Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Kathryn Koehler is active.

Publication


Featured researches published by Kathryn Koehler.


Journal of Experimental Psychology: Human Perception and Performance | 2017

Beyond scene gist: Objects guide search more than scene background.

Kathryn Koehler; Miguel P. Eckstein

Although the facilitation of visual search by contextual information is well established, there is little understanding of the independent contributions of different types of contextual cues in scenes. Here we manipulated 3 types of contextual information: object co-occurrence, multiple object configurations, and background category. We isolated the benefits of each contextual cue to target detectability, its impact on decision bias, confidence, and the guidance of eye movements. We find that object-based information guides eye movements and facilitates perceptual judgments more than scene background. The degree of guidance and facilitation of each contextual cue can be related to its inherent informativeness about the target spatial location as measured by human explicit judgments about likely target locations. Our results improve the understanding of the contributions of distinct contextual scene components to search and suggest that the brain’s utilization of cues to guide eye movements is linked to the cue’s informativeness about the target’s location.


Current Biology | 2017

Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes.

Miguel P. Eckstein; Kathryn Koehler; Lauren Welbourne; Emre Akbas

Even with great advances in machine vision, animals are still unmatched in their ability to visually search complex scenes. Animals from bees [1, 2] to birds [3] to humans [4-12] learn about the statistical relations in visual environments to guide and aid their search for targets. Here, we investigate a novel manner in which humans utilize rapidly acquired information about scenes by guiding search toward likely target sizes. We show that humans often miss targets when their size is inconsistent with the rest of the scene, even when the targets were made larger and more salient and observers fixated the target. In contrast, we show that state-of-the-art deep neural networks do not exhibit such deficits in finding mis-scaled targets but, unlike humans, can be fooled by target-shaped distractors that are inconsistent with the expected targets size within the scene. Thus, it is not a human deficiency to miss targets when they are inconsistent in size with the scene; instead, it is a byproduct of a useful strategy that the brain has implemented to rapidly discount potential distractors.


Journal of Vision | 2014

What do saliency models predict

Kathryn Koehler; Fei Guo; Sheng Zhang; Miguel P. Eckstein


Journal of Vision | 2017

Temporal and peripheral extraction of contextual cues from scenes during visual search

Kathryn Koehler; Miguel P. Eckstein


Cognitive Science | 2015

Scene Inversion Slows the Rejection of False Positives through Saccade Exploration During Search.

Kathryn Koehler; Miguel P. Eckstein


Journal of Vision | 2012

Human versus Bayesian Optimal Learning of Eye Movement Strategies During Visual Search

Kathryn Koehler; Emre Akbas; Matthew F. Peterson; Miguel P. Eckstein


Journal of Vision | 2011

Assessing models of visual saliency against explicit saliency judgments from one hundred humans viewing eight hundred real scenes

Kathryn Koehler; Fei Guo; Sheng Zhang; Miguel P. Eckstein


publisher | None

title

author


Journal of Vision | 2016

Scene Context Leads to Inattentional Scale Blindness during Search

Miguel P. Eckstein; Kathryn Koehler


Journal of Vision | 2015

Independent Contributions of Multiple Types of Scene Context on Eye Movement Guidance and Visual Search Performance

Kathryn Koehler; Miguel P. Eckstein

Collaboration


Dive into the Kathryn Koehler's collaboration.

Top Co-Authors

Avatar
Top Co-Authors

Avatar

Emre Akbas

University of California

View shared research outputs
Top Co-Authors

Avatar

Fei Guo

University of California

View shared research outputs
Top Co-Authors

Avatar

Sheng Zhang

University of California

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Researchain Logo
Decentralizing Knowledge