A signature of human cognitive control is conflict adaptation: improved performance on a high-conflict trial following another high-conflict trial. This phenomenon offers an account for how cognitive control, a scarce resource, is recruited. Using a sequential Stroop task, we find that 12 of 13 vision-language models (VLMs) tested exhibit behavior consistent with conflict adaptation, with the lone exception likely reflecting a ceiling effect. To understand the representational basis of this behavior, we use sparse autoencoders (SAEs) to identify task-relevant supernodes in InternVL 3.5 4B. Partially overlapping supernodes emerge for text and color in both early and late layers, and their relative sizes mirror the automaticity asymmetry between reading and color naming in humans. We further isolate a conflict-modulated supernode in layers 24-25 whose ablation significantly increases Stroop errors while minimally affecting congruent trials.

The rapid growth of the drone industry, particularly in the use of small unmanned aerial systems (sUAS) and unmanned aerial vehicles (UAVs), requires the development of advanced training protocols for remote pilots. Remote pilots must develop a combination of technical and cognitive skills to manage the complexities of modern drone operations. This paper explores the integration of neurotechnology, specifically auricular vagus nerve stimulation (aVNS), as a method to enhance remote pilot training and performance. The scientific literature shows aVNS can safely improve cognitive functions such as attention, learning, and memory. It has also been shown useful to manage stress responses. For safe and efficient sUAS/UAV operation, it is essential for pilots to maintain high levels of vigilance and decision-making under pressure. By modulating sympathetic stress and cortical arousal, aVNS can prime cognitive faculties before training, help maintain focus during training and improve stress recovery post-training. Furthermore, aVNS has demonstrated the potential to enhance multitasking and cognitive control. This may help remote pilots during complex sUAS operations by potentially reducing the risk of impulsive decision-making or cognitive errors. This paper advocates for the inclusion of aVNS in remote pilot training programs by proposing that it can provide significant benefits in improving cognitive readiness, skill and knowledge acquisition, as well as operational safety and efficiency. Future research should focus on optimizing aVNS protocols for drone pilots while assessing long-term benefits to industrial safety and workforce readiness in real-world scenarios.

Summary: Ocular drift, or tiny eye movements that seem random can be influenced by prior knowledge of an expected visual target, researchers report. A very subtle and seemingly random type of eye movement called ocular drift can be influenced by prior knowledge of the expected visual target, suggesting a surprising level of cognitive control over the eyes, according to a study led by Weill Cornell Medicine neuroscientists. The discovery, described Apr. 3 in Current Biology, adds to the scientific understanding of how vision--far from being a mere absorption of incoming signals from the retina--is controlled and directed by cognitive processes. "These eye movements are so tiny that we're not even conscious of them, and yet our brains somehow can use the knowledge of the visual task to control them," says study lead author Dr. Yen-Chu Lin, who carried out the work as a Fred Plum Fellow in Systems Neurology and Neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine. Dr. Lin works in the laboratory of study senior author Dr. Jonathan Victor, the Fred Plum Professor of Neurology at Weill Cornell Medicine. The study involved a close collaboration with the laboratory of Dr. Michele Rucci, professor of brain and cognitive sciences and neuroscience at the University of Rochester.

We are interested in the mechanisms by which individuals monitor and adjust their performance of simple cognitive tasks. We model a speeded discrimination task in which individuals are asked to classify a sequence of stimuli (Jones & Braver, 2001). Response conflict arises when one stimulus class is infrequent relative to another, resulting in more errors and slower reaction times for the infrequent class. How do control pro- cesses modulate behavior based on the relative class frequencies? We explain performance from a rational perspective that casts the goal of individuals as minimizing a cost that depends both on error rate and re- action time.

Cognitive control refers to the flexible deployment of memory and attention in response to task demands and current goals. Control is often studied experimentally by presenting sequences of stimuli, some demanding a response, and others modulating the stimulus-response mapping. In these tasks, participants must maintain information about the current stimulus-response mapping in working memory. Prominent theories of cognitive control use recurrent neural nets to implement working memory, and optimize memory utilization via reinforcement learning. We present a novel perspective on cognitive control in which working memory representations are intrinsically probabilistic, and control operations that maintain and update working memory are dynamically determined via probabilistic inference.

Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.

While French is one of the most popular GCSEs in the UK, many Brits are nervous when it comes to using their language skills later in life. But a new suggests there's nothing to fear - even if it has been decades since you last studied a foreign language. Researchers from the University of York have shown that people tested on foreign languages 50 years after they last sat any exam perform just as well as recent students. 'We often say if you don't use a language, you will lose it, but this doesn't seem to be the case,' said Professor Monika Schmid, Head of the University of York's Department of Language and Linguistics. During recent tests, experts from Abertay University in Dundee, found that speaking more than one language didn't have any cognitive benefit.

In a new study, researchers merged artificial intelligence with targeted electrical brain stimulation to show that it is possible to improve specific human brain functions related to self-control and mental flexibility. The findings come from a human study conducted at Massachusetts General Hospital in Boston among 12 patients undergoing brain surgery for epilepsy--a procedure that places hundreds of tiny electrodes throughout the brain to record its activity and identify where seizures originate. The study is the first to show that a specific human mental function linked to mental illness can be reliably enhanced using precisely targeted electrical stimulation and that there are specific sub-parts of the internal capsule brain structure that are particularly effective for cognitive enhancement. Lastly, they show that a closed-loop algorithm used as a controller was twice as effective as stimulating at random times. This work is published in Nature Biomedical Engineering in the article, "Closed-loop enhancement and neural decoding of cognitive control in humans."

Cognitive control, the ability of a system to adapt to the demands of a task, is an integral part of cognition. A widely accepted fact about cognitive control is that it is context-sensitive: Adults and children alike infer information about a task's demands from contextual cues and use these inferences to learn from ambiguous cues. However, the precise way in which people use contextual cues to guide adaptation to a new task remains poorly understood. This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation. We begin by identifying an essential difference between human learning and current approaches to meta-learning: In contrast to humans, existing meta-learning algorithms do not make use of task-specific contextual cues but instead rely exclusively on online feedback in the form of task-specific labels or rewards. To remedy this, we introduce a framework for using contextual information about a task to guide the initialization of task-specific models before adaptation to online feedback. We show how context-conditioned meta-learning can capture human behavior in a cognitive task and how it can be scaled to improve the speed of learning in various settings, including few-shot classification and low-sample reinforcement learning. Our work demonstrates that guiding meta-learning with task information can capture complex, human-like behavior, thereby deepening our understanding of cognitive control.

Cognitive control refers to the flexible deployment of memory and attention in response to task demands and current goals. Control is often studied experimentally by presenting sequences of stimuli, some demanding a response, and others modulating the stimulus-response mapping. In these tasks, participants must maintain information about the current stimulus-response mapping in working memory. Prominent theories of cognitive control use recurrent neural nets to implement working memory, and optimize memory utilization via reinforcement learning. We present a novel perspective on cognitive control in which working memory representations are intrinsically probabilistic, and control operations that maintain and update working memory are dynamically determined via probabilistic inference.