Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are ...physically realized in neural activity 1. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level 2–4. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making 5, 6, but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain 7–9. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar 5 rather than prototype theory 10, 11. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition.
•Brain response can adjudicate competing theories better than behavior alone•fMRI patterns during visual categorization are consistent with exemplar theory•Exemplar representations are reflected in occipital, parietal, and frontal cortex•Categorization decisions are supported by individual experiences, not prototypes
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.
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.
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.
•The hippocampus integrates across experiences to support complex behaviors.•Activation patterns in the hippocampus are influenced by selective attention.•These hippocampal processes align with ...formal accounts of concept learning.•Recent fMRI evidence supports a role for the hippocampus in concept formation.
Concepts organize our experiences and allow for meaningful inferences in novel situations. Acquiring new concepts requires extracting regularities across multiple learning experiences, a process formalized in mathematical models of learning. These models posit a computational framework that has increasingly aligned with the expanding repertoire of functions associated with the hippocampus. Here, we propose the Episodes-to-Concepts (EpCon) theoretical model of hippocampal function in concept learning and review evidence for the hippocampal computations that support concept formation including memory integration, attentional biasing, and memory-based prediction error. We focus on recent studies that have directly assessed the hippocampal role in concept learning with an innovative approach that combines computational modeling and sophisticated neuroimaging measures. Collectively, this work suggests that the hippocampus does much more than encode individual episodes; rather, it adaptively transforms initially-encoded episodic memories into organized conceptual knowledge that drives novel behavior.
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.
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•Neural pattern similarity reveals item-specific rather than category neural coding.•Item-specific memories are reinstated in the hippocampus and PRC during retrieval.•Hippocampus functionally coupled with occipito-temporal cortex during retrieval.•The fidelity of item reinstatement predicts subsequent memory-based decisions.
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.
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.
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.
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.
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