The concept of informacional entropy can predict sequence learning, in rats?

Author(s)

Leopoldo Francisco Barletta Marchelli

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Instituto de Biociências (IBIOC/SB)
• Defense date: 2011-08-17

Examining board members

Gilberto Fernando Xavier; André Frazão Helene; Andréa Maria Garrido dos Santos

• Advisor: Gilberto Fernando Xavier

Abstract

Prediction of environmental events, relying on memories of past regularities, is one of the fundamental functions of complex nervous systems. Sequences of serially ordered stimuli allow extracting information that defines its serial pattern. These patterns allow prediction of the next item in a sequence of events, facultating previous preparation to deal with its occurrence. Not surprisingly, animals, including humans, can identify rules present in serial structures of stimuli. Serial reaction time tasks (SRTT) have been extensively used in studies involving association, anticipation, attention, and learning and memory. Typically, subjects have to react to stimuli presented either in random or in repetitive sequences. As training proceeds, reaction time to each stimulus decreases, reflecting acquisition of this perceptual-motor skill. However, reaction time reduction is greater for repetitive sequences relative to the random sequences, indicating acquisition about the repetitive structure of the sequence. In human beings, this may occur even when the subject in uncapable of reporting the existence of a sequence, indicating that the acquisition was (at least initially) implicit rather than explicit. The complexity of a sequence of stimuli, at different levels, may be quantifyed by means of a mathematical tool proposed by Shannon (1948), the information entropy (IE). In this study we evaluated to which extent IE can predict performance of rats in SRTT involving sequences of stimuli organized at different levels of complexity. Rats were trained to react (1) a repeated sequence of stimuli which IE at the level \"1\" (i.e., expressing to which extent a given item allow prediction of the next) was 2.75. After reaching an asymptotic level of performance, the animals were exposed (2) a variable sequence of stimuli with the same amount of IE in the level \"1\", but with more IE in the level \"2\" (i.e., expressing to which extent two given items allow prediction of the next). Later the animals were exposed to (3) a new repeated sequence of stimuli, which IE at the level \"1\" was 3.00. Finally, the animals were submitted to (4) a random sequence of stimuli with the same amount of IE at the level \"1\", i.e., 3.00, but with greater IE in level 2. Results showed that rats learned about the serial patterns and, more interestingly, their performance strongly correlated to the amount of IE at the level \"2 \", both in terms of reaction times and in terms of percentage of correct responses. Therefore, IE allows not only to quantify complexity of sequences in studies involving serial learning, but also to predict performance of the subjects. (AU)