Looping Images and Sounds

 

Looping Images and Sounds

Evoke Deep Learning Promise

Rotating Comic Characters

Offer

Deep Learning Practice

 

Current psychological research relates that over-dependence on device/computer screens can lead to short attention spans, with our visual speed crushing our inherent listening capability.  Yet, we still rely heavily on multiple screens for our daily pursuits.

How can we maintain our exposure to the myriad of screen images, with multiple details on split visual fields, and yet use them even further to benefit and escalate us? 

Although our visual memory can dominate with this screen adhesion, our auditory memory (listening) then suffers, out-of- sync.  Unable to integrate auditory, we can only focus on singular image-memory intake. One-item-at-a-time; primarily, visually.

 However, we can remedy this imbalance, and put laborious visual screen intake to our advantage. But, that is, if we agree to undertake the needed effort required; admitting that a better focused, integrated, memory spans has personal value. 

Question:  Why do I need Deep Learning rehearsal looping practice to obtain higher memory spans? I have plenty to do with my screen life as it is, and there are many simple brain games available.

Answer:  Because most jobs now require procedural training with an eye for detail, management acumen. Everyday life demands require fast organizational self-management. Furthermore, our daily life and work productivity could suffer, especially if procedural listening and instructions are required.

 Change needed.

Call in comic characters to the rescue -- inanimate 3D objects become activated

Looping images and sounds can strengthen our minds. Continuous rehearsal segments will affect the frontal brain cortex for improved working memory [1] required to quickly learn procedures. Okay, what will comprise these segments?

 Looping comic characters.

Five specific characters, in a researched and practiced program, entered the rehearsal paradigm stage.  Although they varied in gender, appearance, personality, and temperament, nonetheless, remained non-distracting, timed objects.

But, soon they went to work, becoming free to loop repetively with interplay.[2] Their assignment was mental skill sequencing practice, fundamental to following oral and written directions and procedures.

The looped motion interaction evolves into its own reinforced learning [3] for the participant. Each spoken entity creates a visual and sound unit to reinforce the preceding segment, as Leonardo noted in the early 1500s, Renaissance. [4]

The visual facial patterns become oriented in space-time creating their own local motion/sound/visual energy.[5]

Question. How will these objects create auditory memory integration with Deep Learning features? 
Answer: Through 5^th Dimension counter point, [6] or parallel thought [7]

Question:  What is the 5^th Dimension aspect? Is it strictly an assumption?
Answer:  The 5^th D formulates as a result of combining the first four dimensions that had decades of scientifically documented mechanisms. The three common dimensions now create four and five added complex dimensions into one serial, cumulative, conception.

All five progressive dimensions each had years of scientific, well-documented, authenticated research and practice. [8]

1st Dimension – flat objects

2^nd Dimension – cartoons

3^rd Dimension – cubism, 3-D

4^th Dimension – 4D - quantum sound and image sounds, into higher memory span loops over time and space distances (complex segmented layers in a smooth flow field). [9]

5^th Dimension – 5D - layers of looped thought creates Counterpoint known as parallel thinking[10]. The constant looping span rehearsal formulates Deep Learning practice.

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[1]  Kandel, E. R. (2012). The age of insight. New York: Random House.

[2]  Hofstader, D. (1979). Gödel, Escher, Bach: The eternal golden braid. New York: Basic Books. p. 239.

[3] Wayner. P. (Sept. 5, 2022). What is Reinforcement Learning?  How AI Trains Itself. MetaTech Online Events.

[4] Shlain, L. (1991). Art and physics: Parallel visions in space time, light. New York: William Morrow. p. 433.

[5] Adelman E. H. (August 1991). Mechanisms for Motion Perception.  Optics and Photonics News, pp. 24-30.

[6] Rumelhart, D. E., McClelland, J. and the PDP Research Group. (1986).  Parallel distributed processing:  Explorations in the micro structure of cognition.  Cambridge, MA: MIT Press

[7]  Erland, J. K. Erland, (February 4, 1986; copyright TXu 225 862). Contrapuntal Thinking and Definition of Sweeping Thoughts.

[8] Erland, J. K. (Fall 2000). Brain-Based Longitudinal Study Reveals Subsequent High Academic Achievement Gain for Low-Achieving, Low Cognitive Skills, Fourth Grade Students. Journal of Accelerated Learning and Teaching. 25, (3&4) pp.5-48. ERIC ED # 453-553. & # CS 510 558. https://Books.Google.com/jankuypererland page 41,

[9] Learning Visual Groups from Co-occurrences in Space and Time. (2016). Isola, P., Zoran, D., Krishnan, K., Adelson, E. H., International Conference on Learning Representations, workshop paper. Abstract PDF

[10]  Erland, J. K. (1986), (February 4, 1986; copyright TXu 225 862). Contrapuntal Thinking and Definition of Sweeping Thoughts.

 

Deep Learning Practice Resolves Retention Issues

 This article expands on my recent January “Content Timing Process Realized” and March 2022 blogs on “Deep Learning Applied” findings, to elucidate on how learning retention can be actualized through applied parallel thought (Erland, J. K. February 4, 1986); Rumelhart. D. E. McClelland, J. 1986), and neurological codes, (Hinton, G. 2006). Looping, puppetry dramatization becomes a key deep memory element for re-training career and academic skill retention (Erland, J. K. 1980).

 A highly skilled workforce is a requirement in today’s demanding technological economy. Business and industry now grapple how to create upskilling training that retains and advances eager workers in need of procedural learning. Many have ingrained lack of focus creating erratic behavior and follow-through with written and oral directions that underlie all procedural details.

 Working memory becomes the impetus for activating layered segmented chunks, rotating in spans or units, known as “Deep Learning”, earlier referenced as “Contrapuntal, Sweeping, or Parallel Thinking”© (Erland, Janis L., 1986) in my early writings. This innovative Deep Learning, cognitive process is a vitally needed retention component for up-skilling and re-skilling training. Deep Learning offers a critical component for planning, making coherent decisions, and expressing newly learned skills.

 As a conduit to create the procedural system outcome, are “Deep Learning” practice sessions. Art, science, and computational skills are provided by innovative ventriloquist, prosody speaking, puppets. The participant assumes the role of detecting new patterns and systems.

 The Bridge to Achievement’s (BTA)™ mental agility, a cognitive, span-expansion coding process, has been documented through serial published, juried, award-winning, longitudinal experimental research for academic and career achievement. Outstanding outcomes were documented in math, reading and language skills.

Additionally, the extensive longitudinal data research revealed new mental strength will sustain the enhanced skills over time, when applied consistently. The BTA™ Deep Learning practice becomes a valued supplemental front engine for all reading, math, and language programs, or used independently as a “stand alone, mental jump-starter”. Subsequently, the intense, Deep Learning rehearsal process creates a new, higher functioning, and more optimistic, empowered individual.

 The unique BTA content elements cement learning retention in multiple ways:

 -     Brief, timed, self-paced lessons. Mental focus maintained through ongoing fixed, focal interest.

-     Original, one-of-a-kind, phonetic and coding practice lessons.

      -     Lessons increase gradually in complexity with locked, timing, pacing.

      -     Fourteen to thirty minute short, segmented, daily lessons offer less time involvement.

      -     Whole-brain, peers and puppets, modeling rehearsal regimen (Erland, J. K.  1980).

      -     Authentic, Hollywood Golden Age ventriloquist puppets applied as adjacent role models.

      -     Thirteen choreographed character positions rotate in loops over 800 unique segments.

      -     Solid, verified, data-based published results with multiple 3^rd party reviewers (Erland, J. K. Fall 2000).

_____________________ 

Erland, J. K. (1980). Vicarious modeling using peers and puppets with learning disabled adolescents in following oral directions. The University of Kansas, Lawrence, Kansas.

Erland, Janis L. (February 4, 1986; copyright TXu 225 862). Contrapuntal Thinking and Definition of Sweeping Thoughts.

Erland J. K. (c 1989), Hierarchy of Thinking. Mem-ExSpan, Inc.

Erland, J. K. (Fall, 1998). Cognitive skills and accelerated learning memory training using interactive media improves academic performance in reading and math.  Journal of Accelerative Learning and Teaching,  23, (3 & 4), 3-57.

Erland, J. K. (Fall 2000). Brain-Based Longitudinal Study Reveals Subsequent High Academic Achievement Gain for Low-Achieving, Low Cognitive Skills, Fourth Grade Students. Journal of Accelerated Learning and Teaching. 25, (3&4) pp.5-48. ERIC ED # 453-553. & # CS 510 558. https://Books.Google.com/jankuypererland page 41.

Erland, J. K. (© 2008). Downloadable, unpublished report. Five Generations, 27-years of iterative Brain-Based Accelerative Learning Experimentation Demonstrate Cognitive Skill Improvement Enhances Academic and Career Goals. (https://memspan/jalt).

Hinton, G. (2006). Deep Learning and the recipient of the 2001 Rumelhart Deep Learning Prize.

Rumelhart, D. E., McClelland, J. and the PDP Research Group. (1986).  Parallel distributed processing:  Explorations in the micro structure of cognition. Cambridge, MA: MIT Press.