Cognitive effort assessment through pupillary responses: Insights from multinomial processing tree modeling and neural interconnections

Abstract

The pupillary responses of humans exhibit variations in size, which are mediated by optic and oculomotor cranial nerves. Due to their sensitivity and high resolution of pupillary responses, they are used for a long time as measurement metrics of cognitive effort. Investigating the extent of cognitive effort required during tasks of varying difficulty is crucial for understanding the neural interconnections underlying these pupillary responses. This study aims to assess human cognitive efforts involved in visually presented cognitive tasks using the multinomial processing tree (MPT) model, an analytical tool that disentangles and predicts distinct cognitive processes, resulting in changes in pupil diameter. To achieve this, a pupillary response dataset was collected during mental multiplication (MM) tasks and visual stimuli presentations as cognitive tasks. MPT model describes observed response frequencies across various response categories and determines the transition probabilities from one latent state to the next. The expectation maximization (EM) algorithm is employed with MPT model to estimate parameter values based on response frequency within each category. Both group-level and individual subject-to-subject comparisons are conducted to estimate cognitive effort. The results reveal that in the group comparison and with respect to task difficulty level, that subject’s knowledge on MM task influences the successfully solve the problem. Regarding individual analysis, no significant differences are observed in parameters related to correct recall, problem-solving ability, and time constraint compliance. However, some significant differences are found in parameters associated with the perceived difficulty level and ability to recall the correct answers. MPT model combined with EM algorithm constitutes a probabilistic model that enhances pupillary responses identification related to the cognitive effort. Potential applications of this model include disease diagnostics based on parameter values and identification of neural pathways that are involved in the pupillary response and subject’s cognitive effort. Furthermore, efforts are underway to connect this psychological model with an artificial neural network.