Steve Donaldson Department of Mathematics and Computer Science Samford University

1. An Artificial Neural NetworkforMulti-Level InterleavedandCreative Serial Order Cognitive Behavior Steve Donaldson Department of Mathematics and Computer Science Samford University Birmingham. Alabama

2. 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 Exploration of Intelligent Systems (Adapted from Donaldson, 1999)

3. Basic Requirements for Autonomous Systems • 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

4. Some Cognitive Skills and BehaviorsExhibited 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 •

5. Suggested Comprehensive Explanatory Mechanisms Predictive learning Interleaved processing Sequence creation via generalized variable binding

6. Predictive Learning Alphabet mastery Spelling Acquisition of math facts Memorization of a script Basic motor skills Associative memory Multiple associations Categorizing Cognitive Abilitiesby Required Mental Features Recognition Interleaved Processing • Free association • Transcription • Route following • Memory theatres • Multiple trains of thought • Complex motion • Rehearsal Sequence Creation • Counting • Solving mathematical expressions • Understanding simple pronoun referents • Protolanguage reading comprehension • General inductive reasoning • Acquisition and deployment of external memory strategies • Sophisticated non-stereotypical sequence processing

7. High-Level Schematic of the Major Sub-Systems

8. Detailed Model Schematic

9. Some Temporal Processing Concepts • Pattern – 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. • Sequence - temporally ordered collection of input/output patterns. • Recognition - the competence of a system to identify previously learned features or concepts with minimal ambiguity, possibly from partial sensory input, and in the absence of any singular temporal contextual reference; specifically, the retrieval of a previously stored version of a pattern from long-term recognition memory. • Predictive learning – 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 the current input. • Interleaved processing – the production and use of temporally ordered information 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 creation – production of a new sequence from an existing seed sequence and associations related to its members.

10. Sample 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

11. Recognition in the Long-Term Memory Sub-System

12. Predictive Learning Rosenblatt (1964) Elman (1990) Context state (Si) and input/output (Ii) changes in a predictive learning system

13. Acquisition 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

14. Script 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

15. Basic Motor Skills MuscleArm SegmentMovementMovement 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

16. Two Simple Movement Sequences A “reaching” sequence A “putting” sequence

17. Associative Memory via Predictive Learning Some learned associations Associative Recall

18. Multiple Associations Based on Probabilistic Firing in the Sequence Context Module Two sets of learned multiple associations Recall results from several multiple association tests when probing with [mts]_ _ and [water]_ _

19. Short-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.

20. Associative Memory via Predictive Learning Some learned associations

21. Free Association A trace of the pattern perception module An associative tale A trace of the collective microfeatures module

22. Multiple Trains of Thought Learned sequences “Thinking” several thoughts The effect of parameter adjustment on recall order

23. From To Highway Dumas, 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 A Route Following Experiment Localized route sub-sequences lacking global order

24. Route Following Via Interleaved Processing Correctly ordered route recall after learning randomly ordered components

25. Learning for a Transcription Experiment Patterns Sequences An interleaved processing hierarchy

26. Transcribing a “Thought”

27. Complex Motion Muscle control output for a complex motion

28. MemoryTheatres Conceptual approaches to temporal knowledge representation for memory theatres

29. Story Telling Using Memory Theatres

30. Several Approaches to “Rehearsal” Pattern set for “rehearsal” simulations E934█E934█9██3██4███9██3███4█████████9██████3█████4███████E934█9██3██4██████9████3████4███████████9███████3███████4█████████████████9████████████3████████████4█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ One approach to sequence “repetition” via interleaved processing Π3.14159#Π█Π3.14159#Π█3██.██1██4██1██5███9███#███Π3.14159#Π█3██.██1██4██1██5███9███#███Π3.14159#Π█3██.██1██4██1██5███9███#███Π3.14159#Π█3██.██1██4██1██5███9███#███Π3.14159#Π█3██.██1██4██1██5███9███#██ The results of another approach to “rehearsal” ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ΠΠ3.14159#█ An indication of how “rehearsal” results can depend on sequence format

31. Sequence Creation Seed Sequence P1 P2 P3 P4 … P41 P42 P43 … P4S P31 P32 P33 … P3R P21 P22 P23 … P2N P11 P12 P13 … P1M Previously Learned Sequences Created Sequence P1M P2N P3R P4S …

32. Solving Mathematical Expressions Additional sequence learning requirements A trace of patterns produced during the solution of a mathematical expression

33. Protolanguage Reading Comprehension 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. Patterns required for a reading experiment Previously learned sequences necessary for reading Assimilating letters into words and concepts

34. General Inductive Reasoning Patterns used in an inductive reasoning experiment Sequence learning foundation for inductive reasoning Observations preceding inductive rule formation

35. Sample Details from an Inductive Rule Creation Process Trial 1 Trial 2 Trial 5 Trial 12

36. Inductive 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

37. External Memory Strategies Targets Objects Destination Relations Strategy Relations Control Patterns Object-Target Categorization

38. Sequence Learning for an External Memory Strategies Experiment Observations preceding formation of a memory strategy

39. Learning by example as a foundation for the creation of external memory strategies Trial 1 Trial 8 Trial 10

40. Applying a Learned External Memory Strategy External memory strategies learned by example Some additional facts to be learned before strategy application Recall and application of an external memory strategy

41. A Non-Stereotypical Sequence Processing Experiment 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. Model Expansion to accommodate embedded sequences Key designations for the three octaves mapped below Note to keyboard position transformation maps and a phrase from a song

42. Non-Stereotypical Sequence Processing 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. Flowchart of NSTSP processing in the domain of music “Playing” a song at a designated octave as a form of NSTSP

43. Counting 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

44. Understanding Simple Pronoun Referents Simple pronoun to antecedent conversion

45. Explore low level cognitive mechanisms Maintain close ties to biological systems Seek generic principles subserving intelligence Evaluate a parsimonious approach to systems design Investigate foundations for high-level cognition Explore interaction of multiple memory modalities Demonstrate sufficiency of the proposed foundation Results

46. The End!