An Artificial Neural NetworkAn Artificial Neural Networkforfor

Multi-Level InterleavedMulti-Level Interleavedandand

Creative Serial Order Cognitive Creative Serial Order Cognitive BehaviorBehavior

Steve DonaldsonSteve DonaldsonDepartment of Mathematics and Computer ScienceDepartment of Mathematics and Computer Science

Samford UniversitySamford University BirminghamBirmingham. Alabama. Alabama

Research Concern Example

Variable binding Smolensky, 1990

Central executive function Baddeley, 1992

Similarity matching Sloman & Rips, 1998

Emotional impact on decisions Damasio, 1994

Case based reasoning Kolodner, 1997

Chunking Laird, Newell, & Rosenbloom, 1987

Strategy development Anumolu, Bray, & Reilly, 1997

Goal management and planning Albus, 1991

Analogy development Hofstadter, 1995

Temporal processing Rosenblatt, 1964

Common sense reasoning Sun, 1994

Mathematical reasoning Anderson, 1995

Language Gupta & Dell, 1999

Credit assignment Holland, 1995

Rule processing Goebel, 1991

Creativity Hofstadter, 1995

Some Research Concerns Related to the Some Research Concerns Related to the Exploration of Intelligent SystemsExploration of Intelligent Systems

(Adapted from Donaldson, 1999)

Solve multiple tasks within the framework of a composite, synergistic architecture

Act autonomously under the internal control of neural network type processes

Learn in a biologically realistic manner

Operate at a scale significantly larger than normally found in single purpose networks

Acquire knowledge in a manner consistent with biological constraints

Transfer information across tasks, thus dealing with new situations using previously acquired knowledge

Exhibit multiple memory modalities typical of human information processing

Perform lifetime plastic learning without catastrophic loss of previously acquired knowledge

Learn from internal as well as external stimuli

Basic Requirements for Autonomous SystemsBasic Requirements for Autonomous Systems

Some Cognitive Skills and BehaviorsSome Cognitive Skills and BehaviorsExhibited by HumansExhibited by Humans

Recognition • Alphabet mastery • Spelling • Counting •

Acquisition of math facts • Memorization of a script •

Basic motor skills • Associative memory • Rehearsal •

Multiple associations • Free association • Transcription •

Solving mathematical expressions • Memory theatres •

Understanding simple pronoun referents • Complex motion •

Proto-language reading comprehension • Route following •

General inductive reasoning • Multiple trains of thought •

Acquisition and deployment of external memory strategies •

Sophisticated non-stereotypical sequence processing •

Suggested Comprehensive Explanatory MechanismsSuggested Comprehensive Explanatory Mechanisms

Sequence creation via generalized variable binding

Predictive learning Interleaved processing

Categorizing Cognitive AbilitiesCategorizing Cognitive Abilitiesby Required Mental Featuresby Required Mental Features

Predictive LearningPredictive Learning Alphabet masteryAlphabet mastery SpellingSpelling Acquisition of math factsAcquisition of math facts Memorization of a scriptMemorization of a script Basic motor skillsBasic motor skills Associative memoryAssociative memory Multiple associationsMultiple associations

Interleaved ProcessingInterleaved Processing Free associationFree association TranscriptionTranscription Route followingRoute following Memory theatresMemory theatres Multiple trains of thoughtMultiple trains of thought Complex motionComplex motion RehearsalRehearsal

RecognitionRecognition

Sequence CreationSequence Creation CountingCounting Solving mathematical expressionsSolving mathematical expressions Understanding simple pronoun referentsUnderstanding simple pronoun referents Protolanguage reading comprehensionProtolanguage reading comprehension General inductive reasoningGeneral inductive reasoning Acquisition and deployment of external memory strategiesAcquisition and deployment of external memory strategies Sophisticated non-stereotypical sequence processingSophisticated non-stereotypical sequence processing

High-Level Schematic of the Major Sub-SystemsHigh-Level Schematic of the Major Sub-Systems

Detailed Model SchematicDetailed Model Schematic

Some Temporal Processing ConceptsSome Temporal Processing Concepts

PatternPattern – a vector of values representing an idea or action in the model’s experience, – a vector of values representing an idea or action in the model’s experience, typically treated as a 2D figure to aid in visualization and conceptualization.typically treated as a 2D figure to aid in visualization and conceptualization.

SequenceSequence - temporally ordered collection of input/output patterns. - temporally ordered collection of input/output patterns.

RecognitionRecognition - the competence of a system to identify previously learned features or - the competence of a system to identify previously learned features or concepts with minimal ambiguity, possibly from partial sensory input, and in the concepts with minimal ambiguity, possibly from partial sensory input, and in the absence of any singular temporal contextual reference; specifically, the retrieval of a absence of any singular temporal contextual reference; specifically, the retrieval of a previously stored version of a pattern from long-term recognition memory.previously stored version of a pattern from long-term recognition memory.

Predictive learningPredictive learning – an ability acquired by previous exposure to a sequence to – an ability acquired by previous exposure to a sequence to reproduce patterns in that sequence based on the current state of a context module and reproduce patterns in that sequence based on the current state of a context module and the current input.the current input.

   Interleaved processingInterleaved processing – the production and use of temporally ordered information – the production and use of temporally ordered information

based on sequence hierarchies (e.g. sequence A is composed of sequences B and C, based on sequence hierarchies (e.g. sequence A is composed of sequences B and C, sequence B is composed of sequences C, D, and E, etc.).sequence B is composed of sequences C, D, and E, etc.).

   Sequence creationSequence creation – production of a new sequence from an existing seed sequence and – production of a new sequence from an existing seed sequence and

associations related to its members.associations related to its members.

Sample Pattern RepresentationsSample Pattern Representations

Internal representation for the letter “A”

-1 1 1 1 1 1 1 1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1  

Internal representation for a “boat”

-1 -1 -1 -1 -1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 1 1 1 1 1 -1 -1 -1

Recognition in the Long-Term Memory Sub-SystemRecognition in the Long-Term Memory Sub-System

Predictive LearningPredictive Learning

Context state (Si) and input/output (Ii) changes in a predictive learning system

Rosenblatt (1964) Elman (1990)

Acquisition of Math FactsAcquisition of Math Facts

Pattern set for restricted math fact learning

Some basic math facts considered as temporal sequences

Math fact learning represented as sequence completion

Script Learning as a Form of PredictionScript Learning as a Form of Prediction

S2WE_THE_PEOPLE_OF_THE_UNITED_STATES,_IN_ORDER_

TO_FORM_A_MORE_PERFECT_UNION,_ESTABLISH_JUSTICE,

_INSURE_DOMESTIC_TRANQUILITY,_PROVIDE_FOR_THE_

COMMON_DEFENSE,_PROMOTE_THE_GENERAL_WELFARE,_

AND_SECURE_THE_BLESSINGS_OF_LIBERTY_TO_OURSELVES

_AND_OUR_POSTERITY,_DO_ORDAIN_AND_ESTABLISH_THIS_

CONSTITUTION_FOR_THE_UNITED_STATES_OF_AMERICA.█

S1A_PENNY_SAVED_IS_A_PENNY_EARNED█

Avoiding catastrophic interference via sparse neural firing in sequence context

Basic Motor SkillsBasic Motor Skills

L

L

P

P

P

1

0

2

1

2

A

A

1

2

 

Muscle Arm Segment Movement Movement Code

1 Upper Clockwise M1

2 Upper Counter-clockwise M2

3 Lower Clockwise M3

4 Lower Counter-clockwise M4

M1 M2 M3 M4 M5 M6 M7 M8

Muscle control patterns for a simple arm

Two Simple Movement SequencesTwo Simple Movement Sequences

A “reaching” sequence

A “putting” sequence

Associative Memory via Predictive LearningAssociative Memory via Predictive Learning

Some learned

associations

Associative Recall

Level 1 Level 2 Pattern Trial 1 Trial 2 Trial 1 Trial 2 Reversed Wyoming 13 16 16 14 13 Colorado 3 1 3 3 5 Climber 3 2 1 3 2 Summit 0 0 0 0 0 Camera 1 1 0 0 0 Rain 16 15 20 16 17 Boat 3 3 0 1 2 Cup 1 2 0 3 1

Recall results from several multiple association tests when probing with [mts]_ _ and [water]_ _

Two sets of learned multiple associations

Multiple Associations Based on Probabilistic Firing Multiple Associations Based on Probabilistic Firing in the Sequence Context Modulein the Sequence Context Module

Short-Term Priority MemoryShort-Term Priority Memory

Stylized view of short-term priority module activation gradient changes over time in the process of generating the strokes in the letters of the sequence CAT.

Associative Memory via Predictive LearningAssociative Memory via Predictive Learning

Some learned associations

Free AssociationFree Association

A trace of the pattern perception module

An associative tale

A trace of the collective microfeatures module

Multiple Trains of ThoughtMultiple Trains of Thought

Learned sequences

“Thinking” several

thoughts

The effect of parameter adjustment

on recall order

A Route Following ExperimentA Route Following Experiment 

 

 

From To HighwayDumas, Texas (DU TX) Raton, New Mexico (RAT NM) US64

Glenwood Springs, Colorado (GS CO) Aspen, Colorado (ASP CO) CO82

Birmingham, AL (BIR AL) Memphis, Tennessee (ME TN) US78

Raton, New Mexico (RAT NM) Denver, Colorado (DEN CO) I25

Amarillo, Texas (AM TX) Dumas, Texas (DU TX) US87

Memphis, Tennessee (ME TN) Amarillo, Texas (AM TX) I40

Denver, Colorado (DEN CO) Glenwood Springs, Colorado (GS CO) I70  

Localized route sub-sequences lacking global order

Route Following Via Interleaved ProcessingRoute Following Via Interleaved Processing

Correctly ordered route recall after learning randomly ordered components

Learning for a Transcription ExperimentLearning for a Transcription Experiment

An interleaved processing hierarchy

Patterns

Sequences

Transcribing a “Thought”Transcribing a “Thought”

Complex MotionComplex Motion

Muscle control output for a complex motion

MemoryMemoryTheatresTheatres

Conceptual approaches to temporal knowledge representation for memory theatres

Story Telling Using Memory TheatresStory Telling Using Memory Theatres

Several Approaches to “Rehearsal”Several Approaches to “Rehearsal”

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An indication of how “rehearsal” results can depend on sequence format

The results of another approach to “rehearsal”

One approach to sequence “repetition” via interleaved processing

Pattern set for “rehearsal” simulations

Sequence CreationSequence Creation

P41 P

42 P43 …

P4S

P31 P

32 P33 …

P3R

P21 P

22 P23 …

P2N

P11 P

12 P13 …

P1M

P1 P2 P3 P4 …

P1M P2N P3R P4S …

Seed Sequence

Created Sequence

Previously Learned Sequences

Solving Mathematical ExpressionsSolving Mathematical Expressions

Additional sequence learning requirements

A trace of patterns produced during the solution of a mathematical expression

Protolanguage Reading ComprehensionProtolanguage Reading Comprehension

Assimilating letters into words and concepts

Patterns required for a reading experiment

Previously learned sequences necessary for reading

Donaldson, Steve (2003a). An artificial neural network model for reading comprehension. In Arabnia, H., Joshua, R., & Mun, Y. (Eds.), Proceedings of the Internal Conference on Artificial Intelligence, Volume 1. Las Vegas, NV: CSREA Press.

General Inductive ReasoningGeneral Inductive Reasoning

Patterns used in an inductive reasoning experiment

Sequence learning foundation for inductive reasoning

Observations preceding inductive rule formation

Sample Details from an Inductive Sample Details from an Inductive Rule Creation ProcessRule Creation Process

Trial 1

Trial 2

Trial 5

Trial 12

Inductive Rule Formation and ApplicationInductive Rule Formation and Application

An inductive rule formed via sequence creation

Additional sequence learning for inductive rule application

Application of a rule learned via inductive reasoning

External Memory StrategiesExternal Memory Strategies

Targets

Objects

DestinationRelations

StrategyRelations

ControlPatterns

Object-Target Categorization

Observations preceding formation of a memory strategy

Sequence Learning for an External Sequence Learning for an External Memory Strategies ExperimentMemory Strategies Experiment

Trial 1

Trial 8

Trial 10

Learning by Learning by example as example as

a foundation a foundation for the for the

creation of creation of external external memory memory

strategiesstrategies

External memory strategies learned by example

Some additional facts to be learned before strategy application

Recall and application of an external memory strategy

Applying a Learned External Memory StrategyApplying a Learned External Memory Strategy

A Non-Stereotypical Sequence A Non-Stereotypical Sequence Processing Experiment in the Processing Experiment in the

Domain of MusicDomain of Music

Lo Lo CC

Lo Lo DD

Lo Lo EE

Lo Lo FF

Lo Lo GG

Lo Lo AA

Lo Lo BB

Mid Mid CC

Mid Mid DD

Mid Mid EE

Mid Mid FF

Mid Mid GG

Mid Mid AA

Mid Mid BB

Hi Hi CC

Hi Hi DD

Hi Hi EE

Hi Hi FF

Hi Hi GG

Hi Hi AA

Hi Hi BB

Note to keyboard position transformation maps and a phrase from a song

Key designations for the three octaves mapped below

Model Expansion to accommodate embedded sequences

Donaldson, Steve (2003b). A neural network for high-level cognitive control of serial order behavior. In Ventura, D. & Das, S. (Eds.), Proceedings of the 7th Joint Conference on In-formation Sciences (6th International Conference on Computational Intelligence and Natural Computing). Research Triangle Park, NC: Association for Intelligent Machinery.

Non-Stereotypical Sequence ProcessingNon-Stereotypical Sequence Processing

“Playing” a song at a designated octave as a form of NSTSP

Flowchart of NSTSP processing in the domain of music

Donaldson, Steve (2003b). A neural network for high-level cognitive control of serial order behavior. In Ventura, D. & Das, S. (Eds.), Proceedings of the 7th Joint Conference on In-formation Sciences (6th International Conference on Computational Intelligence and Natural Computing). Research Triangle Park, NC: Association for Intelligent Machinery.

CountingCounting

Patterns for a counting experiment

Sequences learned as a foundation for counting

Representing item abstraction for a counting task

Results of counting the members of a group of people

Understanding Simple Pronoun ReferentsUnderstanding Simple Pronoun Referents

Simple pronoun to antecedent conversion

ResultsResults

Explore low level cognitive mechanismsExplore low level cognitive mechanisms Maintain close ties to biological systemsMaintain close ties to biological systems Seek generic principles subserving intelligenceSeek generic principles subserving intelligence Evaluate a parsimonious approach to systems designEvaluate a parsimonious approach to systems design Investigate foundations for high-level cognitionInvestigate foundations for high-level cognition Explore interaction of multiple memory modalitiesExplore interaction of multiple memory modalities Demonstrate sufficiency of the proposed foundationDemonstrate sufficiency of the proposed foundation

  

The End!The End!