{"id":73710,"date":"2024-08-06T00:25:35","date_gmt":"2024-08-06T04:25:35","guid":{"rendered":"https:\/\/world.edu\/?p=73710"},"modified":"2024-08-06T00:25:35","modified_gmt":"2024-08-06T04:25:35","slug":"ais-encode-language-like-brains-do-%e2%88%92-opening-a-window-on-human-conversations","status":"publish","type":"post","link":"https:\/\/world.edu\/ais-encode-language-like-brains-do-%e2%88%92-opening-a-window-on-human-conversations\/","title":{"rendered":"AIs encode language like brains do \u2212 opening a window on human conversations"},"content":{"rendered":"

\"\"<\/p>\n

Language enables people to transmit thoughts to each other because each person\u2019s brain responds similarly to the meaning of words. In our\u00a0newly published research<\/a>, my\u00a0colleagues<\/a>\u00a0and I<\/a>\u00a0developed a framework to model the brain activity of speakers as they engaged in face-to-face conversations.<\/p>\n

We recorded the electrical activity of two people\u2019s brains as they engaged in unscripted conversations.\u00a0Previous research has shown<\/a>\u00a0that when two people converse, their brain activity becomes coupled, or aligned, and that the degree of neural coupling is associated with better understanding of the speaker\u2019s message.<\/p>\n

A neural code refers to particular patterns of brain activity associated with distinct words in their contexts. We found that the speakers\u2019 brains are aligned on a shared neural code. Importantly, the brain\u2019s neural code resembled the artificial neural code of large language models, or LLMs.<\/p>\n

The neural patterns of words<\/h4>\n

A large language model is a machine learning program that can generate text by predicting what words most likely follow others. Large language models excel at learning the\u00a0structure of language<\/a>, generating humanlike text and holding conversations. They can even pass the\u00a0Turing test<\/a>, making it difficult for someone to discern whether they are interacting with a machine or a human. Like humans, LLMs learn how to speak by reading or listening to text\u00a0produced by other humans<\/a>.<\/p>\n

By giving the LLM a transcript of the conversation, we were able to extract its \u201cneural activations,\u201d or how it translates words into numbers, as it \u201creads\u201d the script. Then, we correlated the speaker\u2019s brain activity with both the LLM\u2019s activations and with the listener\u2019s brain activity. We found that the LLM\u2019s activations could predict the speaker and listener\u2019s shared brain activity.<\/p>\n

To be able to understand each other, people have a shared agreement on the grammatical rules and the\u00a0meaning of words in context<\/a>. For instance, we know to use the past tense form of a verb to talk about past actions, as in the sentence: \u201cHe visited the museum yesterday.\u201d Additionally, we intuitively understand that the same word can have different meanings in different situations. For instance, the word cold in the sentence \u201cyou are cold as ice\u201d can refer either to one\u2019s body temperature or personality trait, depending on the context. Due to the complexity and richness of natural language, until the recent success of large language models, we lacked a precise mathematical model to describe it.<\/p>\n

Our study found that large language models can predict how linguistic information is encoded in the human brain, providing a new tool to interpret human brain activity. The similarity between the human brain\u2019s and the large language model\u2019s linguistic code has enabled us, for the first time, to track how information in the speaker\u2019s brain is encoded into words and transferred, word by word, to the listener\u2019s brain during face-to-face conversations. For example, we found that brain activity associated with the meaning of a word emerges in the speaker\u2019s brain before articulating a word, and the same activity rapidly reemerges in the listener\u2019s brain after hearing the word.<\/p>\n

Powerful new tool<\/strong><\/h4>\n

Our study has provided insights into the neural code for language processing in the human brain and how both humans and machines can use this code to communicate. We found that large language models were better able to predict shared brain activity compared with different features of language, such as syntax, or the order in which words connect to form phrases and sentences. This is partly due to the LLM\u2019s ability to incorporate the contextual meaning of words, as well as integrate multiple levels of the linguistic hierarchy into one model: from words to sentences to conceptual meaning. This suggests important\u00a0similarities<\/a>\u00a0between the brain and artificial neural networks.<\/p>\n

An important aspect of our research is using everyday recordings of natural conversations to ensure that our findings capture the brain\u2019s processing in real life. This is called\u00a0ecological validity<\/a>. In contrast to experiments in which participants are told what to say, we relinquish control of the study and let the participants converse as naturally as possible. This loss of control makes it difficult to analyze the data because each conversation is unique and involves two interacting individuals who are spontaneously speaking. Our ability to model neural activity as people engage in everyday conversations attests to the power of large language models.<\/p>\n

Other dimensions<\/strong><\/h4>\n

Now that we\u2019ve developed a framework to assess the shared neural code between brains during everyday conversations, we\u2019re interested in what factors drive or inhibit this coupling. For example, does linguistic coupling increase if a listener better understands the speaker\u2019s intent? Or perhaps complex language, like jargon, may reduce neural coupling.<\/p>\n

Another factor that can influence linguistic coupling may be the relationship between the speakers. For example, you may be able to convey a lot of information with a few words to a good friend but not to a stranger. Or you may be better neurally coupled to\u00a0political allies rather than rivals<\/a>. This is because differences in the way we use words across groups may make it easier to align and be coupled with people within rather than outside our social groups.<\/p>\n

Author Bio:<\/strong> Zaid Zada is a Ph.D. Candidate in Psychology at Princeton University<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Language enables people to transmit thoughts to each other because each person\u2019s brain responds similarly to the meaning of words. In our\u00a0newly published research, my\u00a0colleagues\u00a0and I\u00a0developed a framework to model the brain activity of speakers as they engaged in face-to-face conversations. We recorded the electrical activity of two people\u2019s brains as they engaged in unscripted […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[434804],"tags":[435050],"coauthors":[11733],"class_list":["post-73710","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-human-conversations"],"acf":[],"yoast_head":"\nAIs encode language like brains do \u2212 opening a window on human conversations - World leading higher education information and services<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/world.edu\/ais-encode-language-like-brains-do-\u2212-opening-a-window-on-human-conversations\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"AIs encode language like brains do \u2212 opening a window on human conversations - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"Language enables people to transmit thoughts to each other because each person\u2019s brain responds similarly to the meaning of words. 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