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Cognitive therapy, pioneered by Aaron T. Beck in the 1960s, is a structured approach to addressing psychological distress by focusing on the influence of thoughts on emotions and behaviors. All cognitive therapies involve the basic assumption that human beings have control over their feelings, and that how individuals feel about something depends on how they think about it. Unlike psychoanalytic methods that delve into unconscious processes or humanistic approaches emphasizing...
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Cognitive-behavioral therapies (CBTs) are grounded in the belief that our thoughts profoundly influence our emotions and actions. Advocates of CBT emphasize three core assumptions: first, that cognitions are identifiable and measurable; second, that they are central to psychological functioning; and third, that irrational or maladaptive beliefs can be replaced with rational and adaptive ones. This transformative approach to therapy has paved the way for specific models such as Albert...
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Computational Psychotherapy System for Mental Health Prediction and Behavior Change with a Conversational Agent.

Tine Kolenik1,2,3, Günter Schiepek3,4, Matjaž Gams1

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This study introduces a novel conversational agent for computational psychotherapy, enhancing mental health prediction and behavior change. Simulating theory of mind significantly improves its effectiveness, outperforming current systems.

Keywords:
artificial cognitive architectureattitude and behavior change support systemsdigital mental healthgenerative artificial intelligenceintelligent cognitive conversational agentmachine learning

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Area of Science:

  • Computational Psychology
  • Artificial Intelligence in Mental Health
  • Natural Language Processing for Healthcare

Background:

  • Rising global mental health issues necessitate advanced computational psychotherapy solutions.
  • Current conversational agents lack sophisticated prediction and behavior change capabilities.

Purpose of the Study:

  • To present a novel computational psychotherapy system for mental health prediction and behavior change.
  • To introduce a new dataset of quantitative and qualitative mental health data.
  • To demonstrate the system's ability to predict and influence mental health outcomes.

Main Methods:

  • Development of a conversational agent simulating theory of mind using cognitive architecture and machine learning.
  • Training computational models on a novel dataset of 1495 instances of stress, anxiety, and depression (SAD) scores and diary entries.
  • Evaluation through computational experiments for mental health prediction and an interventional study with 42 participants.

Main Results:

  • The system achieved higher accuracy (91.41%) in mental health prediction compared to state-of-the-art (84% using LSTM).
  • Achieved 87.68% accuracy in 7-day mental health trend forecasting, surpassing other systems.
  • Significantly reduced stress (p=0.004) and anxiety (p=0.008) levels compared to Woebot in an interventional study.

Conclusions:

  • Simulating theory of mind in conversational agents significantly enhances computational psychotherapy efficacy.
  • The developed system offers a promising advancement for mental health interventions.
  • This approach represents a significant improvement over current state-of-the-art computational psychotherapy systems.