On saliency, affect and focused attention

We study how the visual catchiness (saliency) of relevant information impacts user engagement metrics such as focused attention and emotion (affect). Participants completed tasks in one of two conditions, where the task-relevant information either appeared salient or non-salient. Our analysis provides insights into relationships between saliency, focused attention, and affect. Participants reported more distraction in the non-salient condition, and non-salient information was slower to find than salient. Lack-of-saliency led to a negative impact on affect, while saliency maintained positive affect, suggesting its helpfulness. Participants reported that it was easier to focus in the salient condition, although there was no significant improvement in the focused attention scale rating. Finally, this study suggests user interest in the topic is a good predictor of focused attention, which in turn is a good predictor of positive affect. These results suggest that enhancing saliency of user-interested topics seems a good strategy for boosting user engagement.

[1]  Frank M. Shipman,et al.  The ownership and reuse of visual media , 2011, JCDL '11.

[2]  L. Itti,et al.  Visual causes versus correlates of attentional selection in dynamic scenes , 2006, Vision Research.

[3]  M. Banerjee,et al.  Beyond kappa: A review of interrater agreement measures , 1999 .

[4]  Siddharth Suri,et al.  Conducting behavioral research on Amazon’s Mechanical Turk , 2010, Behavior research methods.

[5]  Gabriella Kazai,et al.  Towards a science of user engagement (Position Paper) , 2011 .

[6]  Joemon M. Jose,et al.  Affective feedback: an investigation into the role of emotions in the information seeking process , 2008, SIGIR '08.

[7]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[8]  L. Itti,et al.  Search Goal Tunes Visual Features Optimally , 2007, Neuron.

[9]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[10]  Heather L. O'Brien,et al.  Exploring user engagement in online news interactions , 2011, ASIST.

[11]  Donna L. Hoffman,et al.  Measuring the Customer Experience in Online Environments: A Structural Modeling Approach , 2000 .

[12]  Laurent Itti,et al.  Applying computational tools to predict gaze direction in interactive visual environments , 2008, TAP.

[13]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[14]  Caroline Hummels,et al.  Let's Make Things Engaging , 2005, Funology.

[15]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[16]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[17]  Ming-Hui Huang,et al.  Designing website attributes to induce experiential encounters , 2003, Comput. Hum. Behav..

[18]  Gabriella Kazai,et al.  Towards a science of user engagement. , 2011 .

[19]  Elaine Toms,et al.  The development and evaluation of a survey to measure user engagement , 2010, J. Assoc. Inf. Sci. Technol..

[20]  Irene Lopatovska.  Searching for good mood: Examining relationships between search task and mood , 2009, ASIST.

[21]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[22]  Jacek Gwizdka,et al.  The role of subjective factors in the information search process , 2009, J. Assoc. Inf. Sci. Technol..

[23]  Steven Pace,et al.  A grounded theory of the flow experiences of Web users , 2004, Int. J. Hum. Comput. Stud..

[24]  Morgan Jennings,et al.  Theory and models for creating engaging and immersive ecommerce Websites , 2000, SIGCPR '00.

[25]  Paul A. Cairns,et al.  Measuring and defining the experience of immersion in games , 2008, Int. J. Hum. Comput. Stud..