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Abstract: I present a new approach for the interpretation of reaction time RT datafrom behavioral experiments. From a physical perspective, the entropy of the RTdistribution provides a model-free estimate of the amount of processingperformed by the cognitive system. In this way, the focus is shifted from theconventional interpretation of individual RTs being either long or short, intotheir distribution being more or less complex in terms of entropy. The newapproach enables the estimation of the cognitive processing load withoutreference to the informational content of the stimuli themselves, thusproviding a more appropriate estimate of the cognitive impact of differentsources of information that are carried by experimental stimuli or tasks. Thepaper introduces the formulation of the theory, followed by an empiricalvalidation using a database of human RTs in lexical tasks visual lexicaldecision and word naming. The results show that this new interpretation of RTsis more powerful than the traditional one. The method provides theoreticalestimates of the processing loads elicited by individual stimuli. These loadssharply distinguish the responses from different tasks. In addition, itprovides upper-bound estimates for the speed at which the system processesinformation. Finally, I argue that the theoretical proposal, and the associatedempirical evidence, provide strong arguments for an adaptive system thatsystematically adjusts its operational processing speed to the particulardemands of each stimulus. This finding is in contradiction with Hick-s law,which posits a relatively constant processing speed within an experimentalcontext.



Author: Fermín Moscoso del Prado Martín

Source: https://arxiv.org/







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