Jordan Barnes

Jordan Barnes
Simon Fraser University · Department of Psychology

MA

About

9
Publications
892
Reads
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38
Citations
Additional affiliations
January 2009 - present
Simon Fraser University
Position
  • Research Assistant
September 2008 - December 2012
Simon Fraser University
Position
  • Research Assistant

Publications

Publications (9)
Article
Full-text available
It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. Here we introduce LAG-1, a dynamic neural field model of learning, attention and gaze, that we fit to human learning and eye-movement data from two category learning experiments. LAG-1 comprises thre...
Article
Active sensing theory is founded upon the dynamic relationship between information sampling and an observer’s evolving goals. Oculomotor activity is a well studied method of sampling; a mouse or a keyboard can also be used to access information past the current screen. We examine information access patterns of StarCraft 2 players at multiple skill...
Conference Paper
Full-text available
Abstract Computational models of category learning and attention have historically focused on capturing trial and experiment level interactions between attention and decision. However, evidence has been accumulating that suggests that the moment-to-moment attentional dynamics of an individual affects both their immediate decision-making processes a...
Conference Paper
Full-text available
Here we introduce a simple actor-critic model of eye movements during category learning that we call RLAttn (Reinforcement Learning of Attention). RLAttn stores the rewards it receives for making decisions or performing actions, while attempting to associate stimuli with particular categories. Over multiple trials, RLAttn learns that a large reward...
Article
Full-text available
Learning how to allocate attention properly is essential for success at many categorization tasks. Advances in our understanding of learned attention are stymied by a chicken-and-egg problem: there are no theoretical accounts of learned attention that predict patterns of eye movements, making data collection difficult to justify, and there are not...
Article
A general logic for data-based test evaluation based on Slaney and Maraun's (2008) framework is described. On the basis of this framework and other well-known test theoretic results, a set of guidelines is proposed to aid researchers in the assessment of the psychometric properties of the measures they use in their research. The guidelines are orga...
Article
In this article, we respond to a commentary by Holden and Marjanovic (this issue) on Slaney, Storey, and Barnes’ article “‘Is My Test Valid?’: Guidelines for the Practicing Psychologist for Evaluating the Psychometric Properties of Measures” (this issue). Specifically, we reply to Holden and Marjanovic's claims that our guidelines: endorse a “const...
Data
Full-text available
mark.blair@sfu.ca) Calen Walshe (calen.walshe@sfu.ca) Jordan I. Barnes (jordanb@sfu.ca) Lihan Chen (bill.lihan@gmail.com) Abstract Learning how to allocate attention properly is essential for success at many tasks. Extant theories of categorization assume that learning to allocate attention is an error-driven process, where shifts in attention are...
Article
This paper attempts to clarify some of the challenges associated with high-level learning and provide a context for future research directions in this area. A moderate to advanced level of familiarity with the fluid-analogy systems developed by Douglas Hofstadter and his team of researchers at Indiana University is presupposed. These are models of...

Projects

Project (1)
Project
Dynamic neural field modeling of eye movements and category learning.