From Concrete Examples to Abstract Relations: The Rostrolateral Prefrontal Cortex Integrates Novel Examples into Relational Categories

Neural Correlates of Analogical Reasoning on Syntactic Patterns

Analogical reasoning is central to thought and learning. However, previous neuroscience studies have focused mainly on neural substrates for visuospatial and semantic analogies. There has not yet been research on the neural correlates of analogical reasoning on syntactic patterns generated by the syntactic rules, a key feature of human language faculty. The present investigation took an initial step to address this paucity. Twenty-four participants, whose brain activity was monitored by fMRI, engaged in first-order and second-order relational judgments of syntactic patterns as well as simple and complex working memory tasks. After scanning, participants rated the difficulty of each step during analogical reasoning; these ratings were related to signal intensities in activated regions of interest using Spearman correlation analyses. After prior research, differences in activation levels during second-order and first-order relational judgments were taken as evidence of analogical reasoning. These analyses showed that analogical reasoning on syntactic patterns recruited brain regions consistent with those supporting visuospatial and semantic analogies, including the anterior and posterior parts of the left middle frontal gyrus, anatomically corresponding to the left rostrolateral pFC and the left dorsolateral pFC. The correlation results further revealed that the posterior middle frontal gyrus might be involved in analogical access and mapping with syntactic patterns. Our study is the first to investigate the process of analogical reasoning on syntactic patterns at the neurobiological level and provide evidence of the specific functional roles of related regions during subprocesses of analogical reasoning.

Category Learning Selectively Enhances Representations of Boundary-Adjacent Exemplars in Early Visual Cortex

Category learning and visual perception are fundamentally interactive processes, such that successful categorization often depends on the ability to make fine visual discriminations between stimuli that vary on continuously valued dimensions. Research suggests that category learning can improve perceptual discrimination along the stimulus dimensions that predict category membership and that these perceptual enhancements are a byproduct of functional plasticity in the visual system. However, the precise mechanisms underlying learning-dependent sensory modulation in categorization are not well understood. We hypothesized that category learning leads to a representational sharpening of underlying sensory populations tuned to values at or near the category boundary. Furthermore, such sharpening should occur largely during active learning of new categories. These hypotheses were tested using fMRI and a theoretically constrained model of vision to quantify changes in the shape of orientation representations while human adult subjects learned to categorize physically identical stimuli based on either an orientation rule (N = 12) or an orthogonal spatial frequency rule (N = 13). Consistent with our predictions, modeling results revealed relatively enhanced reconstructed representations of stimulus orientation in visual cortex (V1-V3) only for orientation rule learners. Moreover, these reconstructed representations varied as a function of distance from the category boundary, such that representations for challenging stimuli near the boundary were significantly sharper than those for stimuli at the category centers. These results support an efficient model of plasticity wherein only the sensory populations tuned to the most behaviorally relevant regions of feature space are enhanced during category learning.

A Fluid Self-Concept: How the Brain Maintains Coherence and Positivity across an Interconnected Self-Concept While Incorporating Social Feedback

People experience instances of social feedback as interdependent with potential implications for their entire self-concept. How do people maintain positivity and coherence across the self-concept while updating self-views from feedback? We present a network model describing how the brain represents the semantic dependency relations among traits and uses this information to avoid an overall loss of positivity and coherence. Both male and female human participants received social feedback during a self-evaluation task while undergoing functional magnetic resonance imaging. We modeled self-belief updating by incorporating a reinforcement learning model within the network structure. Participants learned more rapidly from positive than negative feedback and were less likely to change self-views for traits with more dependencies in the network. Further, participants back propagated feedback across network relations while retrieving prior feedback on the basis of network similarity to inform ongoing self-views. Activation in ventromedial prefrontal cortex (vmPFC) reflected the constrained updating process such that positive feedback led to higher activation and negative feedback to less activation for traits with more dependencies. Additionally, vmPFC was associated with the novelty of a trait relative to previously self-evaluated traits in the network, and angular gyrus was associated with greater certainty for self-beliefs given the relevance of prior feedback. We propose that neural computations that selectively enhance or attenuate social feedback and retrieve past relevant experiences to guide ongoing self-evaluations may support an overall positive and coherent self-concept.SIGNIFICANCE STATEMENT We humans experience social feedback throughout our lives, but we do not dispassionately incorporate feedback into our self-concept. The implications of feedback for our entire self-concept plays a role in how we either change or retain our prior self-beliefs. In a neuroimaging study, we find that people are less likely to change their beliefs from feedback when the feedback has broader implications for the self-concept. This resistance to change is reflected in processing in the ventromedial prefrontal cortex, a region that is central to self-referential and social cognition. These results are broadly applicable given the role that maintaining a positive and coherent self-concept plays in promoting mental health and development throughout the lifespan.

Insight into the Effects of High-Altitude Hypoxic Exposure on Learning and Memory

The earth land area is heterogeneous in terms of elevation; about 45% of its land area belongs to higher elevation with altitude above 500 meters compared to sea level. In most cases, oxygen concentration decreases as altitude increases. Thus, high-altitude hypoxic stress is commonly faced by residents in areas with an average elevation exceeding 2500 meters and those who have just entered the plateau. High-altitude hypoxia significantly affects advanced neurobehaviors including learning and memory (L&M). Hippocampus, the integration center of L&M, could be the most crucial target affected by high-altitude hypoxia exposure. Based on these points, this review thoroughly discussed the relationship between high-altitude hypoxia and L&M impairment, in terms of hippocampal neuron apoptosis and dysfunction, neuronal oxidative stress disorder, neurotransmitters and related receptors, and nerve cell energy metabolism disorder, which is of great significance to find potential targets for medical intervention. Studies illustrate that the mechanism of L&M damaged by high-altitude hypoxia should be further investigated based on the entire review of issues related to this topic.

The Neural Mechanisms of Cognitive Control in the Category Induction Task

According to the conflict monitoring hypothesis, conflict monitoring and inhibitory control in cognitive control mainly cause activity in the anterior cingulate cortex (ACC) and control-related prefrontal cortex (PFC) in many cognitive tasks. However, the role of brain regions in the default mode network (DMN) in cognitive control during category induction tasks is unclear. To test the role of the ACC, PFC, and subregions of the DMN elicited by cognitive control during category induction, a modified category induction task was performed using simultaneous fMRI scanning. The results showed that the left middle frontal gyrus (BA9) and bilateral dorsal ACC/medial frontal gyrus (BA8/32) were sensitive to whether conflict information (with/without) appears, but not to the level of conflict. In addition, the bilateral ventral ACC (BA32), especially the right vACC, a part of the DMN, showed significant deactivation with an increase in cognitive effort depending on working memory. These findings not only offer further evidence for the important role of the dorsolateral PFC and dorsal ACC in cognitive control during categorization but also support the functional distinction of the dorsal/ventral ACC in the category induction task.

Value estimation and latent-state update-related neural activity during fear conditioning predict posttraumatic stress disorder symptom severity

Learning theories of posttraumatic stress disorder (PTSD) purport that fear-learning processes, such as those that support fear acquisition and extinction, are impaired. Computational models designed to capture specific processes involved in fear learning have primarily assessed model-free, or trial-and-error, reinforcement learning (RL). Although previous studies indicated that aspects of model-free RL are disrupted among individuals with PTSD, research has yet to identify whether model-based RL, which is inferential and contextually driven, is impaired. Given empirical evidence of aberrant contextual modulation of fear in PTSD, the present study sought to identify whether model-based RL processes are altered during fear conditioning among women with interpersonal violence (IPV)-related PTSD (n = 85) using computational modeling. Model-free, hybrid, and model-based RL models were applied to skin conductance responses (SCR) collected during fear acquisition and extinction, and the model-based RL model was found to provide the best fit to the SCR data. Parameters from the model-based RL model were carried forward to neuroimaging analyses (voxel-wise and independent component analysis). Results revealed that reduced activity within visual processing regions during model-based updating uniquely predicted higher PTSD symptoms. Additionally, after controlling for model-based updating, greater value estimation encoding within the left frontoparietal network during fear acquisition and reduced value estimation encoding within the dorsomedial prefrontal cortex during fear extinction predicted greater PTSD symptoms. Results provide evidence of disrupted RL processes in women with assault-related PTSD, which may contribute to impaired fear and safety learning, and, furthermore, may relate to treatment response (e.g., poorer response to exposure therapy).

Category learning in a recurrent neural network with reinforcement learning

It is known that humans and animals can learn and utilize category information quickly and efficiently to adapt to changing environments, and several brain areas are involved in learning and encoding category information. However, it is unclear that how the brain system learns and forms categorical representations from the view of neural circuits. In order to investigate this issue from the network level, we combine a recurrent neural network with reinforcement learning to construct a deep reinforcement learning model to demonstrate how the category is learned and represented in the network. The model consists of a policy network and a value network. The policy network is responsible for updating the policy to choose actions, while the value network is responsible for evaluating the action to predict rewards. The agent learns dynamically through the information interaction between the policy network and the value network. This model was trained to learn six stimulus-stimulus associative chains in a sequential paired-association task that was learned by the monkey. The simulated results demonstrated that our model was able to learn the stimulus-stimulus associative chains, and successfully reproduced the similar behavior of the monkey performing the same task. Two types of neurons were found in this model: one type primarily encoded identity information about individual stimuli; the other type mainly encoded category information of associated stimuli in one chain. The two types of activity-patterns were also observed in the primate prefrontal cortex after the monkey learned the same task. Furthermore, the ability of these two types of neurons to encode stimulus or category information was enhanced during this model was learning the task. Our results suggest that the neurons in the recurrent neural network have the ability to form categorical representations through deep reinforcement learning during learning stimulus-stimulus associations. It might provide a new approach for understanding neuronal mechanisms underlying how the prefrontal cortex learns and encodes category information.

Learning and Representation of Hierarchical Concepts in Hippocampus and Prefrontal Cortex

A key aspect of conceptual knowledge is that it can be flexibly applied at different levels of abstraction, implying a hierarchical organization. It is yet unclear how this hierarchical structure is acquired and represented in the brain. Here we investigate the computations underlying the acquisition and representation of the hierarchical structure of conceptual knowledge in the hippocampal-prefrontal system of 32 human participants (22 females). We assessed the hierarchical nature of learning during a novel tree-like categorization task via computational model comparisons. The winning model allowed to extract and quantify estimates for accumulation and updating of hierarchical compared with single-feature-based concepts from behavior. We find that mPFC tracks accumulation of hierarchical conceptual knowledge over time, and mPFC and hippocampus both support trial-to-trial updating. As a function of those learning parameters, mPFC and hippocampus further show connectivity changes to rostro-lateral PFC, which ultimately represented the hierarchical structure of the concept in the final stages of learning. Our results suggest that mPFC and hippocampus support the integration of accumulated evidence and instantaneous updates into hierarchical concept representations in rostro-lateral PFC.SIGNIFICANCE STATEMENT A hallmark of human cognition is the flexible use of conceptual knowledge at different levels of abstraction, ranging from a coarse category level to a fine-grained subcategory level. While previous work probed the representational geometry of long-term category knowledge, it is unclear how this hierarchical structure inherent to conceptual knowledge is acquired and represented. By combining a novel hierarchical concept learning task with computational modeling of categorization behavior and concurrent fMRI, we differentiate the roles of key concept learning regions in hippocampus and PFC in learning computations and the representation of a hierarchical category structure.

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.

A Dissociative Framework for Understanding Same-Different Conceptualization

Cognitive, comparative, and developmental psychologists have long been interested in humans' and animals' ability to respond to abstract relations. Cross-species research has used relational matching-to-sample (RMTS) tasks in which participants try to find stimulus pairs that "match" because they express the same abstract relation (same or different). Researchers seek to understand the cognitive processes that underlie successful matching, and the cognitive constraints that create species differences in these tasks. Here we describe a dissociative framework drawn from cognitive neuroscience. It has strong potential to illuminate the area of same-different conceptualization. It has already influenced comparative research on categorization and metacognition. This dissociative framework also shows that species differences in same-different conceptualization have resonance with species differences in other comparative domains.

Non-verbal IQ Gains from Relational Operant Training Explain Variance in Educational Attainment: An Active-Controlled Feasibility Study

Research suggests that training relational operant patterns of behavior can lead to increases in general cognitive ability and educational outcomes. Most studies to date have been under-powered and included proxy measures of educational attainment. We attempted to extend previous findings with increased experimental control in younger children (aged 6.9–10.1 years). Participants (N = 49) were assigned to either a relational training or chess control group. Over 5 months, teachers assigned class time to complete either relational training or play chess. Those who were assigned relational training gained 8.9 non-verbal IQ (NVIQ) points, while those in the control condition recorded no gains (dppc2 = .99). Regression analyses revealed that post-training NVIQ predicted reading test scores (conducted approximately 1 month later) over and above baseline NVIQ in the experimental condition only, consistent with what we might expect in a full test of far transfer towards educational outcomes.

The Rostrolateral Prefrontal Cortex Mediates a Preference for High-Agency Environments

The ability to exert flexible instrumental control over one's environment is a defining feature of adaptive decision-making. Here, we investigated neural substrates mediating a preference for environments with greater instrumental divergence, the distance between outcome probability distributions associated with alternative actions. A formal index of agency, instrumental divergence allows an organism to flexibly obtain the currently most desired outcome as preferences change. As such, it may have intrinsic utility, guiding decisions toward environments that maximize instrumental power. Consistent with this notion, we found that a measure of expected value that treats instrumental divergence as a reward surrogate provided a better account of male and female human participants' choice preferences than did a conventional model, sensitive only to monetary reward. Using model-based fMRI, we found that activity in the rostrolateral and ventromedial PFC, regions associated with abstract cognitive inferences and subjective value computations, respectively, scaled with the divergence-based account of expected value. Implications for a neural common currency of information theoretic and motivational variables are discussed.SIGNIFICANCE STATEMENT Agency is a central concept in philosophy and psychology. While research thus far has focused on cognitive and perceptual measures of agency, recent work demonstrating a strong preference for high-agency environments indicates a salient motivational dimension. Here, using instrumental divergence, the distance between outcome distributions associated with alternative actions, as a formal index of agency, we found that brain regions associated with directed exploration and subjective value computations, respectively, were selectively modulated by a model that treated agency as a reward surrogate, over models that assigned utility only to monetary payoffs. In a subset of regions, such effects were predicted by the influence of instrumental divergence on economic choice preferences. Our results elucidate neural mechanisms mediating the utility of agency.

Differential Functional Connectivity along the Long Axis of the Hippocampus Aligns with Differential Role in Memory Specificity and Generalization

The hippocampus contributes to both remembering specific events and generalization across events. Recent work suggests that information may be represented along the longitudinal axis of the hippocampus at varied levels of specificity: detailed representations in the posterior hippocampus and generalized representations in the anterior hippocampus. Similar distinctions are thought to exist within neocortex, with lateral prefrontal and lateral parietal regions supporting memory specificity and ventromedial prefrontal and lateral temporal cortices supporting generalized memory. Here, we tested whether functional connectivity of anterior and posterior hippocampus with cortical memory regions is consistent with these proposed dissociations. We predicted greater connectivity of anterior hippocampus with putative generalization regions and posterior hippocampus with putative memory specificity regions. Furthermore, we tested whether differences in connectivity are stable under varying levels of task engagement. Participants learned to categorize a set of stimuli outside the scanner, followed by an fMRI session that included a rest scan, passive viewing runs, and category generalization task runs. Analyses revealed stronger connectivity of ventromedial pFC to anterior hippocampus and of angular gyrus and inferior frontal gyrus to posterior hippocampus. These differences remained relatively stable across the three phases (rest, passive viewing, category generalization). Whole-brain analyses further revealed widespread cortical connectivity with both anterior and posterior hippocampus, with relatively little overlap. These results contribute to our understanding of functional organization along the long axis of the hippocampus and suggest that distinct hippocampal-cortical connections are one mechanism by which the hippocampus represents both individual experiences and generalized knowledge.

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.

Breaking the perceptual-conceptual barrier: Relational matching and working memory

Cognitive, comparative, and developmental psychologists have long been interested in humans' and animals' ability to respond to abstract relations, as this ability may underlie important capacities like analogical reasoning. Cross-species research has used relational matching-to-sample (RMTS) tasks in which participants try to find stimulus pairs that "match" because they both express the same abstract relation (same or different). Researchers seek to understand the cognitive processes that underlie successful matching performance. In the present RMTS paradigm, the abstract-relational cue was made redundant with a first-order perceptual cue. Then the perceptual cue faded, requiring participants to transition from a perceptual to a conceptual approach by realizing the task's abstract-relational affordance. We studied participants' ability to make this transition with and without a working-memory load. The concurrent load caused participants to fail to break the perceptual-conceptual barrier unless the load was abandoned. We conclude that finding the conceptual solution depends on reconstruing the task using cognitive processes that are especially reliant on working memory. Our data provide the closest existing look at this cognitive reorganization. They raise important theoretical issues for cross-species comparisons of relational cognition, especially regarding animals' limitations in this domain.

Building Memory Representations for Exemplar-Based Judgment: A Role for Ventral Precuneus

The brain networks underlying human multiple-cue judgment, the judgment of a continuous criterion based on multiple cues, have been examined in a few recent studies, and the ventral precuneus has been found to be a key region. Specifically, activation differences in ventral precuneus (as measured with functional magnetic resonance imaging, fMRI) has been linked to an exemplar-based judgment process, where judgments are based on memory for previous similar cases. Ventral precuneus is implicated in various episodic memory processes, notably such that increased activity during learning in this region as well as in the ventromedial prefrontal cortex (vmPFC) and the medial temporal lobes (MTL) have been linked to retrieval success. The present study used fMRI during a multiple-cue judgment task to gain novel neurocognitive evidence informative for the link between learning-related activity changes in ventral precuneus and exemplar-based judgment. Participants (N = 27) spontaneously learned to make judgments during fMRI, in a multiple-cue judgment task specifically designed to induce exemplar-based processing. Contrasting brain activity during late learning to early learning revealed higher activity in ventral precuneus, the bilateral MTL, and the vmPFC. Activity in the ventral precuneus and the vmPFC was found to parametrically increase between each judgment event, and activity levels in the ventral precuneus predicted performance after learning. These results are interpreted such that the ventral precuneus supports the aspects of exemplar-based processes that are related to episodic memory, tentatively by building, storing, and being implicated in retrieving memory representations for judgment.

A Similarity-Based Process for Human Judgment in the Parietal Cortex

One important distinction in psychology is between inferences based on associative memory and inferences based on analysis and rules. Much previous empirical work conceive of associative and analytical processes as two exclusive ways of addressing a judgment task, where only one process is selected and engaged at a time, in an either-or fashion. However, related work indicate that the processes are better understood as being in interplay and simultaneously engaged. Based on computational modeling and brain imaging of spontaneously adopted judgment strategies together with analyses of brain activity elicited in tasks where participants were explicitly instructed to perform similarity-based associative judgments or rule-based judgments (n = 74), we identified brain regions related to the two types of processes. We observed considerable overlap in activity patterns. The precuneus was activated for both types of judgments, and its activity predicted how well a similarity-based model fit the judgments. Activity in the superior frontal gyrus predicted the fit of a rule-based judgment model. The results suggest the precuneus as a key node for similarity-based judgments, engaged both when overt responses are guided by similarity-based and rule-based processes. These results are interpreted such that similarity-based processes are engaged in parallel to rule-based-processes, a finding with direct implications for cognitive theories of judgment.

Model-based fMRI reveals dissimilarity processes underlying base rate neglect

Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have posited that it arises either from associative similarity-based mechanisms or from dissimilarity-based processes that may depend on higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.

Abstract Memory Representations in the Ventromedial Prefrontal Cortex and Hippocampus Support Concept Generalization

Memory function involves both the ability to remember details of individual experiences and the ability to link information across events to create new knowledge. Prior research has identified the ventromedial prefrontal cortex (VMPFC) and the hippocampus as important for integrating across events in the service of generalization in episodic memory. The degree to which these memory integration mechanisms contribute to other forms of generalization, such as concept learning, is unclear. The present study used a concept-learning task in humans (both sexes) coupled with model-based fMRI to test whether VMPFC and hippocampus contribute to concept generalization, and whether they do so by maintaining specific category exemplars or abstract category representations. Two formal categorization models were fit to individual subject data: a prototype model that posits abstract category representations and an exemplar model that posits category representations based on individual category members. Latent variables from each of these models were entered into neuroimaging analyses to determine whether VMPFC and the hippocampus track prototype or exemplar information during concept generalization. Behavioral model fits indicated that almost three-quarters of the subjects relied on prototype information when making judgments about new category members. Paralleling prototype dominance in behavior, correlates of the prototype model were identified in VMPFC and the anterior hippocampus with no significant exemplar correlates. These results indicate that the VMPFC and portions of the hippocampus play a broad role in memory generalization and that they do so by representing abstract information integrated from multiple events.SIGNIFICANCE STATEMENT Whether people represent concepts as a set of individual category members or by deriving generalized concept representations abstracted across exemplars has been debated. In episodic memory, generalized memory representations have been shown to arise through integration across events supported by the ventromedial prefrontal cortex (VMPFC) and hippocampus. The current study combined formal categorization models with fMRI data analysis to show that the VMPFC and anterior hippocampus represent abstract prototype information during concept generalization, contributing novel evidence of generalized concept representations in the brain. Results indicate that VMPFC-hippocampal memory integration mechanisms contribute to knowledge generalization across multiple cognitive domains, with the degree of abstraction of memory representations varying along the long axis of the hippocampus.