McGurk doesn’t work: Individual differences and task demands explain the McGurk illusion

2019 ◽  
Vol 146 (4) ◽  
pp. 3083-3083
Author(s):  
Laura M. Getz ◽  
Joseph C. Toscano
Keyword(s):  
Individual Differences ◽  
Task Demands ◽  
Mcgurk Illusion ◽  
And Task

Quantifying the Effects of Multi-Tasking on Processing Efficiency

2018 ◽  
Vol 62 (1) ◽  
pp. 1717-1721
Author(s):  
Elizabeth L. Fox ◽  
Joseph W. Houpt
Keyword(s):  
Individual Differences ◽  
System Design ◽  
Task Demands ◽  
Task Characteristics ◽  
Baseline Model ◽  
Processing Efficiency ◽  
Different Types ◽  
Unique Individual ◽  
And Task ◽  
Task Environments

The type and amount of task demands that humans must simultaneously process and respond to influences how efficient they are in completing the tasks. Capturing how and to what degree human efficiency changes in different task environments is crucial to inform an appropriate system design. An individual-based analytic approach is necessary to accurately capture performance changes and lend practical suggestions. We can provide designers with the amount and type of task demands that we expect a person to sustain adequate performance given their unique underlying cognitive properties. We develop a metric, multi-tasking throughput (MT), that provides the extent to which a person processes tasks more efficiently, the same, or less efficiently when required to complete several different types of tasks at once. This is a cognitive-based, standardized metric; meaning it yields the relative degree of change from a baseline model that is created to accommodate to unique individual differences, numbers of tasks, and task characteristics. We quantify MT by using transformations of RTs to predict the extent that external demands of multi-tasking exceeds what the cognitive system can accommodate to thereby hindering performance. We use a real world dual-task application to highlight the apparent differences in strategy and ability across individuals and alternative task environments.

scholarly journals Individual differences in visual salience vary along semantic dimensions

2018 ◽  
Author(s):  
Benjamin de Haas ◽  
Alexios L. Iakovidis ◽  
D. Samuel Schwarzkopf ◽  
Karl R. Gegenfurtner
Keyword(s):  
Individual Differences ◽  
Image Features ◽  
Task Demands ◽  
Large Set ◽  
Visual Salience ◽  
Biological Mechanisms ◽  
Attentional Guidance ◽  
Complex Scenes ◽  
Human Adults ◽  
And Task

What determines where we look? Theories of attentional guidance hold that image features and task demands govern fixation behaviour, while differences between observers are ‘noise’. Here, we investigated the fixations of > 100 human adults freely viewing a large set of complex scenes. We found systematic individual differences in fixation frequencies along six semantic stimulus dimensions. These differences were large (> twofold) and highly stable across images and time. Surprisingly, they also held for first fixations directed towards each image, commonly interpreted as ‘bottom-up’ visual salience. Their perceptual relevance was documented by a correlation between individual face salience and recognition skills. The dimensions of individual salience and their covariance pattern replicated across samples from three different countries, suggesting they reflect fundamental biological mechanisms of attention. Our findings show stable individual salience differences along semantic dimensions, with meaningful perceptual implications. Salience reflects features of the observer as well as the image.

Individual Differences in Mental Rotation

2013 ◽  
Vol 60 (3) ◽  
pp. 164-171 ◽  
Author(s):  
Peter Khooshabeh ◽  
Mary Hegarty ◽  
Thomas F. Shipley
Keyword(s):  
Individual Differences ◽  
Mental Rotation ◽  
Response Times ◽  
Task Demands ◽  
Mental Rotation Task ◽  
Imagery Ability ◽  
Alternative Strategies

Two experiments tested the hypothesis that imagery ability and figural complexity interact to affect the choice of mental rotation strategies. Participants performed the Shepard and Metzler (1971) mental rotation task. On half of the trials, the 3-D figures were manipulated to create “fragmented” figures, with some cubes missing. Good imagers were less accurate and had longer response times on fragmented figures than on complete figures. Poor imagers performed similarly on fragmented and complete figures. These results suggest that good imagers use holistic mental rotation strategies by default, but switch to alternative strategies depending on task demands, whereas poor imagers are less flexible and use piecemeal strategies regardless of the task demands.

How Effective Feedback for Training Depends on Learner Resources and Task Demands

2006 ◽  
Author(s):  
Anne Collins McLaughlin ◽  
Wendy A. Rogers ◽  
Arthur D. Fisk
Keyword(s):  
Task Demands ◽  
Effective Feedback ◽  
And Task

Construction of Graphic Symbol Utterances by Children, Teenagers, and Adults: The Effect of Structure and Task Demands

2007 ◽  
Vol 50 (5) ◽  
pp. 1314-1329 ◽  
Author(s):  
Natacha Trudeau ◽  
Ann Sutton ◽  
Emmanuelle Dagenais ◽  
Sophie de Broeck ◽  
Jill Morford
Keyword(s):  
Task Demands ◽  
Graphic Symbol ◽  
Effect Of Structure ◽  
And Task

The Effect of Attention on Repetition Suppression and Multivoxel Pattern Similarity

2013 ◽  
Vol 25 (8) ◽  
pp. 1305-1314 ◽  
Author(s):  
Katherine S. Moore ◽  
Do-Joon Yi ◽  
Marvin Chun
Keyword(s):  
Task Demands ◽  
Initial Presentation ◽  
Fmri Signal ◽  
Repetition Suppression ◽  
Specific Memory ◽  
Pattern Similarity ◽  
Parahippocampal Place Area ◽  
And Task

Fundamental to our understanding of learning is the role of attention. We investigated how attention affects two fMRI measures of stimulus-specific memory: repetition suppression (RS) and pattern similarity (PS). RS refers to the decreased fMRI signal when a stimulus is repeated, and it is sensitive to manipulations of attention and task demands. In PS, region-wide voxel-level patterns of responses are evaluated for their similarity across repeated presentations of a stimulus. More similarity across presentations is related to better learning, but the role of attention on PS is not known. Here, we directly compared these measures during the visual repetition of scenes while manipulating attention. Consistent with previous findings, we observed RS in the scene-sensitive parahippocampal place area only when a scene was attended both at initial presentation and upon repetition in subsequent trials, indicating that attention is important for RS. Likewise, we observed greater PS in response to repeated pairs of scenes when both instances of the scene were attended than when either or both were ignored. However, RS and PS did not correlate on either a scene-by-scene or subject-by-subject basis, and PS measures revealed above-chance similarity even when stimuli were ignored. Thus, attention has different effects on RS and PS measures of perceptual repetition.

Extending the User Action Notation for Research in Individual Differences

1995 ◽  
Vol 39 (4) ◽  
pp. 253-257
Author(s):  
Derek Brock ◽  
Deborah Hix ◽  
Lynn Dievendorf ◽  
J. Gregory Trafton
Keyword(s):  
Individual Differences ◽  
User Interfaces ◽  
Expressive Power ◽  
Interaction Techniques ◽  
Standard Format ◽  
Input Devices ◽  
Interface Specification ◽  
Patterns Of Use ◽  
Multiple Input ◽  
And Task

Software user interfaces that provide users with more than one device, such as a mouse and keyboard, for interactively performing tasks, are now commonplace. Concerns about how to represent individual differences in patterns of use and acquisition of skill in such interfaces led the authors to develop modifications to the standard format of the User Action Notation (UAN) that substantially augment the notation's expressive power. These extensions allow the reader of an interface specification to make meaningful comparisons between functionally equivalent interaction techniques and task performance strategies in interfaces supporting multiple input devices. Furthermore, they offer researchers a new methodology for analyzing the behavioral aspects of user interfaces. These modifications are documented and their benefits discussed.

scholarly journals Why Are We Distracted by Social Media? Distraction Situations and Strategies, Reasons for Distraction, and Individual Differences

2021 ◽  
Vol 12 ◽  
Author(s):  
Christina Koessmeier ◽  
Oliver B. Büttner
Keyword(s):  
Social Media ◽  
Individual Differences ◽  
Online Survey ◽  
Media Use ◽  
Self Control ◽  
Social Media Use ◽  
User Perspective ◽  
Affiliation Motive ◽  
Problematic Social Media Use ◽  
And Task

Social media is a major source of distraction and thus can hinder users from successfully fulfilling certain tasks by tempting them to use social media instead. However, an understanding of why users get distracted by social media is still lacking. We examine the phenomenon of social media distraction by identifying reasons for, situations of, and strategies against social media distraction. The method adopted is a quantitative online survey (N = 329) with a demographically diverse sample. The results reveal two reasons for social media distraction: social (e.g., staying connected and being available) and task-related distraction (e.g., not wanting to pursue a task). We find individual differences in these reasons for distraction. For social distraction, affiliation motive and fear of missing out (FoMO) are significant predictors, while for task-related distraction, self-regulatory capabilities (self-control, problematic social media use) and FoMO are significant predictors. Additionally, typical distraction situations are non-interactive situations (e.g., watching movies, facing unpleasant tasks). Strategies used to reduce distractions mostly involved reducing external distractions (e.g., silencing the device). This paper contributes to the understanding of social media use by revealing insights into social media distraction from the user perspective.

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