Bristlenose — Academic Sources

# Academic Sources

Theoretical foundations behind Bristlenose's analysis categories

## Emotion Science

Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172. [DOI]

Valence (positive/negative) and arousal are the two fundamental dimensions underlying all emotional experience — discrete emotion categories are constructed from these core dimensions.

Barrett, L. F. (2017). How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin Harcourt. [Publisher]

Emotions are not universal categories hardwired in the brain but are constructed in the moment from core affect, conceptual knowledge, and context.

Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729. [DOI]

The Geneva Emotion Wheel provides 20 emotion families based on appraisal theory — emotions arise from cognitive evaluations of events along dimensions like goal relevance and coping potential.

Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59. [DOI]

The Self-Assessment Manikin (SAM) measures pleasure, arousal, and dominance using non-verbal pictorial scales — validated across hundreds of studies for self-reported emotional experience.

Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3–4), 169–200. [DOI]

Six basic emotions (anger, disgust, fear, happiness, sadness, surprise) are universally recognised across cultures — influential but now contested by constructionist theories.

Plutchik, R. (1980). A general psychoevolutionary theory of emotion. In R. Plutchik & H. Kellerman (Eds.), Emotion: Theory, Research, and Experience (Vol. 1, pp. 3–33). Academic Press. [DOI]

Eight primary emotions arranged in opposing pairs with three intensity levels each — the wheel structure captures relationships and gradations between emotional states.

## User Experience Research

Hassenzahl, M. (2003). The thing and I: Understanding the relationship between user and product. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology: From Usability to Enjoyment (pp. 31–42). Kluwer. [DOI]

User experience has two distinct quality dimensions: pragmatic (usability, task completion) and hedonic (stimulation, identity expression) — both matter for overall product appeal.

Laugwitz, B., Held, T., & Schrepp, M. (2008). Construction and evaluation of a user experience questionnaire. In A. Holzinger (Ed.), HCI and Usability for Education and Work (USAB 2008, pp. 63–76). Springer. [DOI]

The User Experience Questionnaire (UEQ) measures six dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty — validated across many studies and languages.

Benedek, J., & Miner, T. (2002). Measuring desirability: New methods for evaluating desirability in a usability lab setting. Proceedings of the Usability Professionals Association Conference. [Microsoft Research]

The Product Reaction Cards — 118 words users select to describe their experience — capture the vocabulary people actually use when reacting to products.

## Trust and Credibility

Fogg, B. J. (2003). Prominence-interpretation theory: Explaining how people assess credibility online. CHI '03 Extended Abstracts on Human Factors in Computing Systems (pp. 722–723). ACM. [PDF]

Users assess credibility by noticing elements (prominence) and then interpreting them (positive or negative judgment) — both steps must occur for an element to affect credibility assessment.

Fogg, B. J., et al. (2001). What makes web sites credible? A report on a large quantitative study. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '01, pp. 61–68). ACM. [PDF]

Users judge website credibility primarily on visual design and ease of use rather than content accuracy — surface credibility often outweighs substantive evaluation.

Fogg, B. J. (1999). The elements of computer credibility. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '99, pp. 80–87). ACM. [PDF]

Computer credibility comprises trustworthiness (well-intentioned, unbiased) and expertise (knowledgeable, competent) — four credibility types: presumed, surface, reputed, and earned.

Stanford Web Credibility Research. (2002). Stanford Guidelines for Web Credibility. Stanford Persuasive Technology Lab. [Web]

Ten research-based guidelines for building credible websites — including ease of use, markers of expertise, professional design, and elimination of errors.

## Qualitative Research Methods

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. [DOI]

Thematic analysis is a flexible six-phase method for identifying patterns in qualitative data — can be inductive (data-driven) or deductive (theory-driven).

Saldaña, J. (2021). The Coding Manual for Qualitative Researchers (4th ed.). SAGE. [Publisher]

Comprehensive guide to qualitative coding methods including descriptive, in vivo, process, emotion, values, and evaluation coding — the standard reference for coding practices.

Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data (Rev. ed.). MIT Press. [Publisher]

The foundational text on think-aloud protocols — distinguishes verbalization levels and establishes when verbal reports provide valid data about cognitive processes.

## Affective Computing

Picard, R. W. (1997). Affective Computing. MIT Press. [Publisher]

The founding text of affective computing — computers that recognise, express, and respond to emotion can create more effective and humane human-computer interaction.

Demszky, D., et al. (2020). GoEmotions: A dataset of fine-grained emotions. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4040–4054). ACL. [arXiv]

GoEmotions provides 58,000 Reddit comments labelled with 27 emotion categories plus neutral — the largest fine-grained emotion dataset for training NLP models.