Psycholinguistics ePosters

The sound structures that convey meaning in speech such as phonemes and words vary widely in their duration. As a consequence, integrating across absolute time (e.g., 100 ms) and sound structure (e.g., phonemes) reflect fundamentally distinct neural computations. Auditory and cognitive models have often cast neural…

It is beyond doubt that cognitive phenomena such as language and reasoning are the direct product of the activity of neurons and synapses. Making progress on this open question, often called the bridging problem, was identified by Richard Axel as the most important challenge in neuroscience today. The research…

Speech perception and production are deeply interconnected, yet the cellular mechanisms that link them are unknown. Here, we recorded large-scale single-neuron activity from the human brain while participants performed a delayed sentence repetition task, which included listening to a sentence, waiting a short delay,…

Humans can make an on-the-fly rapid inference when presented with a novel word, using prior knowledge and context. While this is an everyday occurrence necessary for effective communication, the mechanisms of rapid inference and learning during reading are not well understood. One reason for this complexity is that…

Humans possess the remarkable ability to extract discrete words and construct meaningful sentences from continuous streams of acoustic signals. While brain regions such as the superior temporal gyrus (STG) are known to be involved in speech recognition, how populations of neurons coordinate to enable this process…

Mirative markers, such as “surprisingly”, are linguistic elements that encode a violation of expectations (Delancey, 1997, 2012; Aikhenvald, 2012). Such markers are used for expectation management during communication, informing the listener about an upcoming unexpected message. Sensitivity to mirative markers relies…

We investigated whether transformer-based machine learning (ML) models could have similar behaviors to humans and predict electrophysiological data in audiovisual speech recognition tasks. Human audiovisual speech recognition follows principles of Bayes-optimal integration, with each modality weighted by its…

Odor naming is considered as a particularly challenging task. In free odor naming scenarios people often fail to respond with any linguistic label to certain odors they smell, resulting in what is called an omission (i.e., a lack of response). The cognitive demands and the nature of interactions between olfaction and…

Prior neuroimaging studies have shown that words associated with actions, like "grasp" and "kick," activate specific regions in the cortical motor system corresponding to the body parts involved in those actions. This activation follows the somatotopic mapping of these body parts. The variation in activation could…

Perceiving causality, anticipating events, choosing an action or understanding language - all facets of complex behavior are anchored in time and rely on representing, recognizing and expressing temporally patterned sequences. Established approaches in the cognitive sciences formulate these processes in terms that…

Our ability to perceive speech remains resilient to variations in voice, rate, and temporal interruptions. While inference models addressed the first two [1], the computational principles by which speech understanding remains impervious to temporal restructuring are largely unexplored. Previous studies indicated…

The human language system comprises a series of processing stages across multiple brain regions typically lateralized to the left cerebral hemisphere. In the event of stroke or other injury to these systems, neural circuits reorganize in an attempt to restore lost function. However, the neural processes and learned…

The ability to robustly recognize strings of letters, a cornerstone of reading, was observed in Baboons and Pigeons despite their lack of phonological and semantic knowledge. Here, we apply a comparative modeling approach to investigate the neuro-cognitive basis of Human, Baboon, and Pigeon orthographic decision…

Applying the principles of predictive coding to reading has successfully increased our understanding of the neuro-cognitive processes underlying the extraction of meaning from text. Central is integrating prior knowledge with the sensory input to highlight the informative parts of a visual percept, thus increasing the…

Research has identified structural differences between monolinguals and bilinguals in brain regions underlying language, and memory. This is consistent with the Declarative/Procedural language model, which proposes that hippocampal-based and basal ganglia-based networks subserve language learning and…

Introduction and Objective: The hypothesis that language-cognitive system interactions play a role in understanding domain-specific semantic terms is supported by a few neurophysiological studies, mainly on the motor cortex. Our goal was to assess if visuospatial coding affects the semantic comprehension of…

Human conversations are one of the most common methods for communication and social interactions. Engaging in a conversation, our brains are able to flexibly transit between speaker-listener alternations, and actively process language comprehension or production planning with ease. Previous work showed a series of…

Finding a low-dimensional manifold of neural signals is crucial for understanding neural computation and developing a robust brain-computer interface (BCI). Latent variable models have been proposed to find such manifolds, and proved to be successful in various cognitive and behavioral tasks. However, natural speaking…

Rhythms stretching across multiple interacting frequencies and spatial scales are ubiquitous in brain activity during complex cognitive tasks. Yet their functional significance is hotly debated between a structural epiphenomenon of brain activity and mechanisms implementing computations. Speech processing, with its…

Artificial neural networks (ANN) have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans. Here, we use two complementary approaches to ask how the models’ ability to capture human neural…

One of the most essential language skills that humans possess is the ability to correctly execute actions based on linguistic instructions. Here we use the latest advances in Natural Language Processing (NLP) to create a neural model of this cognitive ability. Models are trained on a set of 50 psychophysical tasks…

Learning a word, involves mapping low-level perceptual representations of world-referents to high-level semantic representations. It is widely held that conscious access to the visual world-referents is needed to learn the meaning of words. The aim of this study was to use fMRI-based decoded neurofeedback (DecNef) to…

Which genes contribute to making the human brain capable of language? We analyzed resting state fMRI and genetic single nucleotide polymorphism data of 30,660 people in the UK biobank and found 15 regions in the human DNA that relate to the brain language network. We link two genomic regions on chromosome 3 (EPHA3 and…

One of humans’ most astonishing cognitive feats is the dual ability to interpret linguistic instructions to perform novel tasks in very few practice trials and conversely produce a linguistic description for a task once it has been learned. In contrast, it typically takes thousands of trials for animals to learn even…