The signature-testing approach to mapping biological and artificial intelligences

Making inferences from behaviour to cognition is problematic due to a many-to -one mapping problem, in which any one behaviour can be generated by multiple possible cognitive processes. Attempts to cross this inferential gap when comparing human intelligence to that of animals or machines can generate great debate. Here, we discuss the challenges of making comparisons using 'success-testing' approaches and call attention to an alternate experimental framework, the 'signature-testing' approach. Signature testing places the search for information-processing errors, biases, and other patterns centre stage, rather than focussing predominantly on problem-solving success. We highlight current research on both biological and artificial intelligence that fits within this frame-work and is creating proactive research programs that make strong inferences about the similarities and differences between the content of human, animal, and machine minds.

Automated summary

Inferring cognition from behavior is challenging due to the many-to-one mapping problem. Comparing human, animal, and machine intelligence can generate debate. The signature-testing approach focuses on information-processing errors, biases, and patterns. Research on biological and artificial intelligence is creating proactive programs that make strong inferences about the content of minds.