We probably sense sense probabilities

In this paper, we demonstrate the effects of Information Theory measures on the processing of polysemous nouns and reveal that the sensitivity to multiple related senses can be learned from the linguistic context. We collected large-scale data and applied a correlation design to show that an increase in sense uncertainty (or sense diversity) is followed by a faster visual lexical decision. The facilitatory effect of sense uncertainty was revealed by the predictive power of entropy, followed by the additional analysis, which revealed that both the number of senses and the balance of sense probabilities affected processing. For the first time, the balance of sense probabilities was described via redundancy to demonstrate the effect of the numerical description of the balance of sense probabilities. Finally, we crossed distribution semantics and discrimination learning to show that polysemy effects can arise as a consequence of the principles of error-driven learning.