Eye-tracking Data Research Articles

Perceived Quality in the Automotive Industry: Do Car Exterior and Interior Color Combinations Have an Impact?

Since in the automotive field colors play an important role, the present study tried to answer the following questions: is the perceived quality (PQ) of the vehicle interior color different after visually exploring the car body color? If so, how? Here, exploiting immersive virtual reality simulations and eye-tracking technology, participants were asked to visually explore an unbranded car in different exterior/interior color combinations and rate its PQ. Fixation duration (time eyes are fixed on a target) was considered as an implicit measure of visual attention allocation while PQ evaluations were considered as explicit measures of individual preferences for car colors. As for eye-tracking data, the results showed that white and red car exteriors affected the attention to interiors with the fixation duration being longer for gray than black interiors. Moreover, the subjective evaluations of car PQ predicted eye-tracking patterns: as the negative evaluation increased, the fixation duration on car interiors also increased. Overall, these preliminary results suggested the need to further explore the relationship between PQ and attentional/motivational processing as well as the role of subjective aesthetic preferences for color combinations in the automotive field.

How can eye-tracking data be used to understand cognitive processes when comparing data sets with box plots?

How can eye-tracking data be used to understand cognitive processes when comparing data sets with box plots?

Eye tracking is more sensitive than skin conductance response in detecting mild environmental stimuli.

The skin conductance (SC) and eye tracking data are two potential arousal-related psychophysiological signals that can serve as the interoceptive unconditioned response to aversive stimuli (e.g. electric shocks). The current research investigates the sensitivity of these signals in detecting mild electric shock by decoding the hidden arousal and interoceptive awareness (IA) states. While well-established frameworks exist to decode the arousal state from the SC signal, there is a lack of a systematic approach that decodes the IA state from pupillometry and eye gaze measurements. We extract the physiological-based features from eye tracking data to recover the IA-related neural activity. Employing a Bayesian filtering framework, we decode the IA state in fear conditioning and extinction experiments where mild electric shock is used. We independently decode the underlying arousal state using binary and marked point process (MPP) observations derived from concurrently collected SC data. Eight of 11 subjects present a significantly (P-value ) higher IA state in trials that were always accompanied by electric shock ( ) compared to trials that were never accompanied by electric shock ( ). According to the decoded SC-based arousal state, only five (binary observation) and four (MPP observation) subjects present a significantly higher arousal state in trials than trials. In conclusion, the decoded hidden brain state from eye tracking data better agrees with the presented mild stimuli. Tracking IA state from eye tracking data can lead to the development of contactless monitors for neuropsychiatric and neurodegenerative disorders.

Integrating session recording and eye-tracking: development and evaluation of a Chrome extension for user behavior analysis

The subject of this article is the development, implementation, and evaluation of a Chrome extension designed to record and replay web sessions using integrated eye-tracking data. The goal is to develop a tool that enhances user interaction analysis by combining session recordings with eye-tracking capabilities. The tasks to be solved are as follows: create a functional Chrome extension that utilizes rrweb for session recording and WebGazer.js for eye tracking; implement features, such as session recording, replay with eye-tracking data overlays, and session management; and provide options for exporting and importing recorded sessions. The methods used are: architectural modeling using UML diagrams to design the system architecture, software engineering techniques for developing the extension, integration testing to ensure the smooth operation of combined features, and data preprocessing techniques to prevent redundancy and reduce noise in eye-tracking data. Additionally, a structured user study with detailed questionnaires combining both Likert-scale questions and open-ended responses and feedback analysis were conducted to evaluate usability and gather feedback. The following results were obtained: the extension was successfully developed and evaluated with 25 participants aged 18–35 years in a controlled environment. High usability ratings were obtained, with an average score of 4.5 out of 5 for the session recording, replay, and session management features. However, the eye-tracking feature received a lower rating of 3.8 out of 5 due to occasional inaccuracies in the eye-tracking data. The qualitative feedback indicated the usefulness of the eye-tracking feature and highlighted the need for improved data accuracy. Conclusions. The scientific novelty of this study lies in the integration of session recording and eye tracking within a Chrome extension, which represents a novel and comprehensive tool for user interaction analysis on the web. The tool's ability to capture both behavioral data and visual attention without requiring website code modifications is particularly valuable for researchers, marketing specialists, UI/UX designers, and product developers. The usability study and feedback analysis provided a clear direction for future improvements, including enhancing the eye-tracking accuracy and integrating advanced analytics and customizable reporting options. Future work will also explore the integration of machine learning algorithms to automatically analyze recorded data to provide deeper insights and actionable recommendations.

Evaluation of Cognitive Load Differences between Elderly and Young Pedestrians at the Signalised Intersection

The pedestrian cognitive load has an important effect on the pedestrian crossing decision making. Compared with young adults, old people are characterised by declining physical function and slower reaction ability, which makes them prone to traffic accidents when crossing the street. This study aims to compare the visual information-mental load correlation between elderly and young adults waiting at the signalised intersections and evaluate their cognitive load conditions. Therefore, two signalised intersections with different traffic scenes in the Nan’an District, Chongqing, China were selected. The eye-tracking, electrocardiographic and electrodermal activity data of young and old pedestrians were collected using eye-tracking and physiological instruments. The visual indexes (the total duration of fixations, the number of fixations, the average pupil diameter changing rate, the number of saccades, the average peak speed of saccades, the average amplitude of saccades and the total amplitude of saccades) and physiological indicators (the average growth rate of heart rate, the time-domain analysis indicator of HRV and HRV frequency domain analysis indicators, electrodermal response amplitude and rise time of the EDR amp.) were taken as inputs and outputs parameters, respectively. Then, the comprehensive cognitive load evaluation model for pedestrians was constructed when waiting to cross the street based on the data envelopment analysis method. And the cognitive load characteristic differences between the young adults and the elderly were compared. The results show that in the same crossing scene, compared with the young pedestrian, the elder pedestrian exhibited lower overall perceptual efficiency, lower fixation durations and higher cognitive loads. These results can provide certain references on improving the street crossing safety for the elderly pedestrian.

AI Eye-Tracking Technology: A New Era in Managing Cognitive Loads for Online Learners

Eye-tracking technology has emerged as a valuable tool for evaluating cognitive load in online learning environments. This study investigates the potential of AI-driven consumer behaviour prediction eye-tracking technology to improve the learning experience by monitoring students’ attention and delivering real-time feedback. In our study, we analysed two online lecture videos used in higher education from two institutions: Oxford Business College and Utrecht University. We conducted this analysis to assess cognitive demands in PowerPoint presentations, as this directly affects the effectiveness of knowledge dissemination and the learning process. We utilised a neuromarketing-research consumer behaviour eye-tracking AI prediction software called ‘Predict’, which employs an algorithm constructed on the largest neuroscience database (comprising previous studies conducted on live participants n = 180,000 with EEG and eye-tracking data). The analysis for this study was carried out using the programming language R, followed by a series of t-tests for each video and Pearson’s correlation tests to examine the relationship between ocus and cognitive demand. The findings suggest that AI-powered eye-tracking systems have the potential to transform online learning by providing educators with valuable insights into students’ cognitive processes and enabling them to optimise instructional materials for improved learning outcomes.

A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality

Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer’s Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA’s potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.

Individual differences in developmental trajectories of affective attention and relations with competence and social reticence with peers.

This study examined individual differences in affective attention trajectories in infancy and relations with competence and social reticence at 24 months. Data collection spanned 2017 to 2021. Infants (N = 297, 53% White, 49% reported as assigned male at birth) recruited in South Central and Central Pennsylvania and Northern New Jersey provided eye-tracking data at five assessments. Caregivers self-reported anxiety symptoms, infant temperamental negative affect, and infant competence at the final assessment. A subgroup of infants participated in a peer social dyad at the final assessment. Using group-based trajectory modeling, we found three groups of infants with different affective attention trajectories: affective attention increasers (n = 73), affective attention shifters (n = 156), and affective attention decreasers (n = 50). Affective attention increasers exhibited low intercepts with steep attention increases, particularly to angry facial configurations. Affective attention shifters exhibited middle intercepts with attention decreases to facial configurations but an attention increase to angry facial configurations. Affective attention decreasers exhibited high intercepts with steep attention decreases. Infants in the affective attention increasers group exhibited more competence when accounting for caregiver anxiety symptoms and infant temperamental negative affect. Group membership was not related to social reticence during the peer social dyad. Infants higher in temperamental negative affect exhibited more social reticence, particularly as the social dyad continued. Our results provide evidence for individual differences in developmental trajectories of affective attention and relations with toddler social behavior. Our results are primarily generalizable to rural and urban populations in the Midatlantic United States. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

How do L1 glosses affect EFL learners’ reading comprehension performance? An eye-tracking study

Abstract This study employed eye-tracking technology to examine how English as a foreign language learners at different proficiency levels process L1-glossed words and how this processing relates to L2 reading comprehension. Forty-seven university students were divided into a higher-proficiency group (n = 23) and a lower-proficiency group (n = 24) based on their L2 proficiency. Both groups were asked to read an English passage with L1 (Chinese) glosses. Their eye movements were recorded with an eye-tracker as they read. After reading, they were immediately given a reading comprehension test. Analyses of the eye-tracking data showed that the higher-proficiency L2 learners spent more time on unfamiliar words than the lower-proficiency L2 learners. Furthermore, lower-proficiency L2 learners’ longer processing time on glossed unfamiliar words was related to their higher reading comprehension scores, whereas this relationship was not found in the higher-proficiency group. These results revealed that the contribution of L1 glosses to L2 reading comprehension performance varied across L2 learners’ proficiency levels. Our findings have important implications for second language instruction.

Flashlight model: Integrating attention distribution and attention resources for pilots’ visual behaviour analysis and performance prediction

Pilot performance is almost the last line of defense in aircraft safety. Recent years have seen a surge in research aimed at utilizing eye-tracking technology to predict pilot performance, enhancing aviation safety margins. A decline in pilot performance is often attributed to either misdirected attention towards irrelevant tasks or inefficient information processing owing to limited attention resources. Previous research has shown that eye-tracking data can effectively capture these issues and provide accurate performance predictions. Nevertheless, the existing studies either focus on attention distribution or attention resources separately, neglecting the complex interactions between them. To address this gap, our study proposes a synthesized Flashlight model-based eye-tracking analysis for pilot performance prediction, integrating the two perspectives. Accordingly, the combined AOI-gaze metrics are proposed to offer a more nuanced analysis of information processing across specific Areas of Interest (AOIs), thereby enhancing the analysis of gaze metrics. We examined the efficacy of the combined AOI-gaze metrics in the Gradient-boosted decision trees(GBDT) model for pilot performance prediction and compared them with other widely used eye-tracking metrics in a simulated flight experiment case study. Moreover, we employed the SHapley Additive exPlanations (SHAP) method to identify the most influential eye-tracking measurements for pilots’ performance prediction. The result demonstrated that the selected eye-tracking measurements obtained the highest accuracy in performance prediction.

Unraveling information processes of decision-making with eye-tracking data

Unraveling information processes of decision-making with eye-tracking data

Fixational eye movements and their associated evoked potentials during natural vision are altered in schizophrenia

BackgroundVisual exploration is abnormal in schizophrenia; however, few studies have investigated the physiological responses during selecting objectives in more ecological scenarios. This study aimed to demonstrate that people with schizophrenia have difficulties observing the prominent elements of an image due to a deficit mechanism of sensory modulation (active sensing) during natural vision. MethodsAn electroencephalogram recording with eye tracking data was collected on 18 healthy individuals and 18 people affected by schizophrenia while looking at natural images. These had a prominent color element and blinking produced by changes in image luminance. ResultsWe found fewer fixations when all images were scanned, late focus on prominent image areas, decreased amplitude in the eye-fixation-related potential, and decreased intertrial coherence in the SCZ group. ConclusionsThe decrease in the visual attention response evoked by the prominence of visual stimuli in patients affected by schizophrenia is generated by a reduction in endogenous attention mechanisms to initiate and maintain visual exploration. Further work is required to explain the relationship of this decrease with clinical indicators.

Autistic traits and perspective taking in youths with anorexia nervosa: an exploratory clinical and eye tracking study

BackgroundDespite their apparent dissimilarity, Anorexia Nervosa (AN) and Autism Spectrum Disorder (ASD) share many features, especially in terms of social and emotional difficulties. In recent years, empathic abilities in AN have been frequently assessed using self-report measures. Otherwise, the director task (DT) has been used to investigate the ability to take the visual perspective of another individual in a communicative context, using eye-tracking technology. The aim of the current study was to test the presence of autism-relevant features in AN, through: (i) comparing self-reported autistic traits and empathic abilities in a group of young inpatients with AN and age/gender matched healthy controls (HC); (ii) comparing performance on the director paradigm.MethodsThe participants were females in the age-range between 11 and 18 years: 24 with AN and 23 HC. Autistic traits, empathic abilities, and severity of the eating disorder were respectively measured using: the Autism Quotient (AQ), the Interpersonal Reactivity Index (IRI), and the Eating Disorder Inventory-3 (EDI-3). Both groups performed a computerized task in which a director instructed them to move objects placed on a set of shelves using a mouse, while their eye gaze was tracked. A total of 36 shelf configurations, divided into three categories (with dimensional distractor – with spatial distractor – control), were created.ResultsSubjects with AN showed higher autistic traits than HC. Eye-tracking data revealed that subjects with AN took longer to decide which object to select and where to move it, both in distractor-trials and in control-trials. In the AN group, we found a significant negative correlation between the total score of the AQ and the number of fixations to the irrelevant object in the dimensional control condition -in which the subjects were asked to focus on dimensional aspects of the object (large-small)-.ConclusionsAutistic traits were over-represented in a group of young inpatients with AN. Through the use of eye-tracking technology, this exploratory study documented some differences between AN inpatients and HC in their online processes during the perspective taking tasks, which could be considered a target of tailored intervention. A larger sample of patients is needed to confirm these preliminary findings.

Investigation of the accuracy of integral models of the oculo-motor system

For mathematical modeling of the human oculomotor system (OMS), integral nonlinear models are used, which simultaneously take into account the nonlinear and inertial properties of the research object. Based on the data of experimental studies of the OMS "input-output", transient and diagonal intersections of transient functions of the second and third orders are determined. To obtain experimental data, an innovative eye tracking technology is used, which allows recording eye responses to test visual stimuli. Thus test signals are displayed on the computer monitor at different distances from the starting position in the horizontal direction. The aim of the research is to study the accuracy of OMS identification using eye-tracking data by evaluating the calculation errors of multidimensional transient functions when using methods of nonlinear dynamic identification based on models in the form of Volterra series and polynomials. The object of the study is the process of nonparametric identification of the OMS based on Volterra models in the time domain. The subject of the research is algorithmic and software tools for calculating the dynamic characteristics of OMS based on eye-tracking data, analyzing the accuracy of the obtained models using two identification methods: the approximation method and the least squares method (LSM). The means of nonlinear dynamic identification of the human OMS based on Volterra series and polynomials were developed in the Python programming environment. The accuracy estimates of various OMS (linear, quadratic, and cubic) models were obtained based on data from three responses to test signals of different amplitudes. For the same test signals, the same models in the form of the Volterra series and polynomials were obtained, as these models coincide in the region of convergence of the Volterra series. The analysis of errors in the assessment of the dynamic characteristics of the OMS demonstrated that the model in the form of an integral polynomial of the second degree, which was built using the LSM based on three responses, has an accuracy twice as high as the accuracy of similar models built using the data of two responses. Thus, in further studies of the psychophysiological state of a person based on nonlinear dynamic models of the OMS according to the data of three responses, it is advisable to use a model in the form of a quadratic Volterra polynomial. Figs.: 17. Tabl. 3. Refs.: 10 titles.

A training and assessment system for human-computer interaction combining fNIRS and eye-tracking data

In the domain of engineering interactive product design, the optimization of Human-Computer Interaction (HCI) is central to enhancing user experience and system efficiency. Notably, the application of cognitive load theory holds significant prominence in this field. Efficient evaluation of cognitive load not only facilitates the optimization of resource management and task allocation, but is also the key to creating a fluent interactive experience. With the advancement of Artificial Intelligence (AI) and physiological measurement technologies, new methodologies have emerged to measure and understand cognitive load. This research introduces a system for HCI training and assessment. The system, through the development of digital memory paradigms, induces and verifies varying levels of cognitive load with precision, thereby providing data-driven insights for HCI design. Specifically, this study constructs an AI-assisted multimodal cognitive load assessment framework, integrating physiological data collected from Functional Near-Infrared Spectroscopy (fNIRS) and eye-tracking technologies. By extracting and analyzing 29 physiological features, including channel features, graph-theoretic features, and features pertaining to eyelid and iris movements, we propose an innovative multimodal recognition approach to classify cognitive load across individuals. The experimental results not only verified the validity of the method, but also revealed changes in physiological patterns at different levels of cognitive load. In particular, the significant potential of the fNIRS feature in band I. This discovery suggests that we can monitor and predict cognitive load with greater precision through physiological signals, hence optimizing the design of HCI systems to alleviate user strain, prevent information overload, and enhance the naturalness and intuitiveness of interaction.

Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.

Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model's effectiveness in replicating pathologists' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists.

No such thing as the female eye: ditching gender-binary categories in art perception

ABSTRACT We still know little about whether and how gender informs, shapes, or influences art perception. This multidisciplinary article investigates the role that gender might play in the perception of art through historical analyses and an eye-tracking experiment. We first traced how gender identity, sexual identity, and sexual orientation have been used to theorize the existence of a gender-binary difference between viewers in traditional aesthetics and feminist art theory. To test whether such claims reflect any measurable experience, we collected eye-tracking data from 84 participants and divided them into two groups of cisgender, heterosexual individuals by self-definition. Each group participated in two experiments; the first showed digital reproductions of mixed artworks, varying in subject, style, form, and size; the second showed artworks with only one figure, either explicitly showing sex-linked traits or not. In both cases, we measured the number of fixations, average fixation duration, and saccade amplitude as well as differences in the location of fixations and entropy. Our analysis did not detect any significant difference between the groups. This proves empirically, for the first time, that in spite of having been often reiterated in history, gender binary categories are not suitable for describing the visual perception of art.

Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.

Cognition is crucial to brain function, and accurately classifying cognitive load is essential for understanding the psychological processes across tasks. This paper innovatively combines functional near-infrared spectroscopy (fNIRS) with eye tracking technology to delve into the classification of cognitive load at the neurocognitive level. This integration overcomes the limitations of a single modality, addressing challenges such as feature selection, high dimensionality, and insufficient sample capacity. We employ fNIRS-eye tracking technology to collect neural activity and eye tracking data during various cognitive tasks, followed by preprocessing. Using the maximum relevance minimum redundancy algorithm, we extract the most relevant features and evaluate their impact on the classification task. We evaluate the classification performance by building models (naive Bayes, support vector machine, K-nearest neighbors, and random forest) and employing cross-validation. The results demonstrate the effectiveness of fNIRS-eye tracking, the maximum relevance minimum redundancy algorithm, and machine learning techniques in discriminating cognitive load levels. This study emphasizes the impact of the number of features on performance, highlighting the need for an optimal feature set to improve accuracy. These findings advance our understanding of neuroscientific features related to cognitive load, propelling neural psychology research to deeper levels and holding significant implications for future cognitive science.

Blink-induced changes in pupil dynamics are consistent and heritable.

Pupil size and blink rates are heritable but the extent to which they interact with one another has not been properly investigated. Though changes in pupil size due to eye blinks have been reported, they are considered a pupillary artifact. In this study we used the HCP 7T fMRI dataset with resting state eye-tracking data obtained in monozygous and dizygous twins to assess their heritability and their interactions. For this purpose, we characterized the pupil dilation (positive peak) and constriction (negative peak) that followed blink events, which we describe as blink-induced pupillary response (BIPR). We show that the BIPR is highly consistent with a positive dilatory peak (D-peak) around 500ms and a negative constricting peak (C-peak) around 1s. These patterns were reproducible within- and between- subjects across two time points and differed by vigilance state (vigilant versus drowsy). By comparing BIPR between monozygous and dizygous twins we show that BIPR have a heritable component with significant additive genetic (A) and environmental (E) factors dominating the structural equation models, particularly in the time-domain for both D- and C-peaks and amplitude domain for the C-peak. (a2 between 42-49%). Blink duration, pupil size and blink rate were also found to be highly heritable (a2 up to 62% for pupil size). Our study documents an association between BIPR and wakefulness and indicates that BIPR should not be treated as a coincidental artefact, but part of a larger oculomotor system that we label here as Oculomotor Adaptive System, OAS, that is genetically determined.

Research on visual experience evaluation of fortress heritage landscape by integrating SBE–SD method and eye movement analysis

Urbanization has led to homogenizing heritage site landscapes, and the protective measures have become disconnected from public needs. Additionally, the complex and diverse overall characteristics of heritage sites and the uneven distribution of values across different areas are related to the lack of landscape experience assessment. The “subjective + objective” cognitive evaluation and visual perception framework that adopts the Scenic Beauty Estimation Procedure-Semantic Differential (SBE–SD) method and eye-tracking analysis can compensate for the limitations of a single evaluation method by integrating quantitative and qualitative analysis. This research takes the Yi’an Fortress in Zhangpu County, Fujian Province, as the object and examines the visual experiences of different areas and types of landscapes within the Yi’an Fortress. The findings reveal several key insights: (1) Significant differences were found in the landscape experiences of different areas within the heritage site. The visual experience score of the core building area of Yi’an Fortress is (1.01) > the heritage entrance area (0.897) > the residential area (0.841) > the natural ecological area (0.784), indicating that the natural ecological area should be the focus of future protection and development efforts, with a particular emphasis on enhancing the ‘landscape aesthetic’ and ‘landscape cultural’ aspects. (2) The landscape experience scores can be used to understand the reasons for the differences in participants’ experiences of different landscapes. The architectural heritage landscape of the Yi’an Fortress scored highest in the experience evaluation due to its superior performance in terms of ‘landscape form’ factors. The cultural decorative landscape scored next, while the garden greening landscape scored the lowest, due to their poorer performance in terms of ‘landscape form’ and ‘landscape aesthetics’. (3) The eye-tracking data was consistent with the results of the subjective evaluation, validating the “subjective + objective” cognitive evaluation and visual perception framework, employing the SBE–SD method and eye-tracking analysis is a scientific and effective method for assessing the visual experience of heritage landscapes. These results provide a scientific basis for the heritage planners and managers of the Yi’an fortress to improve the landscape environment, better meet public needs, and preserve the unique character of this vital cultural resource. Furthermore, this study offers a new research method and approach for the protection of other heritage landscapes.