We introduce a novel application of a computerized version of the Trail Making Test (c-TMT), designed to track hand and eye movements during multiple trials to tackle the main limitations of the classic pen-and-pencil version. Adults with mild cognitive impairment (MCI) and a matched control group were studied to obtain fine-grained metrics during the task. Statistically significant differences between groups were found in the scanpath length, while more traditional measures, such as time to complete trials, were not discriminative. We advocate for using new complementary measurements and digital tools in neuropsychology to improve the precision of neuropsychological tests.

Visual search is an essential part of almost any everyday human interaction with the visual environment. Nowadays, several algorithms are able to predict gaze positions during simple observation, but few models attempt to simulate human behavior during visual search in natural scenes. Furthermore, these models vary widely in their design and exhibit differences in the datasets and metrics with which they were evaluated. Thus, there is a need for a reference point, on which each model can be tested and from where potential improvements can be derived. In this study, we select publicly available state-of-the-art visual search models and datasets in natural scenes, and provide a common framework for their evaluation. To this end, we apply a unified format and criteria, bridging the gaps between them, and we estimate the models’ efficiency and similarity with humans using a specific set of metrics. This integration has allowed us to enhance the Ideal Bayesian Searcher by combining it with a neural network-based visual search model, which enables it to generalize to other datasets. The present work sheds light on the limitations of current models and how integrating different approaches with a unified criteria can lead to better algorithms. Moreover, it moves forward on bringing forth a solution for the urgent need for benchmarking data and metrics to support the development of more general human visual search computational models. All of the code used here, including metrics, plots, and visual search models, alongside the preprocessed datasets, are available at $\url{https://github.com/FerminT/VisualSearchBenchmark}$.