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Claudio Frongia

Publications and source records attributed to Claudio Frongia.

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Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances

We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use it to show that several major claims advanced by models such as CENTAUR, proposed as an artificial Unified Model of Cognition, are not theoretically or empirically justified. LAPITHS provides a principled reference point for counteracting the current behaviouristic tendency in AI research to interpret the human level performances of transformer based language models as evidence of human like underlying computation and, by extension, as signs of cognitive abilities. The novelty of LAPITHS lies in making explicit the arguments grounded in two quantitative assessments: (i) the Minimal Cognitive Grid, a theoretically motivated method for estimating the cognitive plausibility of artificial systems, and (ii) a behavioural comparison showing that results similar to those reported for CENTAUR like models can be reproduced by other systems that do not satisfy the structural constraints typically associated with cognitive plausibility, and whose outputs do not provide independent explanatory insight into human cognition.

cs.AI

How time window influences biometrics performance: an EEG-based fingerprints connectivity study

EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results have shown a pronounced effect of the time window on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase of the biometric performance with an increase of the time window. In conclusion, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as EEG fingerprint and that, in this context, it is very important to define a sufficient time window able to collect the subject specific features. Moreover, our preliminary results show that extending the window size beyond a certain maximum does not improve biometric systems' performance.

eess.SP