Day 1 AM (April 5)

7:30 - 8:30 Workshop Registration (Corniche Room)
7:30 - 8:45 Breakfast buffet (Corniche Room)

8:45 - 9:15 Workshop Opening (Room 101)
Greetings: General Co-Chairs: J. McCelland and A. Pentland
Announcements: John Weng and Ida Stockman

9:30 - 12:30 Session 1: (Room 101)
                    Child Mental Development – How Is Human Development Grounded?
                    Topics: 1.b, 1.c, 1.e, 2.d, 2.e
                    (Topics numbers correspond to WDL web. They are also listed following this program.)
                    Session Chair: Susan Carey, Columbia University
                        9:30 – 10:10 Review talk:
                                                Issues in Child Mental Development: Theory and Evidence
                                                Esther Thelen, Indiana University
                      10:10 – 10:30 Learning and Problem Solving in Infants and Young Children
                                                Rachel Clifton, University of Massachusetts – Amherst
                      10:30 – 10:50 Break
                      10:50 – 11:10 Development and Learning
                                                Neil Bertheir, University of Massachusetts – Amherst
                      11:10 – 11: 30 Always Under Construction: Analyzing Learning and Cognitive Development On a Common Complexity Scale
                                                Kurt Fischer, Harvard University
                      11:30 – 12:30 Open Discussion

Day 1 PM (April 5)

12:30 - 1:30 Luncheon Buffet (Centennial AB)

2:00 - 5:00 Session 2: (Room 101)
                    Networks – Role of Experience in Development
                    Topics: 1.a, 1.b, 1.d, 2.b, 2.c, 2.d
                    Session Chair: James McClelland, Carnegie Mellon University
                    2:00 – 2:40 Review talk:
                                        Connectionist Models of Cognitive and Linguistic Development: Perspectives on the Nature/Nurture Debate
                                        Kim Plunkett, Oxford University, UK
                    2:40 – 3:00 Modeling Systems that Learn
                                        Tomaso Poggio, M.I.T.
                    3:00 – 3:20 Break
                    3:20 – 3:40 Structure and Growth: A Model of Development for Robotic Systems
                                         Roderic Grupen, University of Massachusetts - Amherst
                    3:40 – 4:00 Development And Conditioning: A Neural Network Model
                                         Nestor Schmajuk, Duke University
                    4:00 – 5:00 Open Discussion

5:30 - 7:00 Dinner (Centennial BC)

                    7:30 - 9:30 Keynote Lecture:
                                       Perception, Action, and Consciousness: How Matter Becomes Imagination
                                       Speaker: Gerald Edelman, Nobel Prize laureate,
                                       The Neurosciences  Institute, San Diego, CA
                                       Location: Kellogg Auditorium
                                       Moderator: Ida Stockman, Michigan State University

Day 2 AM (April 6)

8:15 - 9:15 Breakfast Buffet (Centennial BC)

9:30 - 12:30 Session 3: (Room 101)
                    Neuroscience – Principles Shared by Development for Different Cognitive and Behavioral Capabilities
                    Topics: 1.d, 2.a, 2.b, 2.c, 2.d, 2.e
                    Session Chair: Kim Plunkett, Oxford University, UK
                    9:30 – 10:10 Review talk:
                                           Rewiring Cortex: Patterned Activity and the Development of Cortical Networks
                                           Mriganka Sur, M.I.T.
                    10:10 – 10: 20 The Role of Value and Functional Connectivity
                                           Olaf Sporns, Neurosciences Institute, San Diego
                    10:30 – 10:50 Break
                    10:50 – 11:10 Functional Self-Organization of Cortical Processing Machinery in Skill Learning
                                            Michael M. Merzenich, University of California at San Francisco
                    11:10 – 11: 30 What Neuroscience Can Teach us about Human Learning and Development
                                            Jay McCelland, Carnegie Mellon University
                    11:30 – 12:30 Open Discussion

Day 2 PM (April 6)

12:30 - 1:30 Lunch (Centennial AB)

2:00 - 5:00 Session 4: (Room 101)
                    Computation – How can Development be Modeled by Computational Processes?
                    Topics: 1.a, 1.b, 1.e, 2.a, 2.b, 2.d,
                    Session Chair: Alex P. Pentland, M.I.T.
                    2:00 – 2:40 Review talk:
                                         Quests for Mental Development Models
                                        Juyang Weng, Michigan State University
                    2:40 – 3:00 Speech and Mind
                                        Stephen Levinson, University of Illinois - Urbana
                    3:00 – 3:20 Break
                    3:20 – 3:40 Great Expectations: Scaling up Learning by Embracing Biology and Complexity
                                        Maja Mataric, University of Southern California
                    3:40 – 4:00 How Developmental Psychology and Robotics Complement Each Other
                                        Brian Scassellati, M.I.T.
                    4:00 – 5:00 Open Discussion

5:30 - 6:30 Dinner (Red Cedar B)

7:00 - 8:00 Some invited talks on campus (information will be available at the workshop)

8:00 - 10:00 Lab Tours
                    Eye Lab, Psychology Research Building
                    M.I.N.D. Lab, Communication and Arts Building
                    Robotics Labs, Engineering Building

Day 3 AM (April 7)

8:00 - 8:45 Breakfast Buffet (Centennial BC)

9:00 - 12:00 Session 5: (Room 101)
                    Future Research Directions and Applications
                    Topics: 3.a, 3.b, 3.c, 3.d, 3.e, 4.a, 4.c
                    Session Chair: Mriganka Sur (M.I.T.)
                    9:00 –   9:25 Future Directions and Applications
                                           Chris Brown, Rochester Universtity
                    9:25 –    9:50 Understanding by Building – Computational Approaches in Development and Learning
                                            Olaf Sporns, The Neurosciences Institute, San Diego, CA
                   9: 50 – 10:10 Break
                  10:10 – 11:35 Some Major Research Problems for Automatic Mental Development by Machines
                                           Juyang Weng, Michigan State University
                  10:35 – 11:00 Development in Human-like Software Information Agents
                                            Stan Franklin, University of Memphis
                  11:00 – 12:00 Open Discussion

Day 3 PM (April 7)

12:00 - 1:00 Working Lunch (Room 101)

1:00 - 3:00 Session 6: How to Proceed as a Community?
                    Topics: 4.a, 4.b, 4.c, 4.d, 4.e
                    Session Co-Chairs:
                    Juyang Weng, Michigan State University
                    Ida Stockman, Michigan State University
                    Open Discussion
                    General Planning Issues:
Creating Collaborative Research Groups

Identifying Funding Sources

Influencing Funding Agenda

Fostering Communication and Information Sharing in

Community of Scholars

Possibility of a Listserve

Possibility of a Website

Possibility of a Special Issue and/or a New Journal

Possibility of a Future Conference

Other
 
 

Each session has a list of topics that the talks and discussion in that session should address.  The topic questions following each topic are suggestive and are not meant to be exclusive.   Suggestions from the participants are welcome. For convenience of each session, the topics have been re-numbered here for each session. Some topics are shared by multiple sessions.

Session 1: Child Mental Development– How Is Human Development Grounded?

1. Mechanisms that enable environment/learner interactions in development and learning.

What mechanisms/processes enable interaction between given (changing) learner architecture and the external world to which learner must adapt and through which the learner develops new skills?

How does a human child gradually (through development) make sense out of what is sensed from the environment and what he acts upon in the environment?

What is the role of the somatosensory and body senses in perceptual/cognitive organization?

 

2. Self-regulation or autonomous systems of development and learning. How does a learner learns from its own internally generated inputs (e.g., through proprioceptive sensors) and from its own exploration of the environment and not just passively from externally driven input?

How does the process of development gradually enables such an active learning capability?

How do internal regulation (learning automation)and external regulation (environmental feedback) interact?

 

3. Scaling-up of capabilities, i.e., development as the scaling up of capabilities from ground, with genetically coded or human programmed bias.

How does a developmental system scale up its cognitive and behavioral capabilities?

 

4. The role of context in learning and developing..

How does an infant cope with bombardment of continuous streams of sensory inputs while learning?

How can the related context be formed through learning?

How does the general concept of "chunking" apply to development and learning (human or machine)?

What are the developmental mechanisms that enable scaling up not only individual capabilities but also their integration, from continuous, real-time, multimodal sensory input streams and effector action streams?

 

5. The role of attention in development and learning.

What is our knowledge about human attention selection?

What roles does attention play in context formation?

What are the mechanisms that enable a learner to develop capabilities of attention selection, including intramodal selection (e.g., selecting a part of sensory signal in sensed visual images) and intermodal selection (e.g., selecting the current visual input but not the current auditory input)?

1. Relationships between development and learning?

Does automatic development require a paradigm change from the current machine learning (or engineering) paradigm? How do children develop to learn things their parents do not understand? Can a human make a machine to learn to understand a language that the human maker does not understand?

What is development and what is learning? What are the fundamental differences between human learning and current machine learning techniques?

Assuming that development includes learning, what are the major capabilities that a human developmental system has but a currently typical machine learning system does not?

 

2. Mechanisms that enable environment/learner interactions in development and learning.

What mechanisms/processes enable interaction between given (changing) learner architecture and the external world to which learner must adapt and through which the learner develops new skills?

How does a human child gradually (through development) make sense out of what is sensed from the environment and what he acts upon in the environment?

What is the role of the somatosensory and body senses in perceptual/cognitive organization?

 

3. Representation issues in development.

Does a human developmental algorithm enables automatic derivation of representation for all the tasks to be learned in the life time?

What are the possible mechanisms that enable a developmental system to autonomously build representation for ever changing, hardly predictable tasks that an individual (natural or artificial)has to tackle over his/her/its lifespan?

4. Illustrations of common principles that cross domains in development and learning.

What are the common principles shared by visual language (e.g., American Sign Language) understanding and spoken language understanding?

What are the common principles shared by speech production and building a Lego toy?

What kind of granularity of representation will allow cross-modality sensor-integration and effector-cooperation?

What neural mechanisms enable an organism to distinguish between aversive and appetitive stimulus?

 

5. Constraints on development and learning for different cognitive and behavioral capabilities.

Is there evidence for or modeling of a constrained modularity or constrained nonmodularity approach?

What constraints on neurobiological mechanisms of learning and developing are posed by our understanding about cross-species genetics and behavioral performance?

What constraints on neurobiological mechanisms of human learning are posed by our understanding of cross-cultural similarities in performances and knowledge?

What constraints on neurobiological mechanisms of human learning are posed by unusual circumstances of development including precocious and impaired development?

6. The role of context in learning and developing.

How does an infant cope with bombardment of continuous streams of sensory inputs while learning?

How can the related context be formed through learning?

How does the general concept of "chunking" apply to development and learning (human or machine)?

What are the developmental mechanisms that enable scaling up not only individual capabilities but also their integration, from continuous, real-time, multimodal sensory input streams and effector action streams?

Session 3: Neuroscience – Principles Shared by Development for Different Cognitive and Behavioral Capabilities

1. Representation issues in development

Does a human developmental algorithm enables automatic derivation of representation for all the tasks to be learned in the life time?

What are the possible mechanisms that enable a developmental system to autonomously build representation for ever changing, hardly predictable tasks that an individual (natural or artificial)has to tackle over his/her/its lifespan?

 

2. Commonality among different domains of capabilities.

Traditionally, capabilities developed in various performance domains were considered very different and thus have been studied separately with different representations and methodologies. What are the benefits and what are the drawbacks of this paradigm of study?

If the unit of representation goes down to millisecond (or finer) level of sensory signal stream, neural signals in the brain and muscle contraction signal stream, is there a computational view that characterizes all these capabilities?

What are the state-of-the-art results from studies about plasticity of human brain, such as those through varying extent of sensory input, redirecting input, transplanting cortex, etc?

What do the results from those studies suggest?

 

3. Illustrations of common principles that cross domains in development and learning.

What are the common principles shared by visual language (e.g., American Sign Language) understanding and spoken language understanding?

What are the common principles shared by speech production and building a Lego toy?

What kind of granularity of representation will allow cross-modality sensor-integration and effector-cooperation?

What neural mechanisms enable an organism to distinguish between aversive and appetitive stimulus?

 

4. Constraints on development and learning for different cognitive and behavioral capabilities.

Is there evidence for or modeling of a constrained modularity or constrained nonmodularity approach?

What constraints on neurobiological mechanisms of learning and developing are posed by our understanding about cross-species genetics and behavioral performances?

What constraints on neurobiological mechanisms of human learning are posed by our understanding of cross-cultural similarities in performances and knowledge?

What constraints on neurobiological mechanisms of human learning are posed by unusual circumstances of development including precocious and impaired development?

 

5. The role of context in learning and developing.

How does an infant cope with bombardment of continuous streams of sensory inputs while learning?

How can the related context be formed through learning?

How does the general concept of "chunking" apply to development and learning (human or machine)?

What are the developmental mechanisms that enable scaling up not only individual capabilities but also their integration, from continuous, real-time, multimodal sensory input streams and effector action streams?

 

6. The role of attention in development and learning.

What is our knowledge about human attention selection?

What roles does attention play in context formation?

What are the mechanisms that enable a learner to develop capabilities of attention selection, including intramodal selection (e.g., selecting a part of sensory signal in sensed visual images) and intermodal selection (e.g., selecting the current visual input but not the current auditory input)?

1. Relationships between development and learning.

Does automatic development require a paradigm change from the current machine learning (or engineering) paradigm? How do children develop to learn things their parents do not understand? Can a human make a machine to learn to understand a language that the human maker does not understand?

What is development and what is learning? What are the fundamental differences between human learning and current machine learning techniques?

Assuming that development includes learning, what are the major capabilities that a human developmental system has but a currently typical machine learning system does not?

 

2. Mechanisms that enable environment/learner interactions in development and learning.

What mechanisms/processes enable interaction between given (changing) learner architecture and the external world to which learner must adapt and through which the learner develops new skills?

How does a human child gradually (through development) make sense out of what is sensed from the environment and what he acts upon in the environment?

What is the role of the somatosensory and body senses in perceptual/cognitive organization?

 

3. Scaling-up of capabilities, i.e., development as the scaling up of capabilities from ground, with genetically coded or human programmed bias.

What are the computational models of development, for a subpart of human/agent or for an overall system?

If human development is controlled by a human developmental algorithm that is inherited from the parents' genes, what are the possible mechanisms that enable autonomous and automated development?

How does a developmental system scale up its cognitive and behavioral capabilities?

 

4. Commonality among different domains of capabilities.

Traditionally, capabilities developed in various performance domains were considered very different and thus have been studied separately with different representations and methodologies. What are the benefits and what are the drawbacks of this paradigm of study?

If the unit of representation goes down to millisecond (or finer) level of sensory signal stream, neural signals in the brain and muscle contraction signal stream, is there a computational view that characterizes all these capabilities?

What are the state-of-the-art results from studies about plasticity of human brain, such as those through varying extent of sensory input, redirecting input, transplanting cortex, etc?

What do the results from those studies suggest?

 

5. Illustrations of common principles that cross domains in development and learning.

What are the common principles shared by visual language (e.g., American Sign Language) understanding and spoken language understanding?

What are the common principles shared by speech production and building a Lego toy?

What kind of granularity of representation will allow cross-modality sensor-integration and effector-cooperation?

What neural mechanisms enable an organism to distinguish between aversive and appetitive stimulus?

 

6. The role of context in learning and developing.

How does an infant cope with bombardment of continuous streams of sensory inputs while learning?

How can the related context be formed through learning?

How does the general concept of "chunking" apply to development and learning (human or machine)?

What are the developmental mechanisms that enable scaling up not only individual capabilities but also their integration, from continuous, real-time, multimodal sensory input streams and effector action streams?

1. Terminology discussion.

If we assume that human has a developmental algorithm that starts to run at the conception time of each new human life, what does the human developmental algorithm do?

How do we name the kind of machines that can automatically develop their cognitive and behavioral capabilities? How do we define the term "automatically" here with consideration of the human developmental process?

How do we define a developmental algorithm?

What basic functions should a developmental algorithm have?

 

2. New and open research topics.

What research topics are raised by our discussion about the development and learning for machines as well as about mechanisms of human development and learning?

In what ways the future studies of cognitive and behavioral development are likely to address very challenging capabilities including vision, speech recognition, language understanding, reasoning, planning, decision making, speech production, navigation, object manipulation. etc?

 

3. Milestones problems.

What milestone problems can we lay out for future research in the development and learning for machines and in understanding of human development and learning?

Can we quantize those milestone problems in certain precise way so that we can measure the "generation" or human-equivalent "age" of future machines that can develop?

 

4. Short-term and long-term tasks.

Why do some tasks that are considered "easy" for humans turn out to be very hard for machines and vice versa?

With regard to development, is it true that what is hard for human infants is also often hard for machines?

From our knowledge about what is hard and what is easy for infants, can we suggest easy and hard tasks for machines in the process of automated machine development?

In the context of development, what tasks are likely to be demonstrated by machines in a relatively short time period and what are likely to require more time for machines to develop skills for them?

 

5. Major breakthroughs envisioned.

What breakthroughs are possible along the direction of making machines that can develop and learn autonomously?

What breakthroughs are possible in our understanding of human development and learning?

What are the implications of those breakthroughs?

 

6. Major capabilities to be identified.

What major new capabilities are likely to be realized for machines along the direction of development and learning?

What benefits are such new machine capabilities likely to bring about?

What benefits are such capabilities likely to bring to us for our understanding of human development and learning?

 

7. Applications of foreseeable research results.

What applications are likely from predictable research results for development and learning, such as understanding of human and development, understanding of consciousness, human educational benefits, human-machine interfaces, multimedia based sensor-integration, situation analysis and decision making, humanoid robots, service robots that work in human environments, smart toys, software for education and entertainment.

1. Major capabilities to be identified.

What major new capabilities are likely to be realized for machines along the direction of development and learning?

What benefits are such new machine capabilities likely to bring about?

What benefits are such capabilities likely to bring to us for our understanding of human development and learning.

 

2. Infra-structural requirements.

What are the major differences between traditional methods of computation and those for machine development and learning?

What kinds of infrastructure are required from industry and government in supporting the related research?

What kinds of new equipment are needed for future related research?

What kinds of advances are envisioned for the future computer industry and robotic industry in meeting the need of related studies? Do humanoid robots with embedded computers help?

 

3. Applications of foreseeable research results.

What applications are likely from predictable research results for development and learning, such as understanding of human and development, understanding of consciousness, human educational benefits, human-machine interfaces, multimedia based sensor-integration, situation analysis and decision making, humanoid robots, service robots that work in human environments, smart toys, software for education and entertainment.

 

4. Opportunities for industrial entrepreneurs and venture capitalists.

What kinds of new industry, new products, new services, and new markets are likely to result from research on development and learning by machines and humans?

 

5. Funding implications to sciences and engineering including the identification of potential funding agencies.

How does the current funding structure provide funding opportunities for development and learning?

What suggestions can the workshop make to governmental and private funding agencies?