One of the most interesting models of emotion is Affect Control Theory (ACT) (Heise, 1987). ACT expresses how social events are construed positing a relation between cognition and emotion. In contrast to psychological theories of social cognition, ACT emphasizes that meanings are culturally shared and deviations from meanings generate arousal that triggers re-appraisals. In this brief article and following to Troyer (2004), an outstanding specialist, I expose some aspects of the theory and how Lisa Troyer applies its concepts to model Human-Computer Interaction (HCI).
ACT looks at individuals as agents seeking consistency across interactions. Humans are categorized into roles with shared expectations regarding actions appropriate for the role. Actions in response to one another are markers of social events. Social events are formed by actors who assume identities, behaviors of actors and objects to whom the actions are directed. For instance, a policeman helping citizen, whose linguistic structure would be "Policeman Helps Citizen". Its meaning is defined in three dimensions: Evaluation (goodness), Potency (powerfulness) and Activity (liveliness). Ratings of each element are called "fundamental sentiments" (for it the word affect). Humans have culturally shared fundamental sentiments and expectations. So, we expect good policemen to behave in right ways. When elements combine in an event, emotion signals the corespondence between the meanings we expect and the actual meanings evoked. Smith-Lovin (1987) introduced impression-formation equations combining ratings of elements to estimate new ratings for inputs combined in events. These equations predict how meanings shift as interaction evolves. The sum of the squared differences between fundamental sentiments of the elements and impressions from the event generates the perceived likelihood of an event.
ACT includes a database of ratings for thousand of elements and modifiers (emotion labels for roles, for instance, "pleasant policeman"). Using the equations and database, ACT predicts the basis for expectations in subsequent interaction. The models and database are combined in the software INTERACT. With this software researchers simulate events and generate testable predictions regarding sequences and redefinitions of events. ACT has focused on Human-Human interaction but Troyer (2004) demonstrates how can be used to model Human-Computer interaction. ACT does not require that computers exhibit emotions, but only that they be able to reason about them, that is, metareasoning.
Troyer ("Affect control theory as a foundation for socially intelligent systems", 2004) designed an experiment with 15 subjects, providing them independent ratings for elements of Human-Computer interaction: "Computer", "Run Analysis", "Provide Output", "Freezes" and "Runtime Error". Correspondent social concepts were "Academic", "Ask about Something", "Educate", "Beg" and "Laughs At". Troyer substituted the correspondent social concepts for HCI terms to simulate events representing Human-Human interaction analogs of HCIs. The simulations explored how meanings shifted when a computer initially behaved as expected, producing unexpected behavior. The simulations showed how different events produced different definitions of the actor eliciting the behavior (grouch/delinquent) and the responses to that actor (scold/avoid). Besides, ACT predicted the emotions of the object receiving the behavior.
ACT may provide an architecture for designing socially intelligent systems.
