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[This corrects the article DOI: 10.1371/journal.pone.0154479.].

Creativity has traditionally been considered an ability exclusive to human beings. However, the rapid development of artificial intelligence (AI) has resulted in generative AI chatbots that can produce high-quality artworks, raising questions about the differences between human and machine creativity. In this study, we compared the creativity of humans (n = 256) with that of three current AI chatbots using the alternate uses task (AUT), which is the most used divergent thinking task. Participants were asked to generate uncommon and creative uses for everyday objects. On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceed those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to fully replicate or surpass with AI technology. The study provides insights into the relationship between human and machine creativity, which is related to important questions about the future of creative work in the age of AI.

Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science 1 , 2 . The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories 3 , 4 – 5 , gradient-boosted decision trees 6 , 7 , 8 – 9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains. Tabular Prior-data Fitted Network, a tabular foundation model, provides accurate predictions on small data and outperforms all previous methods on datasets with up to 10,000 samples by a wide margin.

The aim of this study was to investigate the effects of mental fatigue on physical activity, technical and decision-making performance during small-sided games. Nine sub-elite soccer players were enrolled in the study. The players performed two small-sided games on two occasions within a crossover experimental design. Before each game, they underwent a mental fatiguing task (Stroop task) and a control task (documentary watching) in a randomized, counterbalanced order. Players' physical activity, technical, and decision-making performance were obtained during small-sided games by GPS and video scouting. Results showed that distance in acceleration covered per min, negative passes, passing accuracy, and shot accuracy were likely impaired than control task after a mental fatiguing protocol. Decision-making performance of negative passes, passes accuracy, and dribbling accuracy resulted also likely decreased compared with control task. These findings demonstrated that mental fatigue impacted on technical, GPS-derived, and soccer-specific decision-making performance during SSG. In conclusion, avoiding cognitively demanding tasks before playing soccer-specific activities may be advisable to preserve players' physical activity, technical, and decision-making skills.

Mental imagery can be advantageous, unnecessary and even clinically disruptive. With methodological constraints now overcome, research has shown that visual imagery involves a network of brain areas from the frontal cortex to sensory areas, overlapping with the default mode network, and can function much like a weak version of afferent perception. Imagery vividness and strength range from completely absent (aphantasia) to photo-like (hyperphantasia). Both the anatomy and function of the primary visual cortex are related to visual imagery. The use of imagery as a tool has been linked to many compound cognitive processes and imagery plays both symptomatic and mechanistic roles in neurological and mental disorders and treatments.

Current models of mental effort in psychology, behavioral economics, and cognitive neuroscience typically suggest that exerting cognitive effort is aversive, and people avoid it whenever possible. The aim of this research was to challenge this view and show that people can learn to value and seek effort intrinsically. Our experiments tested the hypothesis that effort-contingent reward in a working-memory task will induce a preference for more demanding math tasks in a transfer phase, even though participants were aware that they would no longer receive any reward for task performance. In laboratory Experiment 1 (n = 121), we made reward directly contingent on mobilized cognitive effort as assessed via cardiovascular measures (β-adrenergic sympathetic activity) during the training task. Experiments 2a to 2e (n = 1,457) were conducted online to examine whether the effects of effort-contingent reward on subsequent demand seeking replicate and generalize to community samples. Taken together, the studies yielded reliable evidence that effort-contingent reward increased participants’ demand seeking and preference for the exertion of cognitive effort on the transfer task. Our findings provide evidence that people can learn to assign positive value to mental effort. The results challenge currently dominant theories of mental effort and provide evidence and an explanation for the positive effects of environments appreciating effort and individual growth on people’s evaluation of effort and their willingness to mobilize effort and approach challenging tasks.

Using a wireless single channel EEG device, we investigated the feasibility of using short-term frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65%-75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.

The organization of temporal information is critical for the encoding and retrieval of episodic memories. In the rodent hippocampus and entorhinal cortex, evidence accumulated over the last decade suggests that populations of “time cells” in the hippocampus encode temporal information. We identify time cells in humans using intracranial microelectrode recordings obtained from 27 human epilepsy patients who performed an episodic memory task. We show that time cell activity predicts the temporal organization of retrieved memory items. We also uncover evidence of ramping cell activity in humans, which represents a complementary type of temporal information. These findings establish a cellular mechanism for the representation of temporal information in the human brain needed to form episodic memories.

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, ) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, ) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging technique of growing interest as a tool for investigation of cortical activity. Due to the on-head placement of optodes, artifacts arising from head motion are relatively less severe than for functional magnetic resonance imaging (fMRI). However, it is still necessary to remove motion artifacts. We present a novel motion correction procedure based on robust regression, which effectively removes baseline shift and spike artifacts without the need for any user-supplied parameters. Our simulations show that this method yields better activation detection performance than 5 other current motion correction methods. In our empirical validation on a working memory task in a sample of children 7–15 years, our method produced stronger and more extensive activation than any of the other methods tested. The new motion correction method enhances the viability of fNIRS as a functional neuroimaging modality for use in populations not amenable to fMRI. [Display omitted] •We describe a novel motion correction method for fNIRS based on robust regression.•Simulations show performance superior to 5 other correction methods.•Experimental child data show stronger and more activation than other methods.