Fixations on objects in natural scenes: dissociating importance from salience.

Front Psychol

Neurophysics, Philipps-University Marburg Marburg, Germany ; Physiological Genomics, Ludwig Maximilian University Munich, Germany.

Published: July 2013

The relation of selective attention to understanding of natural scenes has been subject to intense behavioral research and computational modeling, and gaze is often used as a proxy for such attention. The probability of an image region to be fixated typically correlates with its contrast. However, this relation does not imply a causal role of contrast. Rather, contrast may relate to an object's "importance" for a scene, which in turn drives attention. Here we operationalize importance by the probability that an observer names the object as characteristic for a scene. We modify luminance contrast of either a frequently named ("common"/"important") or a rarely named ("rare"/"unimportant") object, track the observers' eye movements during scene viewing and ask them to provide keywords describing the scene immediately after. When no object is modified relative to the background, important objects draw more fixations than unimportant ones. Increases of contrast make an object more likely to be fixated, irrespective of whether it was important for the original scene, while decreases in contrast have little effect on fixations. Any contrast modification makes originally unimportant objects more important for the scene. Finally, important objects are fixated more centrally than unimportant objects, irrespective of contrast. Our data suggest a dissociation between object importance (relevance for the scene) and salience (relevance for attention). If an object obeys natural scene statistics, important objects are also salient. However, when natural scene statistics are violated, importance and salience are differentially affected. Object salience is modulated by the expectation about object properties (e.g., formed by context or gist), and importance by the violation of such expectations. In addition, the dependence of fixated locations within an object on the object's importance suggests an analogy to the effects of word frequency on landing positions in reading.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3715740PMC
http://dx.doi.org/10.3389/fpsyg.2013.00455DOI Listing

Publication Analysis

Top Keywords

scene
9
object
9
natural scenes
8
contrast
8
unimportant objects
8
natural scene
8
scene statistics
8
objects
5
fixations objects
4
natural
4

Similar Publications

Significance: Decoding naturalistic content from brain activity has important neuroscience and clinical implications. Information about visual scenes and intelligible speech has been decoded from cortical activity using functional magnetic resonance imaging (fMRI) and electrocorticography, but widespread applications are limited by the logistics of these technologies.

Aim: High-density diffuse optical tomography (HD-DOT) offers image quality approaching that of fMRI but with the silent, open scanning environment afforded by optical methods, thus opening the door to more naturalistic research and applications.

View Article and Find Full Text PDF

Background: Classroom behavior is one of the important variables for the curriculum in the learning path of learners. The aim of this study was to explain the classroom behavior process of medical sciences students.

Materials And Methods: In a qualitative study using the grounded theory approach, the classroom behavior of 21 students from different medical fields was assessed.

View Article and Find Full Text PDF

Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.

View Article and Find Full Text PDF

Enhancing doctor-patient communication using large language models for pathology report interpretation.

BMC Med Inform Decis Mak

January 2025

Department of Thoracic Surgery, Guizhou Provincial People's Hospital, No. 83, Zhongshan East Road, Guiyang, Guizhou, 550000, China.

Background: Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to comprehend. This complexity can adversely affect the quality of communication between doctors and patients about their diagnosis and treatment options, potentially impacting patient outcomes such as understanding of their condition, treatment adherence, and overall satisfaction.

View Article and Find Full Text PDF

Background: Moral intelligence is a significant and influential factor in the delivery of principled and high-quality care. This is because moral intelligence is the ability to recognize and be sensitive to moral issues, which contributes to the organization of appropriate behavior in the face of moral issues. This is particularly pertinent given that pre-hospital emergency medical services personnel (prehospital EMS personnel) frequently encounter stressful and tension-filled situations.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!