Saliency and visual attention have been studied in a computational context for decades, mostly in the capacity of predicting spatial topographical saliency maps or simulated heatmaps. Spatial selection by an attentive mechanism is, however, inherently a sequential sampling process in humans. There have been recent efforts in analyzing and modeling scanpaths, however, there is as of yet no universal agreement on what metrics should be applied to measure scanpath similarity or the quality of a predicted scanpath from a computational model. Many similarity measures have been suggested in different contexts and little is known about their behavior or properties. This paper presents in one place a review of these metrics, axiomatic analysis of gaze metrics for scanpaths, and careful analysis of the discriminative power of different metrics in order to provide a roadmap for further future analysis. This is accompanied by experimentation based on classic modeling strategies for simulating sequential selection from traditional representations of saliency, and deep neural networks that produce sequences by construction. Experiments provide strong support for the necessity of sequential analysis of attention and support for certain metrics including a family of metrics introduced in this paper motivated by the notion of scanpath plausibility. General introductionHuman and animal brains have significant yet limited computational power, especially when considering the amount of sensory data available to them. Given this limitation, the brain appears to have evolved to provide an efficient solution to actively control the stream of information. Attention is the process of selectively concentrating on some portion of available information, at the expense of ignoring other perceivable parts. It is a combination of behavioral and cognitive processes and it may be defined subjectively or objectively. It has been widely researched in cognitive and perceptual psychology andregardless of discipline, the usage has been identified to be critical to Information Reduction and filtering.

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The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work drastically reduces the human effort required to apply saliency algorithms to new tasks and datasets, while also ensuring consistency and procedural correctness for results and conclusions produced by different parties. At its launch SMILER already includes twenty three saliency models (fourteen models based in MATLAB and nine supported through containerization), and the open design of SMILER encourages this number to grow with future contributions from the community. The project may be downloaded and contributed to through its GitHub

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