Memory’s Illusion: you Knew it before you Learned it

Memorys Illusion: you Knew it before you Learned it

Alankrita Chaturvedi

St. Marys Convent Inter College, Independent Researcher

Abstract

This paper explores the idea that forgetting is not the loss of knowledge but merely the failure to retrieve what was once known. It discusses implicit and explicit memory, déjà vu, and historical rediscoveries of knowledge, suggesting that learning may often be the recollection of previously acquired but inaccessible information. The study integrates cognitive science, memory research, and historical patterns to present a cyclical view of knowledge retention and rediscovery. Additionally, this paper highlights the implications of memorys illusion in fields such as education, artificial intelligence, and cognitive neuroscience, suggesting that harnessing implicit memory retrieval could transform learning methodologies.

Keywords Memory, Implicit Memory, Explicit Memory, Learning, Déjà Vu, Knowledge Rediscovery, Cognitive Science, AI and Memory, Historical Knowledge

1. INTRODUCTION

   Have you ever heard something new, only to feel an eerie sense that you already knew it? What if forgetting is not the loss of knowledge but merely the failure to retrieve what was once known? This paper explores the illusion of memory and how implicit recall may explain phenomena such as déjà vu and knowledge rediscovery.

   Understanding the mechanisms of memory is crucial not only for personal learning but also for broader scientific and technological advancements. As artificial intelligence models attempt to replicate human cognition, the study of memory retrieval and knowledge rediscovery may provide insights into improving AI learning efficiency and neural networks.

2. IMPLICIT AND EXPLICIT MEMORY: THE HIDDEN LAYERS OF KNOWLEDGE

   1. Explicit Memory: The Knowledge We Are Aware Of

      Explicit memory refers to conscious recall of facts and experiences. It is categorized into:

      • Episodic Memory Personal experiences, such as recalling a childhood trip.

      • Semantic Memory General knowledge, like knowing the capital of France.

        Ebbinghaus' Forgetting Curve (1885) demonstrates that explicit memories decay rapidly without reinforcement. However, even when explicit memory fails, information may still exist in deeper cognitive layers.

   2. Implicit Memory: Knowledge Without Awareness

      Implicit memory is the unconscious retention of information. Examples include:

      • Recognizing a song without remembering where you heard it.

      • Riding a bicycle after years of not practicing.

      • Typing on a keyboard effortlessly without recalling key positions.

        Tulvings Encoding Specificity Principle (1973) supports this idea by showing that memory retrieval depends on the right cues. If the correct trigger is absent, knowledge remains hidden, giving the illusion that we never learned it.

        Additionally, studies in neuroplasticity suggest that forgotten skills or knowledge can be reactivated through repeated exposure, providing further evidence that forgetting is not the same as losing information permanently.

   3. Comparison of Implicit and Explicit Memory

      Feature Explicit Memory Implicit Memory

      Definition Conscious recall of facts and experiences Unconscious retention of knowledge

      Examples Remembering historical facts, recalling a

      trip

      Riding a bike, recognizing a song

      Forgetting Rate Rapid (Ebbinghaus Forgetting Curve) Retained for long periods without conscious

      effort

      Retrieval Method Requires conscious effort Triggered by cues, often unknowingly

      Application Studying, exams, recalling events Skill-based learning, intuition

3. HOW THIS SUPPORTS THE THEORY

   • Implicit memory retains forgotten knowledge even when explicit memory fails.

   • Learning may often be the resurfacing of stored but inaccessible information.

   • Déjà vu and intuition may stem from implicit memory retrieval without full conscious awareness.

   • AI models could benefit from mimicking implicit memory storage and retrieval.

     This challenges conventional ideas of forgetting, suggesting that memory is not lost but simply inaccessible until the right conditions arise.

4. THE CYCLE OF FORGOTTEN KNOWLEDGE: ANCIENT WISDOM REDISCOVERED

   1. Ancient Knowledge and Modern Rediscovery

      Many scientific advancements and technological innovations have predecessors in ancient texts and traditions.

      Examples include:

      • Atomic Theory: Introduced by Acharya Kanad (~600 BCE) and Democritus (~400 BCE) before modern physics rediscovered it.

      • Heliocentrism: Proposed by Aryabhata (~500 CE) before Copernicus in the 16th century.

      • Plastic Surgery & Medical Science: Ancient Indian surgeon Sushruta (600 BCE) documented reconstructive surgery long before Western medicine.

      • Binary Number System: Used in Vedic texts before Leibniz formalized it in the 17th century.

      • The Concept of Neural Networks: Philosophical texts from ancient India and Greece describe knowledge transmission in ways similar to modern synaptic learning models.

   2. The Role of Implicit Cultural Memory Just as individuals unconsciously retain forgotten knowledge, civilizations do the same:

   • Oral Traditions and Lost Manuscripts: Ancient knowledge was often passed down orally, and when texts were lost (e.g., the burning of the Library of Alexandria), societies forgot what was once known.

   • Cognitive Recycling: Just as a person who once learned a language might recall it years later with exposure, societies "rediscover" knowledge when circumstances demand it.

   • Scientific Déjà Vu: Entire cultures often "reinvent" ideas that existed in earlier civilizations.

   • AI and Forgotten Knowledge: Machine learning models experience a similar phenomenon where old patterns resurface in training cycles, mimicking human implicit memory.

5. CONCLUSION: REDISCOVERY OVER DISCOVERY

Knowledgeboth personal and collectiveis cyclical. What we perceive as "new" is often forgotten wisdom resurfacing through time. This theory finds support in implicit memory research, ancient texts, and recurring patterns in human discovery. Whether through déjà vu, intuition, or the revival of ancient sciences, knowledge persists beneath the surface, waiting for the right conditions to be remembered.

Furthermore, as we move toward an era dominated by artificial intelligence, understanding how humans retrieve forgotten knowledge could provide groundbreaking advancements in AI learning mechanisms. Could future AI models be designed to remember in the way humans do? If so, the study of implicit memory may not just explain human cognition but also shape the future of technology.

Thus, ratherthan always seeking innovation in the unknown, perhaps the greatest advancements lie in remembering what we have forgotten.

REFERENCES

1. H. Ebbinghaus, Memory: A Contribution to Experimental Psychology, 1885.

2. E. Tulving, Encoding Specificity Principle in Memory Retrieval, 1973.

3. Sushruta, Sushruta Samhita, 600 BCE.

4. Aryabhata, Aryabhatiya, 500 CE.

5. G. Polya, Mathematical Discovery: On Understanding, Learning, and Teaching Problem Solving, 1962.

6. G. Tononi & C. Koch, The Neuroscience of Consciousness: A Global Workspace Perspective, 2015.

7. J. McClelland et al., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986.

8. L. Feigenbaum, Artificial Intelligence: A Knowledge-Based Approach, 1993.