Representational Similarity Mapping of Distributional Semantics in Left Inferior Frontal, Middle Temporal, and Motor Cortex

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

Regionally specific cortical lateralization of abstract and concrete verb processing: Magnetic mismatch negativity study

The neural underpinnings of processing concrete and abstract semantics remain poorly understood. Previous fMRI studies have shown that multimodal and amodal neural networks respond differentially to different semantic types; importantly, abstract semantics activates more left-lateralized networks, as opposed to more bilateral activity for concrete words. Due to the lack of temporal resolution, these fMRI results do not allow to easily separate language- and task-specific brain responses and to disentangle early processing stages from later post-comprehension phenomena. To tackle this, we used magnetoencephalography (MEG), a time-resolved neuroimaging technique, in combination with a task-free oddball mismatch negativity (MMN) paradigm, an established approach to tracking early automatic activation of word-specific memory traces in the brain. We recorded the magnetic MMN responses in 30 healthy adults to auditorily presented abstract and concrete action verbs to assess lateralization of word-specific lexico-semantic processing in a set of neocortical areas. We found that MMN responses to these stimuli showed different lateralization patterns of activity in the upper limb motor area (BA4) and parts of Broca's area (BA45/BA47) within ∼100-350 ms after the word disambiguation point. Importantly, the greater leftward response lateralization for abstract semantics was due to the lesser involvement of the right-hemispheric homologues, not increased left-hemispheric activity. These findings suggest differential region-specific involvement of bilateral sensorimotor systems already in the early automatic stages of processing abstract and concrete action semantics.

Hippocampal Functions Modulate Transfer-Appropriate Cortical Representations Supporting Subsequent Memory

The hippocampus plays a central role as a coordinate system or index of information stored in neocortical loci. Nonetheless, it remains unclear how hippocampal processes integrate with cortical information to facilitate successful memory encoding. Thus, the goal of the current study was to identify specific hippocampal-cortical interactions that support object encoding. We collected fMRI data while 19 human participants (7 female and 12 male) encoded images of real-world objects and tested their memory for object concepts and image exemplars (i.e., conceptual and perceptual memory). Representational similarity analysis revealed robust representations of visual and semantic information in canonical visual (e.g., occipital cortex) and semantic (e.g., angular gyrus) regions in the cortex, but not in the hippocampus. Critically, hippocampal functions modulated the mnemonic impact of cortical representations that are most pertinent to future memory demands, or transfer-appropriate representations Subsequent perceptual memory was best predicted by the strength of visual representations in ventromedial occipital cortex in coordination with hippocampal activity and pattern information during encoding. In parallel, subsequent conceptual memory was best predicted by the strength of semantic representations in left inferior frontal gyrus and angular gyrus in coordination with either hippocampal activity or semantic representational strength during encoding. We found no evidence for transfer-incongruent hippocampal-cortical interactions supporting subsequent memory (i.e., no hippocampal interactions with cortical visual/semantic representations supported conceptual/perceptual memory). Collectively, these results suggest that diverse hippocampal functions flexibly modulate cortical representations of object properties to satisfy distinct future memory demands.Significance Statement The hippocampus is theorized to index pieces of information stored throughout the cortex to support episodic memory. Yet how hippocampal processes integrate with cortical representation of stimulus information remains unclear. Using fMRI, we examined various forms of hippocampal-cortical interactions during object encoding in relation to subsequent performance on conceptual and perceptual memory tests. Our results revealed novel hippocampal-cortical interactions that utilize semantic and visual representations in transfer-appropriate manners: conceptual memory supported by hippocampal modulation of frontoparietal semantic representations, and perceptual memory supported by hippocampal modulation of occipital visual representations. These findings provide important insights into the neural mechanisms underlying the formation of information-rich episodic memory and underscore the value of studying the flexible interplay between brain regions for complex cognition.

Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models

To explain how the human brain represents and organizes meaning, many theoretical and computational language models have been proposed over the years, varying in their underlying computational principles and in the language samples based on which they are built. However, how well they capture the neural encoding of lexical semantics remains elusive. We used representational similarity analysis (RSA) to evaluate to what extent three models of different types explained neural responses elicited by word stimuli: an External corpus-based word2vec model, an Internal free word association model, and a Hybrid ConceptNet model. Semantic networks were constructed using word relations computed in the three models and experimental stimuli were selected through a community detection procedure. The similarity patterns between language models and neural responses were compared at the community, exemplar, and word node levels to probe the potential hierarchical semantic structure. We found that semantic relations computed with the Internal model provided the closest approximation to the patterns of neural activation, whereas the External model did not capture neural responses as well. Compared with the exemplar and the node levels, community-level RSA demonstrated the broadest involvement of brain regions, engaging areas critical for semantic processing, including the angular gyrus, superior frontal gyrus and a large portion of the anterior temporal lobe. The findings highlight the multidimensional semantic organization in the brain which is better captured by Internal models sensitive to multiple modalities such as word association compared with External models trained on text corpora.

The Italian Sensorimotor Norms: Perception and action strength measures for 959 words

Neuroscience research has provided evidence that semantic information is stored in a distributed brain network involved in sensorimotor and linguistic processing. More specifically, according to the embodied cognition accounts, the representation of concepts is deemed as grounded in our bodily states. For these reasons, normative measures of words should provide relevant information about the extent to which each word embeds perceptual and action properties. In the present study, we collected ratings for 959 Italian nouns and verbs from 398 volunteers, recruited via an online platform. The words were mostly taken from the Italian adaptation of the Affective Norms for English Words (ANEW). A pool of 145 verbs was added to the original set. All the words were rated on 11 sensorimotor dimensions: six perceptual modalities (vision, audition, taste, smell, touch, and interoception) and five effectors (hand-arm, foot-leg, torso, mouth, head). The new verbs were also rated on the ANEW dimensions. Results showed good reliability and consistency with previous studies. Relations between perceptual and motor dimensions are described and interpreted, along with relations between the sensorimotor and the affective dimensions. The currently developed dataset represents an important novelty, as it includes different word classes, i.e., both nouns and verbs, and integrates ratings of both sensorimotor and affective dimensions, along with other psycholinguistic parameters; all features only partially accomplished in previous studies.

Conceptual representations in the default, control and attention networks are task-dependent and cross-modal

Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing involves modality-specific and multimodal brain regions in a task-dependent fashion. However, it remains unclear (1) to what extent conceptual feature representations are also modulated by the task, (2) whether conceptual representations in multimodal regions are indeed cross-modal, and (3) how the conceptual system relates to the large-scale functional brain networks. To address these issues, we conducted multivariate pattern analyses on fMRI data. 40 participants performed three tasks-lexical decision, sound judgment, and action judgment-on written words. We found that (1) conceptual feature representations are strongly modulated by the task, (2) conceptual representations in several multimodal regions are cross-modal, and (3) conceptual feature retrieval involves the default, frontoparietal control, and dorsal attention networks. Conceptual representations in these large-scale networks are task-dependent and cross-modal. Our findings support theories that assume conceptual processing to rely on a flexible, multi-level architecture.

Parallel or sequential? Decoding conceptual and phonological/phonetic information from MEG signals during language production

Speaking requires the temporally coordinated planning of core linguistic information, from conceptual meaning to articulation. Recent neurophysiological results suggested that these operations involve a cascade of neural events with subsequent onset times, whilst competing evidence suggests early parallel neural activation. To test these hypotheses, we examined the sources of neuromagnetic activity recorded from 34 participants overtly naming 134 images from 4 object categories (animals, tools, foods and clothes). Within each category, word length and phonological neighbourhood density were co-varied to target phonological/phonetic processes. Multivariate pattern analyses (MVPA) searchlights in source space decoded object categories in occipitotemporal and middle temporal cortex, and phonological/phonetic variables in left inferior frontal (BA 44) and motor cortex early on. The findings suggest early activation of multiple variables due to intercorrelated properties and interactivity of processing, thus raising important questions about the representational properties of target words during the preparatory time enabling overt speaking.

Shared neural representations and temporal segmentation of political content predict ideological similarity

Despite receiving the same sensory input, opposing partisans often interpret political content in disparate ways. Jointly analyzing controlled and naturalistic functional magnetic resonance imaging data, we uncover the neurobiological mechanisms explaining how these divergent political viewpoints arise. Individuals who share an ideology have more similar neural representations of political words, experience greater neural synchrony during naturalistic political content, and temporally segment real-world information into the same meaningful units. In the striatum and amygdala, increasing intersubject similarity in neural representations of political concepts during a word reading task predicts enhanced synchronization of blood oxygen level-dependent time courses when viewing real-time, inflammatory political videos, revealing that polarization can arise from differences in the brain's affective valuations of political concepts. Together, this research shows that political ideology is shaped by semantic representations of political concepts processed in an environment free of any polarizing agenda and that these representations bias how real-world political information is construed into a polarized perspective.

Brain correlates of action word memory revealed by fMRI

Understanding language semantically related to actions activates the motor cortex. This activation is sensitive to semantic information such as the body part used to perform the action (e.g. arm-/leg-related action words). Additionally, motor movements of the hands/feet can have a causal effect on memory maintenance of action words, suggesting that the involvement of motor systems extends to working memory. This study examined brain correlates of verbal memory load for action-related words using event-related fMRI. Seventeen participants saw either four identical or four different words from the same category (arm-/leg-related action words) then performed a nonmatching-to-sample task. Results show that verbal memory maintenance in the high-load condition produced greater activation in left premotor and supplementary motor cortex, along with posterior-parietal areas, indicating that verbal memory circuits for action-related words include the cortical action system. Somatotopic memory load effects of arm- and leg-related words were observed, but only at more anterior cortical regions than was found in earlier studies employing passive reading tasks. These findings support a neurocomputational model of distributed action-perception circuits (APCs), according to which language understanding is manifest as full ignition of APCs, whereas working memory is realized as reverberant activity receding to multimodal prefrontal and lateral temporal areas.

Neural correlates of embodied action language processing: a systematic review and meta-analytic study

The neural correlates of action language processing are still debated within embodied cognition research and little is known about the flexible involvement of modality-specific pre-motor system and multimodal high-level temporo-parietal regions as a function of explicit and implicit tasks. A systematic review and the Activation likelihood estimation (ALE) meta-analyses on functional neuroimaging studies were performed to identify neural correlates of action language processing activated during explicit and implicit tasks. The contrast ALE meta-analysis revealed activation of modality-specific premotor area and inferior frontal areas during explicit action language tasks while a greater activation of posterior temporo-occipital areas emerged for implicit tasks. The conjunction analysis revealed overlap in the temporo-parietal multimodal high-level regions for both types of tasks. Functional specialization of the middle temporal gyrus was found where the more posterior-occipital part resulted activated during implicit action language tasks whereas the antero-lateral part was involved in explicit tasks. Our findings were discussed within a conceptual flexibility perspective about the involvement of both the modality-specific and multimodal brain system during action language processing depending on different types of tasks.

Brain intrinsic connection patterns underlying tool processing in human adults are present in neonates and not in macaques

Tool understanding and use are supported by a dedicated left-lateralized, intrinsically connected network in the human adult brain. To examine this network’s phylogenetic and ontogenetic origins, we compared resting-state functional connectivity (rsFC) among regions subserving tool processing in human adults to rsFC among homologous regions in human neonates and macaque monkeys (adolescent and mature). These homologous regions formed an intrinsic network in human neonates, but not in macaques. Network topological patterns were highly similar between human adults and neonates, and significantly less so between humans and macaques. The premotor-parietal rsFC had most significant contribution to the formation of the neonatal tool network. These results suggest that an intrinsic brain network potentially supporting tool processing exists in the human brain prior to individual tool use experiences, and that the premotor-parietal functional connection in particular offers a brain basis for complex tool behaviors specific to humans.

Analogy-Related Information Can Be Accessed by Simple Addition and Subtraction of fMRI Activation Patterns, Without Participants Performing any Analogy Task

Analogical reasoning, for example, inferring that teacher is to chalk as mechanic is to wrench, plays a fundamental role in human cognition. However, whether brain activity patterns of individual words are encoded in a way that could facilitate analogical reasoning is unclear. Recent advances in computational linguistics have shown that information about analogical problems can be accessed by simple addition and subtraction of word embeddings (e.g., wrench = mechanic + chalk - teacher). Critically, this property emerges in artificial neural networks that were not trained to produce analogies but instead were trained to produce general-purpose semantic representations. Here, we test whether such emergent property can be observed in representations in human brains, as well as in artificial neural networks. fMRI activation patterns were recorded while participants viewed isolated words but did not perform analogical reasoning tasks. Analogy relations were constructed from word pairs that were categorically or thematically related, and we tested whether the predicted fMRI pattern calculated with simple arithmetic was more correlated with the pattern of the target word than other words. We observed that the predicted fMRI patterns contain information about not only the identity of the target word but also its category and theme (e.g., teaching-related). In summary, this study demonstrated that information about analogy questions can be reliably accessed with the addition and subtraction of fMRI patterns, and that, similar to word embeddings, this property holds for task-general patterns elicited when participants were not explicitly told to perform analogical reasoning.

Task-Irrelevant Semantic Properties of Objects Impinge on Sensory Representations within the Early Visual Cortex

Objects can be described in terms of low-level (e.g., boundaries) and high-level properties (e.g., object semantics). While recent behavioral findings suggest that the influence of semantic relatedness between objects on attentional allocation can be independent of task-relevance, the underlying neural substrate of semantic influences on attention remains ill-defined. Here, we employ behavioral and functional magnetic resonance imaging measures to uncover the mechanism by which semantic information increases visual processing efficiency. We demonstrate that the strength of the semantic relatedness signal decoded from the left inferior frontal gyrus: 1) influences attention, producing behavioral semantic benefits; 2) biases spatial attention maps in the intraparietal sulcus, subsequently modulating early visual cortex activity; and 3) directly predicts the magnitude of behavioral semantic benefit. Altogether, these results identify a specific mechanism driving task-independent semantic influences on attention.

Scene context shapes category representational geometry during processing of tools

Tools are ubiquitous in human environments and to think about them we use concepts. Increasingly, conceptual representation is thought to be dynamic and sensitive to the goals of the observer. Indeed, observer goals can reshape representational geometry within cortical networks supporting concepts. In the present study, we investigated the novel hypothesis that task-irrelevant scene context may implicitly alter the representational geometry of regions within the tool network. Participants performed conceptual judgments on images of tools embedded in scenes that either suggested their use (i.e., a kitchen timer sitting on a kitchen counter with vegetables in a frying pan) or that they would simply be moved (i.e., a kitchen timer sitting in an open drawer with other miscellaneous kitchen items around). We investigated whether representations in the tool network reflect category, grip, and shape information using a representational similarity analysis (RSA). We show that a) a number of regions of the tool network reflect category information about tools and b) category information predicts patterns in supramarginal gyrus more strongly in use contexts than in move contexts. Together, these results show that information about tool category is distributed across different regions of the tool network and that scene context helps shape the representational geometry of the tool network.

Correlated Brain Indexes of Semantic Prediction and Prediction Error: Brain Localization and Category Specificity

With strong and valid predictions, grasping a message is easy, whereas more demanding processing is required in the absence of robust expectations. We here demonstrate that brain correlates of the interplay between prediction and perception mechanisms in the understanding of meaningful sentences. Sentence fragments that strongly predict subsequent words induced anticipatory brain activity preceding the expected words; this potential was absent if context did not strongly predict subsequent words. Subjective reports of certainty about upcoming words and objective corpus-based measures correlated with the size of the anticipatory signal, thus establishing its status as a semantic prediction potential (SPP). Crucially, there was an inverse correlation between the SPP and the N400 brain response. The main cortical generators of SPP and N400 were found in inferior prefrontal cortex and posterior temporal cortex, respectively. Interestingly, sentence meaning was reflected by both measures, with additional category-specific sources of SPPs and N400s falling into parieto-temporo-occipital (visual) and frontocentral (sensorimotor) areas for animal- and tool-related words, respectively. These results show that the well-known brain index of semantic comprehension, N400, has an antecedent with different brain localization but similar semantic discriminatory function. We discuss whether N400 dynamics may causally depend on mechanisms underlying SPP size and sources.

Semantic similarity and associated abstractness norms for 630 French word pairs

The representation of abstract concepts remains a challenge, justifying the need for further experimental investigation. To that end, we introduce a normative database for 630 semantically similar French word pairs and associated levels of abstractness for 1260 isolated words based on data from 900 subjects. The semantic similarity and abstractness norms were obtained in two studies using 7-point scales. The database is organised according to word-pair semantic similarity, abstractness, and associated lexical variables such as word length (in number of letters), word frequency, and other lexical variables to allow for matching of experimental material. The associated variables were obtained by cross-referencing our database with other known psycholinguistic databases including Lexique (New et al., 2004), the French Lexicon Project (Ferrand et al., 2010), Wordlex (Gimenes & New, 2016), and MEGALEX (Ferrand et al., 2018). We introduced sufficient diversity to allow researchers to select pairs with varying levels of semantic similarity and abstractness. In addition, it is possible to use these data as continuous or discrete variables. The full data are available in the supplementary materials as well as on OSF ( https://osf.io/qsd4v/ ).

Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis

BACKGROUND

Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals.

METHODS

Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network.

RESULTS

In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible.

CONCLUSIONS

Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.

Cortical network responses map onto data-driven features that capture visual semantics of movie fragments

Research on how the human brain extracts meaning from sensory input relies in principle on methodological reductionism. In the present study, we adopt a more holistic approach by modeling the cortical responses to semantic information that was extracted from the visual stream of a feature film, employing artificial neural network models. Advances in both computer vision and natural language processing were utilized to extract the semantic representations from the film by combining perceptual and linguistic information. We tested whether these representations were useful in studying the human brain data. To this end, we collected electrocorticography responses to a short movie from 37 subjects and fitted their cortical patterns across multiple regions using the semantic components extracted from film frames. We found that individual semantic components reflected fundamental semantic distinctions in the visual input, such as presence or absence of people, human movement, landscape scenes, human faces, etc. Moreover, each semantic component mapped onto a distinct functional cortical network involving high-level cognitive regions in occipitotemporal, frontal and parietal cortices. The present work demonstrates the potential of the data-driven methods from information processing fields to explain patterns of cortical responses, and contributes to the overall discussion about the encoding of high-level perceptual information in the human brain.

Neural Representations of Procedural Knowledge

Although declarative concepts (e.g., apple) have been shown to be identifiable from their functional MRI (fMRI) signatures, the correspondence has yet to be established for executing a complex procedure such as tying a knot. In this study, 7 participants were trained to tie seven knots. Their neural representations of these seven procedures were assessed with fMRI as they imagined tying each knot. A subset of the trained participants physically tied each knot in a later fMRI session. Findings demonstrated that procedural knowledge of tying a particular knot can be reliably identified from its fMRI signature, and such procedural signatures were found here in frontal, parietal, motor, and cerebellar regions. In addition, a classifier trained on mental tying signatures was able to reliably identify when participants were planning to tie knots before they physically tied them, which suggests that the mental-tying and physical-tying procedural signatures are similar. These findings indicate that fMRI activation patterns can illuminate the representation and organization of procedural knowledge.

Feature-specific neural reactivation during episodic memory

We present a multi-voxel analytical approach, feature-specific informational connectivity (FSIC), that leverages hierarchical representations from a neural network to decode neural reactivation in fMRI data collected while participants performed an episodic visual recall task. We show that neural reactivation associated with low-level (e.g. edges), high-level (e.g. facial features), and semantic (e.g. “terrier”) features occur throughout the dorsal and ventral visual streams and extend into the frontal cortex. Moreover, we show that reactivation of both low- and high-level features correlate with the vividness of the memory, whereas only reactivation of low-level features correlates with recognition accuracy when the lure and target images are semantically similar. In addition to demonstrating the utility of FSIC for mapping feature-specific reactivation, these findings resolve the contributions of low- and high-level features to the vividness of visual memories and challenge a strict interpretation the posterior-to-anterior visual hierarchy.

Memory recollection involves reactivation of neural activity that occurred during the recalled experience. Here, the authors show that neural reactivation can be decomposed into visual-semantic features, is widely synchronized throughout the brain, and predicts memory vividness and accuracy.

A Double Dissociation in Sensitivity to Verb and Noun Semantics Across Cortical Networks

What is the neural organization of the mental lexicon? Previous research suggests that partially distinct cortical networks are active during verb and noun processing, but what information do these networks represent? We used multivoxel pattern analysis (MVPA) to investigate whether these networks are sensitive to lexicosemantic distinctions among verbs and among nouns and, if so, whether they are more sensitive to distinctions among words in their preferred grammatical class. Participants heard 4 types of verbs (light emission, sound emission, hand-related actions, mouth-related actions) and 4 types of nouns (birds, mammals, manmade places, natural places). As previously shown, the left posterior middle temporal gyrus (LMTG+), and inferior frontal gyrus (LIFG) responded more to verbs, whereas the inferior parietal lobule (LIP), precuneus (LPC), and inferior temporal (LIT) cortex responded more to nouns. MVPA revealed a double-dissociation in lexicosemantic sensitivity: classification was more accurate among verbs than nouns in the LMTG+, and among nouns than verbs in the LIP, LPC, and LIT. However, classification was similar for verbs and nouns in the LIFG, and above chance for the nonpreferred category in all regions. These results suggest that the lexicosemantic information about verbs and nouns is represented in partially nonoverlapping networks.

A Guide to Representational Similarity Analysis for Social Neuroscience

Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.

Distinct neural mechanisms underlying conceptual knowledge of manner and instrument verbs

Studies on the organization of conceptual knowledge have examined categories of concrete nouns extensively. Less is known about the neural basis of verb categories suggested by linguistic theories. We used functional MRI to examine the differences between manner verbs, which encode information about the manner of an action, versus instrument verbs, which encode information about an object as part of their meaning. Using both visual and verbal stimuli and a combination of univariate and multivariate pattern analyses, our results show that accessing conceptual representations of instrument class involves brain regions typically associated with complex action and object perception, including the anterior inferior parietal cortex and occipito-temporal cortex. On the other hand, accessing conceptual representations of the manner class involves regions that are commonly associated with the processing of visual and biological motion, in the posterior superior temporal sulcus. These findings support the idea that the semantics of manner and instrument verbs are supported by distinct neural mechanisms.

Effective Augmentation of Creativity-Involving Productivity Consequent to Spontaneous Selectivity in Knowledge Acquisition

The results of many studies have suggested that we actively select information from the environment. However, the functional consequences of such selectivity in knowledge acquisition remain unclear, even though it is a vital factor in determining the characteristics of our future knowledge and cognition. We hypothesized that spontaneous selectivity in knowledge acquisition results in effective augmentation of productivity, especially in creativity-demanding task. To test this, we conducted experiments in which subjects acquired novel compositional words during their rapid presentation, evaluated memory confidence rates for the acquired words, and then produced essays based on these words. First, in experiment 1, we showed that the level of confidence in the recognition memory for the words positively related with the length of the essays (a measure of creativity-involving productivity in quantity). Additionally, we found that the semantic distance from the essay to the components of the compositional word (a measure of creative-productivity in quality) was farther for the word with higher memory confidence than for the word with lower memory confidence, suggesting creative leaps when writing the former. While this result supported our hypothesis, it might also reflect better memory that was independent of spontaneous selection. Thus, in a different subject group, we conducted a similar experiment (experiment 2) in which two of the 20 compositional words were presented more often (five times per block) to force memorization. Again, consistent with our hypothesis, essays based on spontaneously memorized words (presented once per block) were significantly longer than those produced using the forcedly memorized words. Therefore, better memory per se did not explain the higher productivity. Instead, these results suggested that the higher creativity-involving productivity was consequent to spontaneous selectivity in the knowledge acquisition. Additionally, we propose a possible mechanism for the observed results based on the results of a neural network simulation. In this simulation, we found that novel information that was assigned to locations more easily accessible to the entire network was better assimilated and therefore selectively acquired. Based on this simulation, we moderately suggest that spontaneously acquired knowledge effectively confers productivity because it effectively activates large parts of the neural networks.

How pattern information analyses of semantic brain activity elicited in language comprehension could contribute to the early identification of Alzheimer's Disease

Alzheimer's disease (AD) is associated with a loss of semantic knowledge reflecting brain pathophysiology that begins years before dementia. Identifying early signs of pathophysiology induced dysfunction in the neural systems that access and process words' meaning could therefore help forecast dementia. This article reviews pioneering studies demonstrating that abnormal functional Magnetic Resonance Imaging (fMRI) response patterns elicited in semantic tasks reflect both AD-pathophysiology and the hereditary risk of AD, and also can help forecast cognitive decline. However, to bring current semantic task-based fMRI research up to date with new AD research guidelines the relationship with different types of AD-pathophysiology needs to be more thoroughly examined. We shall argue that new analytic techniques and experimental paradigms will be critical for this. Previous work has relied on specialized tests of specific components of semantic knowledge/processing (e.g. famous name recognition) to reveal coarse AD-related changes in activation across broad brain regions. Recent computational advances now enable more detailed tests of the semantic information that is represented within brain regions during more natural language comprehension. These new methods stand to more directly index how pathophysiology alters neural information processing, whilst using language comprehension as the basis for a more comprehensive examination of semantic brain function. We here connect the semantic pattern information analysis literature up with AD research to raise awareness to potential cross-disciplinary research opportunities.

Brain reading and behavioral methods provide complementary perspectives on the representation of concepts

The advent of brain reading techniques has enabled new approaches to the study of concept representation, based on the analysis of multivoxel activation patterns evoked by the contemplation of individual concepts such as animal concepts. The present fMRI study characterized the representation of 30 animal concepts. Dimensionality reduction of the multivoxel activation patterns underlying the individual animal concepts indicated that the semantic building blocks of the brain's representations of the animals corresponded to intrinsic animal properties (e.g. fierceness, intelligence, size). These findings were compared to behavioral studies of concept representation, which have typically collected pairwise similarity ratings between two concepts (e.g. Henley, 1969). Behavioral similarity judgments, by contrast, indicated that the animals were organized into taxonomically defined groups (e.g. canine, feline, equine). The difference in the results between the brain reading and behavioral approaches might derive from differences in cognitive processing during judging similarities versus contemplating one animal at a time. Brain reading approaches may have an advantage in describing thoughts about an individual concept, owing to the ability to decode brain activation patterns elicited by the brief consideration of a single concept (e.g. word reading) without a complex cognitive or behavioral task (e.g. similarity judgments). On the other hand, some behavioral tasks may tend to evoke a concept from numerous perspectives, yielding a representation of the breadth and sophistication of the concept knowledge. These results suggest that neural and behavioral measures offer complementary perspectives that together characterize the content and structure of concept representations.

Neural basis of goal-driven changes in knowledge activation

Depending on a person's goals, different aspects of stored knowledge are accessed. Decades of behavioral work document the flexible use of knowledge, but little neuroimaging work speaks to these questions. We used representational similarity analysis to investigate whether the relationship between brain activity and semantic structure of statements varied in two tasks hypothesized to differ in the degree to which knowledge is accessed: judging truth (semantic task) and judging oldness (episodic task). During truth judgments, but not old/new recognition judgments, a left-lateralized network previously associated with semantic memory exhibited correlations with semantic structure. At a neural level, people activate knowledge representations in different ways when focused on different goals. The present results demonstrate the potential of multivariate approaches in characterizing knowledge storage and retrieval, as well as the ways that it shapes our understanding and long-term memory.

A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity

One of the most controversial debates in cognitive neuroscience concerns the cortical locus of semantic knowledge and processing in the human brain. Experimental data revealed the existence of various cortical regions relevant for meaning processing, ranging from semantic hubs generally involved in semantic processing to modality-preferential sensorimotor areas involved in the processing of specific conceptual categories. Why and how the brain uses such complex organization for conceptualization can be investigated using biologically constrained neurocomputational models. Here, we improve pre-existing neurocomputational models of semantics by incorporating spiking neurons and a rich connectivity structure between the model ‘areas’ to mimic important features of the underlying neural substrate. Semantic learning and symbol grounding in action and perception were simulated by associative learning between co-activated neuron populations in frontal, temporal and occipital areas. As a result of Hebbian learning of the correlation structure of symbol, perception and action information, distributed cell assembly circuits emerged across various cortices of the network. These semantic circuits showed category-specific topographical distributions, reaching into motor and visual areas for action- and visually-related words, respectively. All types of semantic circuits included large numbers of neurons in multimodal connector hub areas, which is explained by cortical connectivity structure and the resultant convergence of phonological and semantic information on these zones. Importantly, these semantic hub areas exhibited some category-specificity, which was less pronounced than that observed in primary and secondary modality-preferential cortices. The present neurocomputational model integrates seemingly divergent experimental results about conceptualization and explains both semantic hubs and category-specific areas as an emergent process causally determined by two major factors: neuroanatomical connectivity structure and correlated neuronal activation during language learning.

The case of CAUSE: neurobiological mechanisms for grounding an abstract concept

How can we understand causal relationships and how can we understand words such as 'cause'? Some theorists assume that the underlying abstract concept is given to us, and that perceptual correlation provides the relevant hints towards inferring causation from perceived real-life events. A different approach emphasizes the role of actions and their typical consequences for the emergence of the concept of causation and the application of the related term. A model of causation is proposed that highlights the family resemblance between causal actions and postulates that symbols are necessary for binding together the different partially shared semantic features of subsets of causal actions and their goals. Linguistic symbols are proposed to play a key role in binding the different subsets of semantic features of the abstract concept. The model is spelt out at the neuromechanistic level of distributed cortical circuits and the cognitive functions they carry. The model is discussed in light of behavioural and neuroscience evidence, and questions for future research are highlighted. In sum, taking causation as a concrete example, I argue that abstract concepts and words can be learnt and grounded in real-life interaction, and that the neurobiological mechanisms realizing such abstract semantic grounding are within our grasp.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain’s knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.

Focal versus distributed temporal cortex activity for speech sound category assignment

When listening to speech, phonemes are represented in a distributed fashion in our temporal and prefrontal cortices. How these representations are selected in a phonemic decision context, and in particular whether distributed or focal neural information is required for explicit phoneme recognition, is unclear. We hypothesized that focal and early neural encoding of acoustic signals is sufficiently informative to access speech sound representations and permit phoneme recognition. We tested this hypothesis by combining a simple speech-phoneme categorization task with univariate and multivariate analyses of fMRI, magnetoencephalography, intracortical, and clinical data. We show that neural information available focally in the temporal cortex prior to decision-related neural activity is specific enough to account for human phonemic identification.

Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision.

Enhanced verbal abilities in the congenitally blind

Numerous studies have found that congenitally blind individuals have better verbal memory than their normally sighted counterparts. However, it is not known whether this reflects superiority of verbal or memory abilities. In order to distinguish between these possibilities, we tested congenitally blind participants and normally sighted control participants, matched for age and education, on a range of verbal and spatial tasks. Congenitally blind participants were significantly better than sighted controls on all the verbal tasks but the groups did not differ significantly on the spatial tasks. Thus, the congenitally blind appear to have superior verbal, but not spatial, abilities. This may reflect greater reliance on verbal information and the involvement of visual cortex in language processing in the congenitally blind.