What we look at in paintings: A comparison between experienced and inexperienced art viewers

Ylitalo, Anna‐Kaisa; Särkkä, Aila; Guttorp, Peter

doi:10.1214/16-aoas921

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…Experts also tend to make less frequent and shorter fixations on narrative elements (Kristjanson & Antes, 1989;Pihko et al, 2011;Vogt & Magnussen, 2007). Ylitalo et al (2016) found that experts tend to have shorter fixation durations and less variability in fixation durations than novices. Fedorovskaya et al (2017) found less local viewing by experts and less re-fixations on already attended areas.…”

Section: Expertisementioning

confidence: 99%

Task-dependent eye-movement patterns in viewing art

Sharvashidze¹,

Schütz²

2020

JEMR

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In art schools and classes for art history students are trained to pay attention to different aspects of an artwork, such as art movement characteristics and painting techniques. Experts are better at processing style and visual features of an artwork than nonprofessionals. Here we tested the hypothesis that experts in art use different, task-dependent viewing strategies than nonprofessionals when analyzing a piece of art. We compared a group of art history students with a group of students with no art education background, while viewing 36 paintings under three discrimination tasks. Participants were asked to determine the art movement, the date and the medium of the paintings. We analyzed behavioral and eye-movement data of 27 participants. Our observers adjusted their viewing strategies according to the task, resulting in longer fixation durations and shorter saccade amplitudes for the medium detection task. We found higher task accuracy and subjective confidence, less congruence and higher dispersion in fixation locations in experts. Expertise also influenced saccade metrics, biasing it towards larger saccade amplitudes, advocating a more holistic scanning strategy of experts in all three tasks.

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Section: Expertisementioning

confidence: 99%

Task-dependent eye-movement patterns in viewing art

Sharvashidze¹,

Schütz²

2020

JEMR

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“…Back to the specific topic of our study, even Cultural Heritage can benefit from eye-tracking studies [36]. In Reference [37], eye movement was evaluated by a spatio-temporal point process model for comparing the eye movements of experienced and inexperienced art viewers. However, the model developed by the authors is not accurate for studying the complete dynamics of the fixation process, since it does not capture how the fixation process changes over time.…”

Section: Related Workmentioning

confidence: 99%

A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage

Pierdicca

Paolanti

Quattrini

et al. 2020

Sensors

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In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than 80 %), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.

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“…Bottom-up factors were indicated as a strong role in children, while in adults the observed patterns were similar in both phases. In [25] eye movement was analysed by the simple model, a spatio-temporal point process model, to compare the eye movements of experienced and inexperienced art viewers. However, the model developed in this paper is not good enough for studying the complete dynamics of the fixation process, since it does not capture how the fixation process is changing in time.…”

Section: Related Workmentioning

confidence: 99%

User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data

et al. 2018

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Today, museum visits are perceived as an opportunity for individuals to explore and make up their own minds. The increasing technical capabilities of Augmented Reality (AR) technology have raised audience expectations, advancing the use of mobile AR in cultural heritage (CH) settings. Hence, there is the need to define a criteria, based on users’ preference, able to drive developers and insiders toward a more conscious development of AR-based applications. Starting from previous research (performed to define a protocol for understanding the visual behaviour of subjects looking at paintings), this paper introduces a truly predictive model of the museum visitor’s visual behaviour, measured by an eye tracker. A Hidden Markov Model (HMM) approach is presented, able to predict users’ attention in front of a painting. Furthermore, this research compares users’ behaviour between adults and children, expanding the results to different kind of users, thus providing a reliable approach to eye trajectories. Tests have been conducted defining areas of interest (AOI) and observing the most visited ones, attempting the prediction of subsequent transitions between AOIs. The results demonstrate the effectiveness and suitability of our approach, with performance evaluation values that exceed 90%.

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What we look at in paintings: A comparison between experienced and inexperienced art viewers

Cited by 12 publications

References 37 publications

Task-dependent eye-movement patterns in viewing art

Task-dependent eye-movement patterns in viewing art

A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage

User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data

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