Neurobiology of Language

Sensory feedback is essential for the fine-tuning of motor actions, and speech production is no exception. It depends on continuous self-monitoring to ensure that produced sounds match intended targets. Delaying auditory feedback (DAF) disrupts this alignment and impairs fluency, providing a powerful tool to investigate sensorimotor control. We combined functional and diffusion-weighted MRI in 31 participants performing a word-production task under delayed (DAF) and immediate (no-DAF) auditory feedback. While all participants slowed their speech under DAF, the extent of this effect varied across individuals and was quantified using a susceptibility index (SI). At the group level, DAF elicited increased activation in a right-lateralized network encompassing the superior temporal gyrus, supramarginal gyrus, inferior frontal gyrus, supplementary motor area, and left cerebellum. Incorporating individual differences revealed that higher susceptibility was associated with greater activation in left-hemisphere speech motor homologues and larger volume of the right long arcuate fasciculus, a white-matter pathway connecting auditory and motor speech regions. This pattern suggests that vulnerability reflects increased recruitment of neural resources and a stronger reliance on auditory-motor coupling. In contrast, resilience was associated with greater engagement of the bilateral angular gyrus and higher fiber density in the right posterior arcuate fasciculus, which connects auditory and somatosensory speech regions. This finding indicates that resilience is supported by a posterior circuit that efficiently integrates multi-modal sensory feedback. Together, these findings link functional dynamics with underlying structural connectivity to reveal how a right-lateralized network supports speech control, while accounting for individual differences in susceptibility to fluency disruption. Significance StatementFluent speech depends on the brains ability to monitor self-produced sounds and sensations from articulatory organs to adjust motor commands in real time. To uncover the neural basis of this process, we combined a fluency-disrupting paradigm, delayed auditory feedback (DAF), with functional and structural neuroimaging. This multimodal approach revealed that while DAF processing relies on a right-lateralized network, more susceptible individuals show enhanced recruitment of left-hemisphere monitoring regions. We also found that stronger white-matter connections in the right posterior arcuate fasciculus predict greater resilience and fluency. These findings provide an anatomically grounded account of how auditory and somatosensory feedback interact to support speech production, offering new insight into why some individuals are more susceptible to fluency breakdowns and related disorders.

Humans comprehend language incrementally, updating the representation of sentence meaning with each incoming word. These updates are guided by the distance between each perceived word and prior expectations--the prediction error. The alignment between large language models (LLMs) and cortical activity inspires the hypothesis that the cortical computation of prediction error is Surface-based, driven by statistical patterns of word form co-occurrence. In contrast, psycholinguistic models propose that prediction error computation is Meaning-based, driven by word semantics. We used polysemic words with ambiguous semantics to distinguish these models: ambiguity would introduce uncertainty into meaning representations and hence the prediction error, if Meaning-based, but would not affect the prediction error, if Surface-based. We examined how ambiguity influenced prediction error signatures in self-paced reading times and magnetoencephalographic (MEG) neural responses during sentence processing. While an LLM-based proxy of prediction error robustly predicted reading times and neural responses to unambiguous words, it failed to predict either under ambiguity. That is, prediction error computation was altered by uncertainty in word meaning, which supports the Meaning-based model and corroborates the essential role of word meaning in predictive language processing. Our findings highlight an important limitation of LLMs as in silico models of the human language faculty.

Two decades of research have provided evidence for gray matter volume (GMV) differences in developmental dyslexia (or reading disability, RD) in the left perisylvian cortex. However, there are concerns about result inconsistencies, likely attributable to small sample sizes, lenient statistical thresholds, and insufficient accounting for demographic variables and global GMV (Ramus et al., 2018). To address these concerns, we conducted a Discovery and Replication Study (N=262) using data from the Adolescent Brain Cognitive Development Study. We found GMV differences between the RD and Control Groups did not replicate across the Discovery and Replication Studies using voxel-based morphometry (VBM) in Statistical Parametric Mapping (SPM), and that a more conservative threshold yielded far fewer results. We then conducted Reproducibility Studies and first found that when using surface-based morphometry in FreeSurfer instead of VBM, the Discovery and the Replication Study results again failed to converge. Second, we combined all groups in a factorial VBM/SPM analysis and the interaction analysis provided quantitative confirmation for diverging between-group difference results across the two studies. Third, we tested for the role of covariates of no interest and found that when total GMV is not controlled for, this divergence dissipates and group differences in RD (main effect of Reading Ability) are amplified. In conclusion, replication of GMV differences in RD is low, even when using large, well-matched groups, and analyses approaches play a modulating role. As such, results from prior studies using lenient statistical thresholds and not accounting for total GMV should therefore be viewed with caution.

Sentence comprehension requires the incremental construction of syntactic structure and semantic interpretation. Prior neural work (Nelson et al., 2017) identified key neural events at major phrase boundaries during sentence comprehension. To investigate a behavioral correlation of these processes, we used self-paced reading to examine the impact of syntactic phase boundaries, semantic congruence, and sentence structure on sentence processing. Participants read object-relative, subject-relative, and canonical control sentences one word at a time and a subsequent comprehension task. Reading times were analyzed relative to phrase boundaries, node-closing operations, and semantic congruence. Object-relative sentences produced the greatest processing difficulty, demonstrated by increased reading times and decreased comprehension accuracy. Reading times peaked at the phrase boundaries, indicating that processing costs are tied to constituent completion rather than individual lexical categories. Reading times also increased with the number of syntactic constituents completed at a phrase boundary. Agent-patient semantic congruence produced its largest effects in object-relative sentences, suggesting that semantic information interacts with syntactic computations when processing demands are greatest. These findings demonstrate that self-paced reading is sensitive to the incremental processing associated with syntactic constituent completion. Processing costs are tied more closely to phrase completion than to individual lexical categories, scale with the amount of syntactic structure completed at a boundary and interact with agent-patient semantic interpretation during object-relative sentence comprehension. Together, these findings support a view of sentence comprehension in which syntactic structure building and semantic interpretation proceed incrementally and interact continuously throughout online language processing.

The internal representations of large language models (LLMs) correlate, or "align", with human neural activity during language comprehension. One view holds that this alignment reflects shared sensitivity to statistical patterns in LLMs and humans, while others hold that it reflects, at least in part, the emergence of shared linguistic representations in these systems. Here, we investigate whether hierarchical linguistic composition, a property believed to be fundamental to human language, modulates LLM-brain alignment. To this end, we manipulated syntax, compositional semantics, and associative semantics in English sentences that were presented to both an LLM and human participants during an electroencephalography (EEG) experiment. We matched linguistically manipulated stimuli in predictability, which allows us to tease apart alignment induced by linguistic structure from statistical factors. By comparing LLM-EEG alignment scores that were derived using a linear encoding model across predictability-matched conditions, we evaluate how linguistic manipulations modulate the alignment between human EEG reading data and contextual embeddings extracted word-by-word from the hidden layers of GPT2-XL. Three key patterns emerge: (1) increased alignment for word sequences with syntactic structure, (2) decreased alignment for sentences with compositional semantics, and (3) associative semantics does not modulate alignment. These observed linguistic modulations of LLM-EEG alignment take place above and beyond predictability. Our results indicate that associative semantics is encoded similarly by LLMs and the brain, as are at least some aspects of syntactic structure, while compositional semantics is more uniquely encoded in the human brain.

Successful speech perception requires listeners to bin continuous acoustic information into discrete phonetic categories. However, some people maintain within-category acoustic information (gradient) while others discard category-irrelevant information (discrete) during perception. Listeners also vary in how consistently they label speech sounds and more gradient/consistent labeling has been linked with better speech-in-noise (SIN) perception. Here, we test how neuroanatomical properties of the brain's major speech-language and auditory pathways relate to individual differences in speech categorization and SIN processing. We measured phonetic categorization and SIN comprehension via phoneme labeling and QuickSIN tasks. Diffusion-weighted imaging (DWI) with probabilistic tractography estimated axonal density within the bilateral arcuate fasciculi and brainstem-cortical auditory projections. Anatomical morphology (surface area, gray matter volume, thickness) was also quantified in the adjacent frontotemporal cortical areas and midbrain. Behaviorally, we found more consistent categorizers had better performance on the QuickSIN. DWI showed that more gradient listeners had greater white matter density in the left arcuate fasciculus and brainstem-cortical auditory pathways, while better SIN performance was predicted by denser white matter in the brainstem-cortical auditory pathways. Morphometric results revealed more consistent listening was associated with greater cortical thickness in right superior temporal gyrus and more gradient listening was associated with greater surface area in right pars opercularis. We infer that individual differences in phonetic categorization relate to SIN comprehension and are at least partially explained by neuroanatomical properties of the auditory-linguistic brain.

Neural decoding can be viewed as a representation learning problem in which neural activity is mapped into an intermediate representation before downstream reconstruction. The choice of intermediate representation influences both performance and learning difficulty. Here we developed a novel framework for studying how intermediate representation choice influences downstream learning and reconstruction. As a proof-of-concept, we instantiated our framework using diffusion latent representations extracted from different diffusion timesteps for neural speech decoding. Component-wise evaluation showed that reconstruction performance differed substantially across diffusion timesteps, with teacher-forced Word Error Rates of 44.7%, 7.5%, and 3.5% for different latent models. These results demonstrate that diffusion latent representations can serve as effective intermediate representations for learning from neural activity, but that their effectiveness depends strongly on the selected diffusion timestep. More broadly, our framework provides a basis for systematically studying how intermediate representation choice influences downstream learning and reconstruction.

Computational models are a linchpin in our understanding of the neurocognitive basis of reading. These models can simulate idealized profiles of alexia syndromes, but in reality, individuals with alexia present with a wide range of mixed deficits rather than idealized syndromes. To provide a complete cognitive theory of reading, computational models must be able to account for this individual variation. However, this has never been demonstrated. We test oral reading and non-reading phonological and semantic processing in 83 left-hemisphere stroke survivors. We show that individual alexia profiles can be simulated by applying graded phonology and semantic lesions to an artificial neural network model of reading, creating "matched models" that represent individual stroke survivors. The severity of damage to the semantic and phonological layers of the matched models was highly correlated with directly-measured semantic and phonological processing deficits. However, we also identify systematic ways in which the models fail to simulate the reading performance of their matched stroke survivors. Our results support theories of alexia that rely on process-based deficits, demonstrate the feasibility of large-scale individualized modelling of alexia, and suggest ways to further improve the correspondence of models and human reading behavior.

Context is critical for both human and artificial speech comprehension systems. While the role of preceding context in speech processing has been well documented, the neural mechanisms supporting the integration of subsequent input -- phonemes and words that occur in the future -- remain poorly understood. Here, we leverage advances in artificial speech systems to model the contribution of different sources of context on the neural encoding of speech in the human brain. For neural encoding, context-informed but not context-uninformed speech model embeddings explain unique variance in human neural activity beyond acoustics, including in early speech processing regions. In particular, model embeddings informed by past, future, and surrounding context explain activity in distinct intracranial electrodes. These electrodes are left-lateralised, and spatially intermixed in the temporal lobe. We find that beyond-word context is crucial for the representational quality of speech model embeddings, and in particular for the encoding of abstract linguistic information. Our finding that spatially neighboring yet distinct neural populations in the temporal lobe encode representations shaped by different contextual sources (past, future, and surrounding input) provides key insight into the neural circuitry that integrates multiple forms of contextual information. Furthermore, our results may inform the downstream use of self-supervised speech representations in language technology tasks, and in models of speech comprehension in the human brain.

Prior studies have reported inconsistent results for neuroanatomical differences between early bilinguals and monolinguals. These studies primarily measured gray matter volume (GMV), involved small samples, and prioritized adults. Few studies of early bilinguals have measured cortical thickness (CT), which offers more anatomical specificity. It remains unclear whether results derived from differing metrics and approaches (e.g., vertex-versus parcel-wise analyses) converge. Using data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we compared neuroanatomy between large groups of early cultural Spanish-English bilingual and English monolingual children (N = 1,209) matched on age, pubertal status, sex, handedness, socioeconomic status (SES), and nonverbal reasoning. Whole-brain voxel-based morphometry revealed areas of greater and of lesser GMV in bilinguals than monolinguals across all lobes. Vertex-wise CT analyses similarly identified widespread differences, with bilinguals showing areas of both thicker and thinner cortex. We contextualized these findings with parcel-wise CT analyses (average CT values), utilizing two atlases of differing spatial granularity. Parcel-wise results showed good correspondence with vertex-wise findings when implementing the more fine-grained atlas (Destrieux), but use of the coarser atlas (Desikan-Killiany) provided results that led to different conclusions. Finally, we tested for interaction effects between bilingualism and SES on CT and found several regions where differences between bilinguals and monolinguals in CT were modulated by SES. Together, these findings indicate that early bilingualism is associated with extensive neuroanatomical differences relative to monolinguals during childhood, and that these results can vary as a function of neuroanatomical metric, analysis approach, atlas granularity, and SES. Research HighlightsEarly Spanish-English bilingual and monolingual children differ in gray matter volume and cortical thickness across multiple brain regions. Cortical thickness differences between bilinguals and monolinguals cannot be firmly attributed to adaptations associated with language or executive control. Socioeconomic status modulates cortical differences between early bilinguals and monolinguals, revealing unique thickness patterns for those with lower versus higher SES backgrounds. Parcel-wise between-group cortical thickness results are affected by atlas choice and can influence the interpretation of the findings.

Time perception difficulties are frequently reported in Autism Spectrum Disorder, yet empirical findings remain inconsistent. A key methodological limitation is the failure to separate perceptual sensitivity from decision-making strategies. We applied Signal Detection Theory (SDT) to a subsecond duration discrimination task (100 and 500 ms) in 65 non-clinical adults varying in autistic traits, assessed via the Autism-Spectrum Quotient (AQ) and a Principal Component Analysis (PCA) of its subscales. Autistic traits did not predict reduced perceptual sensitivity (d'): temporal discrimination remained intact across the full autism-trait continuum, with Bayesian analyses providing converging evidence against a perceptual deficit. Instead, a PCA-derived cognitive component -- combining heightened Attention to Detail with reduced Imagination -- was systematically associated with a shift in decision bias (c). Individuals with this profile showed a graded attenuation of standard-based anchoring, with ordinal position progressively filling the gap. This shift operated consistently across both temporal scales, as confirmed by trial-level generalized linear mixed modelling, and reflects a quantitative redistribution of anchoring weight rather than a categorical switch in strategy. These findings reframe temporal "rigidity" in ASD not as a perceptual deficit, but as a suboptimal yet internally consistent decision-making style favouring within-trial information over accumulated representational knowledge. Lay AbstractMany autistic people report difficulties with time in daily life, but scientists have long disagreed on whether this reflects a genuine perceptual problem. This study found that autistic traits do not impair the basic ability to judge duration. Instead, people with more autistic traits tend to rely on which event came first, rather than accumulating experience across trials to refine their judgments -- a less effective but internally consistent strategy.

Verbal fluency is a behavioral task that requires the generation of words from a semantic category (category fluency) or words beginning with a specific letter (letter fluency). Although word production engages a frontal-temporal-parietal network, no studies have tested how lesions to temporal and parietal lobe areas that represent semantic and phonological knowledge dampen neural responses in the left pars triangularis and the left pars opercularis, two adjacent regions in the left inferior frontal gyrus implicated in word search and retrieval. Here, 52 patients with temporal lobe lesions underwent clinical functional MRI while performing the category and letter fluency tasks. We investigated where lesion presence was inversely related to the magnitude of task-specific neural responses in pars triangularis and pars opercularis using a technique referred to as voxel-based lesion activity mapping (VLAM). We found that lesions to the left anterior superior temporal gyrus, left temporal pole, left hippocampus, left insula, and underlying inferior fronto-occipital fasciculus were associated with reduced neural responses in the left pars triangularis during the category fluency task. Lesion damage to the right hippocampus was associated with reduced neural responses in the left pars opercularis during category fluency. By contrast, lesions to the left posterior superior temporal gyrus, left supramarginal gyrus, left parietal operculum, and the inferior fronto-occipital fasciculus and left arcuate fasciculus were associated with reduced neural responses in the left pars triangularis and the left pars opercularis during the letter fluency task. These results suggest that anatomically dissociable brain networks interact with the left inferior frontal gyrus when different search strategies constrain the retrieval of word representations.

Human speech is inherently social. Yet our understanding of the neural substrates underlying continuous speech perception relies largely on neural responses to monologues, leaving substantial uncertainty about how social interactions shape the neural encoding of speech. Here, we bridge this gap by studying how EEG responses to speech change when the input includes a social element. In Experiment 1, we compared the neural encoding of synthesised undirected monologues, directed monologues, and dialogues. In Experiment 2, we extended this by using podcasts, addressing the additional challenges of real speech dialogue, such as dysfluency. Using temporal response function analyses, we show that the presence of a social component strengthens the cortical tracking of the speech envelope, despite identical acoustic properties. Neural responses to synthesised speech showed a strong correlation with those for real speech podcasts, with a stronger alignment emerging for more socially-relevant speech material. In addition, we demonstrate that robust neural indices of sound and lexical-level processing can be derived using real podcast recordings despite the presence of dysfluencies. Finally, we present a simulation to put to the test the robustness of temporal response function analyses under increasing levels of dysfluency. Together, these findings highlighting the impact of social elements in shaping auditory neural processing, providing a framework for future investigation and analysis of social speech listening and speech interaction. Significance StatementHuman speech is rarely produced or processed in a social vacuum. Yet, our understanding of continuous speech neurophysiology mostly comes from experiments involving speech monologues. This study reveals how social context modulates the neural encoding of speech. We directly contrast neural signals recorded when participants listened to monologues and dialogues, using controlled material from speech synthesis and real podcast recordings. We found that the social element amplifies the neural encoding of speech features, reflecting greater engagement. We also show strong correlation between synthetic and real podcast neural responses, scaling with social relevance. Finally, we demonstrate that lexical processing can be measured robustly even amid natural dysfluencies. These insights advance our understanding of speech neurophysiology, informing future research on social speech.

Prior studies in bilinguals have reported relationships between brain structure and the dimensions of (i) language proficiency or (ii) language balance (the discrepancy between a bilinguals two proficiencies), but rarely both, even though they are highly related. These studies were often conducted in late bilinguals and the analyses limited to regions of interest. Here, we tested for relationships between brain structure and these two dimensions in 46 early cultural Spanish-English bilinguals (mean age = 16.7 years) at the level of the whole brain for gray matter volume (GMV) and cortical thickness (CT). Results revealed a positive association between GMV and proficiency in the weaker language in the right angular gyrus (AG; BA 39) extending into the superior temporal gyrus (BA 22). More balanced bilingualism was also associated with more GMV in the AG (BA 39), in addition to less GMV in left postcentral gyrus (BA 1), right cerebellum lobule IX and right superior occipital gyrus (BA 18). However, these relationships between GMV and balance disappeared after controlling for language proficiency. No significant associations were observed for CT and these two dimensions of language. Our findings suggest that relationships between GMV and balance are driven by language proficiency, and that the relationship between GMV and language proficiency likely does not involve language-specific mechanisms, given the location of the association is in the right inferior parietal cortex. Together, this study separates the neuroanatomical bases of these two language dimensions and places them in brain regions outside those usually targeted in prior studies. HighlightsO_LINeuroanatomy was correlated with proficiencies in early Spanish-English bilinguals C_LIO_LIRight angular gyrus gray matter volume (GMV) was positively related to proficiency C_LIO_LIGMV was positively related to balance, but not after controlling for proficiency C_LIO_LIRelations with these language dimensions are located outside of language cortex C_LIO_LINo significant associations were observed for cortical thickness C_LI

PurposeTo examine speech iconicity for shape in aphasia, we compared iconicity ratings from people with aphasia to those from neurologically intact individuals and evaluated how iconicity relates to phonological and semantic processing profiles in aphasia. MethodEleven people with aphasia and 11 age- and gender-matched neurologically intact participants rated how rounded or pointed 50 auditory pseudowords sounded using a 5-point scale. Ratings from participants with aphasia were compared to predicted iconicity ratings derived from reference ratings from prior work and to ratings from neurologically intact participants. For each participant with aphasia, correlations between individual ratings and predicted ratings were related to measures of phonological and semantic processing. ResultsRatings from people with aphasia were significantly correlated with both the predicted ratings and the ratings from neurologically intact participants. The strength of the correlation between individual ratings and predicted ratings did not differ significantly between groups, although there was a trend toward weaker correlations in the aphasia group. There were indications that greater language impairment was associated with greater disruption of iconicity ratings; in particular, deficits in phonological segmentation and semantic processing were associated with reduced sensitivity to shape iconicity. ConclusionThese findings suggest that sensitivity to shape iconicity is preserved in individuals with aphasia to varying degrees. The specific nature of language impairment appears to play an important role in determining iconicity processing in aphasia.

The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in aligning model behavior with task objectives. Existing reasoning chain tuning methods often rely on black-box heuristics or gradient-free search, which lack interpretability, generalization, and sample efficiency. In this work, we introduce Thoughts-as-Planning, a novel framework that formalizes reasoning chain optimization as a sequential decision-making process over a latent semantic space. We model the LLM as a partially observable environment and learn a latent world model that simulates the effect of reasoning chain edits on downstream outputs. A proximity-preserving embedding space is constructed to encode reasoning chain-response dynamics, enabling planning via gradient descent or reinforcement learning. Our method supports multi-scale abstraction, allowing reasoning chain edits at token, segment, and instruction levels to be integrated into a unified planner. Through extensive experiments on language understanding and generation tasks, we demonstrate that Thoughts-as-Planning outperforms state-of-the-art reasoning chain tuning baselines in efficiency, robustness, and generalization, while offering interpretability through its structured planning trajectory. Our code is available at https://github.com/FastLM/Thoughts-as-Planning.

The present study examined whether thematic reversal anomalies are processed similarly across subject and object experiencer constructions in Malayalam. Event-related brain potentials (ERPs) were recorded as 30 first-language speakers of Malayalam read transitive sentences with the two types of experiencer verbs, in which the thematic role assignment for the preceding arguments was either correct or reverse. The reversal anomaly became apparent only at the position of the experiencer verb. A linear mixed-models analysis confirmed a biphasic N400-P600 effect at the verb for both verb types when the argument roles were reverse. Thus, our results suggest a uniform processing strategy for TRAs irrespective of the type of experiencer verb involved. However, the N400 amplitude was larger for the object experiencer verb compared to subject experiencer verbs. We suggest that the quantitative difference observed for object experiencer verbs is due to the inverse linking of grammatical function and thematic roles associated with these verbs. In other words, verb-specific linking properties modulate the processing of TRAs involving object experiencer verbs. We argue that this modulation occurs because the parser recalibrates cue weighting when the expected form-to-meaning mappings are overridden by the inverse linking properties of object experiencer verbs.

Classic neurobiological models of human speech and language have emphasized the dominant role of temporal lobe in speech perception, while premotor regions including the ventral premotor cortex (PMv) are situated at the level of articulatory processing. However, accumulating evidence from neuroimaging, clinical, and computational studies suggests that premotor cortex may contribute to speech processing beyond articulation. The precise extent and functional organization of these speech-related representations, however, remain unclear. In this study, we combined naturalistic speech perception with computational encoding models to characterize the organization of speech representations within PMv. We functionally localized a cortical region that encompasses previously described premotor speech areas, which we term the premotor language area (PML). Using acoustic, phonemic, semantic, and deep neural speech representations, we found that PML contains representations spanning the full speech-processing hierarchy, from low-level acoustic features to high-level semantic information. These representations are arranged along a smooth posterior-anterior gradient, with increasingly abstract speech representations emerging toward anterior PML. Moreover, this organizational gradient mirrors the canonical speech processing hierarchy in the temporal auditory regions. These findings challenge the traditional view of premotor cortex as primarily an acoustic-articulatory unit, and instead identify PML as a hierarchically organized speech-processing region that parallels the temporal auditory cortex. This provides a new framework for understanding the role of premotor cortex in speech perception.

While evidence suggests magnocellular deficits in the geniculostriate pathway in adults with dyslexia, neural deficits in the subcortical pathways during childhood remain unclear. Here, we used high-resolution fMRI to investigate subcortical abnormalities in Chinese children with developmental dyslexia. Fast achromatic motion stimuli and slowly drifting chromatic gratings were used to assess magnocellular (M) and parvocellular (P) functions, respectively. Relative to controls, children with dyslexia showed a selective reduction in responses to the M stimulus in the ventromedial pulvinar (vmPul) and the superficial layers of the superior colliculus (SCs), along with significantly reduced SCs-vmPul connectivity. Importantly, while vmPul responses to the M stimulus were positively associated with reading skills in healthy controls, this correlation was absent in children with dyslexia. Unlike previous findings in adults, the lateral geniculate nucleus (LGN) exhibited a non-selective reduction in responses to both stimuli, no volume reduction, and no correlation with reading ability. These findings demonstrate a selective deficit to achromatic motion processing in the colliculus-pulvinar pathway in children with dyslexia, which contributes to their reading difficulties. This early subcortical disruption differs from, and precedes, the neural deficits previously reported in the adult LGN, offering new insight into the developmental trajectory of dyslexia.

Impaired reading, i.e., alexia, is common after left hemisphere stroke. The most common deficit in alexia is a difficulty reading aloud pronounceable novel words, also called pseudowords. While semantic and phonological processes both subserve reading real words, pseudoword reading deficits in alexia are typically ascribed to phonological deficits alone. Some theories, however, suggest that pseudoword reading relies in part on lexical-semantic knowledge, such that semantic deficits could also contribute to poor pseudoword reading in alexia. Leveraging a large sample of left-hemisphere stroke survivors, we examine the cognitive and neural substrates of pseudoword reading accuracy and two error types: lexicalization errors, where a pseudoword is incorrectly read as a real word, and nonword errors, where a pseudoword is read as an incorrect nonword. 76 left-hemisphere stroke survivors read 60 pseudowords aloud, and performed two pseudoword repetition tasks to assess phonological processing and two picture naming tasks to assess mappings between lexical semantics and phonology. Regression models assessed how pseudoword repetition and naming related to overall accuracy and rates of lexicalization and nonword errors in pseudoword reading. Voxel-based and connectome lesion-symptom mapping localized the neural territory responsible for these errors. Both pseudoword repetition and naming independently related to pseudoword reading accuracy. Pseudoword repetition but not naming deficits predicted higher rates of lexicalization errors, while naming but not pseudoword repetition deficits predicted higher rates of nonword errors. Greater nonword error rate also predicted smaller imageability effects in real word reading (t(71)=-3.2, p=0.002). Lexicalization errors were associated with lesions to and disconnections of the left putamen and basal ganglia. Nonword errors were associated with lesions to the superior and middle temporal gyri, as well as broad temporo-parietal disconnections, overlapping with previous lesion-mapping results implicating these regions in semantic contributions to word reading. These results suggest that lexicalization errors result from impaired planning and execution of novel motor plans, causing a reliance on the well-learned motor plans associated with lexical items. In contrast, greater rates of nonword errors, relative to lexicalization errors, occur when semantic contributions to reading are impaired. Overall, these findings demonstrate that semantic processes are involved in reading pseudowords, at least in stroke alexia. These findings support connectionist accounts of reading in which damage in the direct orthography to phonology route for reading leads to reliance on semantic representations, even for pseudowords, suggesting a reinterpretation of pseudoword reading as a pure measure of phonological reading deficits.