Benedict Singleton and Dr. Kev Hilton

Centre for Design Research © 2008

Benedict Singleton and Dr. Kev Hilton

The Emotional Spectrum Analyser


Introduction

• ‘Understanding’ changes our beliefs and needs

• From design of effective product interfaces, to affective products.

• From a historical lack of interest, to perceived competitive advantage for product innovation.

• This developed a need to reliably quantify emotions and develop technical solutions.

Emotional Spectrum Analysis

• ESA16 software, using electro-encephalogram technology


Contemporary Conceptions

• Emotional state is complex and difficult to articulate

• Often characterised as a blending or layering of core emotions

• Technologists looked for solutions to provide a ‘Cognitive representation’ of a ‘Physiological state’.

• However, emotion is led by changing context or situation and environment, a potentially ‘chaotic’ multi-factorial system of influence.

Emotional Spectrum Analysis


Contemporary Conceptions

• ‘Pure’ emotions, e.g. anger or happiness, can still be used as discussion points around Emotional Space (Russell and Feldman Barratt, 1999).

• However, mono-dimensional models do not adequately represent the complexity of emotional evidence for effective application to design.


Assessing Emotion Objectively

• ‘Objective’ observation of participant’s emotions is unreliable.

• Self-report of emotions has also proven unreliable (Turkkan, 2000).

• Post-hoc categorization of emotions is problematic.

This has led to discussions around ‘universal’ words and images.


PrEmo V5 (Desmet, 2002)



Kansei Engineering, scaling experience

Happiness Sadness

Fast Slow

X

X



• These approaches still require ‘reflective’ reporting.

• There is a need to record data in real-time.

• Technology might work in combination with universals to develop this field of knowledge.


Physiological Traces of Emotion

• Reliable automatic means of monitoring immersive experiences.

• ‘Immersion’ and ‘verbalizing’ tasks distract one another.

• Neuroscience technologies, such as ESA may provide the physical means.

• Universals need to be further developed to provide reliable cross-cultural categorization.

• However we still face the complexity of influences on experience.



• There is no simple way to map neural activity onto emotion (Prohovnik et al, 2004).

• The Brain Function Laboratory’s ESA software takes an orthogonally rotated approach to mapping four independent and dissimilar forms of neural activity.

• Labeling them with the ‘state’ terms which were commonly used in self-report.

• It is of course the universality and applicability of these terms which challenge development.



• Emotional intensity on the recording does not consistently match the experienced, ‘remembered’, intensity.

• BFL stated that it is not possible to compare one individual’s recordings against another individual’s, only against their own.


Conclusion

• The hope of ESA-16 providing a non-invasive emotional assessment.

• Products do elicit emotional responses but reflection upon these responses can distort the memory of these emotions.

• However, designers and technologists first need an validated model of emotion in order to progress.


Conclusion

• It was therefore concluded that in the short term, this technology might be repurposed for monitoring other physiological changes, used for enquiries into immersive experiences, for example, computer gaming.

• The ESA-16 might be viewed as a stepping stone towards a clearer understanding of experiences.

• Nevertheless, a key question for further investigation that came out of this project was ‘just how reliable are our emotional responses to product?’


Dr. Kev Hilton ([email protected])

Benedict Singleton and Dr. Kev Hilton

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