Beyond the Turing test: Performance metrics for evaluating a computer simulation of the human mind

Performance metrics for machine intelligence (e.g., the Turing test) have traditionally consisted of pass/fail tests. Because the tests devised by psychologists have been aimed at revealing unobservable processes of human cognition, they are similarly capable of revealing how a computer accomplishes a task, not simply its success or failure. Here we propose the adaptation of a set of tests of abilities previously measured in humans to be used as a benchmark for simulation of human cognition. Our premise is that if a machine cannot pass these tests, it is unlikely to be able to engage in the more complex cognition routinely exhibited by animals and humans. If it cannot pass these sorts of tests, it will lack fundamental capabilities underlying such performance. 1