A systematic approach to answering the easy problems of consciousness based on an executable cognitive system

Abstract / Extracted Text

Consciousness is the window of the brain and reflects many fundamental cognitive properties involving both computational and cognitive mechanisms. A collection of these properties was described as the "easy problems" by Chalmers, including the ability to discriminate, categorize, and react to stimuli; information integration; reportability; information access; attention; deliberate control; and the difference between wakefulness and sleep. These "easy problems" have not been systematically addressed. This study presents a first attempt to address them systematically based on an executable cognitive system and its implemented computational mechanisms, built upon an understanding of conceptual knowledge proposed by Kant. The study suggests that the abilities to discriminate, categorize, react, report, and integrate information can all be derived from the system's learning mechanism; attention and deliberate control are goal-oriented and can be attributed to emotional states and its information-manipulation mechanism; and the difference between wakefulness and dream sleep lies mainly in the source of stimuli. The connections between the implemented mechanisms in the executive system and conclusions drawn from empirical findings are also discussed, and many of these discussions and conclusions are supported by demonstrations of the executive system.

Latest Summary

• Consciousness encompasses fundamental cognitive properties termed the "easy problems," including discrimination, categorization, reaction to stimuli, information integration, reportability, information access, attention, deliberate control, and wakefulness versus sleep.
• The study systematically addresses these properties using an executable cognitive system grounded in Kantian conceptual knowledge.
• Key cognitive abilities such as discrimination, categorization, reaction, reporting, and information integration emerge from the system's learning mechanisms.
• Attention and deliberate control are linked to goal-oriented behavior driven by emotional states and information manipulation, while wakefulness versus dream sleep differences relate primarily to the source of stimuli.

Practical Takeaways:

This research provides a computational framework that models core cognitive functions underlying consciousness, highlighting the role of learning mechanisms and emotional states in attention and control. Practitioners developing AI or cognitive systems can leverage these insights to design systems that better mimic human-like perception, decision-making, and state-dependent processing by integrating learning-based discrimination and goal-driven control influenced by internal states.

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