This is basically in keeping with the work of one other psychologist, Robert Rescorla, whose work within the ’70s and ’80s influenced each Wasserman and Sutton. Rescorla encouraged people to think about association not as a “low-level mechanical process” but as “the training that results from exposure to relations amongst events within the environment” and “a primary means by which the organism represents the structure of its world.”
That is true even of a laboratory pigeon pecking at screens and buttons in a small experimental box, where scientists rigorously control and measure stimuli and rewards. However the pigeon’s learning extends outside the box. Wasserman’s students transport the birds between the aviary and the laboratory in buckets—and experienced pigeons jump immediately into the buckets at any time when the scholars open the doors. Much as Rescorla suggested, they’re learning the structure of their world contained in the laboratory and the relation of its parts, just like the bucket and the box, though they don’t all the time know the particular task they’ll face inside.
Comparative psychologists and animal researchers have long grappled with an issue that suddenly seems urgent due to AI: How can we attribute sentience to other living beings?
The identical associative mechanisms through which the pigeon learns the structure of its world can open a window to the form of inner life that Skinner and plenty of earlier psychologists said didn’t exist. Pharmaceutical researchers have long used pigeons in drug-discrimination tasks, where they’re given, say, an amphetamine or a sedative and rewarded with a food pellet for accurately identifying which drug they took. The birds’ success suggests they each experience and discriminate between internal states. “Is that not tantamount to introspection?” Wasserman asked.
It is difficult to assume AI matching a pigeon on this specific task—a reminder that, though AI and animals share associative mechanisms, there may be more to life than behavior and learning. A pigeon deserves ethical consideration as a living creature not due to the way it learns but due to what it feels. A pigeon can experience pain and suffer, while an AI chatbot cannot—even when some large language models, trained on corpora that include descriptions of human suffering and sci-fi stories of sentient computers, can trick people into believing otherwise.
UNIVERSITY OF IOWA/WASSERMAN LAB
“The intensive private and non-private investments into AI research in recent times have resulted within the very technologies which can be forcing us to confront the query of AI sentience today,” two philosophers of science wrote in in 2023. “To these current questions, we want an analogous degree of investment into research on animal cognition and behavior.” Indeed, comparative psychologists and animal researchers have long grappled with questions that suddenly seem urgent due to AI: How can we attribute sentience to other living beings? How can we distinguish true sentience from a really convincing performance of sentience?
Such an undertaking would yield knowledge not only about technology and animals but in addition about ourselves. Most psychologists probably wouldn’t go so far as Sutton in arguing that reward is enough to clarify most if not all human behavior, but nobody would dispute that individuals often learn by association too. In truth, most of Wasserman’s undergraduate students eventually succeeded at his recent experiment with the striped discs, but only after they gave up trying to find rules. They resorted, just like the pigeons, to association and couldn’t easily explain afterwards what they’d learned. It was just that with enough practice, they began to get a feel for the categories.
It’s one other irony about associative learning: What has long been considered probably the most complex type of intelligence—a cognitive ability like rule-based learning—may make us human, but we also call on it for the simplest of tasks, like sorting objects by color or size. Meanwhile, among the most refined demonstrations of human learning—like, say, a sommelier learning to taste the difference between grapes—are learned not through rules, but only through experience.
Learning through experience relies on ancient associative mechanisms that we share with pigeons and countless other creatures, from honeybees to fish. The laboratory pigeon shouldn’t be only in our computers but in our brains—and the engine behind a few of humankind’s most impressive feats.
