π What is Information Integration Theory?
Information Integration Theory (IIT), developed by Norman Anderson, proposes that individuals combine and weigh different pieces of information to form overall judgments or evaluations. It posits that this integration process follows algebraic rules, meaning that the impact of each piece of information can be mathematically modeled.
π History and Background
Norman Anderson introduced IIT in the late 1960s. His work challenged prevailing psychological theories that emphasized the limitations of human information processing. Anderson argued that people are capable of quite sophisticated cognitive operations when integrating information.
π Key Principles of IIT
- βοΈ Valuation: Each piece of information is assigned a subjective value or scale value representing its perceived magnitude or importance.
- πΆ Weighting: Each piece of information is also assigned a weight, reflecting its relative importance in the overall judgment.
- β Integration: The values and weights are combined according to an algebraic rule (e.g., adding or averaging) to produce an overall evaluation.
- π’ Response: The integrated evaluation is then translated into an observable response, such as a judgment or decision.
π§ͺ Famous Experiments and Examples
1. Impression Formation
Experiment: Participants were presented with descriptions of hypothetical individuals, each consisting of several adjectives (e.g., intelligent, kind, honest). The adjectives varied in their valence (positive or negative) and intensity.
Results: Anderson found that participants' overall impressions of the individuals could be accurately predicted by averaging the scale values of the adjectives. This supported the averaging model of information integration.
2. Moral Judgments
Experiment: Participants were asked to rate the moral acceptability of different actions, described in terms of their consequences and intentions.
Results: The study showed that both consequences and intentions influenced moral judgments, and that their effects could be mathematically modeled using IIT principles. Participants weighted intentions more heavily when assessing moral acceptability. The following additive formula was used to model this relationship:
$Moral Judgement = w_1 * Intentions + w_2 * Consequences$
Where $w_1$ and $w_2$ represent the weights for intentions and consequences respectively.
3. Consumer Preferences
Experiment: Participants evaluated various products (e.g., cars, restaurants) based on several attributes (e.g., price, quality, service).
Results: IIT principles could predict consumer preferences by combining the values and weights assigned to each attribute. For example, a car with a high quality score but a high price might be evaluated differently than a car with moderate quality and a low price. IIT helps to model these trade-offs. An averaging model might be used:
$Overall\_Preference = \frac{w_1 * Quality + w_2 * Price}{w_1 + w_2}$
π Real-World Examples
- π§ββοΈ Legal Decisions: Juries integrate evidence from various sources (witness testimony, forensic analysis) to reach verdicts.
- π₯ Medical Diagnoses: Doctors combine symptoms, test results, and patient history to make diagnoses.
- πΌ Business Negotiations: Negotiators weigh different factors (price, terms, relationships) to reach agreements.
- π½οΈ Restaurant Choices: Considering price, ambiance, and food quality to decide where to eat.
π‘ Conclusion
Information Integration Theory provides a powerful framework for understanding how people make judgments and decisions by combining multiple sources of information. The famous experiments conducted by Anderson and his colleagues have demonstrated the validity and utility of IIT principles in diverse domains, from impression formation to moral judgment to consumer behavior. IIT continues to be an influential theory in psychology and related fields.