Context reinstatement requires a schema relevant virtual environment to benefit object recall.

How does our environment impact what we will later remember? Early work in real-world environments suggested that having matching encoding/retrieval contexts improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects and examine an influence of memory schema and dynamic environments. Participants (N = 240) remembered more objects when in the same virtual environment (context) as during encoding. This traded-off with falsely "recognizing" more similar lures. Experimentally manipulating the virtual objects and environments revealed that a congruent object/environment schema aids recall (but not recognition), though a dynamic background does not. These findings further our understanding of when and how context affects our memory through a naturalistic approach to studying such effects.

Novel approaches to functional lateralization: Assessing information in activity patterns across hemispheres and more accurately identifying structural homologues.

Functional lateralization is typically measured by comparing activation levels across the right and left hemispheres of the brain. Significant additional information, however, exists within distributed multi-voxel patterns of activity - a format not detectable by traditional activation-based analysis of functional magnetic resonance imaging (fMRI) data. We introduce and test two methods -one anatomical, one functional- that allow hemispheric information asymmetries to be detected. We first introduce and apply a novel tool that draws on brain 'surface fingerprints' to pair every location in one hemisphere with its hemispheric homologue. We use anatomical data to show that this approach is more accurate than the common distance-from-midline method for comparing bilateral regions. Next, we introduce a complementary analysis method that quantifies multivariate laterality in functional data. This new 'multivariate Laterality Index' (mLI) reflects both quantitative and qualitative information-differences across homologous activity patterns. We apply the technique here to functional data collected as participants viewed faces and non-faces. Using the previously generated surface fingerprints to pair-up homologous searchlights in each hemisphere, we use the novel multivariate laterality technique to identify face-information asymmetries across right and left counterparts of the fusiform gyrus, inferior temporal gyrus, superior parietal lobule, and early visual areas. The typical location of the fusiform face area has greater information asymmetry for faces than for shapes. More generally, we argue that the field should consider an information-based approach to lateralization.

Representations within the Intraparietal Sulcus Distinguish Numerical Tasks and Formats.

How does our brain understand the number five when it is written as an Arabic numeral, and when presented as five fingers held up? Four facets have been implicated in adult numerical processing: semantic, visual, manual, and phonological/verbal. Here, we ask how the brain represents each, using a combination of tasks and stimuli. We collected fMRI data from adult participants while they completed our novel "four number code" paradigm. In this paradigm, participants viewed one of two stimulus types to tap into the visual and manual number codes, respectively. Concurrently, they completed one of two tasks to tap into the semantic and phonological/verbal number codes, respectively. Classification analyses revealed that neural codes representing distinctions between the number comparison and phonological tasks were generalizable across format (e.g., Arabic numerals to hands) within intraparietal sulcus (IPS), angular gyrus, and precentral gyrus. Neural codes representing distinctions between formats were generalizable across tasks within visual areas such as fusiform gyrus and calcarine sulcus, as well as within IPS. Our results identify the neural facets of numerical processing within a single paradigm and suggest that IPS is sensitive to distinctions between semantic and phonological/verbal, as well as visual and manual, facets of number representations.

A five-factor model of perseverative thought.

Like diagnostic status, clinically relevant thought remains overwhelmingly conceptualized in terms of discrete categories (e.g., worry, rumination, obsessions). However, definitions can vary widely. The area of perseverative thought (or clinically relevant thought more broadly) would benefit substantially from a consensus-based, empirically grounded taxonomy similar to the Hierarchical Taxonomy of Psychopathology (Kotov et al., 2017) or the Big Five for personality. This article addresses three major barriers to establishing such a taxonomy: (a) a lack of research explicitly comparing categorical (subtype) versus dimensional models, (b) primary reliance on between-person measures rather than modeling at the level of the thought (within person), and (c) insufficient emphasis on replication and refinement. Participants included an unselected crowdsourced sample (790 observations from 286 participants) and an independent anxious-depressed replication sample (808 observations from 277 participants). Participants made dimensional ratings for three idiographic clinically relevant thoughts on a range of features. Multilevel latent class analysis and multilevel exploratory factor analysis were applied to identify and extract natural patterns of covariation among features at the level of the thought, controlling for person-level tendencies. A consistent five-dimension solution emerged across both samples and reliably outperformed the best-fitting categorical solution in terms of fit, replicability, and explanatory power. Identified dimensions were dyscontrol, self-focus, valence, interpersonal, and uncertainty. Findings support a five-factor latent structure of perseverative thought. Theoretical, empirical, and clinical implications and future directions are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Numerical estrangement and integration between symbolic and non-symbolic numerical information: Task-dependence and its link to math abilities in adults.

Most adults have access to two different number systems to represent numerical information: an exact number system, which relies on different forms of number symbols to represent exact numerical information, and an approximate number system, which allows for approximate estimates of numerical quantities. Here we investigate the integration between the symbolic and non-symbolic numerical information (i.e., "numerical integration"), and how numerical integration relates to adults' formal math abilities. We administered two tasks to measure numerical integration. For a number comparison task with non-symbolic dot arrays and Arabic numerals, participants indicated the larger of two sequentially presented stimuli that were same-format (dot-dot or numeral-numeral), or mixed-format (dot-numeral or numeral-dot). For a number-letter discrimination task, participants identified Arabic numerals or letter pairs that co-occurred with dot arrays (matching or mismatching the quantity represented by the numeral). In the number comparison task, participants were significantly slower when comparing mixed-format stimuli, especially when Arabic numerals were presented first and dot arrays second, suggesting estrangement between symbolic and non-symbolic numerical information and an asymmetry depending on the order in which the numerical information is presented. In contrast, in the number-letter discrimination task, participants were significantly faster in number-letter discrimination for matching dot arrays and numerals, suggesting integration between symbolic and non-symbolic numerical information. Surprisingly, some measures of numerical estrangement derived from the number comparison task significantly correlated with adults' performance on a standardized math assessment. Thus, we conclude that numerical integration or estrangement is task-dependent, and adults with greater levels of symbolic estrangement tend to have higher math skills.

Sleep reduces the semantic coherence of memory recall: An application of latent semantic analysis to investigate memory reconstruction.

Sleep is thought to help consolidate hippocampus-dependent memories by reactivating previously encoded neural representations, promoting both quantitative and qualitative changes in memory representations. However, the qualitative nature of changes to memory representations induced by sleep remains largely uncharacterized. In this study, we investigated how memories are reconstructed by hypothesizing that semantic coherence, defined as conceptual relatedness between statements of free-recall texts and quantified using latent semantic analysis (LSA), is affected by post-encoding sleep. Short naturalistic videos of events featuring six animals were presented to 115 participants who were randomly assigned to either 12- or 24-h delay groups featuring sleep or wakefulness. Participants' free-recall responses were analyzed to test for an effect of sleep on semantic coherence between adjacent statements, and overall. The presence of sleep reduced both forms of semantic coherence, compared to wakefulness. This change was robust and not due to shifts in conciseness or repetitiveness with sleep. These findings support the notion that sleep-dependent consolidation qualitatively changes the features of reconstructed memory representations by reducing semantic coherence.

Image memorability is predicted by discriminability and similarity in different stages of a convolutional neural network.

The features of an image can be represented at multiple levels-from its low-level visual properties to high-level meaning. What drives some images to be memorable while others are forgettable? We address this question across two behavioral experiments. In the first, different layers of a convolutional neural network (CNN), which represent progressively higher levels of features, were used to select the images that would be shown to 100 participants through a form of prospective assignment. Here, the discriminability/similarity of an image with others, according to different CNN layers dictated the images presented to different groups, who made a simple indoor versus outdoor judgment for each scene. We found that participants remember more scene images that were selected based on their low-level discriminability or high-level similarity. A second experiment replicated these results in an independent sample of 50 participants, with a different order of postencoding tasks. Together, these experiments provide evidence that both discriminability and similarity, at different visual levels, predict image memorability.

Neural pattern similarity across concept exemplars predicts memory after a long delay.

The irregularities of the world ensure that each interaction we have with a concept is unique. In order to generalize across these unique encounters to form a high-level representation of a concept, we must draw on similarities between exemplars to form new conceptual knowledge that is maintained over a long time. Two neural similarity measures - pattern robustness and encoding-retrieval similarity - are particularly important for predicting memory outcomes. In this study, we used fMRI to measure activity patterns while people encoded and retrieved novel pairings between unfamiliar (Dutch) words and visually presented animal species. We address two underexplored questions: 1) whether neural similarity measures can predict memory outcomes, despite perceptual variability between presentations of a concept and 2) if pattern similarity measures can predict subsequent memory over a long delay (i.e., one month). Our findings indicate that pattern robustness during encoding in brain regions that include parietal and medial temporal areas is an important predictor of subsequent memory. In addition, we found significant encoding-retrieval similarity in the left ventrolateral prefrontal cortex after a month's delay. These findings demonstrate that pattern similarity is an important predictor of memory for novel word-animal pairings even when the concept includes multiple exemplars. Importantly, we show that established predictive relationships between pattern similarity and subsequent memory do not require visually identical stimuli (i.e., are not simply due to low-level visual overlap between stimulus presentations) and are maintained over a month.

Influences on memory for naturalistic visual episodes: sleep, familiarity, and traits differentially affect forms of recall.

The memories we form are composed of information that we extract from multifaceted episodes. Static stimuli and paired associations have proven invaluable stimuli for understanding memory, but real-life events feature spatial and temporal dimensions that help form new retrieval paths. We ask how the ability to recall semantic, temporal, and spatial aspects (the "what, when, and where") of naturalistic episodes is affected by three influences-prior familiarity, postencoding sleep, and individual differences-by testing their influence on three forms of recall: cued recall, free recall, and the extent that recalled details are recombined for a novel prompt. Naturalistic videos of events with rare animals were presented to 115 participants, randomly assigned to receive a 12- or 24-h delay with sleep and/or wakefulness. Participants' immediate and delayed recall was tested and coded by its spatial, temporal, and semantic content. We find that prior familiarity with items featured in events improved cued recall, but not free recall, particularly for temporal and spatial details. In contrast, postencoding sleep, relative to wakefulness, improved free recall, but not cued recall, of all forms of content. Finally, individuals with higher trait scores in the Survey of Autobiographical Memory spontaneously incorporated more spatial details during free recall, and more event details (at a trend level) in a novel recombination recall task. These findings show that prior familiarity, postencoding sleep, and memory traits can each enhance a different form of recall. More broadly, this work highlights that recall is heterogeneous in response to different influences on memory.

Neural Patterns are More Similar across Individuals during Successful Memory Encoding than during Failed Memory Encoding.

After experiencing the same episode, some people can recall certain details about it, whereas others cannot. We investigate how common (intersubject) neural patterns during memory encoding influence whether an episode will be subsequently remembered, and how divergence from a common organization is associated with encoding failure. Using functional magnetic resonance imaging with intersubject multivariate analyses, we measured brain activity as people viewed episodes within wildlife videos and then assessed their memory for these episodes. During encoding, greater neural similarity was observed between the people who later remembered an episode (compared with those who did not) within the regions of the declarative memory network (hippocampus, posterior medial cortex [PMC], and dorsal Default Mode Network [dDMN]). The intersubject similarity of the PMC and dDMN was episode-specific. Hippocampal encoding patterns were also more similar between subjects for memory success that was defined after one day, compared with immediately after retrieval. The neural encoding patterns were sufficiently robust and generalizable to train machine learning classifiers to predict future recall success in held-out subjects, and a subset of decodable regions formed a network of shared classifier predictions of subsequent memory success. This work suggests that common neural patterns reflect successful, rather than unsuccessful, encoding across individuals.

Semantic knowledge influences whether novel episodic associations are represented symmetrically or asymmetrically.

We provide new evidence concerning two opposing views of episodic associations: The independent-association hypothesis posits that associations are unidirectional and separately modifiable links (A→B and A←B); in contrast, the associative-symmetry hypothesis proposes that a single, bidirectional association exists between A and B (A↔B). We used a novel method to demonstrate that whether or not episodic associations are symmetric depends on whether there is a preexisting semantic relationship between A and B. In two experiments, participants studied 30 semantically unrelated and 30 semantically related pairs intermixed in a single list and then performed a series of up to eight cued-recall test cycles. All pairs were tested in each cycle, and the testing direction (A-? or B-?) alternated between cycles. Unrelated pairs exhibited associative symmetry-that is, accuracy and response times improved gradually on each test-suggesting that testing in both directions strengthened the same association. In contrast, semantically related pairs exhibited a stair-like pattern, in which performance did not change from odd to even tests when the test direction changed; it only improved between tests in the same direction. We concluded that episodic associations can have either a single bidirectional representation or separate directional representations, depending on the semantic relatedness of their constituent items.

Addressing misconceptions of fast mapping in adults.

Studies of fast mapping (FM) in adults have included both positive results and failures to replicate. I argue that although conflicting studies warrant caution, FM findings are nonetheless promising and intriguing. I separate the issue into distinct questions: whether FM has hippocampal independence, and whether it has unique cognitive consequences. I clarify some misunderstandings and identify limitations that may contribute to failures to find learning from FM in some amnesic patients. Finally, I argue that the array of behavioral findings in healthy adults is consistent with computational and neural models.

Word inversion sensitivity as a marker of visual word form area lateralization: An application of a novel multivariate measure of laterality.

An area within the ventral occipitotemporal cortex (vOTC), the "visual word form area" (VWFA), typically exhibits a strongly left-lateralized response to orthographic stimuli in skilled readers. While individual variation in VWFA lateralization has been observed, the behavioral significance of laterality differences remains unclear. Here, we test the hypothesis that differences in VWFA lateralization reflect differing preferences for holistic orthographic analysis. To examine this hypothesis, we implemented a new multivariate method that uses machine learning to assess functional lateralization, along with a traditional univariate lateralization method. We related these neural metrics to behavioral indices of holistic orthographic analysis (inversion sensitivity). The multivariate measure successfully detected the lateralization of orthographic processing in the VWFA, and as hypothesized, predicted behavioral differences in holistic orthographic analysis. An exploratory whole brain analysis identified further regions with a relationship between inversion sensitivity and lateralization: one near the junction of the inferior frontal and precentral sulci, and another along the superior temporal gyrus. We conclude that proficient native readers of English exhibit differences in cortical lateralization of the VWFA that have significant implications for reading behavior.

The VWFA Is the Home of Orthographic Learning When Houses Are Used as Letters.

Learning to read specializes a portion of the left mid-fusiform cortex for printed word recognition, the putative visual word form area (VWFA). This study examined whether a VWFA specialized for English is sufficiently malleable to support learning a perceptually atypical second writing system. The study utilized an artificial orthography, HouseFont, in which house images represent English phonemes. House images elicit category-biased activation in a spatially distinct brain region, the so-called parahippocampal place area (PPA). Using house images as letters made it possible to test whether the capacity for learning a second writing system involves neural territory that supports reading in the first writing system, or neural territory tuned for the visual features of the new orthography. Twelve human adults completed two weeks of training to establish basic HouseFont reading proficiency and underwent functional neuroimaging pre and post-training. Analysis of three functionally defined regions of interest (ROIs), the VWFA, and left and right PPA, found significant pre-training versus post-training increases in response to HouseFont words only in the VWFA. Analysis of the relationship between the behavioral and neural data found that activation changes from pre-training to post-training within the VWFA predicted HouseFont reading speed. These results demonstrate that learning a new orthography utilizes neural territory previously specialized by the acquisition of a native writing system. Further, they suggest VWFA engagement is driven by orthographic functionality and not the visual characteristics of graphemes, which informs the broader debate about the nature of category-specialized areas in visual association cortex.

Neural activity in human visual cortex is transformed by learning real world size.

The way that our brain processes visual information is directly affected by our experience. Repeated exposure to a visual stimulus triggers experience-dependent plasticity in the visual cortex of many species. Humans also have the unique ability to acquire visual knowledge through instruction. We introduced human participants to the real-world size of previously unfamiliar species, and to the functional motion of novel tools, during a functional magnetic resonance imaging scan. Using machine learning, we compared activity patterns evoked by images of the new items, before and after participants learned the animals' real-world size or tools' motion. We found that, after acquiring size information, participants' visual activity patterns for the new animals became more confusable with activity patterns evoked by similar-sized known animals in early visual cortex, but not in ventral temporal cortex, reflecting an influence of new size knowledge on posterior, but not anterior, components of the ventral stream. In contrast, learning the functional motion of new tools did not lead to an equivalent change in recorded activity. Finally, the time-points marked by evidence of new size information in early visual cortex were more likely to show size information and greater activation in the right angular gyrus, a key hub of semantic knowledge and spatial cognition. Overall, these findings suggest that learning an item's real-world size by instruction influences subsequent activity in visual cortex and in a region that is central to semantic and spatial brain systems.

An Empirical Analysis of Popular Press Claims Regarding Linguistic Change in President Donald J. Trump.

Linguistic features of a person's speech can change over time. It has been proposed that characteristics in the speech of President Donald J. Trump (DJT) have changed across time, though this claim has been based on subjective and anecdotal reports. A previous study of speech by Presidents of the United States identified an increase in the use of conversational fillers and non-specific nouns, and lower unique word counts, in the speech of President Ronald W. Reagan, but not in the speech of President George H.W. Bush. To empirically test claims of a systematic change in speech by DJT, we applied the same analysis by transcribing and analyzing publicly available Fox News interviews with DJT between 2011 and 2017. A regression analysis revealed a significant increase in the use of filler words by DJT over time. There was no significant change in numbers of unique words. The observed rise in filler words was significantly greater than filler-word change in President George H.W. Bush, and was not significantly different from the rise previously found in the speech of President Ronald W. Reagan. Identifying the reason for this linguistic change is not possible from speech samples alone, and the variables index linguistic change rather than being validated measures of change in cognitive ability. Nonetheless, features of the data such as the trajectory starting years before announcement of candidacy rule-out several potential explanations. To summarize, we find statistical evidence to support suggestions that speech by DJT has changed over time.

Expertise Moderates Incidentally Learned Associations Between Words and Images.

Individuals with expertise in a domain of knowledge demonstrate superior learning for information in their area of expertise, relative to non-experts. In this study, we investigated whether expertise benefits extend to learning associations between words and images that are encountered incidentally. Sport-knowledge-experts and non-sports-experts encountered previously unknown faces through a basic perceptual task. The faces were incidentally presented as candidates for a position in a sports team (a focus of knowledge for only the sports-experts) or for a job in a business (a focus of knowledge for both the sports-experts and non-sports-experts). Participants later received a series of surprise memory tests that tested: ability to recognize each face as being old, the amount of information recalled about each face, and ability to select a correct face from equally familiar alternatives. Relative to non-sports-experts, participants with superior sports expertise were able to better recall the information associated with each face and could better select associated faces from similarly familiar options for the hypothetical prospective athletes. Hypothetical job candidates were recalled and selected at similar levels of performance in both groups. The groups were similarly familiar with the images (in a yes/no recognition memory test) when the faces were prospective athletes or job candidates. These findings suggest a specific effect of expertise on associative memory between words and images, but not for individual items, supporting a dissociation in how expertise modulates the human memory system for word-image pairings.

Creatures great and small: Real-world size of animals predicts visual cortex representations beyond taxonomic category.

Human occipitotemporal cortex contains neural representations for a variety of perceptual and conceptual features. We report a study examining neural representations of real-world size along the visual ventral stream, while carefully accounting for taxonomic categories that typically co-vary with size. We recorded brain activity during a functional Magnetic Resonance Imaging (fMRI) scan from eighteen participants as they were presented with images of twelve animal species. The animals were selected to vary on a number of dimensions, including taxonomic group, real-world size and prior familiarity. We apply multivariate analysis methods, including representational similarity analysis (RSA) and machine learning classifiers, to probe the distributed patterns of neural activity evoked by these presentations. We find that the real-world size of visually presented animate items is represented in posterior, but not anterior, regions of the ventral stream. A significant linear relationship is present for real-world size representation along the ventral stream. These representations remain after controlling for factors such as taxonomic category, familiarity and models of visual similarity, and even after restricting examinations to within-taxonomic category comparisons, suggesting that size information is found for within, as well as between, taxonomic categories. These findings are consistent with real-world size having an influence on activity patterns in early regions of the visual system.

Beyond Functional Connectivity: Investigating Networks of Multivariate Representations.

For over two decades, interactions between brain regions have been measured in humans by asking how the univariate responses in different regions co-vary ('Functional Connectivity'). Thousands of Functional Connectivity studies have been published investigating the healthy brain and how it is affected by neural disorders. The advent of multivariate fMRI analyses showed that patterns of responses within regions encode information that is lost by averaging. Despite this, connectivity methods predominantly continue to focus on univariate responses. In this review, we discuss the recent emergence of multivariate and nonlinear methods for studying interactions between brain regions. These new developments bring sensitivity to fluctuations in multivariate information, and offer the possibility to ask not only whether brain regions interact, but how they do so.