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 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.
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…
(Please also see the Disclaimer page.)