Affective valence: meaning and application

You ask employees what a new scheduling system brings to mind, and 'overview', 'control' and 'yet another system' clearly point in different directions. That difference in the direction of feeling is called affective valence. This article covers where the concept comes from, how it is measured, how it applies to individual associations and where interpretation stops.

Affective valence is the positive or negative direction of feeling attached to an experience, object or association: how pleasant or unpleasant something feels.

Valence describes the direction of a feeling. It does not tell you how strongly an association is anchored in memory, how activating something is or which emotion someone feels. In the circumplex model, valence is one of two core dimensions, alongside arousal. Association research often records an association's charge on an ordinal scale, which is a research choice, not a universal measure.

One root lies in research on meaning. Charles Osgood, George Suci and Percy Tannenbaum developed the semantic differential (Osgood, Suci & Tannenbaum, 1957). Respondents rate a concept on pairs of opposite adjectives, such as good–bad. Such ratings yielded three recurring dimensions: evaluation, potency and activity. The evaluation dimension is close to what was later called valence; that is an interpretation of the concept's history, not an equivalence.

In 1980, James Russell published his circumplex model of affect (Russell, 1980). He showed that 28 words for affective states can be arranged in a circle around two bipolar dimensions: pleasure–displeasure and arousal–sleep. Each affect word is then a combination of both. Later literature usually calls the first axis valence and the second arousal.

Margaret Bradley and Peter Lang described the Self-Assessment Manikin (SAM), a wordless pictorial scale (Bradley & Lang, 1994). Respondents choose a figure that matches their reaction on three dimensions: pleasure (valence), arousal and dominance. The authors compared SAM with the semantic differential.

In the circumplex model, valence is bipolar: pleasant and unpleasant sit opposite each other on one axis. John Cacioppo and Gary Berntson proposed a bivariate model instead (Cacioppo & Berntson, 1994). Here, positivity and negativity are separable dimensions, each from "not at all" to "extremely". Both can be high or low at the same time, or work against each other.

This has practical consequences. On one bipolar scale, someone with strongly positive and strongly negative feelings may land near the middle, just like someone with no pronounced feeling. Two separate scales make such ambivalence visible. If you expect mixed feelings, for example about a change with both opportunities and risks, one axis is often too coarse.

The relation between valence and arousal is not fixed either. An analysis of eight datasets found, on average, a weak V-shaped relation: with more strongly positive or negative feelings, arousal tends to be somewhat higher (Kuppens et al., 2013). Differences between individuals were large. So the valence of a reaction does not tell you how activating it is for a given person.

Each related concept answers its own question:

• Affective valence: does the feeling point towards pleasant or unpleasant?
• Association strength: how strongly is a concept connected to something else?
• Association salience: how quickly and prominently does an association come to mind in a situation?
• Arousal: how activated or calm does someone feel?
• Dominant emotion: which specific emotion prevails, such as irritation or pride? That concept has its own article. Two reactions with the same negative valence can contain very different emotions.

Valence and strength are related, though: an object's evaluation is activated automatically more reliably when the object–evaluation link is strong (Fazio, 2001). They remain two properties that you record separately. Emotional drivers are different again: they describe what someone wants to achieve or avoid. Valence can help you investigate such a driver.

No single measure reads valence directly, so researchers often combine methods.

Self-report and rating scales

The most common route is asking people how pleasant or unpleasant something feels, using a bipolar scale, adjective pairs or a pictorial scale such as SAM. If you expect mixed feelings, ask about positive and negative separately. Self-report is direct and practical, but you measure what people can and want to report; wording, scale design and context affect the answer.

Implicit measures

In evaluative priming, a person briefly sees a concept and then judges as fast as possible whether a following word means good or bad. Responses are faster when both carry the same charge (Fazio, 2001). The effect was mainly visible at a short interval of about 300 milliseconds. Implicit here means inferred from performance on a task, not automatically unconscious. The measure is also relative and task-dependent. See also the method page Implicit association research / RIAT.

Physiology: facial muscles

With electromyography (EMG), you record small electrical signals from facial muscles. In laboratory research, the frowning muscle (corrugator supercilii) is more active with unpleasant and less with pleasant stimuli (Larsen, Norris & Cacioppo, 2003). The smiling muscle (zygomaticus major) does not respond in that two-directional way and proved a less reliable indicator with linguistic stimuli ('t Hart et al., 2018). EMG requires sensors on the face and a controlled set-up.

EEG and frontal asymmetry

With EEG, you measure electrical voltage differences through electrodes on the scalp. A much-discussed measure is frontal asymmetry: the difference in activity between left and right frontal regions. Early research linked relatively greater left frontal activity to positive affect. Later research, mainly on anger, points to a link with approach motivation, which can be positive or negative in valence (Harmon-Jones, Gable & Peterson, 2010). Anger is unpleasant and yet approach-oriented. Frontal asymmetry is therefore not a valence meter.

Association research collects what people spontaneously connect with a brand, service or situation. Each association can carry its own charge: positive, neutral or negative. At Neurofactor, we use charge as a working term for the valence of a single association within a decision context. It is often recorded on an ordinal scale: ordered points from negative through neutral to positive.

Number of points, labels and a possible neutral midpoint are research choices. There is no universal valence scale. So record the scale, who assigns the charge (the respondent or a coder) and the context of the question.

A charge score does tell you the direction of feeling for that association and how points on one scale are ordered. A charge score does not tell you:

• that the distances between points are equal;
• how strongly or quickly the association is available;
• how activating the association is or which emotion goes with it;
• that someone will act on it;
• how scores relate to another scale or context.

Treating ordinal data as interval numbers can produce false and missed effects and even reversed conclusions (Liddell & Kruschke, 2018). An average near neutral can also hide ambivalence: half positive and half negative produces the same average as everyone neutral. Report the distribution across points and use analyses suited to ordinal data.

1. Start with the decision. Define the choice you want to support and the context in which people respond.
2. Choose the model. One bipolar scale if direction matters most; separate positive and negative scales if you expect mixed feelings.
3. Fix the scale. Record points, labels, the neutral midpoint and who assigns the charge.
4. Combine methods where needed. Self-report, an implicit task and physiology each answer a different part of the question.
5. Analyse at the right level of measurement. Look at distributions and use ordinal analyses.
6. Report in context. Place charge next to strength, salience and context, and state what you cannot conclude.

This example is fictional and intended as an illustration.

Situation. An organisation is introducing a new scheduling system. The HR team wants to align internal communication with it.

Decision question. Should communication mainly stress the benefits or first address concerns?

Available information. Employees give free associations with "the new scheduling system" and rate each one's charge themselves on an ordinal scale with a neutral midpoint. Which associations come first is also recorded.

Suitable approach. The team examines the distribution of charges per association, not just an average. "Control" is often mentioned early, but with strongly varying charge: some mean a grip on their schedule, others the feeling of being monitored.

Possible interpretation. The same association carries a different meaning for different people. That is a hypothesis, not an established result. An average near neutral would have hidden this difference.

Next step. Short interviews to understand both meanings, then a test of two message variants, one explaining what data the system does and does not record. Which works better is only known after that test.

• Equating valence with strength. A positive association is not automatically strong or frequently mentioned.
• Reading an average as an exact measure. Ordinal steps are not guaranteed to be equal, and opposite reactions cancel out.
• Reading neutral as unimportant. Neutral can also mean mixed.
• Reading a physiological signal as a feeling. EMG and EEG measure bodily or electrical activity; linking it to valence remains an interpretation.
• Comparing scores across different scales or contexts. A charge belongs to the scale, the question and the situation.
• Using positive valence to predict behaviour. Without further research, a pleasant association says nothing about choosing or buying.

Affective valence describes the direction of feeling attached to an experience, object or association. In research, valence is valuable when you keep it separate from strength, arousal and specific emotion, fix the scale explicitly and analyse what it actually measures. The charge of associations then shows which way a reaction points, without inferring more than the measurement supports.