A neural approach to the Turing Test: The role of emotions

Abstract

As is well known, the Turing Test proposes the possibility of distinguishing the behavior of a machine from that of a human being through an experimental session. The Turing Test assesses whether a person asking questions to two different entities, can tell from their answers which of them is the human being and which is the machine. With the progress of Artificial Intelligence, the number of contexts in which the capacities of response of a machine will be indistinguishable from those of a human being is expected to increase rapidly. In order to configure a Turing Test in which it is possible to distinguish human behavior from machine behavior independently from the advances of Artificial Intelligence, at least in the short-medium term, it would be important to base it not on the differences between man and machine in terms of performance and dialogue capacity, but on some specific characteristic of the human mind that cannot be reproduced by the machine even in principle. We studied a new kind of test based on the hypothesis that such characteristic of the human mind exists and can be made experimentally evident. This peculiar characteristic is the emotional content of human cognition and, more specifically, its link with memory enhancement. To validate this hypothesis we recorded the EEG signals of 39 subjects that underwent a specific test and analyzed their signals with a neural network able to label similar signal patterns with similar binary codes. The results showed that, with a statistically significant difference, the test participants more easily recognized images associated in the past with an emotional reaction than those not associated with such a reaction. This distinction in our view is not accessible to a software system, even AI-based, and a Turing Test based on this feature of the mind may make distinguishable human versus machine responses.