The study explores the role of mixed selectivity in prefrontal cortex (PFC) neurons during complex cognitive tasks. Mixed selectivity refers to neurons that respond to multiple task-related aspects simultaneously, rather than being specialized for a single aspect. The research shows that mixed selectivity neurons encode distributed information about all task-relevant aspects, allowing for more flexible and efficient information processing. This type of selectivity provides a computational advantage over specialized responses, as it enables a wider range of input-output functions through readout neurons. The high-dimensional nature of mixed selectivity is linked to the ability of these neurons to represent complex information in a way that is suitable for local processing. The study analyzed neural activity in monkeys during a sequence memory task, revealing that mixed selectivity neurons can encode information about task type, object identity, and temporal order. Even when individual neurons lose their classical selectivity to a specific aspect, the population of neurons can still decode the task-relevant information. This suggests that information is distributed across the neural population, rather than being confined to individual neurons. The dimensionality of the neural representations was found to be predictive of animal behavior. In error trials, the dimensionality of the neural representations collapses, which is linked to the disruption of non-linear mixed selectivity. This collapse impairs the ability of downstream readout neurons to produce the correct response. The study also shows that high-dimensional representations are crucial for the generation of complex behaviors, as they allow for a wide range of input-output functions and support flexible adaptation to new tasks. The findings suggest that the focus of attention in neuroscience should shift from neurons with easily interpretable response tuning to the widely observed but rarely analyzed mixed selectivity neurons. These neurons play a critical role in complex cognitive tasks and their high-dimensional representations are essential for the flexibility and adaptability of neural circuits. The study highlights the importance of mixed selectivity in the functioning of the prefrontal cortex and its role in supporting complex cognitive functions.The study explores the role of mixed selectivity in prefrontal cortex (PFC) neurons during complex cognitive tasks. Mixed selectivity refers to neurons that respond to multiple task-related aspects simultaneously, rather than being specialized for a single aspect. The research shows that mixed selectivity neurons encode distributed information about all task-relevant aspects, allowing for more flexible and efficient information processing. This type of selectivity provides a computational advantage over specialized responses, as it enables a wider range of input-output functions through readout neurons. The high-dimensional nature of mixed selectivity is linked to the ability of these neurons to represent complex information in a way that is suitable for local processing. The study analyzed neural activity in monkeys during a sequence memory task, revealing that mixed selectivity neurons can encode information about task type, object identity, and temporal order. Even when individual neurons lose their classical selectivity to a specific aspect, the population of neurons can still decode the task-relevant information. This suggests that information is distributed across the neural population, rather than being confined to individual neurons. The dimensionality of the neural representations was found to be predictive of animal behavior. In error trials, the dimensionality of the neural representations collapses, which is linked to the disruption of non-linear mixed selectivity. This collapse impairs the ability of downstream readout neurons to produce the correct response. The study also shows that high-dimensional representations are crucial for the generation of complex behaviors, as they allow for a wide range of input-output functions and support flexible adaptation to new tasks. The findings suggest that the focus of attention in neuroscience should shift from neurons with easily interpretable response tuning to the widely observed but rarely analyzed mixed selectivity neurons. These neurons play a critical role in complex cognitive tasks and their high-dimensional representations are essential for the flexibility and adaptability of neural circuits. The study highlights the importance of mixed selectivity in the functioning of the prefrontal cortex and its role in supporting complex cognitive functions.