Speech prosody is essential for conveying communicative intentions. Although neurophysiological data has shown that communicative functions conveyed through prosody are processed rapidly in the human brain, it is still unclear when and to what extent prosodic information is needed for the conscious speech act recognition as speech unfolds. Using a gating paradigm, we investigated the point at which listeners recognise the function of identical Italian sentences – whether they express a question or statement – based on vocal intonation. Comparing cross-spliced and natural sentences, we found that, rising or falling nuclear accentual movement on the sentence-final word seems to be the primary cue for recognition, with questions identified slightly later than statements. Furthermore, we discuss the limitations of splicing techniques in filtering out natural prosodic variations, the presence of a “statement bias” in perceiving incomplete sentences, along with a visual examination of interindividual responses. These findings offer valuable insights into the timing of conscious recognition of different communicative functions based on speech prosody.

Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of abstract concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and abstract meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, abstract concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms.

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 blindness. This neural recruitment is fundamentally governed by the biological principles of neural circuit expansion and the absence of uncorrelated sensory input. Critically, the degree of these changes is constrained by the availability of neural matter previously allocated to visual experiences, as in the case of late blindness. Moreover, we here shed light on the implication of visual deprivation on the neural underpinnings of verbal working memory, revealing longer reverberatory neural activity in ‘blind models’ as compared to the sighted ones. These findings provide a better understanding of the interplay between visual deprivations, neuroplasticity, language processing and verbal working memory.

Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model-internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of ‘shared’ neurons responding to all category instances – the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances (‘ proper names ’) or symbols related to classes of instances sharing common features (‘ category terms ’). Learning category terms remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper-name learning prevented substantial reduction of instance-specific neurons and blocked the overgrowth of category-general cells. Representational Similarity Analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with proper names and without any symbols. These network-based mechanisms for concepts, proper names and category terms explain why and how symbol learning changes object perception and memory, as revealed by experimental studies. Significance Statement How do verbal symbols for specific individuals ( Micky Mouse ) and object categories ( house mouse ) causally influence conceptual representation and processing? Category terms and proper names have been shown to respectively promote category formation and instance learning, potentially by respectively directing attention to category-critical and object-specific features. Yet the mechanisms underlying these observations at the neural circuit level remained unknown. Using a mathematically precise deep neural network model constrained by properties of the human brain, we show category-term learning strengthens and solidifies conceptual representations, whereas proper names support object-specific mechanisms. Based on network-internal mechanisms and unsupervised correlation-based learning, this work offers neurobiological explanations for causal effects of symbol learning on concept formation, category building and instance representation in the human brain.