Study shows humans possess "remote touch," accurately detecting buried objects in sand by sensing subtle mechanical reflections. Humans outperformed a robot using an LSTM algorithm, suggesting remarkable tactile perception.
This Perspective outlines ways in which generative artificial intelligence aligns with and supports the core ideas of generative linguistics, and how generative linguistics can provide criteria to evaluate and improve neural language models.
This study identifies genetic factors associated with quantitative ability, using data from large-scale genome-wide association studies. The research reveals 53 SNPs linked to a latent trait, distinct from general intelligence, and implicates genes involved in neuron projection development and brain function.
A new study published in Psychophysiology used electroencephalography (EEG) and advanced modeling to track brain dynamics during metaphor generation, finding that specific sequences of brain states are associated with more creative metaphors. Early on, higher alpha-band synchronization predicted novelty, while later, alpha-band desynchronization became more prominent.
Despite widespread computer training, many individuals still struggle with basic computer tasks. This study suggests that cognitive abilities, specifically fluid intelligence, are a stronger predictor of computer skill than the amount of training received. The research indicates that inherent intelligence plays a more crucial role in computer literacy than previously believed, challenging the focus on simply providing more training.
Bee is an AI wearable that aims to solve memory retention issues by summarizing conversations and generating daily diary entries. However, the author's experience with the device was marred by inaccuracies and privacy concerns.
This study explores a more general notion of novelty, including novel combinations of existing elements, by introducing higher-order Heaps' laws and a new model (ERRWT) to reproduce the empirical properties of higher-order novelties.
The default mode network (DMN) plays a critical role in human creativity and the generation of ideas, according to recent studies. Researchers are exploring how manipulating this network can influence creativity.
A study found that by age three, children can understand the intentions behind others' actions through the mirror neuron system. This system allows them to mimic and anticipate actions, which is crucial for social development.
This paper describes the structure-mapping engine (SME), a program for studying analogical processing . SME has been built to explore Gentner's structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well . We review the structure-mapping theory and describe the design of the engine . We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial . typically bounded by O(N). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work.
