Forgetting who we’ve been

Most probably, many of my neuroscience colleagues will deeply disagree with what I am about to write here. However, there is also a movement towards a paradigm shift around how we define the brain and/or its functionalities. So, I guess this post is a humble contribution to the movement, the paradigm shift of how we understand the brain.

I have come to realize that if we continue to think about the brain and define it as it exists today in the text books, our deep questions will simply never be answered.

For the sake of this post, I am sharing two of my long standing fundamental (stupid) questions: 1) where exactly exactly is the memory and 2) what exactly exactly is information? I double-write exactly because every time that I ask these questions, the answers that our current body of knowledge offers appear to only touch the surface. In other words, they fail to provide in-depth answers that would quench my thirst.

While spending years in the tangible world of science, simultaneously, I have also spent years in the untangible world of the art and spirituality. To me, both worlds complement one another and have helped me build a more holistic experience, understanding, and knowledge of this world. As a result, I believe that if we want to define the brain we need to look at it holistically and not just scientifically. I know… it sounds cliche. But hear me out!

Everything that we see, hear, touch, etc is made up of energy! I don’t believe anyone disagrees with that. Hence, the brain is made up of energy too. In fact, in labs, we measure things such as brainwaves, which does indicate the emanation of energy. So, if the brain is all energy, can we assume that what we call the memory is also energy, waves, frequencies? If so, then there is no such thing as the brain being the storage cabinet or somewhere we store files and files of so called information. In that case, there is no memory as we define it.

The brain, then, is simply a generator and receiver of these waves, i.e. it does not store anything but that it tunes itself into different waves of energy, which in turn have/carry different types of information. This means that, at any point in time, we forget who we are because there is no real memory in the brain that makes us remember our name, gender, address, etc. The brain, depending on the habits of the individual, tunes itself into the same frequencies, which in turn carry the same information. Hence, we remember things from the past.

The job of the individual, then, is to either stay with the usual and let the brain be the same receiver as before or, if change is needed, increase their awareness and through focus of the mind adjust the reception of the brain to different types of frequencies of energy.

The free-will, in this sense, will then be for the individual to shift the focus of the mind towards the types of frequencies of the will instead of going on autopilot. Wait… is this what they meant when they said “God [the creator] Wills it” ?

For fact, I tend to forget names and events easily when I move forward in my life. Knowing that there is no such thing as memory but shifting of brain reception of energy, I now understand why I tend to forget names and events easily. It simply is because I am so focused at my life at the moment that I tune into that energy only. Meaning to say, I tune out of the old energy.

The topic of Information is another big question of mine… I will keep pondering until I figure it out.

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PhD Defense: February 21, 2018

Dear Friends & Family,
I finally did it! After 6 years, I am having my final seminar (PhD defense) on Wednesday, February 21. Please feel free to attend remotely. See below for more info.
Thanks for all your support!
Nilo
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Please find below the announcement of Nilo Sarraf’s PhD final seminar. You are cordially invited to attend the presentation, to be held in-person at the San José State campus and virtually via Zoom. Anyone who is interested is welcome to attend, so please forward as appropriate. Announcement is also attached as a PDF. 

Science and Engineering Faculty – PhD Final Seminar
School of Information Systems – Information Science Discipline

Date:  21 February, 2018 
Time: 
4:00 p.m. (Pacific Time)

Venue: Clark Hall, Room 322, School of Information
San José State University, San Jose CA 95129

Online via Zoom: Join from PC, Mac, Linux, iOS or Android:
https://sjsu.zoom.us/j/3607477007

Speaker: Niloufar Sarraf

Supervisors:
Prof. Virginia Tucker, SJSU External Supervisor
Prof. Sylvia Edwards, QUT,Associate Supervisor
Prof. Ian Stoodley, QUT, Principal Supervisor
Prof. Christine Bruce, JCU, External Supervisor

Review Panel:
Prof. Bill Fisher, SJSU, QUT Adjunct HOS Representative and Panel Chair
Prof. Christine Bruce, External Representative
Prof. Michelle Chen, SJSU Discipline Expert
Prof. Virginia Tucker, External Supervisor

Title
Mapping the Neurophysiological and the Affective Dimensions of the Information Search Process Model

ABSTRACT
The affective and neurological components of information retrieval system design have increasingly become an essential part of research in human-information interaction and interactive information retrieval. These sophisticated processes are composed of not only human cognitive processes but also emotional and neuropsychological (NP) responses. One of the most cited information search process models, the Information Search Process (ISP) model (Kuhlthau, 1991), identified three realms of user experience; affective (feelings), cognitive (thoughts), and physical (actions) realms. While the ISP model identified three dimensions of user experience, it does not include the NP dimension of the brain. Neither does it examine the impact, if any, of emotional states on the NP dimension.

This research contributes three original findings to the field of Information Science, positioned in Neuro Information Science. First, this experimental research discovered and mapped the neurophysiological and the emotional dimensions of information search processes. Second, this thesis connected the dots between the discrete emotions of Kuhlthau’s model (1991), the continuous dimensions of emotion scale of Scherer (2005), and the neurophysiological and emotional aspects of information search processes (Sarraf, 2018). Third, this research contributed to the body of knowledge for detection of dimensions of emotions using EEG devices. 48 participants performed search tasks during neutral, positive, and negative emotional states. This study collected brain frequencies through the Emotiv EEG neuroheadset. The results indicated that there were clear differences in the brain frequencies within different locations of the brain, depending on the ISP stage and the emotional state.

One of the major findings of this study discovered that, regardless of the information search stages and/or emotional states, the dominant active part of the brain was the upper left brain, which primarily handles logical and analytical thoughts. Moreover, this study showed that when investigating (exploration stage) and forming focus (formulation stage) in searching for information, the brain was extremely active, thinking logical/analytical thoughts. But the brain slowed down in logical/analytical thinking when gathering for information (collection stage).

On the other hand, positive feelings harmonized the neural activities of the brain regardless of the stages of information search. During information search stages, the brain activities balanced out, thinking only logical analytical thoughts. Yet negativity affected the brain drastically in that, when investigating information, while the logical/analytical thoughts increased, so did intuition and interpersonal feelings.

This study also connected the discrete emotions and the continuous emotions on the valence-arousal scale. The continuous emotions roughly changed from (a) negative-excited to (b) positive-calm to (c) positive-excited. This study suggest that, the corresponding neurophysiological aspects of the ISP stages change from (a) gamma to (b) gamma to (c) beta in the upper left brain, which handles logical and analytical thinking.

Lastly, this study supported the existing experimental research methods and results when detecting dimensions of emotions using EEG devices. During positive emotional states, beta waves in the upper left brain were the most dominant. During negative emotions, beta and gamma waves were dominant both in the upper left and in the right brain hemisphere.The right brain hemisphere was active with beta and gamma waves when feeling negative emotions and in positive emotions the brain was active in the upper left quadrant eliciting beta waves.

Speaker Background
Niloufar Sarraf has over 10 years of academic and industry user-centered research experience in web and mobile technologies from IBM, Google, Yahoo, Stanford University, VMware, Unity Technologies, and SAP Labs. She is natively interested in how humans interact with everything and is a student of human emotional motivation and sensory cognition, studying how these forces interact with computer and user interfaces. She is a thought leader in the industry with research paper publications and has been invited to speak at BayCHI, iConference, guest lecturing at universities, Cognitive Computing Forum conference, and TTI Vanguard to speak about her doctoral work in the new and emerging discipline of Neuro Information Science.


Final Thesis Abstract: Mapping the Neurophysiological and the Affective Dimensions of the Information Search Process Model

I am in the last stages of my thesis and it feels like a dream come true! It is an extraordinary experience when you approach the finish line, finalizing a six-year doctoral research. My most recent speaking experience was at the TTI Vanguard Conference at the Ritz-Carlton in December 2017. As I am concluding the first draft thesis, I thought of sharing the Abstract here with you. Happy New Year!

ABSTRACT
The affective and neurological components of information retrieval system design have increasingly become an essential part of research in human-information interaction and interactive information retrieval. These sophisticated processes are composed of not only human cognitive processes but also emotional and neuropsychological (NP) responses. One of the most cited information search process models, the Information Search Process (ISP) model (Kuhlthau, 1991), identified three realms of user experience; affective (feelings), cognitive (thoughts), and physical (actions) realms. While the ISP model identified three dimensions of user experience, it does not include the NP dimension of the brain. Neither does it examine the impact, if any, of emotional states on the NP dimension.

This research contributes three original findings to the field of Information Science, positioned in Neuro Information Science. First, this experimental research discovered and mapped the neurophysiological and the emotional dimensions of information search processes. Second, this thesis connected the dots between the discrete emotions of Kuhlthau’s model (1991), the continuous dimensions of emotion scale of Scherer (2005), and the neurophysiological and emotional aspects of information search processes (Sarraf, 2018). Third, this research contributed to the body of knowledge for detection of dimensions of emotions using EEG devices. 48 participants performed search tasks during neutral, positive, and negative emotional states. This study collected brain frequencies through the Emotiv EEG neuroheadset. The results indicated that there were clear differences in the brain frequencies within different locations of the brain, depending on the ISP stage and the emotional state.

One of the major findings of this study discovered that, regardless of the information search stages and/or emotional states, the dominant active part of the brain was the upper left brain, which primarily handles logical and analytical thoughts. Moreover, this study showed that when investigating (exploration stage) and forming focus (formulation stage) in searching for information, the brain was extremely active, thinking logical/analytical thoughts. But the brain slowed down in logical/analytical thinking when gathering for information (collection stage).

On the other hand, positive feelings harmonized the neural activities of the brain regardless of the stages of information search. During information search stages, the brain activities balanced out, thinking only logical analytical thoughts. Yet negativity affected the brain drastically in that, when investigating information, while the logical/analytical thoughts increased, so did intuition and interpersonal feelings.

This study also connected the discrete emotions and the continuous emotions on the valence-arousal scale. The continuous emotions roughly changed from (a) negative-excited to (b) positive-calm to (c) positive-excited. This study suggest that, the corresponding neurophysiological aspects of the ISP stages change from (a) gamma to (b) gamma to (c) beta in the upper left brain, which handles logical and analytical thinking.

Lastly, this study supported the existing experimental research methods and results when detecting dimensions of emotions using EEG devices. During positive emotional states, beta waves in the upper left brain were the most dominant. During negative emotions, beta and gamma waves were dominant both in the upper left and in the right brain hemisphere.The right brain hemisphere was active with beta and gamma waves when feeling negative emotions and in positive emotions the brain was active in the upper left quadrant eliciting beta waves.


Mapping ISP Model’s Neurological Affective Components

Raw EEG

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.


EEG Data Process Using EEGLAB on MatLab

P1NT1Many 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.
  1. The ‘Load Data Using BIOSIG’ will open.
  2. 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.
  3. Clic ‘Ok’
  1. Name the file in the field ‘Name It’.
  2. Click ‘Ok’
  1. Go to the ‘Edit’ menu and click on ‘Channel Location’ from the Edit menu options.
  1. 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.
  1. Go back to EEGLAB software and choose the ‘Read Locations’ button. (14location.ced)
  2. Choose the above CED file from your computer and highlight it.
  3. Click ‘Open’
  1. Choose ‘Autodetect’ from the ‘File Format’ menu.
  2. Click ‘Ok’.
  1. Click ‘Ok’.
  1. Go to the ‘Tool’ menu and click on ‘Remove Baseline’ from the Tool menu options.
  1. Click ‘Ok’.
  1. Go to the ‘Tool’ menu and click on ‘Run ICA from the Tool menu options.
  2. Click ‘Ok’.
  1. 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.
  1. Go to the ‘Plot’ menu and click on ‘Channel Data’ from the Plot menu options.
  1. The brainwaves look like this graph and include outlier data that shows as irregularities in the brainwaves.
  1. Highlight the outliers of the brainwaves. These outliers show as peaks in the brainwaves.
  2. Click on the ‘>>’ button in order to move forward on the screen
  3. Repeat highlighting until the end of the data.
  4. Click ‘Reject’ in order to delete all the highlighted outlier data.
  1. Name this new data set in the field ‘Save it as File’.
  2. Click ‘Ok’.
  1. Go to the ‘Plot’ menu and click on ‘Channel Spectra and Maps’ from the Plot menu options.
  1. In the ‘Frequencies to Plot as Scalp Maps (HZ)’ indicate the desired brainwave frequencies to be graphed and plotted
  2. Click ‘Ok’.
  1. Depending on the chosen brainwave frequencies, such graph will be displayed.
  2. Save this plot as JPG file

 

 

 

 

 

 

 


Mapping the Affective Brain Activities of the Information Search Process Model

EEG HeatmapsWhile 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.


Paper Presentation at the Smart Data Conference 2015

SmartData 2015 Header

I am honored to be presenting my (in-progress) doctoral work at the upcoming Smart Data Conference 2015, presented by the DataVersity, a provider of high quality educational resources for business and information technology professionals on the uses and management of data. The Smart Data Conference is designed to accommodate all levels of technical understanding. It will bring together emerging disciplines that are focused on more intelligent information gathering and analysis. So, for those interested, I thought of sharing part of this paper here. And if you are planning on attending the conference, please attend my presentation on: Wednesday, August 19, 2015 – 04:45 PM – 05:30 PM at the San Jose Convention Center – 150 West San Carlos Street, San Jose, CA 95113 USA


Affective Search: How Does Affect Impact Web Search Performance? Towards Smart Emotional Neuro Search Engines: An Extension of the Human Brain

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?

Nilo Sarraf (@nilosarraf) is pursuing her PhD in Neuro Information Science in the SJSU School of Information Gateway PhD program, in partnership with Queensland University of Technology. Her doctoral research aims to examine cognitive relationships between dimensions of emotions and web search performance. Most recently, she was the first person to propose that “Smart Affective (Neuro)” engines, through wearable computing, read human emotions in order to improve search results based on the neurological feedback they receive from user’s brain waves.