Two-mode Threshold Learning

Item

Title
Two-mode Threshold Learning
Date
1964
Index Abstract
Not Available
Photo Quality
Not Needed
Report Number
AMRL TDR 64-39
Creator
Sklansky, J.
Corporate Author
RCA Laboratories
Laboratory
Biophysics Laboratory
Extent
53
Identifier
AD0602966
Access Rights
OTS
Distribution Classification
1
Contract
AF 33(657)-11336
DoD Project
7233 - Biological Information Handling Systems and Their Functional Analogs
DoD Task
723305 - Theory of Information Handling
DTIC Record Exists
No
Distribution Change Authority Correspondence
None
Distribution Conflict
No
Cover Price
1.75
Abstract
In certain 'threshold learning processes' (TLPs) associated with pattern recognition and sensory perception, the process of training an observer to recognize patterns or distinguish levels of sensory excitation may be modeled by a finite-state Markov chain. The statistics of the signals received by the observer move at random between two sets of parameters in a 'two-mode' TLP, modeled by a two-mode Markov chain. Using a probabilistic measure of effectiveness, the effectiveness of a 'simple incremental' feedback policy is shown to be greater for two-mode TLPs than for one-mode TLPs over a certain range of environmental and structural statistics. A method of designing periodic train-work schedules for two-mode TLPs is described. ('Train' and 'work' correspond to 'closed-loop' and 'open-loop' respectively.) In many real adaptive processes an 'RC approximation' of the train-work dynamics is applicable. For these processes the ratio of working time to retraining time, yielding a desired performance level, is maximized when the work-retrain period is made as small as possible. Many stochastic processes present modeling problems of near psychological complexity. Ways in which open-loop/closed-loop relationships can help the life scientist or engineer model adaptive stochastic processes by two-mode TLPs are indicated.
Report Availability
Full text available
Date Issued
1964-05
Provenance
RAF Centre of Aviation Medicine
Type
report
Format
1 online resource
Media
AMRLTDR64-039.pdf

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