Can We Hear Temperature? New Study Says Yes
Researchers at Reichman University found that humans can detect water temperature through sound, indicating an implicit skill developed over time, with implications for multisensory integration and sensory technology advancements.
Read original article
Researchers at Reichman University have found that humans can detect water temperature through sound, a skill likely acquired through lifelong exposure to auditory cues. The study utilized machine learning techniques to analyze how people perceive thermal properties via auditory signals. By employing a deep neural network and classification algorithms, the researchers demonstrated that participants could consistently discern the temperature of water being poured, even when they were unaware of their ability to do so. This suggests an implicit learning process at work. The findings highlight the potential for multisensory integration, where the brain combines information from different sensory modalities to enhance perception. The study also raises questions about whether humans develop unique sensory maps in the brain for this experience, similar to those for vision and touch. The implications of this research could extend to advancements in sensory technologies and a deeper understanding of human perception.
- Humans can hear and discern water temperature through sound.
- The ability to detect temperature via auditory cues is likely an implicit skill developed over time.
- Machine learning was used to classify sounds of water at different temperatures accurately.
- The study suggests potential for multisensory integration in human perception.
- Future research may explore the development of unique sensory maps in the brain for temperature perception.
Related
Light can vaporize water without the need for heat
MIT researchers discovered light can evaporate water without heat, introducing the "photomolecular effect." This finding challenges traditional beliefs, offering potential in climate, desalination, and industrial processes. Companies show interest in utilizing this phenomenon.
Semantic encoding during language comprehension at single-cell resolution
Researchers studied how neurons in the human brain encode linguistic meaning during language comprehension. Neurons in the prefrontal cortex responded selectively to word meanings, reflecting semantic information based on sentence context. The study identified nine semantic domains where neurons selectively responded, shedding light on cortical organization of semantic representations.
Ultra-detailed brain map shows neurons that encode words' meaning
Scientists map neurons encoding word meanings in the prefrontal cortex. Neurons respond to related words, revealing brain's word categorization process. Research in Nature enhances understanding of language processing for future brain-computer interfaces.
Semantic encoding during language comprehension at single-cell resolution
Researchers studied how neurons in the human brain encode linguistic meaning during language comprehension. Neurons in the left prefrontal cortex responded selectively to word meanings, showing dynamic activities based on sentence contexts. The study reveals detailed semantic representations at the neuron scale, offering insights into real-time language processing.
AI method rapidly speeds predictions of materials' thermal properties
Researchers developed VGNN, a machine-learning framework predicting materials' thermal properties faster than other AI techniques. It aids energy systems and microelectronics by reducing waste heat, offering accurate predictions for various material properties.
5 commentsI also think I can hear atmospheric conditions in the area where I grew up. I mean temperature, humidity, and inversion layers in calm conditions. Obviously most people can recognize active weather sounds like wind, rain, or hail. And some can hear fog and snow, while others seem more oblivious to those details. This is an extension of that same thing, where the ambient sound environment is attenuated in a consistent way. It sounds different on a frosty morning versus a hot dry morning versus a cool damp morning with coastal marine layer influence.
I don't know how universal this is for careful observers. I can only do it well where I grew up, and I don't know if that is about length of exposure, age of exposure (more receptive to learning), or the local geography of hills and valleys and distant noise sources giving me more cues.
Related
Light can vaporize water without the need for heat
MIT researchers discovered light can evaporate water without heat, introducing the "photomolecular effect." This finding challenges traditional beliefs, offering potential in climate, desalination, and industrial processes. Companies show interest in utilizing this phenomenon.
Semantic encoding during language comprehension at single-cell resolution
Researchers studied how neurons in the human brain encode linguistic meaning during language comprehension. Neurons in the prefrontal cortex responded selectively to word meanings, reflecting semantic information based on sentence context. The study identified nine semantic domains where neurons selectively responded, shedding light on cortical organization of semantic representations.
Ultra-detailed brain map shows neurons that encode words' meaning
Scientists map neurons encoding word meanings in the prefrontal cortex. Neurons respond to related words, revealing brain's word categorization process. Research in Nature enhances understanding of language processing for future brain-computer interfaces.
Semantic encoding during language comprehension at single-cell resolution
Researchers studied how neurons in the human brain encode linguistic meaning during language comprehension. Neurons in the left prefrontal cortex responded selectively to word meanings, showing dynamic activities based on sentence contexts. The study reveals detailed semantic representations at the neuron scale, offering insights into real-time language processing.
AI method rapidly speeds predictions of materials' thermal properties
Researchers developed VGNN, a machine-learning framework predicting materials' thermal properties faster than other AI techniques. It aids energy systems and microelectronics by reducing waste heat, offering accurate predictions for various material properties.