities to encode the same information increases over time. However, as the number of encoding opportunities increases, so do other factors such as the number of identical encoded VWM representations and chances to engage in successful retrieval during each opportunity. Thus, across two experiments, we disentangled the contributions of each of these factors to the MEB by having participants encode a varying number of identical objects across multiple encoding opportunities. Along with behavioural data, we also examined two established EEG correlates that track the number of maintained VWM representations, namely the posterior alpha suppression and the negative slow wave. Here, we identified that the primary mechanism behind the MEB was the number of encoding opportunities. That is, recognition memory performance was higher following an increase in the number of encoding opportunities, and this could not be attributed solely to an increase in the number of encoded VWM representations or successful retrieval. Our results thus contribute to the understanding of the fundamental mechanisms behind the influence of VWM on VLTM encoding.

Revealing motor insensitivity to food in Anorexia Nervosa as a potential mechanism contributing to calorie restriction: A mobile Approach Avoidance Task study.

A change in implicit behavioural tendencies toward foods may contribute to the maintenance of calorie restriction in Anorexia Nervosa (AN). To test this hypothesis, we assessed approach-avoidance tendencies toward different categories of stimuli using a novel mobile version of the approach-avoidance task (AAT). The sample included 66 patients with restrictive AN and 84 healthy controls, all females. All participants performed the AAT in which they were required to approach or avoid stimuli (high-calorie foods, low-calorie foods, and neutral objects) by respectively pulling their phone towards themselves of pushing it away. Both the response time and the force of each movement were collected by means of the smartphone's accelerometer. The results revealed that patients with AN had a reduced tendency to approach food stimuli compared to healthy controls, who instead presented faster and stronger movements in approaching rather than avoiding foods as compared to neutral objects. This finding was particularly pronounced in patients with greater levels of malnutrition. No differences were instead observed comparing high-calorie and low-calorie foods. The observed reduction in the natural tendency to approach food stimuli is consistent with patients' eating behaviour and may contribute to the maintenance of calorie restriction, thus representing a possible target for novel therapeutic approaches.

Cognition of Time and Thinking Beyond.

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

Prediction errors disrupt hippocampal representations and update episodic memories.

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.