Monthly Archives: June 2014

Challenges & Limitations: Smart Affective Neuro Search

In my previous post, Smart Affective Neuro Search, partially I discussed my (in progress) dissertation as well as my somewhat unconventional proposal in regards to the implications this model may have in the industry. Here, I intend to examine and discuss some of the challenges and limitations of this model.

In my pilot study, I was able to test my research design along with its proposed methods and measurement techniques. One of these techniques included the Observer Methods, more specifically body movements.

Shortly, I will discuss some of the challenges and limitations of my model. But before that, let’s review the different views when it comes to observing and measuring body movements.

Body Movement – Observer Methods

Some researchers debate whether body movements are valid indicators of human emotions. However, many studies include strong evidence that associates body movements with specific emotions (Wallbott, 1988; de Meijer, 2005). Boone and

Cunningham (1998) were also able to show connection between certain emotions and certain body movements. These findings pertain to the Darwinian theory of discrete emotions, where some body movements directly relate to specific emotional states (Wallbott, 1998). Furthermore, Gunes and Piccardi (2007) identified six facial and body gestures, connecting them with various emotions (see table below).

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Table. List of Bodily Emotions (Gunes & Piccardi, 2007)

 

This table suggests that certain emotions may be assessed by certain body movements. For example, this table suggests that hands resting on the waists or made to fists appear to be indicators of anger in an individual. Moreover, the table indicates that signs of anxiety in participants may be detected through observations of the location of their hands on the table surface.

Moreover, researchers have been monitoring hand movements in hope of establishing correlations between emotions and these hand movements. In these studies, researchers either study glove movements of the users or they have computer programs observe the movements. While glove-based studies analyze the hand gestures using model-based techniques, the computer-based approach observes hands in images using appearance-based techniques (Chen et al., 2003).

Challenges & Limitations

Although human body movements play a big role in emotional communication (Fasel & Luettin, 2003), the complex motor movements involved may contribute to major amount of ‘noise’ when it comes to the readings of brain electrical signals. The current EEG devices in the market today, are partly designed to include noise suppressions. However, they still may not be able to fully suppress all the ‘noise’ emanating from major body movements, such as head or hand movements while participants conduct search tasks on various computer devices.

These, I believe are some of the challenges and limitations when it comes to this proposed model for developing Affective Neuro Search. It is my intention to, through continuous research, address these challenges and limitations.

In future posts, and as I progress in my dissertation, I will discuss proposed ways in which raw EEG data may be best analyzed for the purpose of Affective Neuro Search and such…

 

Please also see here about exciting and emerging wearable computing and AI devices here: Brain controlled airplanesneurogaming, and robots that learn behavior.


Smart (Affective) Neuro Search…

I am humbled by the overwhelming number of views and comments on my previous post, Affective Smart Search. Here, I intend to elaborate on my proposal in regards to the implications that my doctoral dissertation (in progress) may have in the industry.

(Please also see the Disclaimer page.)

Through a pilot study, I was able to test one of my hypotheses that: ‘Aroused dimensions of emotions (high intensity emotions, such as anger) that include high-frequency Beta (and possibly Gamma) brain waves impact search performance (efficiency and effectiveness) negatively.’

Shortly, I will discuss my proposal in regards to the implications that this doctoral dissertation may have in the industry. But before that, let’s review the different views of dimensions of emotions and how they may correlate with high and low frequency brain waves.

Views on Structure of Emotions

There are two main views on the structure of emotion: 1) discrete and 2) continuous approaches. Darwin, the father of the discrete approach, claimed that there exist six basic emotions: fear, happiness, surprise, anger, sadness, and disgust (Darwin, 1872; Ekman, 1992). These theorists argue that these six basic emotions are universal and that humans, regardless of their cultural background, appear to both display and recognize these six distinct emotions. On the other hand, the continuous approach addresses different ‘dimensions’ of emotions (Russel & Mehrabian, 1977; Russel, 1994). These theorists state that there are two dimensions of emotions, valence and arousal (Russell, 1994; Russell & Mehrabian, 1977; Russell & Steiger, 1982; Barrett & Russell, 1999).

While the discrete approach includes the list of discrete emotions, the dimensional self-report approach utilizes dimensions of emotions, arousal and valence (Wundt, 1904). The arousal dimension, as Wundt explains, measures the calmness versus the excitement of an emotion, ranging from calming to exciting (or agitating) states. On the other hand, valence indicates the positivity versus the negativity of an emotion, ranging from highly positive to negative states. As a result, in this method, participants indicate their subjective experience through these two coordinates.

Emotional Dimensions Associated with Brain Waves

While valence assesses the pleasantness (positivity/negativity of an emotion), arousal is explained to represent the intensity of an emotion. Valence (or positive happy emotions) result in a higher frontal coherence in alpha, and higher right parietal beta power, compared to negative emotion Arousal (or excitation) appear to present a higher beta power and coherence in the parietal lobe, plus lower alpha activity.

Russel’s (1989) research shows that the following two emotional dimensions are associated with various brain waves:

  • Theta waves, also seen in meditative states (Cahn & Polich, 2006), show arousal or drowsiness in adults
  • Alpha waves are exhibited when closing the eyes and during relaxation
  • Beta waves, linked with motor behavior, occur when the individual is actively moving (Pfurtscheller and da Silva, 1999). Low beta frequencies are often associated with concentration and/or active thinking
  • Gamma waves represent cognitive or motor functions (Niedermeyer & da Silva, 2004)

Neurophysiologic Methods

Neurophysiologic methods aim to monitor and read human body responses in reaction, such as skin conductance, blood pressure, heart pulse rate, and most recently brain activities, in order to infer human emotional states. Most recently, and the most non-invasive EEG devices, such as Emotiv or Interaxon, are gaining increased respect in the research community.

Industry Implications
And here comes my unconventional ‘out of the box’ proposal… I envision my dissertation add to the body of knowledge of Neuro Information Science in developing search engines that, through wearable computing devices, are able to read human brain waves, and dimensions of emotions thereof, in order to improve search results based on the neurological feedback that the search engines receive from user’s 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.

For example, at the time when the search result is being presented on the screen, high frequencies of brain waves may be an indication of high intensity emotions, such as frustration. All the while, the search engines read user brain waves by receiving the brain signals through wearable computing devices. Gradually, the search engine may ‘learn to improve’ its search results based on, for example, alpha (or calmer) brain waves received.

I my future posts, and as I progress in my dissertation, I will elaborate more and will discuss challenges and limitations of this model…

(Read more about exciting and emerging wearable computing and AI devices here: Brain controlled airplanesneurogaming, and robots that learn behavior.)

(Please also see the Disclaimer page.)


‘Smart’ Affective Search: Brain Activities to Help Improve Search Results?

In an era where we are creating brain controlled airplanes, neurogaming, 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 user’s brain waves. Thanks to companies such as Interaxon and Emotiv, EEG devices have readily been made available to researchers interested in neuro-related studies, who otherwise would have not had access to expensive fMRI machines. Although the two devices measure different entities of the brain, nonetheless, EEG devices help enthusiastic, but low budget, researchers (such as me!) conduct neuro-related studies.

My doctoral research topic aims to examine cognitive relationships between dimensions of human emotions and information retrieval, as in search performance, in the field of neuro information science (Gwizdka, 2012). This study aims 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.

To illustrate, emerging expressions, such as “pleasurable engineering” or “emotional design”, have not only become the driving factors in information retrieval system design (Nahl & Bilal, 2007) but also illustrate the important role of emotions in human-computer-interaction. Information retrieval entails complicated cognitive processes, composed of human cognitive processes as well as human physiological and neurological reactions (Picard, 2001). However, our understanding of how emotions affect information retrieval is limited (Nahl & Bilal, 2007), so is our understanding when it comes to the effects of physiological and neurological responses on information retrieval, more specifically on web search performance.

Hence, for us to be able to design better search engines, we need to understand both ‘human-computer-interaction’ as well as ‘brain-computer-interaction’ processes, such that the two not be treated separately.

Keywords: affective information retrieval, affective search, neuro-information science, web search performance, affective information behavior, EEG in information retrieval, emotional design, brain computer interaction