TL;DR
It is proposed that the best-performing LLMs can be used as models to understand human performance, response times, and the timing of Event-Related Potentials (ERPs) as recorded by electroencephalography (EEG) during the task.
A novel taxonomy of symbolic integration in LLMs is proposed, along with a roadmap to merge symbolic techniques with LLMs to enhance transparency and identifies commonly used benchmarks, recent advances and important research gaps.
This work investigates whether written narratives alone can evoke human temporal illusions, using a new benchmark of 6,684 narrative pairs spanning five illusions and finds that human readers prefer expected scenarios in only two of the five illusions, those where the manipulation is directly visible in text rather tha...
We compared four large language models (LLMs; ChatGPT, Grok, Gemini, and DeepSeek) with humans on tests of cognitive development, assessing relational integration, linguistic awareness, general and domain-specific reasoning, and cognitive self-awareness to specify how LLMs compare with humans along cognitive developmen...
Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortices, where the...
The TRANSCEND framework, a dual-route neurological model that begins with concept retrieval and leads to either verbal or AAC output, provides a foundation for future research that teases apart the neural underpinnings of motor, speech, and language aspects of AAC use.
Human language processing can be studied through both behavior and brain activity, yet it remains unclear whether these two data types reflect sensitivity to the same information. One influential view holds that both behavioral and neural responses are largely determined by processing effort, often estimated by word su...
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