Back

SFU Cognitive Science Lab

About

 

The Cognitive Science Lab at Simon Fraser University  studies learning, visual attention, and their interconnections to understand how learning changes the way we access information—both with our eyes, and via computer interfaces—and how accessing the correct information can improve our learning. Our methodologies include: experimental studies, naturalistic datasets of real world tasks (e.g., video games) in combination with eye-tracking, computational modelling of cognition, and big data analyses.

StarCraft 2

Using Esports to Study Cognition and Expertise

StarCraft 2 is a real time strategy video game in which players manage a military campaign against opponents. There are three important factors of StarCraft 2 that make it suitable for attention and learning research.

  1. Players use the interface to move their display to different portions of the environment, perform actions on the units/structures at that location, and shift their view elsewhere. This pattern bears a strong resemblance to eye-gaze fixations, and they reflect the spatial and temporal sampling of information.
  2. StarCraft 2 is a complex game and a domain of genuine expertise and diversity of skill. As in traditional sports, there are full-time professional players who compete for substantial prize pools.
  3.  StarCraft 2 stores every action a player performs in replay files which are available to researchers, allowing us investigate performance of a naturalistic task non-invasively with a high degree of precision.
➕ Digit eyes: Learning-related changes in information access in a computer game parallel those of oculomotor attention in laboratory studies【 Author Credit
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 levels. The first measures are analogous to existing eye-movement studies: fixation frequency, fixation targets, and fixation duration all change as a function of skill, and are commensurate with known properties of eye movements in learning. Actions that require visual attention at moderate skill levels are eventually performed with little visual attention at all. This (a) confirms the generalizability of laboratory studies of attention and learning using eye movements to digital interface use, and (b) suggests that a wide variety of information access behaviors may be considered as a unified set of phenomena.
Full Text
➕ Over the Hill at 24: Persistent Age-Related Cognitive-Motor Decline in Reaction Times in an Ecologically Valid Video Game Task Begins in Early Adulthood
Typically studies of the effects of aging on cognitive-motor performance emphasize changes in elderly populations. Although some research is directly concerned with when age-related decline actually begins, studies are often based on relatively simple reaction time tasks, making it impossible to gauge the impact of experience in compensating for this decline in a real world task. The present study investigates age-related changes in cognitive motor performance through adolescence and adulthood in a complex real world task, the real-time strategy video game StarCraft 2. In this paper we analyze the influence of age on performance using a dataset of 3,305 players, aged 16-44, collected by Thompson, Blair, Chen & Henrey. Using a piecewise regression analysis, we find that age-related slowing of within-game, self-initiated response times begins at 24 years of age. We find no evidence for the common belief expertise should attenuate domain-specific cognitive decline. Domain-specific response time declines appear to persist regardless of skill level. A second analysis of dual-task performance finds no evidence of a corresponding age-related decline. Finally, an exploratory analyses of other age-related differences suggests that older participants may have been compensating for a loss in response speed through the use of game mechanics that reduce cognitive load.
Full Text
➕ Robustness of performance during domain change in an esport: A study of within-expertise transfer
Research on the transfer of skill from the circumstances in which it was learned to partially or completely novel tasks or situations is a foundational topic in the study of learning, memory, education, and expertise. A long history of transfer research has led to the conclusion that skill learning is generally domain specific. One important transfer problem occurs when a domain of expertise undergoes a fundamental shift, as when experts must adapt to changes in technology, rules, or professional practice. Here we examine skill maintenance in StarCraft 2, a video game of skills which undergoes frequent changes due to updates and includes a variety of gameplay options. Of particular interest are two competing predictions about how transfer will interact with expertise in this domain. The first approach emphasizes perceived similarity of the domains and predicts that skilled individuals will exhibit more favourable transfer than novices as these people will know enough to avoid processes, methods, and strategies which no longer apply after a domain change. The second emphasizes maximal adaptation to task constraints and predicts that experts will suffer the most during a domain change because of the loss of exploitable affordances. Neither approach did a good job explaining behaviour after the major game update called ‘StarCraft 2: Heart of the Swarm,’ perhaps because transfer was generally strong across all players. However, when examining transfer in the context of larger changes to gameplay, transfer seemed slightly better in more experienced players. The theoretical implications of this apparent interaction effect, and of the apparent resilience of more experienced StarCraft 2 players to transfer costs, are discussed.
Full Text

ExNovo

Human Computer Interfaces

ExNovo is aimed at designing and testing a new computer interface that is grounded in what we know about human cognition. The effectiveness of Human-Computer Interfaces (HCI) are constrained by the limits human memory and attention, and existing interfaces leave a lot to be desired in how they can often overwhelm users. Think for instance about how many hotkeys your favourite text editor has, versus how many you actually use, versus having to dig through layer after layer after layer of GUI menus to find a function.

  

The ExNovo (*from the beginning*) interface aims to unify the speed of hotkeys with the learnability of a GUI, investigates how speed and performance differs between ExNovo and traditional menu-based interfaces, and explores how interface elements such as sound and visual coding might help to make interfaces easier to learn. 

 

【 Paper out for review】

Virtual Reality

We are starting work into testing the viability of immersive virtual reality in conducting cognitive science research. Using the categroy learning paradigm, we are exploring how people change their behaviour over time as they learn to categorize different stimuli into their appropriate groups.

By the end of the 2023 edition, I have reviewed more than 100 papers, ranging from Germanic linguists to using deep learning with EEGs; from the philosophy of modular consciousness to false memory production, and beyond.

➕ Comparing virtual reality, desktop-based 3D, and 2D versions of a category learning experiment【 Author Credit
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 levels. The first measures are analogous to existing eye-movement studies: fixation frequency, fixation targets, and fixation duration all change as a function of skill, and are commensurate with known properties of eye movements in learning. Actions that require visual attention at moderate skill levels are eventually performed with little visual attention at all. This (a) confirms the generalizability of laboratory studies of attention and learning using eye movements to digital interface use, and (b) suggests that a wide variety of information access behaviors may be considered as a unified set of phenomena.
Full Text

Category Learning

The lab’s foundation is experiments on category learning, using eyetracking data to measure attention allocation as participants learn a categorization task. By varying feature set, information access, cost of errors, and more, we’re seeking insights into attentional allocation, learning, and cognitive modeling.