Thermodynamic fluctuation theorems govern human sensorimotor learning.

The application of thermodynamic reasoning in the study of learning systems has a long tradition. Recently, new tools relating perfect thermodynamic adaptation to the adaptation process have been developed. These results, known as fluctuation theorems, have been tested experimentally in several physical scenarios and, moreover, they have been shown to be valid under broad mathematical conditions. Hence, although not experimentally challenged yet, they are presumed to apply to learning systems as well. Here we address this challenge by testing the applicability of fluctuation theorems in learning systems, more specifically, in human sensorimotor learning. In particular, we relate adaptive movement trajectories in a changing visuomotor rotation task to fully adapted steady-state behavior of individual participants. We find that human adaptive behavior in our task is generally consistent with fluctuation theorem predictions and discuss the merits and limitations of the approach.

Optimized Detection of the Infrequent Response in P300-Based Brain-Computer Interfaces / Detección optimizada de la respuesta infrecuente en interfaces cerebro-computadora

This paper presents an application developed on the BCI2000 platform which reduces the average spelling time per symbol on the Donchin speller. The motivation was to reduce the compromise between spelling rate and spelling accuracy due to the large amount of responses required in order to perform coherent average techniques. The methodology was made under a Bayesian approach which allows calculation of each target's class posterior probability. This result indicates the probability of each response of belonging to the infrequent class. When there is enough evidence to make a decision the system stops the stimulation process and moves on with the next symbol, otherwise it continues stimulating the user until it finds the selected letter. The average spelling rate, after using the proposed methodology with 14 healthy users and a maximum number of 5 stimulation sequences, was of 6.1 ± 0.63 char/min, compared to a constant rate of 3.93 char/min with the standard system.

Este trabajo presenta una aplicación desarrollada sobre la plataforma BCI2000 que disminuye el tiempo promedio de selección de los símbolos del deletreador de Donchin. La motivación consistió en reducir el compromiso entre la taza de deletreo y la precisión correspondiente, la cual surge como consecuencia de la gran cantidad de respuestas necesarias para realizar técnicas de promediación coherente. La metodología propuesta se basa en un enfoque Bayesiano que permite calcular la probabilidad posterior asociada con la clasificación de cada objetivo, resultado que indica la evidencia que presentan las respuestas de pertenecer a la clase infrecuente. Cuando existe evidencia suficiente para tomar una decisión, el sistema detiene el proceso de estimulación y continúa con el siguiente símbolo, de lo contrario permanece estimulando al usuario hasta conseguir identificar la letra seleccionada. Después de utilizar la metodología propuesta sobre los registros de 14 usuarios sanos con un número máximo de 5 series de estimulación, el tiempo promedio de deletreo reportado es de 6.1 ± 0.63 letras/min, el cual es comparado con una taza constante de 3.93 letras/min obtenido con un sistema convencional.