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William Cowan

dblp:36/203 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Human-computer interaction and ubiquitous computing · 2Applied, interdisciplinary, general and emerging computing · 2Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Accessibility and assistive technology · 56% Interaction techniques and input · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Accessibility and assistive technology › assistive technology for visual impairment
assistive technology for blind users
0.112009
On the audio representation of distance for blind users · CHI 2009
Interaction techniques and input › non-visual interaction › auditory interaction
auditory display
0.112009
On the audio representation of distance for blind users · CHI 2009
Accessibility and assistive technology › assistive technology
wearable assistive device
0.012009
On the audio representation of distance for blind users · CHI 2009

Methods — techniques the papers use, named apart from their topics

user study · 0.1response time measurement · 0.1
YearPublicationVenuePosition
2017 Thinking and Guessing: Bayesian and Empirical Models of How Humans Search
Marta Kryven, Tomer D. Ullman, William Cowan, Josh Tenenbaum
CogSci3
2016 Outcome or Strategy? A Bayesian Model of Intelligence Attribution
Marta Kryven, Tomer D. Ullman, William Cowan, Josh Tenenbaum
CogSci3
2013 Modelling perceptually efficient aquatic environments
abstract
Water is fluid and changeable, its colour and shape are determined by its environment. For example, deep and shallow water require very different physical simulations [Darles et al. 2011]. Yet humans effortlessly discriminate between water and non-water: there must be simple and unique perceptual features making discrimination possible. We are conducting an empirical study of water recognition and here report the preliminary results.
Marta Kryven, William Cowan
SAP2
2009 On the audio representation of distance for blind users
abstract
This study examines methods for displaying distance information to blind travellers using sound, focussing on abstractions of methods currently used in commercial Electronic Travel Aids (ETAs). Ten blind participants assessed three sound encodings commonly used to convey distance information by ETAs: sound frequency (Pitch), Ecological Distance (ED), and temporal variation or Beat Rate (BR). Response time and response correctness were chosen for measures.
Martin Talbot, William Cowan
CHI2