Human decision-making belongs to the foundation of our society and civilization, but we are on the verge of a future where much of it will be delegated to artificial intelligence. The arrival of Large Language Models (LLMs) has transformed the nature and scope of AI-supported decision-making; however, the process by which they learn to make decisions, compared to humans, remains poorly understood. In this study, we examined the decision-making behavior of five leading LLMs across three core dimensions of real-world decision-making: uncertainty, risk, and set-shifting. Using three well-established experimental psychology tasks designed to probe these dimensions, we benchmarked LLMs against 360 newly recruited human participants. Across all tasks, LLMs often outperformed humans, approaching near-optimal performance. Moreover, the processes underlying their decisions diverged fundamentally from those of humans. On the one hand, our finding demonstrates the ability of LLMs to manage uncertainty, calibrate risk, and adapt to changes. On the other hand, this disparity highlights the risks of relying on them as substitutes for human judgment, calling for further inquiry.

In recent years, the application of deep learning in task functional Magnetic Resonance Imaging (tfMRI) decoding has led to significant advancements. However, most studies remain constrained by assumption of temporal stationarity in neural activity, resulting in predominantly block - wise a nalysis with limited temporal resolution on the order of tens of seconds. This limitation restricts the ability to decode cognitive functions in detail . To address these limitations, this study proposes a deep neural networ k designed for volume - wise identification of task states within tfMRI data, thereby overcoming the constraints of conventional methods. Evaluated on Human Connectome Project (HCP) motor and gambling tfMRI datasets, the model achieved impressive mean accuracy rates of 94.0 8.2% and 79.6 7.1%, respectively. These results demonstrate a substantial enhancement in temporal resolution, enabling more detailed exploration of cognitive processes. The study further employs visualization algorithms to investigate dyna mic brain mappings during different tasks, marking a significant step forward in deep learning - based frame - level tfMRI decoding. This approach offers new methodologies and tools for examining dynamic changes in brain activities and understanding the underlying cognitive mechanisms.