Biology subjects

Fedorenko, E.

Publications and source records attributed to Fedorenko, E..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Intact reading ability in spite of a spatially distributed visual word form 'area' in an individual born without the left superior temporal lobe

The visual word form area (VWFA) is an experience-dependent region in the left ventral temporal cortex (VTC) of literate adults that responds selectively to visual words. Why does it emerge in this stereotyped location? Past research shows the VWFA is preferentially connected to the left-lateralized frontotemporal language network. However, it remains unclear whether the presence of a typical language network and its connections with VTC are critical for the VWFAs emergence, and whether alternative functional architectures may support reading ability. We explored these questions in an individual (EG) born without the left superior temporal lobe but exhibiting normal reading ability. We recorded fMRI activation to visual words, objects, faces, and scrambled words in EG and neurotypical controls. We did not observe word selectivity either in EGs right homotope of the VWFA (rVWFA)--the most expected location given that EGs language network is right-lateralized--or in her spared left VWFA (lVWFA), despite typical face selectivity in both the right and left fusiform face area (rFFA, lFFA). We replicated these results across scanning sessions (5 years apart). Moreover, in contrast with the idea that the VWFA is simply part of the language network that responds to general linguistic information, no part of EGs VTC showed selectivity to higher-level linguistic processing. Interestingly, multivariate pattern analyses revealed sets of voxels in EGs rVWFA and lVWFA that showed 1) higher within- than between-category correlations for words (e.g., Words-Words>Words-Faces), and 2) higher within-category correlations for words than other categories (e.g., Words-Words>Faces-Faces). These results suggest that a typical left-hemisphere language network may be necessary for the emergence of focal word selectivity within the VTC, and that orthographic processing can be supported by a distributed neural code.

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bioRxiv2021

The language network supports both lexical access and sentence generation during language production

A fronto-temporal brain network has long been implicated in language comprehension. However, this networks role in language production remains debated. In particular, it remains unclear whether all or only some language regions contribute to production, and which aspects of production these regions support. Across three fMRI experiments that rely on robust individual-subject analyses, we characterize the language networks response to high-level production demands. We report three novel results. First, sentence production, spoken or typed, elicits a strong response throughout the language network. Second, the language network responds to both phrase-structure building and lexical access demands, although the response to phrase-structure building is stronger and more spatially extensive, present in every language region. Finally, contra some proposals, we find no evidence of brain regions--within or outside the language network--that selectively support phrase-structure building in production relative to comprehension. Instead, all language regions respond more strongly during production than comprehension, suggesting that production incurs a greater cost for the language network. Together, these results align with the idea that language comprehension and production draw on the same knowledge representations, which are stored in a distributed manner within the language-selective network and are used to both interpret and generate linguistic utterances.

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bioRxiv2021

The universal language network: A cross-linguistic investigation spanning 45 languages and 11 language families.

To understand the architecture of human language, it is critical to examine diverse languages; yet most cognitive neuroscience research has focused on a handful of primarily Indo-European languages. Here, we report an investigation of the fronto-temporo-parietal language network across 45 languages and establish the robustness to cross-linguistic variation of its topography and key functional properties, including left-lateralization, strong functional integration among its brain regions, and functional selectivity for language processing.

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bioRxiv2021

High-level language brain regions are sensitive to sub-lexical regularities

A network of left frontal and temporal brain regions supports high-level language processing-- including the processing of word meanings, as well as word-combinatorial processing--across presentation modalities. This core language network has been argued to store our knowledge of words and constructions as well as constraints on how those combine to form sentences. However, our linguistic knowledge additionally includes information about sounds (phonemes) and how they combine to form clusters, syllables, and words. Is this knowledge of phoneme combinatorics also represented in these language regions? Across five fMRI experiments, we investigated the sensitivity of high-level language processing brain regions to sub-lexical linguistic sound patterns by examining responses to diverse nonwords--sequences of sounds/letters that do not constitute real words (e.g., punes, silory, flope). We establish robust responses in the language network to visually (Experiment 1a, n=605) and auditorily (Experiments 1b, n=12, and 1c, n=13) presented nonwords relative to baseline. In Experiment 2 (n=16), we find stronger responses to nonwords that obey the phoneme-combinatorial constraints of English. Finally, in Experiment 3 (n=14) and a post-hoc analysis of Experiment 2, we provide suggestive evidence that the responses in Experiments 1 and 2 are not due to the activation of real words that share some phonology with the nonwords. The results suggest that knowledge of phoneme combinatorics and representations of sub-lexical linguistic sound patterns are stored within the same fronto-temporal network that stores higher-level linguistic knowledge and supports word and sentence comprehension.

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bioRxiv2021

The human language system does not support music processing

Language and music are two human-unique capacities whose relationship remains debated. Some have argued for overlap in processing mechanisms, especially for structure processing. Such claims often concern the inferior frontal component of the language system located within Brocas area. However, others have failed to find overlap. Using a robust individual-subject fMRI approach, we examined the responses of language brain regions to music stimuli, and probed the musical abilities of individuals with severe aphasia. Across four experiments, we obtained a clear answer: music perception does not engage the language system, and judgments about music structure are possible even in the presence of severe damage to the language network. In particular, the language regions responses to music are generally low, often below the fixation baseline, and never exceed responses elicited by non-music auditory conditions, like animal sounds. Further, the language regions are not sensitive to music structure: they show low responses to intact and structure-scrambled music, and to melodies with vs. without structural violations. Finally, in line with past patient investigations, individuals with aphasia who cannot judge sentence grammaticality perform well on melody well-formedness judgments. Thus the mechanisms that process structure in language do not appear to process music, including music syntax.

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bioRxiv2021

Frontal language areas do not emerge in the absence of temporal language areas: A case study of an individual born without a left temporal lobe

Language relies on a left-lateralized fronto-temporal brain network. How this network emerges ontogenetically remains debated. We asked whether frontal language areas emerge in the absence of temporal language areas through a deep-data investigation of an individual (EG) born without her left temporal lobe. Using fMRI methods that have been validated to elicit reliable individual-level responses, we find that--as expected for early left hemisphere damage--EG has a fully functional language network in her right hemisphere (comparable to that in n=145 controls) and performs normally on language assessments. However, we detect no response to language in EGs left frontal lobe (replicated across two sessions, 3 years apart). Another network--the multiple demand network--is robustly present in frontal lobes bilaterally, suggesting that EGs left frontal cortex can support non-linguistic cognition. The existence of temporal language areas therefore appears to be a prerequisite for the emergence of the frontal language areas.

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bioRxiv2021

Differential tracking of linguistic vs. mental state content in naturalistic stimuli by language and Theory of Mind (ToM) brain networks

Language and social cognition, especially the ability to reason about mental states, known as Theory of Mind (ToM), are deeply related in development and everyday use. However, whether these cognitive faculties rely on distinct, overlapping, or the same mechanisms remains debated. Some evidence suggests that, by adulthood, language and ToM draw on largely distinct--though plausibly interacting--cortical networks. However, the broad topography of these networks is similar, and some have emphasized the importance of social content / communicative intent in the linguistic signal for eliciting responses in the language areas. Here, we combine the power of individual-subjects functional localization with the naturalistic-cognition inter-subject correlation approach to illuminate the language-ToM relationship. Using fMRI, we recorded neural activity as participants (n=43) listened to stories and dialogs with mental state content (+linguistic, +ToM), viewed silent animations and live action films with mental state content but no language (-linguistic, +ToM), or listened to an expository text (+linguistic, -ToM). The ToM network robustly tracked stimuli rich in mental state information regardless of whether mental states were conveyed linguistically or non-linguistically, while tracking a +linguistic/-ToM stimulus only weakly. In contrast, the language network tracked linguistic stimuli more strongly than a) non-linguistic stimuli, and than b) the ToM network, and showed reliable tracking even for the linguistic condition devoid of mental state content. These findings suggest that in spite of their indisputably close links, language and ToM dissociate robustly in their neural substrates--and thus plausibly cognitive mechanisms--including during the processing of rich naturalistic materials.

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bioRxiv2021

Is it that simple? Linear mapping models in cognitive neuroscience

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models instead; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated. Here, we discuss the choice of a mapping model in the context of three overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that, contrary to popular intuition, these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model. Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals. Finally, we outline several complexity metrics that can be used to effectively evaluate mapping models.

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bioRxiv2021

No evidence for a special role of language in feature-based categorization

The relationship between language and human thought is the subject of long-standing debate. One specific claim implicates language in feature-based categorization. According to this view, language resources facilitate object categorization based on a certain feature (e.g., color). Specifically, it is hypothesized that verbal labels help maintain focus on a relevant categorization criterion and reduce interference from irrelevant features. As a result, language impairment is expected to affect categorization of items grouped according to a single feature (low-dimensional categories, e.g., Things that are yellow), where many irrelevant features need to be inhibited, more than categorization of items that share many features (high-dimensional categories, e.g., Animals), where few irrelevant features need to be inhibited. In two behavioral studies with individuals with aphasia, we failed to find consistent support for the role of language in low-dimensional categorization. We also collected fMRI data from healthy adults and observed little activity in language-responsive brain regions during both low-dimensional and high-dimensional categorization. Combined, these results demonstrate that the language system is not implicated in object categorization. Our work adds to the growing evidence that, although language may assist in accessing task-relevant information (e.g., instructions), many cognitive tasks in adult brains proceed without recruiting the language system.

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bioRxiv2020

Artificial Neural Networks Accurately Predict Language Processing in the Brain

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a first systematic study taking this approach to higher-level cognition: human language processing, our species signature cognitive skill. We find that the most powerful transformer models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (fMRI, ECoG). Models neural fits ( brain score) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain. SignificanceLanguage is a quintessentially human ability. Research has long probed the functional architecture of language processing in the mind and brain using diverse brain imaging, behavioral, and computational modeling approaches. However, adequate neurally mechanistic accounts of how meaning might be extracted from language are sorely lacking. Here, we report an important first step toward addressing this gap by connecting recent artificial neural networks from machine learning to human recordings during language processing. We find that the most powerful models predict neural and behavioral responses across different datasets up to noise levels. Models that perform better at predicting the next word in a sequence also better predict brain measurements - providing computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.

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bioRxiv2020

Comprehension of computer code relies primarily on domain-general executive resources

Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used fMRI to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

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bioRxiv2020

Incremental language comprehension difficulty predicts activity in the language network but not the multiple demand network

What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general "multiple demand" (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, fMRI data collected during naturalistic story listening are compared to theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant-or trial-level variability, the neuroimaging and behavioral datasets were collected in non-overlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal "core language network", in both left hemispheric areas and their right hemispheric homologues. These results argue against strong involvement of domain-general executive circuits in language comprehension.

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bioRxiv2020

Reduced language lateralization in autism and the broader autism phenotype as assessed with robust individual-subjects analyses

One of the few replicated functional brain differences between individuals with autism spectrum disorders (ASD) and neurotypical (NT) controls is reduced language lateralization. However, most prior reports relied on comparisons of group-level activation maps or functional markers that had not been validated at the individual-subject level, and/or used tasks that do not isolate language processing from other cognitive processes, complicating interpretation. Furthermore, few prior studies have examined functional responses in other functional networks, as needed to determine the selectivity of the effect. Using fMRI, we compared language lateralization between 28 ASD participants and carefully pairwise-matched controls, with the language regions defined individually with a well-validated language localizer. ASD participants showed less lateralized responses due to stronger right hemisphere activations. Further, this effect did not stem from a ubiquitous reduction in lateralization across the brain: ASD participants did not differ from controls in the lateralization of two other large-scale networks--the Theory of Mind network and the Multiple Demand network. Finally, in an exploratory study, we tested whether reduced language lateralization may also be present in NT individuals with high autistic trait load. Indeed, autistic trait load in a large set of NT participants (n=189) was associated with less lateralized language activations. These results suggest that reduced language lateralization is a robust and spatially selective neural marker of autism, present in individuals with ASD, but also in NT individuals with higher genetic liability for ASD, in line with a continuum model of underlying genetic risk.

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bioRxiv2020

The P600 ERP component as an index of rational error correction within a noisy-channel framework of human communication

Recent evidence suggests that language processing is well-adapted to noise in the input (e.g., speech errors, mishearing) and readily corrects the input via rational inference over possible intended sentences and probable noise corruptions. However, it remains unclear whether this inference takes the form of an offline re-analysis or a rapid, real-time correction to the representations of the input. We hypothesize that noise inferences happen online during processing and that well-studied ERP components may serve as a useful index of this process. In particular, a reduced N400 effect and increased P600 effect appear to accompany sentences where the probability that the message was corrupted by noise exceeds the probability that it was produced intentionally and perceived accurately. Indeed, semantic violations that are attributable to noise--for example, in "The storyteller could turn any incident into an amusing antidote", where the implausible word "antidote" is orthographically and phonologically close to the intended "anecdote"--elicit a reduced N400 effect and larger P600 effect. Further, the magnitude of this P600 effect is shown to relate to the probability that the comprehender will retrieve a plausible alternative. This work thus adds to the growing body of literature that suggests that many aspects of language processing are well-adapted to noise in the input and opens the door to electrophysiologic investigations of these processes

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bioRxiv2019

The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation

Aside from the language-selective left-lateralized fronto-temporal network, language comprehension sometimes additionally recruits a domain-general bilateral fronto-parietal network implicated in executive functions: the multiple demand (MD) network. However, the nature of the MD networks contributions to language comprehension remains debated. To illuminate the role of this network in language processing, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants, 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in stark contrast with the language-selective network, the MD network responded more strongly (i) to lists of unconnected words than to sentences, and critically, (ii) in paradigms with an explicit task compared to passive comprehension paradigms. In fact, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. In tandem, these results argue against a role for the MD network in core aspects of sentence comprehension like inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD networks engagement during language processing likely reflects effort associated with extraneous task demands. Significance StatementDomain-general executive processes, like working memory and cognitive control, have long been implicated in language comprehension, including in neuroimaging studies that have reported activation in domain-general multiple demand (MD) regions for linguistic manipulations. However, much prior evidence has come from paradigms where language interpretation is accompanied by extraneous tasks. Using a large fMRI dataset (30 experiments/481 participants/678 sessions), we demonstrate that MD regions are engaged during language comprehension in the presence of task demands, but not during passive reading/listening--conditions that strongly activate the fronto-temporal language network. These results present a fundamental challenge to proposals whereby linguistic computations, like inhibiting irrelevant meanings, keeping representations active in working memory, or predicting upcoming elements, draw on domain-general executive resources.

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bioRxiv2019

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brains response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

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bioRxiv2019

The small and efficient language network of polyglots and hyper-polyglots

Acquiring a foreign language is challenging for many adults. Yet certain individuals choose to acquire sometimes dozens of languages, and often just for fun. Is there something special about the minds and brains of such polyglots? Using robust individual-level markers of language activity, measured with fMRI, we compared native language processing in polyglots versus matched controls. Polyglots (n=17, including 9 \"hyper-polyglots\" with proficiency in 10-55 languages) used fewer neural resources to process language: their activations were smaller in both magnitude and extent. This difference was spatially and functionally selective: the groups were similar in their activation of two other brain networks - the multiple demand network and the default mode network. We hypothesize that the activation reduction in the language network is experientially driven, such that the acquisition and use of multiple languages makes language processing generally more efficient. However, genetic and longitudinal studies will be critical to distinguish this hypothesis from the one whereby polyglots brains already differ at birth or early in development. This initial characterization of polyglots language network opens the door to future investigations of the cognitive and neural architecture of individuals who gain mastery of multiple languages, including changes in this architecture with linguistic experiences.

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bioRxiv2019

No evidence for functional distinctions across fronto-temporal language regions in their temporal receptive windows

The \"core language network\" consists of left temporal and frontal regions that are selectively engaged in linguistic processing. Whereas the functional differences across these regions have long been debated, many accounts propose distinctions in terms of representational grain-size--e.g., words vs. sentences--or processing time-scale, i.e., operating on local linguistic features vs. larger spans of input. Indeed, the topography of language regions appears to overlap with a cortical hierarchy reported by Lerner et al. (2011) wherein mid-posterior temporal regions are sensitive to low-level features of speech, surrounding areas--to word-level information, and inferior frontal areas--to sentence-level information and beyond. However, the correspondence between the language network and this hierarchy of \"temporal receptive windows\" (TRWs) is difficult to establish because the precise anatomical locations of language regions vary across individuals. To directly test this correspondence, we first identified language regions in each participant with a task-based localizer, which confers high functional resolution to the study of TRWs (traditionally based on stereotactic coordinates); then, we characterized regional TRWs with the naturalistic story listening paradigm of Lerner et al. (2011), which augments task-based characterizations of the language network by more closely resembling comprehension \"in the wild\". We find no region-by-TRW interactions across temporal and inferior frontal regions, which are all sensitive to both word-level and sentence-level information. Therefore, the language network as a whole constitutes a unique stage of information integration within a broader cortical hierarchy.\n\nHighlightsO_LILanguage regions are identified with task-based, participant-specific localization.\nC_LIO_LIA progressively scrambled naturalistic story probes regional processing timescales.\nC_LIO_LIWidespread sensitivity to scrambling at the timescales of both words and sentences.\nC_LIO_LINo processing timescale distinctions across temporal and inferior-frontal regions.\nC_LIO_LIThese regions all occupy a common, unique stage in a broader processing hierarchy.\nC_LI

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