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.

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.

The ability to adapt and switch between tasks is a fundamental Empirical studies further established the prefrontal cortex aspect of cognitive flexibility, shaping decision-making (PFC) as a key region in task-switching, with experiments such and behavioral efficiency in dynamic environments. Taskswitching as the Wisconsin Card Sorting Test (WCST) demonstrating its has been widely studied across disciplines such as role in adaptive behavior [14]-[16]. Spiking Neural Networks psychology, cognitive neuroscience, and artificial intelligence (SNNs) have emerged as a biologically realistic approach to [1], [2]. While humans often shift between tasks seamlessly, modeling neural dynamics, particularly due to their ability to performance variations arise depending on prior experience, replicate synaptic plasticity mechanisms such as Spike Timing-task familiarity, and cognitive load. Understanding these processes Dependent Plasticity (STDP) [10], [17]. Prior studies have requires computational models that can capture the successfully applied SNNs to pattern recognition and classification underlying neural mechanisms driving adaptive control and tasks [18] and have modeled sensory processing systems decision-making. Empirical studies have identified increased like the mammalian olfactory system [19]. These findings neural activity in the cognitive control network, particularly in establish a computational foundation for implementing taskswitching the prefrontal cortex (PFC), when engaging in task-switching models with biologically grounded learning dynamics.

Flexible cognition demands discovering hidden rules to quickly adapt stimulus-response mappings. Standard neural networks struggle in such tasks requiring rapid, context-driven remapping. Recently, Hummos (2023) introduced a fast-and-slow learning algorithm to mitigate this shortcoming, but its scalability to complex, image-computable tasks was unclear. Here, we propose the Wisconsin Neural Network (WiNN), which extends Hummos' fast-and-slow learning to image-computable tasks demanding flexible rule-based behavior. WiNN employs a pretrained convolutional neural network for vision, coupled with an adjustable "context state" that guides attention to relevant features. If WiNN produces an incorrect response, it first iteratively updates its context state to refocus attention on task-relevant cues, then performs minimal parameter updates to attention and readout layers. This strategy preserves generalizable representations in the sensory and attention networks, reducing catastrophic forgetting. We evaluate WiNN on an image-based extension of the Wisconsin Card Sorting Task, revealing several markers of cognitive flexibility: (i) WiNN autonomously infers underlying rules, (ii) requires fewer examples to do so than control models reliant on large-scale parameter updates, (iii) can perform context-based rule inference solely via context-state adjustments-further enhanced by slow updates of attention and readout parameters, and (iv) generalizes to unseen compositional rules through context-state updates alone. By blending fast context inference with targeted attentional guidance, WiNN achieves "sparks" of flexibility. This approach offers a path toward context-sensitive models that retain knowledge while rapidly adapting to complex, rule-based tasks.

Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wisdom of crowds" within a single individual, allowing them to "think with many minds." While mental simulation enables imagined deliberation, cognitive constraints limit its effectiveness. We propose synthetic deliberation, a Large Language Model (LLM)-based method that simulates discourse between agents embodying diverse perspectives, as a solution. Using a custom GPT-based model, we showcase its benefits: concurrent processing of multiple viewpoints without cognitive degradation, parallel exploration of perspectives, and precise control over viewpoint synthesis. By externalizing the deliberative process and distributing cognitive labor between parallel search and integration, synthetic deliberation transcends mental simulation's limitations. This approach shows promise for strategic planning, policymaking, and conflict resolution.

This study aimed to analyze brain activity during various STEM activities, exploring the feasibility of classifying between different tasks. EEG brain data from twenty subjects engaged in five cognitive tasks were collected and segmented into 4-second clips. Power spectral densities of brain frequency waves were then analyzed. Testing different k-intervals with XGBoost, Random Forest, and Bagging Classifier revealed that Random Forest performed best, achieving a testing accuracy of 91.07% at an interval size of two. When utilizing all four EEG channels, cognitive flexibility was most recognizable. Task-specific classification accuracy showed the right frontal lobe excelled in mathematical processing and planning, the left frontal lobe in cognitive flexibility and mental flexibility, and the left temporoparietal lobe in connections. Notably, numerous connections between frontal and temporoparietal lobes were observed during STEM activities. This study contributes to a deeper understanding of implementing machine learning in analyzing brain activity and sheds light on the brain's mechanisms.

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.

New research shows that monkeys outperform humans in a test meant to measure cognitive flexibility. The experiment, conducted by a team of psychology researchers at George State University, pitted humans against capuchin and rhesus macaque monkeys. Both groups were asked to interact with a touchscreen computer that featured four squares with different patterns in them. When subjects pressed on the squares in the right sequence, a triangle would appear in place of one of the squares, and when pressed the triangle would produce a reward. For the monkeys, the reward was a banana pellet, and for humans it was either a short audio jingle or a sign of points being tallied up.

Last December, entrepreneur Amin Khoury gave Northeastern University's College of Computer Science a $50 million gift. The money was slated for programs that would help new graduates compete in a marketplace increasingly dominated by artificial intelligence and automation. The university's press release touted, "As the global economy adapts to the influence of artificial intelligence … Northeastern is empowering humans to be agile learners, thinkers, and creators, beyond the capacity of any machine." The school, like quite a few others, is reimagining itself as an incubator for skills that are difficult to automate: creativity, imagination, mental flexibility. Indeed, Joseph Aoun, Northeastern's president, literally wrote the book on this.

In 1848, the 25-year-old Phineas Gage was working on a railroad in Vermont, packing explosive powder into a hole with an iron tamper. Unexpectedly, the powder exploded, sending the tamper backwards through Gage's skull and brain. That he survived is a miracle, but astonishingly he even seemed capable of functioning effectively, maintaining normal memory, speech, and motor skills. Those that knew him, however, thought he was anything but the same, with friends remarking he was "no longer Gage." "…his equilibrium, or balance, so to speak, between his intellectual faculties and animal propensities seems to have been destroyed. He is fitful, irreverent, indulging in the grossest profanity (which was not previously his custom), manifesting but little deference for his fellows, impatient of restraint or advice when it conflicts with his desires."