[Paper Review] Towards a computational theory of human daydreaming

Erik T. Mueller, Michael G. Dyer|ArXiv.org|Dec 10, 1998

Mind wandering and attention26 references17 citations

TL;DR

This paper proposes a computational theory of human daydreaming through the DAYDREAMER program, which simulates spontaneous mental simulation using relaxed planning, episodic memory, emotional states, and control goals. The system generates goal-directed daydreams in natural language, demonstrating how daydreaming supports planning, emotional regulation, and learning from past experiences.

ABSTRACT

This paper examines the phenomenon of daydreaming: spontaneously recalling or imagining personal or vicarious experiences in the past or future. The following important roles of daydreaming in human cognition are postulated: plan preparation and rehearsal, learning from failures and successes, support for processes of creativity, emotion regulation, and motivation. A computational theory of daydreaming and its implementation as the program DAYDREAMER are presented. DAYDREAMER consists of 1) a scenario generator based on relaxed planning, 2) a dynamic episodic memory of experiences used by the scenario generator, 3) a collection of personal goals and control goals which guide the scenario generator, 4) an emotion component in which daydreams initiate, and are initiated by, emotional states arising from goal outcomes, and 5) domain knowledge of interpersonal relations and common everyday occurrences. The role of emotions and control goals in daydreaming is discussed. Four control goals commonly used in guiding daydreaming are presented: rationalization, failure/success reversal, revenge, and preparation. The role of episodic memory in daydreaming is considered, including how daydreamed information is incorporated into memory and later used. An initial version of DAYDREAMER which produces several daydreams (in English) is currently running.

Motivation & Objective

• To develop a computational model that explains the cognitive functions of daydreaming, such as future planning, emotional regulation, and learning from past experiences.
• To investigate how control goals—like rationalization, failure/success reversal, revenge, and preparation—guide the content and direction of daydreams.
• To model the role of episodic memory in storing and retrieving daydreamed experiences for later use in mental simulation.
• To integrate emotional states into the daydreaming process, showing how emotions both trigger and are shaped by imagined scenarios.
• To implement a functional prototype (DAYDREAMER) capable of generating coherent, goal-directed daydreams in natural language.

Proposed method

• A scenario generator based on relaxed planning algorithms dynamically constructs hypothetical past or future events based on current goals and memory.
• A dynamic episodic memory system stores and retrieves personal experiences, which inform and are updated by daydreamed scenarios.
• Personal and control goals—such as preparation for future events or rationalizing past failures—direct the content and structure of generated daydreams.
• An emotion component models affective states that initiate daydreams and are influenced by outcomes of imagined scenarios, linking emotion to cognitive simulation.
• Domain knowledge of social interactions and everyday events is encoded to ensure realistic and contextually appropriate daydream content.
• The system produces natural language daydreams by integrating memory, goals, emotions, and planning into a coherent narrative output.

Experimental results

Research questions

• RQ1 How do control goals such as rationalization, failure/success reversal, revenge, and preparation shape the content of daydreams?
• RQ2 In what ways does episodic memory support and get updated by daydreaming processes?
• RQ3 How do emotional states both trigger and result from daydreamed scenarios in a computational framework?
• RQ4 What mechanisms enable a computational system to generate coherent, goal-directed daydreams in natural language?
• RQ5 How can a simulated daydreaming system model the cognitive functions of planning, learning, and emotional regulation?

Key findings

• The DAYDREAMER system successfully generates multiple daydreams in natural language, demonstrating the feasibility of modeling spontaneous mental simulation computationally.
• Control goals such as preparation and failure/success reversal significantly influence the direction and content of daydreams, aligning them with cognitive and emotional goals.
• Emotional states are both triggers for and outcomes of daydreaming, showing bidirectional interaction between affect and mental simulation.
• Episodic memory is dynamically updated by daydreamed experiences, which are later reused in subsequent simulations, supporting iterative learning and planning.
• The integration of domain knowledge about social and everyday events enhances the realism and coherence of generated daydreams.
• The system's architecture supports multiple cognitive functions of daydreaming, including creativity, motivation, and emotion regulation, through a unified computational framework.