Time and memory costs jointly determine a speed-accuracy trade-off and set-size effects

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Decision Science

Background:

• Policies, which map states to actions, require memory, with complexity determined by mutual information between states and actions.
• High-complexity policies retain state information for greater rewards but incur a decoding time cost, creating a speed-accuracy trade-off.
• Low-complexity policies discard state information, exploiting regularities for faster, less accurate actions.

Purpose of the Study:

• To investigate the relationship between policy complexity, memory costs, and decision-making speed and accuracy.
• To test the theory that policy complexity underlies speed-accuracy trade-offs and set-size effects in human behavior.
• To examine how humans modulate policy complexity in response to time and memory constraints.

Main Methods:

• Three experiments were conducted manipulating intertrial intervals, environmental regularities, and state set sizes.
• Participants' responses were analyzed to assess policy complexity, decoding speed, and accuracy.
• Behavioral data were used to model the interplay between policy complexity and cognitive costs.

Main Results:

• Humans demonstrated sensitivity to both time and memory costs when adjusting policy complexity.
• Response times increased with larger state set sizes, consistent with higher policy complexity.
• Findings support the hypothesis that policy complexity influences the observed speed-accuracy trade-offs.

Conclusions:

• Policy complexity is a critical factor influencing decision-making speed and accuracy.
• Constraints imposed by policy complexity may explain common speed-accuracy trade-offs and set-size effects.
• Understanding policy complexity offers insights into the cognitive mechanisms underlying adaptive behavior.