Task complexity moderates the influence of descriptions in decisions from experience

Area of Science:

• Cognitive psychology
• Decision science
• Human-computer interaction

Background:

• Decision-makers often use both descriptive and experiential information.
• Limited research exists on decisions combining both information types.
• Understanding how task complexity influences information use is crucial.

Purpose of the Study:

• To investigate the relationship between task complexity and the influence of descriptive information on decision-making.
• To explore how descriptions affect performance in experience-based tasks of varying complexity.
• To develop and validate a cognitive model explaining these effects.

Main Methods:

• Three experiments were conducted to examine decision-making behavior.
• Participants made choices in tasks with varying complexity, with and without descriptive information.
• A cognitive model integrating descriptive information into reinforcement learning was proposed and tested.

Main Results:

• In simple tasks, descriptions had minimal impact compared to experience alone.
• In moderately complex tasks, descriptions significantly improved performance and choice identification.
• A non-monotonic pattern emerged: description influence increased with complexity up to a point, then decreased for overly complex tasks.

Conclusions:

• Task complexity moderates the influence of descriptive information on decision-making.
• A novel cognitive model accurately predicts behavior and the non-monotonic influence pattern.
• Findings have implications for designing effective warning labels and information systems in complex environments.