Neural Representational Similarity Research Articles

Short-term number sense training recapitulates long-term neurodevelopmental changes from childhood to adolescence.

Number sense is fundamental to the development of numerical problem-solving skills. In early childhood, children establish associations between non-symbolic (e.g., a set of dots) and symbolic (e.g., Arabic numerals) representations of quantity. The developmental estrangement theory proposes that the relationship between non-symbolic and symbolic representations of quantity evolves with age, with increased dissociation across development. Consistent with this theory, recent research suggests that cross-format neural representational similarity (NRS) between non-symbolic and symbolic quantities is correlated with arithmetic fluency in children but not in adolescents. However, it is not known if short-term training (STT) can induce similar changes as long-term development. In this study, children aged 7-10 years underwent a theoretically motivated 4-week number sense training. Using multivariate neural pattern analysis, we investigated whether short-term learning could modify the relation between cross-format NRS and arithmetic skills. Our results revealed a significant correlation between cross-format NRS and arithmetic fluency in distributed brain regions, including the parietal and prefrontal cortices, prior to training. However, this association was no longer observed after training, and multivariate predictive models confirmed these findings. Our findings provide evidence that intensive STT during early childhood can promote behavioral improvements and neural plasticity that resemble and recapitulate long-term neurodevelopmental changes that occur from childhood to adolescence. More generally, our study contributes to our understanding of the malleability of number sense and highlights the potential for targeted interventions to shape neurodevelopmental trajectories in early childhood. RESEARCH HIGHLIGHTS: We tested the hypothesis that short-term number sense training induces the dissociation of symbolic numbers from non-symbolic representations of quantity in children. We leveraged a theoretically motivated intervention and multivariate pattern analysis to determine training-induced neurocognitive changes in the relation between number sense and arithmetic problem-solving skills. Neural representational similarity between non-symbolic and symbolic quantity representations was correlated with arithmetic skills before training but not after training. Short-term training recapitulates long-term neurodevelopmental changes associated with numerical problem-solving from childhood to adolescence.

Predicting Cued and Oddball Visual Search Performance from fMRI, MEG, and DNN Neural Representational Similarity.

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed task-specific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing.

Shared vivid remembering: age-related differences in across-participants similarity of neural representations during encoding and retrieval

ABSTRACT Recent advances in multivariate neuroimaging analyses have made possible the examination of the similarity of the neural patterns of activations measured across participants, but it has not been investigated yet whether such measure is age-sensitive. Here, in the scanner, young and older participants viewed scene pictures associated with labels. At test, participants were presented with the labels and were asked to recollect the associated picture. We used Pattern Similarity Analyses by which we compared patterns of neural activation during the encoding or the remembering of each picture of one participant with the averaged pattern of activation across the remaining participants. Results revealed that across-participants neural similarity was higher in young than in older adults in distributed occipital, temporal and parietal areas during encoding and retrieval. These findings demonstrate that an age-related reduction in specificity of neural activation is also evident when the similarity of neural representations is examined across participants.

Unity and diversity of neural representation in executive functions.

Although the unity and diversity model of executive functions (EFs) has been replicated, there are some studies questioning the validity of the EFs construct. This debate can be partially resolved by directly combining the brain activity pattern in different executive control processes. Previous univariate activation studies have suggested that the neural substrates of different EFs (e.g., updating, inhibiting, and shifting) involve common and distinct brain regions. However, the underlying multivariate neural representation of EFs in terms of unity and diversity is still elusive. Here, we employed the n-back task, stop signal task, and category switching task to investigate the characteristic of the neural representation in the three EF domains. At the global level, multivoxel pattern analysis revealed that a three-way classifier built with global activation pattern successfully distinguished the three EF tasks. At the local level, although most overlapping activations exhibit lower neural representational similarity, the inferior frontal junction showed similar neural representation across the three EFs, which was further confirmed by searchlight analysis that additionally revealed other similar representational regions were located in the presupplementary motor area extend to dorsal midcingulate cortex. In addition, using machine learning-based predictive framework, the resting-state functional networks built with the representational regions of EFs predicted intellectual abilities to some extent in a large independent sample. These findings suggest that different EFs are characterized by dissociable global neural representation but also share similar local neural representation, which contributes to understanding the neural correlates of the unity and diversity of EFs from an integrated framework. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neural Evidence for Representational Persistence Within Events

How does the brain process continuous experiences so they can be remembered? Evidence suggests that people perceive their experience as a series of distinct and meaningful events. Information encountered within the same event shows greater temporal integration into memory as well as enhanced neural representational similarity. Although these data support the theory that the brain builds and maintains a mental model of the current event that represents recently encountered stimulus information, this hypothesis has not been directly tested. We used fMRI in humans (N = 21, 13 female) to test whether within-event neural similarity indicates the persistence of stimulus representations in a mental model. Participants viewed trial-unique visual images that were grouped into events. We calculated neural pattern similarity across time in the category-selective visual cortex to measure stimulus persistence. Pattern similarity was enhanced within, compared with between, events in the object-sensitive left lateral occipital (LO) cortex. This was specific to situations when objects could persist within a mental model, suggesting modulation of neural activity based on the features and structure of the event. Left LO object persistence was correlated with activity in a medial prefrontal cortex (mPFC) region linked to representing mental models within events. mFPC activity also correlated with pattern similarity in the hippocampus but more generally across stimulus categories. Critically, left LO similarity was related to estimates of temporal proximity in memory. The data suggest that temporal neural stability reflects stimulus persistence in mental models and highlight the importance of within-event representational stability in the transformation of experience to memory.SIGNIFICANCE STATEMENT How does the brain process continuous experiences so they can be remembered? One idea is that information persists in mental models during stable events, facilitating the organization of events in memory. Using fMRI pattern similarity analysis, we found enhanced similarity within, compared with between, events in the object-sensitive LO but only when objects could persist within a mental model. mPFC activity correlated with left LO similarity when objects persisted within an event; in contrast, mPFC activity correlated with hippocampal similarity across stimulus categories. Left LO persistence was also related to the remembered temporal proximity of stimuli. The data suggest the brain dynamically maintains stimulus information in mental models during events, supporting the transformation of experience into memory.

Emerging native-similar neural representations underlie non-native speech category learning success.

Learning non-native phonetic categories in adulthood is an exceptionally challenging task, characterized by large inter-individual differences in learning speed and outcomes. The neurobiological mechanisms underlying the inter-individual differences in the learning efficacy are not fully understood. Here we examined the extent to which training-induced neural representations of non-native Mandarin tone categories in English listeners (n = 53) are increasingly similar to those of the native listeners (n = 33) who acquired these categories early in infancy. We particularly assessed whether the neural similarities in representational structure between non-native learners and native listeners are robust neuromarkers of inter-individual differences in learning success. Using inter-subject neural representational similarity (IS-NRS) analysis and predictive modeling on two functional magnetic resonance imaging (fMRI) datasets, we examined the neural representational mechanisms underlying speech category learning success. Learners' neural representations that were significantly similar to the native listeners emerged in brain regions mediating speech perception following training; the extent of the emerging neural similarities with native listeners significantly predicted the learning speed and outcome in learners. The predictive power of IS-NRS outperformed models with other neural representational measures. Furthermore, neural representations underlying successful learning are multidimensional but cost-efficient in nature. The degree of the emergent native-similar neural representations was closely related to the robust neural sensitivity to feedback in the frontostriatal network. These findings provide important insights on experience-dependent representational neuroplasticity underlying successful speech learning in adulthood and could be leveraged in designing individualized feedback-based training paradigms that maximize learning efficiency.

Neural representational similarity between symbolic and non-symbolic quantities predicts arithmetic skills in childhood but not adolescence.

Mathematical knowledge is constructed hierarchically from basic understanding of quantities and the symbols that denote them. Discrimination of numerical quantity in both symbolic and non-symbolic formats has been linked to mathematical problem-solving abilities. However, little is known of the extent to which overlap in quantity representations between symbolic and non-symbolic formats is related to individual differences in numerical problem solving and whether this relation changes with different stages of development and skill acquisition. Here we investigate the association between neural representational similarity (NRS) across symbolic and non-symbolic quantity discrimination and arithmetic problem-solving skills in early and late developmental stages: elementary school children (ages 7-10 years) and adolescents and young adults (AYA, ages 14-21 years). In children, cross-format NRS in distributed brain regions, including parietal and frontal cortices and the hippocampus, was positively correlated with arithmetic skills. In contrast, no brain region showed a significant association between cross-format NRS and arithmetic skills in the AYA group. Our findings suggest that the relationship between symbolic-non-symbolic NRS and arithmetic skills depends on developmental stage. Taken together, our study provides evidence for both mapping and estrangement hypotheses in the context of numerical problem solving, albeit over different cognitive developmental stages.

Enhanced inter-regional coupling of neural responses and repetition suppression provide separate contributions to long-term behavioral priming

Stimulus identification commonly improves with repetition over long delays (“repetition priming”), whereas neural activity commonly decreases (“repetition suppression”). Multiple models have been proposed to explain this brain-behavior relationship, predicting alterations in functional and/or effective connectivity (Synchrony and Predictive Coding models), in the latency of neural responses (Facilitation model), and in the relative similarity of neural representations (Sharpening model). Here, we test these predictions with fMRI during overt and covert naming of repeated and novel objects. While we find partial support for predictions of the Facilitation and Sharpening models in the left fusiform gyrus and left frontal cortex, the data were most consistent with the Synchrony model, with increased coupling between right temporoparietal and anterior cingulate cortex for repeated objects that correlated with priming magnitude across participants. Increased coupling and repetition suppression varied independently, each explaining unique variance in priming and requiring modifications of all current models.

Inferior parietal lobule is sensitive to different semantic similarity relations for concrete and abstract words.

Similarity measures, the extent to which two concepts have similar meanings, are the key to understand how concepts are represented, with different theoretical perspectives relying on very different sources of data from which similarity can be calculated. While there is some commonality in similarity measures, the extent of their correlation is limited. Previous studies also suggested that the relative performance of different similarity measures may also vary depending on concept concreteness and that the inferior parietal lobule (IPL) may be involved in the integration of conceptual features in a multimodal system for the semantic categorization. Here, we tested for the first time whether theory-based similarity measures predict the pattern of brain activity in the IPL differently for abstract and concrete concepts. English speakers performed a semantic decision task, while we recorded their brain activity in IPL through fNIRS. Using representational similarity analysis, results indicated that the neural representational similarity in IPL conformed to the lexical co-occurrence among concrete concepts (regardless of the hemisphere) and to the affective similarity among abstract concepts in the left hemisphere only, implying that semantic representations of abstract and concrete concepts are characterized along different organizational principles in the IPL. We observed null results for the decoding accuracy. Our study suggests that the use of the representational similarity analysis as a complementary analysis to the decoding accuracy is a promising tool to reveal similarity patterns between theoretical models and brain activity recorded through fNIRS.

Divergence in cortical representations of threat generalization in affective versus perceptual circuitry in childhood: Relations with anxiety

Children at risk for anxiety display elevated threat sensitivity and may inaccurately classify safe stimuli as threatening, a process known as overgeneralization. Little is known about whether such overgeneralization might stem from altered sensory representations of stimuli resembling threat, especially in youth. Here we implement representational similarity analysis of fMRI data to examine the similarity of neural representations of threat versus ambiguous or safe stimuli in threat and perceptual neurocircuitry among children at varying levels of anxiety traits. Three weeks after completing threat conditioning and extinction, children underwent an fMRI extinction recall task, during which they viewed the extinguished threat cue (CS+), safety cue (CS-) and generalization stimuli (GS) consisting of CS-/CS+ blends. Multivoxel BOLD signal patterns were measured in seven regions of interest: four affective areas (ventromedial prefrontal cortex (vmPFC), anterior insular cortex (AIC), dorsomedial prefrontal cortex (dmPFC), and amygdala) and three perceptual areas (inferior temporal cortex (ITC) and visual areas V1 and V4). Compared to low anxious children, children with high trait anxiety evidenced less neural pattern differentiation between the CS+ and similar GS, particularly in the vmPFC. Together, these results demonstrate the utility of multivariate neuroimaging approaches in arbitrating the relative contributions of perceptual versus affective sources to threat generalization.

Faster learners transfer their knowledge better: Behavioral, mnemonic, and neural mechanisms of individual differences in children’s learning

Why some children learn, and transfer their knowledge to novel problems, better than others remains an important unresolved question in the science of learning. Here we developed an innovative tutoring program and data analysis approach to investigate individual differences in neurocognitive mechanisms that support math learning and “near” transfer to novel, but structurally related, problems in elementary school children. Following just five days of training, children performed recently trained math problems more efficiently, with greater use of memory-retrieval-based strategies. Crucially, children who learned faster during training performed better not only on trained problems but also on novel problems, and better discriminated trained and novel problems in a subsequent recognition memory task. Faster learners exhibited increased similarity of neural representations between trained and novel problems, and greater differentiation of functional brain circuits engaged by trained and novel problems. These results suggest that learning and near transfer are characterized by parallel learning-rate dependent local integration and large-scale segregation of functional brain circuits. Our findings demonstrate that speed of learning and near transfer are interrelated and identify the neural mechanisms by which faster learners transfer their knowledge better. Our study provides new insights into the behavioral, mnemonic, and neural mechanisms underlying children’s learning.

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias.

The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).

The effect of expertise upon behavioral and neural representational spaces

When people become an expert in a particular domain, they process objects of expertise in a different manner. Previous neuroimaging studies have demonstrated how this expertise alters brain activity by comparing objects of expertise with other objects (e.g., Gauthier et al., 2000, Nature Neuroscience; Harel et al., 2010, Cerebral Cortex). However, everyone knows whether an image depicts a bird or a car. In contrast, only a bird expert can tell apart a great-tailed from a boat-tailed grackle. Here we focus upon the neural basis of expertise at this detailed level by comparing the similarities and differences of the representational spaces of birds between 20 ornithologists and 20 control participants. We scanned subjects with functional magnetic resonance imaging while presenting images of 24 different bird types. The image set was organized in 8 triplets, which each contained two birds that belonged to the same species but were visually different (e.g. male and female) and one bird that resembled one of the other two birds but belonged to a different species. As such, the triplets dissociated species-level semantics from visual similarity. Behaviorally, ornithologists showed more within-group consistency in subjective similarity ratings compared to controls. In addition, the ornithologists showed a sensitivity to the dissociation between species and visual appearance when rating semantic similarity. A neural representational similarity analysis analyzed the multi-voxel patterns in low-level visual cortex (LVC), high-level visual cortex (HVC), and prefrontal cortex (PFC). In PFC, the neural patterns distinguished bird types more reliably in ornithologists than in controls. In addition, the similarity structure of the neural patterns was more shared between ornithologists than between controls, in particular in HVC and in PFC. Overall, the fMRI findings suggest that expertise results in an overall expansion of the neural representational space of objects of expertise, but not in a qualitatively different organization. Meeting abstract presented at VSS 2018

Dyadic Neural Similarity During Stress in Mother-Child Dyads.

Shared psychological processes between individuals occur most between a mother and her child because the mother-child bond is one of the closest forms of human attachment, in which a mother and her child are essentially wired to connect. We recruited mother-child pairs (Ndyad =19; adolescent: Mage =13.74, 11 males; mothers: Mage =44.26), who each completed an fMRI scan. We examined dyadic neural representational similarity as adolescents completed a stress task and mothers observed their child's performance during the same task. On average, mothers and their children did not show similar neural patterns during stress. However, neural similarity varied depending on family connectedness, such that only dyads reporting high family connectedness showed similar neural profiles. Importantly, increased neural similarity was associated with reduced stress in youth, suggesting that shared neural profiles in mother-child dyads enhance adolescents' psychological well-being.

Should you trust your RSA result? A Bayesian method for reducing bias in neural representational similarity analysis.

Should you trust your RSA result? A Bayesian method for reducing bias in neural representational similarity analysis.

Predicting behavior change from persuasive messages using neural representational similarity and social network analyses

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals’ social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition.

Monitoring the growth of the neural representations of new animal concepts.

Although enormous progress has recently been made in identifying the neural representations of individual object concepts, relatively little is known about the growth of a neural knowledge representation as a novel object concept is being learned. In this fMRI study, the growth of the neural representations of eight individual extinct animal concepts was monitored as participants learned two features of each animal, namely its habitat (i.e., a natural dwelling or scene) and its diet or eating habits. Dwelling/scene information and diet/eating-related information have each been shown to activate their own characteristic brain regions. Several converging methods were used here to capture the emergence of the neural representation of a new animal feature within these characteristic, a priori-specified brain regions. These methods include statistically reliable identification (classification) of the eight newly acquired multivoxel patterns, analysis of the neural representational similarity among the newly learned animal concepts, and conventional GLM assessments of the activation in the critical regions. Moreover, the representation of a recently learned feature showed some durability, remaining intact after another feature had been learned. This study provides a foundation for brain research to trace how a new concept makes its way from the words and graphics used to teach it, to a neural representation of that concept in a learner's brain.

Implicit Race Bias Decreases the Similarity of Neural Representations of Black and White Faces

Implicit race bias has been shown to affect decisions and behaviors. It may also change perceptual experience by increasing perceived differences between social groups. We investigated how this phenomenon may be expressed at the neural level by testing whether the distributed blood-oxygenation-level-dependent (BOLD) patterns representing Black and White faces are more dissimilar in participants with higher implicit race bias. We used multivoxel pattern analysis to predict the race of faces participants were viewing. We successfully predicted the race of the faces on the basis of BOLD activation patterns in early occipital visual cortex, occipital face area, and fusiform face area (FFA). Whereas BOLD activation patterns in early visual regions, likely reflecting different perceptual features, allowed successful prediction for all participants, successful prediction on the basis of BOLD activation patterns in FFA, a high-level face-processing region, was restricted to participants with high pro-White bias. These findings suggest that stronger implicit pro-White bias decreases the similarity of neural representations of Black and White faces.

Associative Learning Increases Trial-by-Trial Similarity of BOLD-MRI Patterns

Associative learning is a dynamic process that allows us to incorporate new knowledge within existing semantic networks. Even after years, a seemingly stable association can be altered by a single significant experience. Here, we investigate whether the acquisition of new associations affects the neural representation of stimuli and how the brain categorizes stimuli according to preexisting and emerging associations. Functional MRI data were collected during a differential fear conditioning procedure and at test (4-5 weeks later). Two pictures of faces and two pictures of houses served as stimuli. One of each pair coterminated with a shock in half of the trials (partial reinforcement). Applying Multivoxel Pattern Analysis (MVPA) in a trial-by-trial manner, we quantified changes in the similarity of neural representations of stimuli over the course of conditioning. Our findings show an increase in similarity of neural patterns throughout the cortex on consecutive trials of the reinforced stimuli. Furthermore, neural pattern similarity reveals a shift from original categories (faces/houses) toward new categories (reinforced/unreinforced) over the course of conditioning. This effect was differentially represented in the cortex, with visual areas primarily reflecting similarity of low-level stimulus properties (original categories) and frontal areas reflecting similarity of stimulus significance (new categories). Effects were not dependent on overall response amplitude and were still present during follow-up. We conclude that trial-by-trial MVPA is a useful tool for examining how the human brain encodes relevant associations and forms new associative networks.