Information retrieval processes entail complicated cognitive processes, which are also composed human emotion responses (Picard, 2001). These entail physiological and neurological reactions. In order to understand the role of affective responses in information retrieval, more specifically within search process, researchers need to investigate these interactions from multiple perspectives (Scherer, 2005). However, our understanding of how emotions affect information retrieval, as revealed in search performance, is limited (Nahl & Bilal, 2007).
There is a gap in the body of knowledge on the effects of physiological and neurological responses on information retrieval, more specifically on web search performance. My doctoral research aims to examine cognitive relationships between dimensions of human emotions and information retrieval, as in search performance. My aim is to increase our understanding in regards to affective search, improving information systems design practices, and investigating ways to design ‘smart’ information systems that learn and improve search results based on neuro feedback. This pilot study examined the neurological relationship between dimensions of emotions and web search performance by applying emerging and cutting edge research technologies, such as electroencephalography (EEG), thereby increasing our understanding of affective search and improving information systems design practices.
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.
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.
In this session we will cover:
- Q1: How do dimensions of emotions affect search effectiveness?
- Q2: How do dimensions of emotions affect search efficiency?