The neurological aspect and component of information retrieval system design is gaining increased attention within the field of Information Science, as it encompasses complicated human brain activities.

On one hand, the physiological components hold the physiological human responses, such as heart rate or facial expressions, and on the other hand the neurological components, such as electroencephalogram (EEG), hold the neurological or human brain activities and response. While there are numerous LIS studies that have studied and included the physiological responses, few have examined the neurological components of information seeking processes.

Furthermore, the affective component of information retrieval system design is becoming increasingly essential within the field of Information Science, as it encompasses complicated human cognitive processes. Cognitive processes include not only mental processes but also emotion (or affective) processes and responses.  

In the field of Psychology, there are two major disciplines of emotions; discrete and continuous emotions. Within the field of LIS the studies that have examined the role of emotions have mainly examined the role of discrete emotions, such as happiness or frustration. However,  few have examined the role continuous emotions, or so called the dimensions of emotions (valence or arousal). Since these are two major types of human emotions, and in order to build a more holistic view of the role of emotions in information seeking processes, it is important to move toward understanding the affective dimension of information retrieval as well.

One of the most established theoretical frameworks of information search processes is Carol Kuhlthau’s Information Search Process (ISP) model. In this model, through decades of empirical research, Kuhlthau established a six-step model of the holistic experience of the information seekers. These holistic experiences include the affective, cognitive, and physical experiences of the users. Three of these six stages (exploration, formulation, and collection) entail the actual act and process of the users seeking and searching for information using a search system, for example an online search engine. For this reason, this study mainly focuses on these three steps of the information search stages.

The goal of this study is to explore and examine the role of the neurological components as well as the dimensions of emotions of information seeking processes, more specifically when it comes to online information search processes, pertaining to the ISP Model. More clearly, this study explores, examines, and maps the neurological components of  the ISP Model and examines the impact of dimensions of emotions on the neurological responses of the ISP model, more specifically when it comes to online information search processes, pertaining to the ISP Model.

Many times I have been asked about the way in which I processed and graphed the EEG data that I collected for my doctoral studies. For the purpose of my dissertation, I collected the EEG data using the Emotiv neuroheadset and used the EEGLAB open source software to process and graph the EEG data. In this post, I have simplified the steps that I took in order to process my EEG data. Please note that I self-educated myself by reading through tutorials, forum discussions, help pages, and much much more… I am positioning my doctoral work in the field of Neuro Information Science, which is marriage between neuroscience and information science. By no means do I claim to be a neuroscientist or a medical professional.  Hope this helps some of you out there. Happy EEGLABing!

———————————————————————

I used the EEGLAB software, an interactive Matlab toolbox that is used for processing continuous and event-related EEG data, among others, in order to analyze the EEG data that I had collected for my research experiment. I used EEGLAB because it has been widely used in academia as well as in professional institutions, helping process complex EEG data while providing solid robust graphic user interface of the processed and the analyzed EEG data. Moreover, EEGLAB provided several data visualization graphs that helped me greatly in my work to find and establish patterns of brainwaves during each phase of the ISP model.

More specifically, I installed the EEGLAB Compiled version for Windows OS. Next, I will list the step-by-step ways in which I used EEGLAB to process my EEG data:

1. Open the EEGLAB software.
2. Go to the ‘File’ menu and click on ‘Import Data’ from the File menu options. Choose ‘From EDF File’.
3. Find and choose the EEG data that is an EDF file saved on the hard drive and hit ‘Open’ in order to import it into EEGLAB.

4. The ‘Load Data Using BIOSIG’ will open.
5. In the ‘Channel List’ box, type numbers 3 through 16 with one spacebar between each number. This will map the 14 channels of the Emotiv neuroheadset data correctly to the EEGLAB software.
6. Clic ‘Ok’

7. Name the file in the field ‘Name It’.
8. Click ‘Ok’

9. Go to the ‘Edit’ menu and click on ‘Channel Location’ from the Edit menu options.

10. Go to the Text Editor of the computer and create a file as shown below. Save as a CED file. These numbers will map the 14 sensor channels of the Emotiv neuroheadset channel locations correctly to the EEGLAB software.

11. Go back to EEGLAB software and choose the ‘Read Locations’ button. (14location.ced)
12. Choose the above CED file from your computer and highlight it.
13. Click ‘Open’

14. Choose ‘Autodetect’ from the ‘File Format’ menu.
15. Click ‘Ok’.

16. Click ‘Ok’.

17. Go to the ‘Tool’ menu and click on ‘Remove Baseline’ from the Tool menu options.

18. Click ‘Ok’.

19. Go to the ‘Tool’ menu and click on ‘Run ICA from the Tool menu options.
20. Click ‘Ok’.

21. At this point, you will see a window like this. Depending on the memory of the computer, this part may be time consuming, if the memory is low.

22. Go to the ‘Plot’ menu and click on ‘Channel Data’ from the Plot menu options.

23. The brainwaves look like this graph and include outlier data that shows as irregularities in the brainwaves.

24. Highlight the outliers of the brainwaves. These outliers show as peaks in the brainwaves.
25. Click on the ‘>>’ button in order to move forward on the screen
26. Repeat highlighting until the end of the data.
27. Click ‘Reject’ in order to delete all the highlighted outlier data.

28. Name this new data set in the field ‘Save it as File’.
29. Click ‘Ok’.

30. Go to the ‘Plot’ menu and click on ‘Channel Spectra and Maps’ from the Plot menu options.

31. In the ‘Frequencies to Plot as Scalp Maps (HZ)’ indicate the desired brainwave frequencies to be graphed and plotted
32. Click ‘Ok’.

33. Depending on the chosen brainwave frequencies, such graph will be displayed.
34. Save this plot as JPG file

While quite challenging, it has been exciting to work towards positioning my thesis in the (new) field of Neuro Information Science, a marriage between neuroscience and information science. One of my main undertakings with this research is to map the affective and the neural patterns of the information search processes. To my knowledge, this would be the first attempt in the field.

In the field of information science, the affective component of information retrieval system design is increasingly becoming part of the design processes and design roadmaps. In addition, Artificial Neural Networks strive to model the human brain’s biological structure. These system designs and computations, strive to understand and 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 digital data sets, such as pixel, binary, digital, etc. However, the human brain also entails emotional cognitive processes. Hence, It is essential to recognize that, if we are to mimic the human brain we need to also add human emotions – one of the main components of the human cognitive processes – to the equation.

In order to do this, we need to first map the affective and neural patterns, in this case, the information search processes. For these reasons, I decided to map and establish the neural patterns of the information search process during different affective states.

I propose that adding additional data inputs of human emotions may improve not only information system designs but also the design of the artificial neural networks.

One way to read these affective neural activities is to gather user brainwaves via wearable devices and to use these as additional data input onto the information system designs. However, in order to do this, we need to know how to input the affective neural types of data. This doctoral research sets the foundation for continued investigation of the ways in which to design ‘smart’ information systems that learn and improve information retrieval results based on user affective neuro feedback. By developing information search systems that become an extension of the brain via neuro wearable devices we may be able to add human emotions readings as additional data input when developing information search system as well as artificial neural networks. I call this the Smart Affective Search.

In order to map the affective and neural patterns of the information search processes, using Electroencephalogram (EEG) devices as one of my methods, I measured user electrical brain activities during information search processes and during different affective states. Next, I give an overview of information search process model, the underlying theoretical framework used for this study, and why it is important.

Information Search Process Model (ISP)

Kuhlthau (1991) was the first to successfully develop the information-seeking phases of users. She established her findings as the Information Search Process (ISP) model. The ISP model attempts to define various steps of the information search processes in terms of the affective, cognitive, and physical realms. Kuhlthau (2004) developed six steps of the ISP model as listed below:

1. Initiation: when a person first becomes aware of a lack of knowledge or understanding and feelings of uncertainty and apprehension are common.
2. Selection: when a general area, topic, or problem is identified and initial uncertainty often gives way to a brief sense of optimism and a readiness to begin the search.
3. Exploration: when inconsistent, incompatible information is encountered, uncertainty, confusion, and doubt frequently increase, and people find themselves “in the dip” of confidence.
4. Formulation: when a focused perspective is formed and uncertainty diminishes as confidence begins to increase.
5. Collection: when information pertinent to the focused perspective is gathered and uncertainty subsides as interest and involvement deepens.
6. Presentation: when the search is completed with a new understanding enabling the person to explain his or her learning to others or in someway put the learning to use.

While the ISP model is well-established and widely used in the field of information science, to my knowledge, the affective neurological patterns of this model was never investigated nor established. In order to map the affective and neural patterns of the information search processes, I gathered extensive EEG data on the electrical activities of the various stages of the information search process during different affective states. As a result, and after months of data analysis, finally and excitingly, I was able to create heat maps (see the thumbnail of this post!) of the affective neural activities during specific stages of the ISP model! In my next posts, I will go into further details.

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!

1        Introduction

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.

4        Conclusion

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.