Eye movements in real-world scene photographs: General characteristics and effects of viewing task

Cronin, Deborah; Hall, Elizabeth H.; Goold, Jessica; Hayes, Taylor R.; Henderson, John M.

doi:10.31234/osf.io/e74kb

Cited by 3 publications

(3 citation statements)

References 66 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have previously used this eye-movement data set to study general eye-movement characteristics in scenes (Cronin et al, 2020). The ConceptNet and center-proximity results are presented here for the first time.…”

Section: Methodsmentioning

confidence: 99%

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

Hayes

Henderson

2021

Psychol Sci

Self Cite

View full text Add to dashboard Cite

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

show abstract

Section: Methodsmentioning

confidence: 99%

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

Hayes

Henderson

2021

Psychol Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have previously used this eye movement data set to study general eye movement characteristics in scenes (Cronin, Hall, Goold, Hayes, & Henderson, 2020). The Con-ceptNet and center proximity results are presented here for the first time.…”

Section: Participantsmentioning

confidence: 99%

Looking for Semantic Similarity: What a Vector Space Model of Semantics Can Tell Us About Attention in Real-world Scenes

Hayes¹,

Henderson²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real- world scenes but is poorly understood. Here we examined the relationship between object semantics and attention by combining a vector space model of semantics with eye movements in scenes. Within this approach, the vector space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention, with greater attention to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

show abstract

“…Far fewer examples exist that demonstrate adult adaptive flexibility in active sensing. Humans, for example, depend on eye movements to visually sense the world [ 11 – 15 ]. Humans are known to move their eyes differently depending on the task [ 15 ], but it is not known if they can learn a new eye movement strategy that is beneficial for a task they have never encountered before.…”

Section: Introductionmentioning

confidence: 99%

Adaptive learning and recall of motor-sensory sequences in adult echolocating bats

Taub

Yovel

2021

BMC Biol

View full text Add to dashboard Cite

Background Learning to adapt to changes in the environment is highly beneficial. This is especially true for echolocating bats that forage in diverse environments, moving between open spaces to highly complex ones. Bats are known for their ability to rapidly adjust their sensing according to auditory information gathered from the environment within milliseconds but can they also benefit from longer adaptive processes? In this study, we examined adult bats’ ability to slowly adapt their sensing strategy to a new type of environment they have never experienced for such long durations, and to then maintain this learned echolocation strategy over time. Results We show that over a period of weeks, Pipistrellus kuhlii bats gradually adapt their pre-takeoff echolocation sequence when moved to a constantly cluttered environment. After adopting this improved strategy, the bats retained an ability to instantaneously use it when placed back in a similarly cluttered environment, even after spending many months in a significantly less cluttered environment. Conclusions We demonstrate long-term adaptive flexibility in sensory acquisition in adult animals. Our study also gives further insight into the importance of sensory planning in the initiation of a precise sensorimotor behavior such as approaching for landing.

show abstract

Eye movements in real-world scene photographs: General characteristics and effects of viewing task

Cited by 3 publications

References 66 publications

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

Looking for Semantic Similarity: What a Vector Space Model of Semantics Can Tell Us About Attention in Real-world Scenes

Adaptive learning and recall of motor-sensory sequences in adult echolocating bats

Contact Info

Product

Resources

About