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Category learning is our ability to generalize across experiences and apply existing knowledge to new situations. Many real-world categories adhere to a “rule-plus-exceptions” structure, wherein most items are rule-followers, but a subset of “exceptions” violate category rules. Rule-plus-exception learning seems tightly coupled with hippocampal function. Though past work has demonstrated that prediction error drives hippocampus to form distinct representations of exceptions, limited work has investigated how this process impacts existing rule-follower representations. Here we use a neural network model of hippocampus to quantify how rule- follower representations are altered by the introduction of exceptions. By recording model representations of rule- followers before and after exceptions are introduced, we computed the shift in rule-follower representation elicited by exceptions. A rule-follower’s similarity to exceptions along category-relevant, but not irrelevant, dimensions predicted its degree of representational shift. This work furthers our understanding of how hippocampus supports the integration of surprising information in dynamic environments.

Paradigms using the free recall of word lists have furthered our understanding of the organizational structure of memory by elucidating the role of contextual associations on memory search. We adapted the traditional word list-learning paradigm to investigate whether emphasizing contextual associations between items influences subsequent retrieval. Specifically, we introduced a review period between encoding and recall of word lists where items were repeated to highlight either the temporal or semantic associations at encoding. We found that temporal review led to stronger temporal clustering compared to a semantic or control review, and semantic review led to stronger semantic clustering compared to a temporal or control review. Moreover, participants recalled more list items when semantic associations were emphasized, with the degree of semantic clustering at recall predicting memory performance. These results demonstrate that emphasizing contextual associations during a repeated viewing after initial encoding can affect subsequent memory organization and recall.

Ways in which ovarian hormones affect cognition have been long overlooked in psychology and neuroscience research despite strong evidence of their effects on the brain. In order 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. Results show that the menstrual cycle distinctly affects learning of exceptions in a manner that matches the typical estradiol cycle. Furthermore, participants in their high estradiol phase outperform participants in their low estradiol phase, and show steeper learning slopes than men in exception-learning. These results provide novel evidence of the role of estradiol in category learning, underscore the importance of recruiting diverse samples in cognitive neuroscience research, and highlight the ways in which cognition varies throughout the fundamental biological cycles of the human experience.

The learning of rule-plus-exception categories relies on pattern integration and differentiation, but how the representations of rule-followers and exceptions develop through these two operations remains obscure. Here, we inspected the representational shifts in rule-plus-exception category learning by fitting a computational model to behavioral categorization data. We found that exceptions were differentiated from rule-followers within and between categories through learning. The distanced rule-follower and exception representations in each category formed distinct clusters that together constituted a hierarchically structured categorical representation. Moreover, exception learning increased the representational overlap between rule-followers of opposite categories, thereby blurring the category boundary. Our findings illuminate the representational dynamic underlying the acquisition of rule-plus-exception categories and highlight the roles of pattern integration and differentiation in category learning.

Hippocampus (HPC) is a key structure in category learning, forming organized concepts from individual episodes to adaptively represent shared and distinct elements. Most of the evidence for the role of HPC in concept formation, however, relies on neural coding measured at the end of learning. Thus, it remains unclear how neural mechanisms early in learning support the formation of flexible knowledge. We explored this by combining human functional MRI with a computational model of learning, SUSTAIN. Participants learned to categorize complex visual objects with multiple features. A multidimensional rule linked features to categories with exception items in each category. We observed distinct HPC engagement along its primary axis for exception learning that also predicted learning outcomes. The model predicted that better learners use exceptions to update their knowledge, which was reflected in HPC activation. These findings suggest that HPC discriminates initial informative experiences during learning to support flexible knowledge formation.

In addition to saliency and goal-based factors, a scene’s semantic content has been shown to guide attention in visual search tasks. Here, we ask if this rapidly available guidance signal can be leveraged to learn new attentional strategies. In a variant of the scene preview paradigm (Castelhano & Heaven, 2010), participants searched for targets embedded in real-world scenes with target locations linked to scene gist. We found that activating gist with scene previews significantly increased search efficiency over time in a manner consistent with formal theories of skill acquisition. We combine VGG16 and EBRW to provide a biologically inspired account of the gist preview advantage and its effects on learning in gist-guided attention. Preliminary model results suggest that, when preview information is useful, stimulus features may amplify the similarities and differences between exemplars.

We explore the impact of learning sequence on performance in a rule-plus-exception categorization task. Behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-following stimuli. Simulations of this task using a neural network model of hippocampus and its subfields replicate these behavioural findings. Representational similarity analysis of the model’s hidden layers suggests that model representations are also impacted by trial sequence. These results provide novel computational evidence of hippocampus’s sensitivity to learning sequence and further support this region’s proposed role in category learning.

Successful categorization requires a careful coordination ofattention, representation, and decision making. Comprehensivetheories that span levels of analysis are key to understandingthe computational and neural dynamics of categorization. Here,we build on recent work linking neural representations ofcategory learning to computational models to investigate howcategory decision making is driven by neural signals across thebrain. We combine functional magnetic resonance imagingwith hierarchical drift diffusion modelling to show that trial-by-trial fluctuations in neural activation from regions ofoccipital, cingulate, and lateral prefrontal cortices are linked tocategory decisions. Notably, lateral prefrontal cortex activationwas also associated with exemplar-based model predictions oftrial-by-trial category evidence. We propose that these brainregions underlie distinct functions that contribute to successfulcategory learning.

We investigated whether there exists a behavioral dependency between object detection and categorization. Previous work (Grill-Spector & Kanwisher, 2005) suggests that object detection and basic-level categorization may be the very same perceptual mechanism: As objects are parsed from the background they are categorized at the basic level. In the current study, we decouple object detection from categorization by manipulating the between-category contrast of the categorization decision. With a superordinate-level contrast with people as one of the target categories (e.g., cars vs. people), which replicates Grill-Spector and Kanwisher, we found that success at object detection depended on success at basic-level categorization and vice versa. But with a basic-level contrast (e.g., cars vs. boats) or superordinate-level contrast without people as a target category (e.g., dog vs. boat), success at object detection did not depend on success at basic-level categorization. Successful object detection could occur without successful basic-level categorization. Object detection and basic-level categorization do not seem to occur within the same early stage of visual processing. (Contains 2 tables and 7 figures.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]