Working memory, or the ability to hold and manipulate information in the mind, is a critical component of human intelligence and executive functioning. It is correlated with performance on various cognitive tasks, including measures of fluid intelligence, which encompasses reasoning and problem solving. We use a comprehensive set of classic working memory tasks to estimate the working memory capacity of large language models (LLMs). We find that in most cases, LLMs exceed normative human scores. However, we do not find that the increased capacity of working memory is associated with higher performance on other executive functioning tasks or problem solving benchmarks. These results suggest that LLMs may have deficits in attentional control and cognitive flexibility, which result in difficulties with inhibiting automatic responses and adapting to shifting information. Our findings suggest that current reasoning models have mixed results in compensating for these deficits.

Tens of millions of Americans engage in a taboo habit that scientists have warned could pose risks to cognitive health. A recent study found that regularly viewing pornography can immediately reduce a person's performance on tasks requiring attention and cognitive control right after exposure to explicit content. Impaired executive function and reduced cognitive performance are known early markers of cognitive decline, a precursor to dementia. Research has suggested that if such effects are sustained or repeated over time, they could potentially contribute to long-term health risks. In the study, college students watched a 10-minute internet pornographic video chosen for its high viewership.

-- Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiological processes during a night's sleep. T o address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed to process multi-modal polysomno-graphic data. This model integrates one-channel ECG and EEG signals along with extracted features, including EEG power bands and heart rate variability parameters, to capture complementary information across modalities. A thorough evaluation of the CogPSGFormer architecture was conducted to optimize the processing of extended sleep signals and identify the most effective configuration. The proposed framework was evaluated on 817 individuals from the ST AGES dataset using cross-validation. The model achieved 80.3% accuracy in classifying individuals into low vs. high cognitive performance groups on unseen data based on Penn Conditional Exclusion T est (PCET) scores. I. INTRODUCTION Cognitive decline linked to changes in sleep characteristics--such as variations in sleep architecture, quality, and duration--represents a significant global health challenge.

Cognitive flexibility has been extensively studied in human cognition but remains relatively unexplored in the context of Visual Large Language Models (VLLMs). This study assesses the cognitive flexibility of state-of-the-art VLLMs (GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet) using the Wisconsin Card Sorting Test (WCST), a classic measure of set-shifting ability. Our results reveal that VLLMs achieve or surpass human-level set-shifting capabilities under chain-of-thought prompting with text-based inputs. However, their abilities are highly influenced by both input modality and prompting strategy. In addition, we find that through role-playing, VLLMs can simulate various functional deficits aligned with patients having impairments in cognitive flexibility, suggesting that VLLMs may possess a cognitive architecture, at least regarding the ability of set-shifting, similar to the brain. This study reveals the fact that VLLMs have already approached the human level on a key component underlying our higher cognition, and highlights the potential to use them to emulate complex brain processes.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. There's nothing like a nostalgic song to transport you back to a special time and place -- and now a new study has shown that music could help protect those memories for a lifetime. Researchers at the University of Exeter discovered that people who "engage in music" over the course of their lives tend to have improved memory and better overall brain health as they age, according to a press release. The findings were published in the International Journal of Geriatric Psychiatry.

Artificial Intelligence (AI) has been rapidly advancing and has demonstrated its ability to perform a wide range of cognitive tasks, including language processing, visual recognition, and decision-making. Part of this progress is due to LLMs (Large Language Models) like those of the GPT (Generative Pre-Trained Transformers) family. These models are capable of exhibiting behavior that can be perceived as intelligent. Most authors in Neuropsychology consider intelligent behavior to depend on a number of overarching skills, or Executive Functions (EFs), which rely on the correct functioning of neural networks in the frontal lobes, and have developed a series of tests to evaluate them. In this work, we raise the question of whether LLMs are developing executive functions similar to those of humans as part of their learning, and we evaluate the planning function and working memory of GPT using the popular Towers of Hanoi method. Additionally, we introduce a new variant of the classical method in order to avoid that the solutions are found in the LLM training data (dataleakeage). Preliminary results show that LLMs generates near-optimal solutions in Towers of Hanoi related tasks, adheres to task constraints, and exhibits rapid planning capabilities and efficient working memory usage, indicating a potential development of executive functions. However, these abilities are quite limited and worse than well-trained humans when the tasks are not known and are not part of the training data.

Having regular exercise while studying can help boost pupils' exam grades in both French maths, according to researchers, who say it develops their cognitive skills. To understand the influence fitness has on learning, experts from the University of Geneva, Switzerland, tested education and activity levels of 193 pupils aged 8 to 12. By combining data on fitness, and exam results, they found a link between better cardiorespiratory fitness and higher marks in mathematics and French grammar. However, the team say the link was indirect, with physical fitness improving executive functions and cognitive flexibility, which in turn helps with subjects that rely on specific and structured answers, such as mathematics. The researchers say schools and administrators should consider the importance of exercise and movement when planning timetables and allocating budgets.

Many have noted that the big contenders in the last two American presidential elections were well into their 70s, raising questions of the mental capacity, going forward, of these potential leaders. "Starting after middle age, say around 60 or so, memory and other abilities decline," says Dilip Jeste, professor of psychiatry and neuroscience at UC San Diego and director of the UCSD Center for Healthy Aging. But what actually declines--and what abilities might improve, as well as when, how, and at what speed--is a complex issue. It turns out, according to a new study in Nature Human Behavior, that many things improve with age, including some cognitive aspects that had previously been thought to get worse. John Verssimo, of the University of Lisbon, and his colleagues, looked at a large sample of people between the ages of 58 and 98 and measured their performance on a broad range of cognitive tasks to get a more detailed picture of cognitive aging.

Mice are extremely social animals, but researchers have programmed them to form instant social bonds with a single beam of light. Scientists at Northwestern University designed tiny, wireless brain implant that activate single neurons to force mice to socially interact with one another in real time - and when stimulation is desynchronized, socializing stops. This was done by targeting a set of neurons in a brain region related to higher order executive function, which helps facilitate relationships, causing them to increase the frequency and duration of social interactions. The device used on the mice is smaller than a human fingertip, thin and flexible, but the breakthrough is its wireless nature that allows the mice to look normal and behave in a realistic environment. Previous research using optogenetics required fiberoptic wires, which restrained mouse movements and caused them to become entangled during social interactions or in complex environments.

Using non-invasive, dry-electroencephalography (EEG) and machine-learning computer algorithms, researchers were able to distinguish between Parkinson's disease patients with and without mild cognitive impairments with 80% accuracy, early results of a study found. Follow-up assessments will be conducted after 12 months to validate the method's ability to predict cognitive impairment in this patient population. The results were presented in a poster, "The Identification of Mild Cognitive Impairment in Parkinson's disease using EEG and Machine Learning, "at the Alzheimer's Association International Conference 2020. The poster abstract was published in the journal Alzheimer's & Dementia. Cognitive impairment is a common symptom of Parkinson's disease characterized by difficulties in executive function, attention, vision, word-finding, and problems with learning and remembering information.