Rosario Tomasello, PhD

Neural circuits related to language exhibit a remarkable ability to reorganize and adapt in response to visual deprivation. Particularly, early and late blindness induce distinct neuroplastic changes in the visual cortex, repurposing it for language and semantic processing. Interestingly, these functional changes provoke a unique cognitive advantage – enhanced verbal working memory, particularly in early blindness. Yet, the underlying neuromechanisms and the impact on language and memory-related circuits remain not fully understood. Here, we applied a brain-constrained neural network mimicking the structural and functional features of the frontotemporal-occipital cortices, to model conceptual acquisition in early and late blindness. The results revealed differential expansion of conceptual-related neural circuits into deprived visual areas depending on the timing of visual loss, which is most prominent in early …

Comprehenders are known to generate expectations about upcoming linguistic input at the sentence and discourse level. However, most previous studies on prediction focused mainly on word-induced brain activity rather than examining neural activity preceding a critical stimulus in discourse processing, where prediction actually takes place. In this EEG study, participants were presented with multiple sentences resembling a discourse including conditional sentences with either only if or if, which are characterized by different semantics, triggering stronger or weaker predictions about the possible continuation of the presented discourses, respectively. Results revealed that discourses including only if, as compared to discourses with bare if, triggered an increased predictive neural activity before the expected critical word, resembling the readiness potential. Moreover, word-induced P300 brain responses were found …

What makes human communication exceptional is the ability to grasp speaker’s intentions beyond what is said verbally. How the brain processes communicative functions is one of the central concerns of the neurobiology of language and pragmatics. Linguistic-pragmatic theories define these functions as speech acts, and various pragmatic traits characterise them at the levels of propositional content, action sequence structure, related commitments and social aspects. Here I discuss recent neurocognitive studies, which have shown that the use of identical linguistic signs in conveying different communicative functions elicits distinct and ultra-rapid neural responses. Interestingly, cortical areas show differential involvement underlying various pragmatic features related to theory-of-mind, emotion and action for specific speech acts expressed with the same utterances. Drawing on a neurocognitive model, I posit that …

Methods:Here, we used brain-constrained neural network of the frontotemporal and occipital cortices mimicking key neuroanatomical distinctions between human and non-human primates. Results: Our comparative analysis of models shows that the human model, characterized by denser inter-area connectivity, gives rise to larger cell assemblies with distinct semantic-specific topography compared to the less densely connected monkey models. Additionally, by simulating auditory word recognition, we observed the emergence of verbal working memory by longer neural reverberation activity in the human architecture compared to those of monkeys. Interestingly, these observations are consistent across different model types, including a basic meanfield and a spiking neural model.Conclusions:These findings shed light on the structural underpinnings of human-specific verbal working memory, a crucial feature for the acquisition of an expansive vocabulary.

Although teaching animals a few meaningful signs is usually time-consuming, children acquire words easily after only a few exposures, a phenomenon termed “fast-mapping.” Meanwhile, most neural network learning algorithms fail to achieve reliable information storage quickly, raising the question of whether a mechanistic explanation of fast-mapping is possible. Here, we applied brain-constrained neural models mimicking fronto-temporal-occipital regions to simulate key features of semantic associative learning. We compared networks (i) with prior encounters with phonological and conceptual knowledge, as claimed by fast-mapping theory, and (ii) without such prior knowledge. Fast-mapping simulations showed word-specific representations to emerge quickly after 1–10 learning events, whereas direct word learning showed word-meaning mappings only after 40–100 events. Furthermore, hub regions …

The offer of some cake can be declined by saying “I am on a diet” – an indirect reply. Here, we asked whether certain well-established psychological and conceptual features are linked to the (in)directness of speech acts – an issue unexplored so far. Subjects rated direct and indirect speech acts performed by the same critical linguistic forms in different dialogic contexts. We find that indirect replies were understood with less certainty, were less predictable by, less coherent with and less semantically similar to their context question. These effects were smaller when direct and indirect replies were matched for the type of speech acts for which they were used, compared to when they were not speech act matched. Crucially, all measured cognitive dimensions were strongly associated with each other. These findings suggest that indirectness goes hand-in-hand with a set of cognitive features, which should be taken into …

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 …

Comprehending conditional statements is fundamental for hypothetical reasoning about situations. However, the online comprehension of conditional statements containing different conditional connectives is still debated. We report two self-paced reading experiments on German conditionals presenting the conditional connectives wenn (‘if’) and nur wenn (‘only if’) in identical discourse contexts. In Experiment 1, participants read a conditional sentence followed by the confirmed antecedent p and the confirmed or negated consequent q. The final, critical sentence was presented word by word and contained a positive or negative quantifier (ein/kein ‘one/no’). Reading times of the two quantifiers did not differ between the two conditional connectives. In Experiment 2, presenting a negated antecedent, reading times for the critical positive quantifier (ein) did not differ between conditional connectives, while reading …

During conversations, speech prosody provides important clues about the speaker’s communicative intentions. In many languages, a rising vocal pitch at the end of a sentence typically expresses a question function, whereas a falling pitch suggests a statement. Here, the neurophysiological basis of intonation and speech act understanding were investigated with high-density electroencephalography (EEG) to determine whether prosodic features are reflected at the neurophysiological level. Already approximately 100 ms after the sentence-final word differing in prosody, questions, and statements expressed with the same sentences led to different neurophysiological activity recorded in the event-related potential. Interestingly, low-pass filtered sentences and acoustically matched nonvocal musical signals failed to show any neurophysiological dissociations, thus suggesting that the physical intonation alone …

Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in …

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 …

People normally know what they want to communicate before they start speaking. However, brain indicators of communication are typically observed only after speech act onset, and it is unclear when any anticipatory brain activity prior to speaking might first emerge, along with the communicative intentions it possibly reflects. Here, we investigated brain activity prior to the production of different speech act types, request and naming actions performed by uttering single words embedded into language games with a partner, similar to natural communication. Starting ca. 600 msec before speech onset, an event-related potential maximal at fronto-central electrodes, which resembled the Readiness Potential, was larger when preparing requests compared to naming actions. Analysis of the cortical sources of this anticipatory brain potential suggests a relatively stronger involvement of fronto-central motor regions for …

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational and corpus linguistics, analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and computational corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.

In the 1960s, optical magnification and the concept of microsurgery entered the operating room and revolutionized the effectiveness and safety of neurosurgical procedures. By utilizing natural anatomic pathways within the brain, the new conceptual foundation, combined with the technological advances in surgical microscopy, allowed manually invading the brain with fewer harmful effects. In recent years, the rapidly growing field of computational neuroscience has opened up new perspectives for exploring brain function at the micro and macro scales. Neural network simulations approximate the mechanistic prerequisites of even the highest cognitive functions by modeling their material basis in terms of neural systems. 1 Those systems are characterized by interactive, distributed, dynamic, and adaptable processing within neuron populations interlinked by local and long-distance connectivity within and across …

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Depending on the desired evaluation, these conditions, in turn, have to contain dozens of items to achieve a good signal to noise ratio. Designing those paradigms can be very time-consuming. Subsequently, a group of participants is stimulated with the new paradigm, while brain activity is assessed, e.g. with EEG/MEG. The measured data are then pre-processed and finally contrasted according to the different stimulus conditions. In this way, only a limited number of analyses and hypothesis tests can be performed, while for alternative or further analyses, completely new paradigms usually need to be designed. This traditional approach is necessarily data-limited, and the cost-benefit ratio is therefore rather poor. In contrast, in computational linguistics analyses are based on text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By combining the two approaches, we here present a unified approach of continuous natural speech and MEG to generate a corpus-like database of speech-evoked neuronal activity.