Generalization of cognitive maps across space and time.

Prominent theories posit that associative memory structures, known as cognitive maps, support flexible generalization of knowledge across cognitive domains. Here, we evince a representational account of cognitive map flexibility by quantifying how spatial knowledge formed one day was used predictively in a temporal sequence task 24 hours later, biasing both behavior and neural response. Participants learned novel object locations in distinct virtual environments. After learning, hippocampus and ventromedial prefrontal cortex (vmPFC) represented a cognitive map, wherein neural patterns became more similar for same-environment objects and more discriminable for different-environment objects. Twenty-four hours later, participants rated their preference for objects from spatial learning; objects were presented in sequential triplets from either the same or different environments. We found that preference response times were slower when participants transitioned between same- and different-environment triplets. Furthermore, hippocampal spatial map coherence tracked behavioral slowing at the implicit sequence transitions. At transitions, predictive reinstatement of virtual environments decreased in anterior parahippocampal cortex. In the absence of such predictive reinstatement after sequence transitions, hippocampus and vmPFC responses increased, accompanied by hippocampal-vmPFC functional decoupling that predicted individuals' behavioral slowing after a transition. Collectively, these findings reveal how expectations derived from spatial experience generalize to support temporal prediction.

The impact of emissions from an essential oil diffuser on cognitive performance.

Essential oil products are increasingly used in indoor environments and have been found to negatively contribute to indoor air quality. Moreover, the chemicals and fragrances emitted by those products may affect the central nervous system and cognitive function. This study uses a double-blind between-subject design to investigate the cognitive impact of exposure to the emissions from essential oil used in an ultrasonic diffuser. In a simulated office environment where other environmental parameters were maintained constant, 34 female and 25 male university students were randomly allocated into four essential oil exposure scenarios. The first two scenarios contrast lemon oil to pure deionized water, while the latter two focus on different levels of particulate matter differentiated by HEPA filters with non-scented grapeseed oil as the source. Cognitive function was assessed using a computer-based battery consisting of five objective tests that involve reasoning, response inhabitation, memory, risk-taking, and decision-making. Results show that exposure to essential oil emissions caused shortened reaction time at the cost of significantly worse response inhabitation control and memory sensitivity, indicating potentially more impulsive decision-making. The cognitive responses caused by scented lemon oil and non-scented grapeseed oil were similar, as was the perception of odor pleasantness and intensity.

Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space.

Precisely characterizing mental representations of visual experiences requires careful control of experimental stimuli. Recent work leveraging such stimulus control has led to important insights; however, these findings are constrained to simple visual properties like color and line orientation. There remains a critical methodological barrier to characterizing perceptual and mnemonic representations of realistic visual experiences. Here, we introduce a novel method to systematically control visual properties of natural scene stimuli. Using generative adversarial networks (GANs), a state-of-the-art deep learning technique for creating highly realistic synthetic images, we generated scene wheels in which continuously changing visual properties smoothly transition between meaningful realistic scenes. To validate the efficacy of scene wheels, we conducted two behavioral experiments that assess perceptual and mnemonic representations attained from the scene wheels. In the perceptual validation experiment, we tested whether the continuous transition of scene images along the wheel is reflected in human perceptual similarity judgment. The perceived similarity of the scene images correspondingly decreased as distances between the images increase on the wheel. In the memory experiment, participants reconstructed to-be-remembered scenes from the scene wheels. Reconstruction errors for these scenes resemble error distributions observed in prior studies using simple stimulus properties. Importantly, perceptual similarity judgment and memory precision varied systematically with scene wheel radius. These findings suggest our novel approach offers a window into the mental representations of naturalistic visual experiences.

The structure of hippocampal circuitry relates to rapid category learning in humans.

Prior work suggests that complementary white matter pathways within the hippocampus (HPC) differentially support the learning of specific versus general information. In particular, while the trisynaptic pathway (TSP) rapidly forms memories for specific experiences, the monosynaptic pathway (MSP) slowly learns generalities. However, despite the theorized significance of such circuitry, characterizing how information flows within the HPC to support learning in humans remains a challenge. We leveraged diffusion-weighted imaging as a proxy for individual differences in white matter structure linking key regions along with TSP (HPC subfields CA3 and CA1 ) and MSP (entorhinal cortex and CA1 ) and related these differences in hippocampal structure to category learning ability. We hypothesized that learning to categorize the "exception" items that deviated from category rules would benefit from TSP-supported mnemonic specificity. Participant-level estimates of TSP- and MSP-related integrity were constructed from HPC subfield connectomes of white matter streamline density. Consistent with theories of TSP-supported learning mechanisms, we found a specific association between the integrity of CA3 -CA1 white matter connections and exception learning. These results highlight the significant role of HPC circuitry in complex human learning.

Brain Mechanisms of Concept Learning.

Concept learning, the ability to extract commonalities and highlight distinctions across a set of related experiences to build organized knowledge, is a critical aspect of cognition. Previous reviews have focused on concept learning research as a means for dissociating multiple brain systems. The current review surveys recent work that uses novel analytical approaches, including the combination of computational modeling with neural measures, focused on testing theories of specific computations and representations that contribute to concept learning. We discuss in detail the roles of the hippocampus, ventromedial prefrontal, lateral prefrontal, and lateral parietal cortices, and how their engagement is modulated by the coherence of experiences and the current learning goals. We conclude that the interaction of multiple brain systems relating to learning, memory, attention, perception, and reward support a flexible concept-learning mechanism that adapts to a range of category structures and incorporates motivational states, making concept learning a fruitful research domain for understanding the neural dynamics underlying complex behaviors.

Indoor CO2 concentrations and cognitive function: A critical review.

Poor indoor air quality indicated by elevated indoor CO2 concentrations has been linked with impaired cognitive function, yet current findings of the cognitive impact of CO2 are inconsistent. This review summarizes the results from 37 experimental studies that conducted objective cognitive tests with manipulated CO2 concentrations, either through adding pure CO2 or adjusting ventilation rates (the latter also affects other indoor pollutants). Studies with varied designs suggested that both approaches can affect multiple cognitive functions. In a subset of studies that meet objective criteria for strength and consistency, pure CO2 at a concentration common in indoor environments was only found to affect high-level decision-making measured by the Strategic Management Simulation battery in non-specialized populations, while lower ventilation and accumulation of indoor pollutants, including CO2 , could reduce the speed of various functions but leave accuracy unaffected. Major confounding factors include variations in cognitive assessment methods, study designs, individual and populational differences in subjects, and uncertainties in exposure doses. Accordingly, future research is suggested to adopt direct air delivery for precise control of CO2 inhalation, include brain imaging techniques to better understand the underlying mechanisms that link CO2 and cognitive function, and explore the potential interaction between CO2 and other environmental stimuli.

Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample.

Episodic memory function has been shown to depend critically on the hippocampus. This region is made up of a number of subfields, which differ in both cytoarchitectural features and functional roles in the mature brain. Recent neuroimaging work in children and adolescents has suggested that these regions may undergo different developmental trajectories-a fact that has important implications for how we think about learning and memory processes in these populations. Despite the growing research interest in hippocampal structure and function at the subfield level in healthy young adults, comparatively fewer studies have been carried out looking at subfield development. One barrier to studying these questions has been that manual segmentation of hippocampal subfields-considered by many to be the best available approach for defining these regions-is laborious and can be infeasible for large cross-sectional or longitudinal studies of cognitive development. Moreover, manual segmentation requires some subjectivity and is not impervious to bias or error. In a developmental sample of individuals spanning 6-30 years, we assessed the degree to which two semi-automated segmentation approaches-one approach based on Automated Segmentation of Hippocampal Subfields (ASHS) and another utilizing Advanced Normalization Tools (ANTs)-approximated manual subfield delineation on each individual by a single expert rater. Our main question was whether performance varied as a function of age group. Across several quantitative metrics, we found negligible differences in subfield validity across the child, adolescent, and adult age groups, suggesting that these methods can be reliably applied to developmental studies. We conclude that ASHS outperforms ANTs overall and is thus preferable for analyses carried out in individual subject space. However, we underscore that ANTs is also acceptable and may be well-suited for analyses requiring normalization to a single group template (e.g., voxelwise analyses across a wide age range). Previous work has supported the use of such methods in healthy young adults, as well as several special populations such as older adults and those suffering from mild cognitive impairment. Our results extend these previous findings to show that ASHS and ANTs can also be used in pediatric populations as young as six.

Dynamic updating of hippocampal object representations reflects new conceptual knowledge.

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.

Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex.

When faced with a new challenge, we often reflect on related past experiences to guide our behavior. The ability to retrieve memories that overlap with current experience, a process known as pattern completion, is theorized as a critical function of the hippocampus. Although this view has influenced research for decades, there is little empirical support for hippocampal pattern completion to individual memory elements and its influence on behavior. We used pattern analysis of brain activity measured with functional magnetic resonance imaging to demonstrate that specific elements of past experiences are reinstated in the hippocampus, as well as perirhinal cortex (PRC), when making decisions about those experiences. Linking neural measures of specific memory reinstatement in the hippocampus and PRC to behavior with computational modeling revealed that reinstatement predicts the speed of memory-based decisions. Moreover, hippocampal activation during retrieval was selectively coupled to regions of occipito-temporal cortex that showed content-specific item reinstatement. These results provide evidence for hippocampal pattern completion and its role in the mechanisms of decision making.

Reconciling category exceptions through representational shifts.

Real-world categories often contain exceptions that disobey the perceptual regularities followed by other members. Prominent psychological and neurobiological theories indicate that exception learning relies on the flexible modulation of object representations, but the specific representational shifts key to learning remain poorly understood. Here, we leveraged behavioral and computational approaches to elucidate the representational dynamics during the acquisition of exceptions that violate established regularity knowledge. In our study, participants (n = 42) learned novel categories in which regular and exceptional items were introduced successively; we then fitted a computational model to individuals' categorization performance to infer latent stimulus representations before and after exception learning. We found that in the representational space, exception learning not only drove confusable exceptions to be differentiated from regular items, but also led exceptions within the same category to be integrated based on shared characteristics. These shifts resulted in distinct representational clusters of regular items and exceptions that constituted hierarchically structured category representations, and the distinct clustering of exceptions from regular items was associated with a high ability to generalize and reconcile knowledge of regularities and exceptions. Moreover, by having a second group of participants (n = 42) to judge stimuli's similarity before and after exception learning, we revealed misalignment between representational similarity and behavioral similarity judgments, which further highlights the hierarchical layouts of categories with regularities and exceptions. Altogether, our findings elucidate the representational dynamics giving rise to generalizable category structures that reconcile perceptually inconsistent category members, thereby advancing the understanding of knowledge formation.

Brief category learning distorts perceptual space for complex scenes.

The formation of categories is known to distort perceptual space: representations are pushed away from category boundaries and pulled toward categorical prototypes. This phenomenon has been studied with artificially constructed objects, whose feature dimensions are easily defined and manipulated. How such category-induced perceptual distortions arise for complex, real-world scenes, however, remains largely unknown due to the technical challenge of measuring and controlling scene features. We address this question by generating realistic scene images from a high-dimensional continuous space using generative adversarial networks and using the images as stimuli in a novel learning task. Participants learned to categorize the scene images along arbitrary category boundaries and later reconstructed the same scenes from memory. Systematic biases in reconstruction errors closely tracked each participant's subjective category boundaries. These findings suggest that the perception of global scene properties is warped to align with a newly learned category structure after only a brief learning experience.

Distinct hippocampal mechanisms support concept formation and updating.

Learning systems must constantly decide whether to create new representations or update existing ones. For example, a child learning that a bat is a mammal and not a bird would be best served by creating a new representation, whereas updating may be best when encountering a second similar bat. Characterizing the neural dynamics that underlie these complementary memory operations requires identifying the exact moments when each operation occurs. We address this challenge by interrogating fMRI brain activation with a computational learning model that predicts trial-by-trial when memories are created versus updated. We found distinct neural engagement in anterior hippocampus and ventral striatum for model-predicted memory create and update events during early learning. Notably, the degree of this effect in hippocampus, but not ventral striatum, significantly related to learning outcome. Hippocampus additionally showed distinct patterns of functional coactivation with ventromedial prefrontal cortex and angular gyrus during memory creation and premotor cortex during memory updating. These findings suggest that complementary memory functions, as formalized in computational learning models, underlie the rapid formation of novel conceptual knowledge, with the hippocampus and its interactions with frontoparietal circuits playing a crucial role in successful learning. Significance statement: How do we reconcile new experiences with existing knowledge? Prominent theories suggest that novel information is either captured by creating new memories or leveraged to update existing memories, yet empirical support of how these distinct memory operations unfold during learning is limited. Here, we combine computational modeling of human learning behaviour with functional neuroimaging to identify moments of memory formation and updating and characterize their neural signatures. We find that both hippocampus and ventral striatum are distinctly engaged when memories are created versus updated; however, it is only hippocampus activation that is associated with learning outcomes. Our findings motivate a key theoretical revision that positions hippocampus is a key player in building organized memories from the earliest moments of learning.

Learning exceptions to category rules varies across the menstrual cycle.

Ways in which ovarian hormones affect cognition have been long overlooked despite strong evidence of their effects on the brain. To address this gap, we study performance on a rule-plus-exception category learning task, a complex task that requires careful coordination of core cognitive mechanisms, across the menstrual cycle (N = 171). Results show that the menstrual cycle distinctly affects exception learning in a manner that parallels the typical rise and fall of estradiol across the cycle. Participants in their high estradiol phase outperform participants in their low estradiol phase and demonstrate more rapid learning of exceptions than a male comparison group. A likely mechanism underlying this effect is estradiol's impact on pattern separation and completion pathways in the hippocampus. These results provide novel evidence for the effects of the menstrual cycle on category learning, and underscore the importance of considering female sex-related variables in cognitive neuroscience research.

Revealing a competitive dynamic in rapid categorization with object substitution masking.

Categorization at different levels of abstraction have distinct time courses, but the different levels are often considered separately. Superordinate-level categorization is typically faster than basic-level categorization at ultra-rapid exposure durations (< 33 ms) while basic-level categorization is faster than superordinate-level categorization at longer exposure durations. This difference may be due to a competitive dynamic between levels of categorization. By leveraging object substitution masking, we found a distinct time course of masking effects for each level of categorization. Superordinate-level categorization showed a masking effect earlier than basic-level categorization. However, when basic-level categorization first showed a masking effects, superordinate-level categorization was spared despite its earlier masking effect. This unique pattern suggests a trade-off between the two levels of categorization over time. Such an effect supports an account of categorization that depends on the interaction of perceptual encoding, selective attention, and competition between levels of category representation.

Brain Connectivity Changes in Postconcussion Syndrome as the Neural Substrate of a Heterogeneous Syndrome.

Background: Postconcussion syndrome (PCS) or persistent symptoms of concussion refers to a constellation of symptoms that persist for weeks and months after a concussion. To better capture the heterogeneity of the symptoms of patients with PCS, we aimed to separate patients into clinical subtypes based on brain connectivity changes. Methods: Subject-specific structural and functional connectomes were created based on diffusion weighted and resting state functional magnetic resonance imaging, respectively. Following an informed dimensionality reduction, a Gaussian mixture model was used on patient-specific structural and functional connectivity matrices to find potential patient clusters. For validation, the resulting patient subtypes were compared in terms of cognitive, neuropsychiatric, and postconcussive symptom differences. Results: Multimodal analyses of brain connectivity were predictive of behavioral outcomes. Our modeling revealed two patient subtypes: mild and severe. The severe subgroup showed significantly higher levels of depression, anxiety, aggression, and a greater number of symptoms than the mild patient subgroup. Conclusion: This study suggests that structural and functional connectivity changes together can help us better understand the symptom severity and neuropsychiatric profiles of patients with PCS. This work allows us to move toward precision medicine in concussions and provides a novel machine learning approach that can be applicable to other heterogeneous conditions.

Learning exceptions to the rule in human and model via hippocampal encoding.

Category learning helps us process the influx of information we experience daily. A common category structure is "rule-plus-exceptions," in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following items, but improved exception categorization has been positively associated with hippocampal function. In light of model-based predictions that the nature of existing memories of related experiences impacts memory formation, here we use behavioural and computational modelling data to explore how learning sequence impacts performance in rule-plus-exception categorization. Our behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-followers. To explore whether hippocampal learning systems also benefit from this manipulation, we simulate our task using a computational model of hippocampus. The model successful replicates our behavioural findings related to exception learning, and representational similarity analysis of the model's hidden layers suggests that model representations are impacted by trial sequence: delaying the introduction of an exception shifts its representation closer to its own category members. Our results provide novel computational evidence of how hippocampal learning systems can be targeted by learning sequence and bolster extant evidence of hippocampus's role in category learning.

Identifying the neural dynamics of category decisions with computational model-based functional magnetic resonance imaging.

Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.

Progression of neuropsychiatric symptoms in young-onset versus late-onset Alzheimer's disease.

Young-onset and late-onset Alzheimer's disease has different clinical presentations with late-onset presenting most often with memory deficits while young-onset often presents with a non-amnestic syndrome. However, it is unknown whether there are differences in presentation and progression of neuropsychiatric symptoms in young- versus late-onset Alzheimer's disease. We aimed to investigate differences in the prevalence and severity of neuropsychiatric symptoms in patients with young- and late-onset Alzheimer's disease longitudinally with and without accounting for the effect of medication usage. Sex differences were also considered in these patient groups. We included 126 young-onset and 505 late-onset Alzheimer's disease patients from National Alzheimer's Coordinating Center-Uniform Data Set (NACC-UDS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). We investigated the prevalence and severity of neuropsychiatric symptoms using the Neuropsychiatric Inventory-Questionnaire over 4 visits with 1-year intervals, using a linear mixed-effects model. The prevalence of depression was significantly higher in young-onset than late-onset Alzheimer's disease over a 4-year interval when antidepressant usage was included in our analyses. Our findings suggest that neuropsychiatric symptom profiles of young- and late-onset Alzheimer's disease differ cross-sectionally but also display significant differences in progression.

Modeling categorization of scenes containing consistent versus inconsistent objects.

How does object perception influence scene perception? A recent study of ultrarapid scene categorization (O. R. Joubert, G. A. Rousselet, D. Fize, & M. Fabre-Thorpe, 2007) reported facilitated scene categorization when scenes contained consistent objects compared to when scenes contained inconsistent objects. One proposal for this consistent-object advantage is that ultrarapid scene categorization is influenced directly by ultrarapid recognition of particular objects within the scene. We instead asked whether a simpler mechanism that relied only on scene categorization without any explicit object recognition could explain this consistent-object advantage. We combined a computational model of scene recognition based on global scene statistics (A. Oliva & A. Torralba, 2001) with a diffusion model of perceptual decision making (R. Ratcliff, 1978). This model is sufficient to account for the consistent-object advantage. The simulations suggest that this consistent-object advantage need not arise from ultrarapid object recognition influencing ultrarapid scene categorization, but from the inherent influence certain objects have on the global scene statistics diagnostic for scene categorization.

Ventromedial prefrontal cortex compression during concept learning.

Prefrontal cortex (PFC) is thought to support the ability to focus on goal-relevant information by filtering out irrelevant information, a process akin to dimensionality reduction. Here, we test this dimensionality reduction hypothesis by relating a data-driven approach to characterizing the complexity of neural representation with a theoretically-supported computational model of learning. We find evidence of goal-directed dimensionality reduction within human ventromedial PFC during learning. Importantly, by using computational predictions of each participant's attentional strategies during learning, we find that that the degree of neural compression predicts an individual's ability to selectively attend to concept-specific information. These findings suggest a domain-general mechanism of learning through compression in ventromedial PFC.