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Covert Attention with Eye Movements: Computational Modeling and Empirical Data Simulation
Covert Attention with Eye Movements: Computational Modeling and Empirical Data Simulation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105319
- ISBN
- 9798297666030
- DDC
- 612.88
- 저자명
- Tam, Joyce.
- 서명/저자
- Covert Attention with Eye Movements: Computational Modeling and Empirical Data Simulation
- 발행사항
- [Sl] : The Pennsylvania State University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 184 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 주기사항
- Advisor: Wyble, Bradley.
- 학위논문주기
- Thesis (Ph.D.)--The Pennsylvania State University, 2025.
- 초록/해제
- 요약Visual attention is a set of cognitive mechanisms that allows us to selectively prioritize a subset of visual input for further processing. Attentional selection can often be guided by overt movements, such as orienting one's body, head, or eyes towards a region of interest; but information can also be prioritized covertly. The integrative use of covert and overt attention is fundamental to finding relevant information in the world, where visual input is rich, complex, and dynamic. However, most work on attention is based on desktop paradigms where overt movements is often strictly controlled. The current goal therefore was to take a step towards bridging the gap between laboratory and naturalistic paradigms through understanding the integrative use of eye movements and covert attention in visual search.A combination of computational and empirical approaches was used. First, I developed a novel computational model: Reflexive Attention Gradient through Neural AttractOr Competition - Pulvinar gUided Selective Tuning (RAG+). The model was parametrized on a battery of laboratory findings and offered a biologically plausible theory of how covert and overt visual attention could be programmed within an integrated system but observed independently. Particularly, the model simulated saccade decisions using both feedforward and feedback operations but linked the N2pc component - an indicator of covert selection - exclusively to feedback. As feedback functioned to enhance the spatial specificity of selection, it was theorized to be task-dependent and implemented only if high-resolution selections were required. With the optionality of feedback, the model simulated the optionality of the N2pc component, explaining how the presence of this component could be dissociated from saccade decisions.Secondly, empirical data were collected using a visual search task hosted in virtual reality (VR), where participants could freely move their eyes while searching for targets. Model predictions were tested on VR data to assess the ecological validity of laboratory-based theories. RAG+ simulated both covert and overt attentional phenomena above-chance, showing that laboratory-based theories encapsulated in the model's mechanisms could apply to search within a more naturalistic setting. Moreover, the pyramidal structure of RAG+ allowed for flexible visual remapping across small viewpoint changes, providing new insights into the maintenance of visual stability amid the integrative use of covert and overt attention. Overall, by building a formal theory of the integrative use of covert and overt attention and taking a step to evaluate these theory-based predictions in a virtual immersive search environment, I seek to lay the foundation for understanding visual attention in its naturalistic form.
- 일반주제명
- Eye movements
- 일반주제명
- Electroencephalography
- 일반주제명
- Brain research
- 일반주제명
- Virtual reality
- 일반주제명
- Keyboards
- 일반주제명
- Information technology
- 일반주제명
- Neurosciences
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■035 ▼a(MiAaPQ)PennState19232jzt5630
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a612.88
■1001 ▼aTam, Joyce.
■24510▼aCovert Attention with Eye Movements: Computational Modeling and Empirical Data Simulation
■260 ▼a[Sl]▼bThe Pennsylvania State University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a184 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Wyble, Bradley.
■5021 ▼aThesis (Ph.D.)--The Pennsylvania State University, 2025.
■520 ▼aVisual attention is a set of cognitive mechanisms that allows us to selectively prioritize a subset of visual input for further processing. Attentional selection can often be guided by overt movements, such as orienting one's body, head, or eyes towards a region of interest; but information can also be prioritized covertly. The integrative use of covert and overt attention is fundamental to finding relevant information in the world, where visual input is rich, complex, and dynamic. However, most work on attention is based on desktop paradigms where overt movements is often strictly controlled. The current goal therefore was to take a step towards bridging the gap between laboratory and naturalistic paradigms through understanding the integrative use of eye movements and covert attention in visual search.A combination of computational and empirical approaches was used. First, I developed a novel computational model: Reflexive Attention Gradient through Neural AttractOr Competition - Pulvinar gUided Selective Tuning (RAG+). The model was parametrized on a battery of laboratory findings and offered a biologically plausible theory of how covert and overt visual attention could be programmed within an integrated system but observed independently. Particularly, the model simulated saccade decisions using both feedforward and feedback operations but linked the N2pc component - an indicator of covert selection - exclusively to feedback. As feedback functioned to enhance the spatial specificity of selection, it was theorized to be task-dependent and implemented only if high-resolution selections were required. With the optionality of feedback, the model simulated the optionality of the N2pc component, explaining how the presence of this component could be dissociated from saccade decisions.Secondly, empirical data were collected using a visual search task hosted in virtual reality (VR), where participants could freely move their eyes while searching for targets. Model predictions were tested on VR data to assess the ecological validity of laboratory-based theories. RAG+ simulated both covert and overt attentional phenomena above-chance, showing that laboratory-based theories encapsulated in the model's mechanisms could apply to search within a more naturalistic setting. Moreover, the pyramidal structure of RAG+ allowed for flexible visual remapping across small viewpoint changes, providing new insights into the maintenance of visual stability amid the integrative use of covert and overt attention. Overall, by building a formal theory of the integrative use of covert and overt attention and taking a step to evaluate these theory-based predictions in a virtual immersive search environment, I seek to lay the foundation for understanding visual attention in its naturalistic form.
■590 ▼aSchool code: 0176.
■650 4▼aEye movements
■650 4▼aElectroencephalography
■650 4▼aBrain research
■650 4▼aVirtual reality
■650 4▼aKeyboards
■650 4▼aInformation technology
■650 4▼aNeurosciences
■690 ▼a0489
■690 ▼a0317
■71020▼aThe Pennsylvania State University.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0176
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360191▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
