Classifying the difficulty levels of working memory tasks by using pupillary response.

Knowing the difficulty of a given task is crucial for improving the learning outcomes. This paper studies the difficulty level classification of memorization tasks from pupillary response data. Developing a difficulty level classifier from pupil size features is challenging because of the inter-subject variability of pupil responses.

Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study.

Background: A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty.

Testing multiple polynomial models for eye-tracker calibration.

The straightforward approach to eye-tracker calibration considers that the calibration data do not have erroneous associations, and the calibration function is defined. The violation of the non-erroneous assumption could cause an arbitrarily large bias. The MMransac algorithm proposed in this paper is a modified version of the Random Sample Consensus.

Boron and Coumaphos Residues in Hive Materials Following Treatments for the Control of Aethina tumida Murray.

In the search of alternatives for controlling Aethina tumida Murray, we recently proposed the BAA trap which uses boric acid and an attractant which mimics the process of fermentation caused by Kodamaea ohmeri in the hive. This yeast is excreted in the feces of A. tumida causing the fermentation of pollen and honey of infested hives and releasing compounds that function as aggregation pheromones to A.