mlsanityData to 14 Oct 2026

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

arXiv 2603.04440 ↗First posted 20 Feb 2026q-bio.NC cs.AI cs.ETcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 60 references that are in the map. First posted on arXiv 20 Feb 2026.

Builds on

3 of 60 references.

References · 3

  1. 2026A computational account of dreaming: learning and memory consolidationcited by 3
  2. 2026Cognitive algorithms and systems of episodic memory, semantic memory and their learningscited by 1
  3. 2026A consequence of failed sequential learning: A computational account of developmental amnesiacited by 1

Built on by

Not cited yet.