For some psychologists, it's becoming more common to use AI systems to replace human thinking in research. That's a very risky choice based on misconceptions, warn Iris van Rooij and Olivia Guest in a new paper appearing today in Current Directions in Psychological Science . ‘Research based on artificial intelligence models will never be an adequate substitute for understanding and replicating human thought.’
The technology companies behind the latest popular artificial intelligence models often make big promises, such as claiming to have developed artificial minds that can rival human brains. ‘That's a promise that can be very alluring to psychologists’, explains Iris van Rooij, professor of computational cognitive science at Radboud University. ‘It's easy to think you can do psychological experiments with artificial participants, but you simply can’t automate science.’
‘Following the replication crisis [where it was found that many results in peer-reviewed studies in psychology could not be reproduced], some researchers are pushing for a more methodological, statistical approach to psychology research, something that can be proceduralised and maybe even automated’, says Olivia Guest, associate professor of computational cognitive science at Radboud University. ‘But the whole point of doing science is to produce knowledge. That’s why it’s so important to emphasise AI can never meaningfully replicate human cognition.’
In their paper, Van Rooij and Guest point out three traps to avoid for fellow researchers in their field. ‘First, AI systems are not minds. As we’ve discussed in our previous research, AI systems can never be sufficiently trained to reach human-level cognition. Even if tech companies continue to use astronomical levels of resources to train them, they won’t even be able to get close’, says van Rooij. ‘At best, you’ll be able to produce a decoy: something that may look impressive and trick you into thinking it can act like a human, but in no way a replacement for the real thing.’
Guest adds: ‘Furthermore, these systems are based on predicting, that’s not a basis for actual new theories. Just because an AI system can predict what a human would say or do, doesn’t mean it can explain what humans do. Compare it for example to the tides. Long before humans understood what caused the tides, we created tide tables to predict when ebb and flood would happen. But no one would argue that those tables explain the tides – yet that’s what some people are claiming AI models do.’
And finally, the researchers warn that it’s a fallacy to think that cognitive science can be automated. Guest: ‘Doing theoretical work is very difficult, and sometimes researchers might fall for instant gratification, or maybe they haven’t been taught how to do it. But if you ask AI to take over, you run into many risks, from getting stuck in existing theories to deskilling future scientists. It creates inconsistencies, which runs the risk of introducing pseudoscientific ideas into the field.’
If we truly want to advance the study of cognition, the authors argue, we can’t rely on AI models to take shortcuts. Van Rooij: ‘Part of the problem is inherent to the system: we ask researchers to write as many papers as possible, while good science is slow. But we can’t rely on AI as if it’s a cheat code, and the best way to avoid the traps of AI is to be aware of them. Only once we’ve acknowledged that can we expect to move cognitive science forward.’
Current Directions in Psychological Science
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