Nicoletta Lanese is the health channel editor at Live Science and was previously a news editor and staff writer at the site. She holds a graduate certificate in science communication from UC Santa Cruz and degrees in neuroscience and dance from the University of Florida.
The research, published Wednesday in the journal Nature, signals a shift in a decades-long debate in cognitive science — a field that explores what kind of computer would best represent the human mind. Since the 1980s, a subset of cognitive scientists have argued that neural networks, a type of artificial intelligence , aren't viable models of the mind because their architecture fails to capture a key feature of how humans think.
In the new study, Lake and study co-author Marco Baroni of Pompeu Fabra University in Barcelona tested both AI models and human volunteers using a made-up language with words like"dax" and"wif." These words either corresponded with colored dots, or with a function that somehow manipulated those dots' order in a sequence. Thus, the word sequences determined the order in which the colored dots appeared.
The human participants produced the correct dot sequences about 80% of the time. When they failed, they made consistent types of errors, such as thinking a word represented a single dot rather than a function that shuffled the whole dot sequence. The authors also pitted MLC against two neural network-based models from OpenAI, the company behind ChatGPT, and found both MLC and humans performed far better than OpenAI models on the dots test. MLC also aced additional tasks, which involved interpreting written instructions and the meanings of sentences.
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