Article

Characterising Eye Movement Events With an Unsupervised Hidden Markov Model

2020-11-18

Abstract excerpt

<p>Eye-tracking allows researchers to infer cognitive processes from eye movements that are classified into distinct events. Parsing the events is typically done by algorithms. Previous algorithms have successfully used hidden Markov models (HMMs) for classification but can still be improved in several aspects. To address these aspects, we developed \texttt{gazeHMM}, an algorithm that uses an HMM as a generative m...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fc3d4dc7-af9e-548e-8987-2ab4c5f711b7
DOI: 10.31234/osf.io/wvp2f

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Characterising Eye Movement Events With an Unsupervised Hidden Markov ModelDOI 10.31234/osf.io/wvp2fRead

A Hidden Markov Model for Analyzing Eye-Tracking of Moving ObjectsSemantic proximity Read

Model-based eye-tracking: a new window to understand individual differences and psychiatric disordersSemantic proximity Read

Deep Learning Neural Networks as a Component of a Model of Saccadic GenerationSemantic proximity Read

(Micro)saccade-related potentials during face recognition: A study combining EEG, eye-tracking, and deconvolution modelingSemantic proximity Read

Where we look, what we know: How hippocampal predictions shape, and are shaped by, eye movementsSemantic proximity Read

A Mathematical Model of Exploration and Exploitation in Natural Scene ViewingSemantic proximity Read

Human-level saccade detection performance using deep neural networksSemantic proximity Read

Generative Artificial Intelligence for Secure and Scalable Multimodal Eye Movement DatasetsSemantic proximity Read

Visual Attention Through Uncertainty Minimization in Recurrent Generative ModelsSemantic proximity Read

Just-in-time: gaze guidance in natural behaviorSemantic proximity Read

Motor “laziness” constrains fixation selection in real-world tasksSemantic proximity Read

A generative learning model for saccade adaptationSemantic proximity Read