I am excited to have been invited to present a position paper on my doctoral work at the upcoming iConference, presented by the iSchools association. The conference focuses on research work by information science scholars worldwide and will be hosted by The Donald Bren School of Information and Computer Sciences at the University of California, Irvine. So, for those interested, I thought of sharing part of this paper here. And if you are planning on attending the conference, please come by the conference workshop!
The affective component of information retrieval system design is becoming increasingly essential within the field of information retrieval, as it encompasses complicated human cognitive processes. Cognitive processes include not only mental processes but also emotion (or affective) processes and responses (Picard, 2001). Therefore, it is important to move toward understanding the affective dimension of information retrieval. The goal of this study is to explore and examine the neurological affective components of information retrieval systems, more specifically in web search processes and search performance.
Over the past decade, information retrieval research studies have evolved and become increasingly sophisticated. System-oriented approach was one of the first types of studies where information retrieval systems were the focal point of attention. However, researchers began to realize that not only do we need to examine machines but we also need to study user interaction with the systems. This, in turn, led to user-oriented approach. Shortly thereafter, researchers began to detect sophisticated cognitive processes when dealing with information retrieval systems. As a result, studies began to turn to cognitive-oriented approaches.
Most recently, research communities are detecting how human emotions may play a significant role in human-computer-interaction. Expressions such as “pleasurable engineering” or “emotional design” have become the driving factors in system design, and these expressions have also been extended to information retrieval system design (Nahl & Bilal, 2007). These emerging factors and expressions indicate the important role of emotions in human-computer-interaction, highlighting the importance of including the affective dimensions when designing information retrieval systems. However, our understanding of how emotions affect search processes, as revealed in search performance—search effectiveness and search efficiency—is limited (Nahl & Bilal, 2007).
As a result, the emotion-oriented approach has risen to the surface, making researchers realize the potential effects of affective dimensions on user information retrieval processes. More specifically, researchers are increasingly exploring the neurological aspects of cognitive and emotion responses. This research intends to contribute to the emotion-oriented approach studies, aiming to add value to the evolution of information retrieval research approaches by further exploring neuro-information science.
2 Problem Statement and Research Questions
There is a gap in the current body of knowledge on the effects of physiological and neurological emotion responses in information retrieval, more specifically on web search. This pilot study aimed to examine the effect of different dimensions of emotions on web search performance, as revealed in search efficiency and search effectiveness.
- Q1: How do dimensions of emotions affect search effectiveness?
- Q2: How do dimensions of emotions affect search efficiency?
- Q3: Are there any interactional effects between dimensions of emotions and search performance?
The hypothesis is that positive emotional states have positive effects on information retrieval and negative emotional states affect users’ web search performance negatively.
3 Industry implications
In an era when humans are creating brain controlled airplanes, neuro-gaming, and robots that learn behavior by reading human emotions, there appear to be no limits in having search engines read human emotions in order to improve search results based on the neurological feedback they receive from brain waves. Thanks to technologies such as Interaxon and Emotiv, EEG devices have readily been made available to researchers interested in neuro-related studies that otherwise would have not had access to expensive fMRI machines. Although the two devices measure different aspects of the brain, nonetheless, EEG devices help researchers conduct neuro-related studies.
I envision my dissertation adding to the body of knowledge of Neuro Information Science in developing search engines that, through wearable computing devices that are able to read brain waves and dimensions of emotions in order to improve search results based on the neurological feedback that the search engines receive from brain waves. In other words, search engines become an extension of the human brain by receiving brain waves that constantly provide neurological feedback in terms of the search results that they provide. All the while, the search engine reads brain waves by receiving the brain signals through wearable computing devices. Gradually, the search engine may ‘learn to improve’ its results based on, for example, alpha (calm) brain waves received.
This research topic will be a beneficial addition to the current body of knowledge in the field of Neuro Information Science. We need to increase our body of knowledge and strive to understand how human affective responses impact human-computer-interaction. This, in turn, will help us design smarter information retrieval systems.
Most recently, Artificial Neural Networks, the complex adaptive deep learning systems (a step beyond machine learning) that use statistical learning algorithms, increasingly strive to model the human brain’s biological neuron networks and architecture. These computations, although artificial, strive to model human decision-making processes and aim to estimate a wide range of computational functions based on large sets of data inputs. It is worth noting that artificial neural networks, while quite sophisticated in computing and recognizing patterns, at the moment, primarily receive their input from data types, such as pixel, binary, digital, etc. These artificial neural networks are codes that aim to stimulate the way in which the human brain learns, more specifically in recognizing patterns or creating memories. The codes are organized in layers in order for the systems to learn to understand various data inputs.
While the artificial neural networks are still in their infancy, it is essential to recognize that, to this day and to my knowledge, they are based solely on digital data input. System programmers and architectures fail to approach these efforts based on a holistic view of the human brain. In other words, the main component of emotion is missing from this equation. I propose that adding one additional data input of human emotion may improve these artificial neural networks. One of the main contributions of this research paper is my proposal to the scholars of Artificial Intelligence to include human emotions readings via wearable computing devices as an additional data put for their statistical learning algorithms when creating these artificial neural networks.