Uncovering the Unspoken: Using Implicit Measures to Understand User Experience
What Can Psychology Teach Us About Unconscious User Experience?
Traditionally, UX researchers have relied extensively on explicit measures – techniques like surveys, interviews that ask users to directly self-report their conscious thoughts and feelings (Bargas-Avila & Hornbæk, 2011). However, decades of research in psychology suggest that when users make an evaluation they also rely on implicit processes outside conscious awareness or control. In particular, dual process theories state that in addition to deliberate, explicit attitudes, people also hold implicit attitudes – spontaneous associations, preferences, and evaluations (Wilson et al., 2000).
This article will focus on implicit measures and how they can be used to complement explicit measures in UX.
Limitations of Explicit Measures
While valuable, explicit UX measures have some limitations. Self-reports, for example, reflect conscious access and description of experience accurately. When using self-reports we cam measure the conscious experience of users but aspects of emotion and cognition influencing behaviours often remain unconscious. As a result, users cannot accurately report on processes they are unaware of or fail to introspect upon.
Explicit measures also are subject to bias from factors like social desirability affecting what participants are willing to disclose (Nosek et al., 2007). They require accuracy in self-perception and verbal report, making them cognitively demanding to users. As a result, exclusive relying on explicit measures provides an incomplete picture of the drivers of user experience.
The Potential of Implicit Measures
In contrast, implicit measures aim to capture attitudes manifesting automatically outside conscious control or awareness (De Houwer et al., 2009). Usually, implicit attitudes can be inferred from performances in computerised tasks, reducing self-presentation biases.
The most popular measure, the Implicit Associate Test (IAT), was developed by Greenwald, McGhee, and Schwartz in 1998 (for a critical review see this). The IAT indirectly measures the strength of associations between concepts (e.g., black people, gay people) and evaluations (e.g., good, bad) or stereotypes (e.g., athletic, clumsy). The main idea is that making a response is easier when closely related items share the same response key. For example, having an implicit preference towards Category A would lead a participant to respond faster when concepts related to being Category A share the same response as positive evaluations. If you’re curious and want to find out more about the IAT check out the Project Implicit website.
Other less frequently implicit tasks are briefly discussed below:
The Affect Misattribution Procedure (AMP; Payne et al., 2005) briefly shows participants a prime image that elicits an emotional reaction, followed by a neutral target image (e.g. Chinese character). Participants rate targets as more or less pleasant, with primes unconsciously biasing judgments.
The Approach Avoidance Task (AAT; Heuer et al., 2007) requires participants to respond to positive and negative visual stimuli by pushing and pulling a joystick, with the expectation that people are faster to push negative stimuli away and pull positive stimuli closer.
Semantic priming tasks (Banaji & Hardin, 1996) show participants a prime followed by a target. Faster responses when the prime and target are semantically related reflect unconscious activation of associated concepts by the prime.
The Stroop priming task (Schmettow et al., 2013) combines colour-naming of words with concept primes. Interference when word meaning and colour are incongruent demonstrates implicit associations between the prime idea and target word.
Tasks like the ones described above offer us a window into user attitudes otherwise unseen. Combined use of implicit and explicit measures can allows us to compare conscious and unconscious evaluations.
Dual Process Theories
Dual process theories propose that people form attitudes and evaluations through two different systems - one that is associative and automatic, and another that is more rational and rule-based (Gawronski & Bodenhausen, 2011).
The associative system underlies implicit attitudes. These attitudes are the result of emotional reactions and learned associations towards something over time. For example, through repeated exposure to flowers and positive situations, we unconsciously start feeling more positively towards flowers in general.
In contrast, explicit attitudes come from the more logical, thinking-driven part of our brains. We deliberately and rationally process factual information about the attributes of something to evaluate it. For instance, reviewing specs of different smartphone models to decide which best fits our needs.
In essence, dual process theories state that both associative, experience-driven reactions, and rational deliberation shape attitudes - sometimes working together and sometimes contradicting each other. Evaluating both implicit and explicit attitudes thus leads to more comprehensive understanding of the mental drivers underlying how people evaluate technologies and experiences.
Current Use of Implicit Measures in UX Research
Even though both implicit and explicit measures can help us better understand UX, current application of implicit measures in UX research remains limited. A recent scoping review by Maisto and colleagues found only 12 studies on the topic to date. The majority of them employed variants of the IAT discussed above, assessing primarily aesthetic/experiential aspects of UX.
A common finding in the studies reviewed was divergence between implicit and explicit measures - “implicit-explicit discrepancy” - with implicit measures occasionally detecting effects unobserved by explicit measures (Briñol et al., 2006). Discrepancy can arise from poorer psychometric properties of a measure, or the distinct representations captured.
Another observation was that most studies using implicit measures focused on interactions with conversational agents and social robots. However, there is evidence that humans likely apply implicit social biases even towards technologies lacking humanoid features (Actis-Grosso et al, 2021).
Weaknesses of Implicit Measures
Like most measures implicit measures have limitations and those should be considered before employing them. The information value offered by implicit measures must be weighed against methodological challenges in their implementation. Carefully constructed, rigorously validated implicit measures can provide unique predictive and discriminant validity. However, many require further research confirming incremental validity over existing tools in applied contexts (Bar-Anan & Nosek, 2014).
Implicit measures also demand controlled administration for internal reliability. Subtle design decisions can substantially impact outcomes. They require careful design and can be much harder to be implemented by non experts and outside the lab.
Recommendations for UX Professionals
If you’re intrigued and interested in applying implicit measures in your research you should consider the following:
Select the appropriate implicit measure suited to dimensions of interest after reviewing evidence on validation. Psychometric rigour varies widely across different measures. For example, IAT can be a good choice if you’re interested in aesthetic/experiential aspects of UX.
Consider the strengths and weaknesses of the measures discussed above. Planning and running implicit tests requires more effort and time than most common explicit tests. For example, you might need to invest on specific software.
Employ implicit measures alongside explicit measures for triangulation and to reveal any contradictions.
Incorporating implicit alongside explicit measures allows us to paint a fuller picture of the conscious and unconscious mental processes that shape user experience. Although applying them requires more effort, implicit measures can offer us a means to understand and evaluate people’s perception and experience.