Mapping the Affective Brain Activities of the Information Search Process Model

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.