why metacognition and social cognition will become critical … · 2019. 5. 23. · •solutions...

Jerald D. KralikDepartment of Bio & Brain Engineering

KAIST

Why Metacognition and Social Cognition

will become critical components of AI

Jerald D. KralikDepartment of Bio & Brain Engineering

KAIST

Why the (Human) Mind/Brain, Metacognition and

Social Cognition are so important for AI

Why (Human) Mind/Brain for AI?

• Important work not using it as model

– More purely derived from problem being solved

– But good luck

• Why not use it?

– Does what most of us are trying to do/accomplish

– Both expert and artificial general intelligence

• A suboptimal kluge

– Mostly myth

Why Study the Mind/Brain?

• Extraordinary system/device that works

• Solutions provide further benefits

– Insight to inform our choices

• Natural tradeoffs

• Suboptimal from different perspectives

– Health/Dysfunction

What Does the Brain Tell Us?

• We do know a great deal

• Yet still determining principles, general structure (and details)

• Especially higher cognitive abilities

• So: cognitive and brain science a challenge

Why Not Farther at this Point?

• Missing overarching, principled theoretical approach

– Lacking a theory of brain function

• Even with the most fundamental units—neurons

– What are they trying to do?

• And at the top: the purpose/structure of high-level

function

– A theory of human intelligence

• Or even a consensus re: general cognitive architecture

NMSNST

MCN output

High-level cognition in the primate brain

basal

ganglia

posterior

parietal

S1/S2

V1-V5

IT

basal

forebrain

Amyg Hipp

M1PM

PFdl

PFm

OFC

Thal

Hθ

Anterior Posterior

Need Theoretical Framework

A Standard Model of the Mind: Toward a Common Computational

Framework Across Artificial Intelligence, Cognitive Science,

Neuroscience, and Robotics.

JE Laird, C Lebiere, PS Rosenbloom - Ai Magazine, 2017From Wikipedia

My Approach

• Literature review from relevant fields

– Evolutionary biology

– Psychology

– Neuroscience

– Anthropology

– Sociology

• Evolutionary/Anthropological Approach

• Logical analysis

Kralik, J. D., Mao, T., Zhao, C., Nguyen, H. T., and Ray, L. E. (2016). Modeling incubation and restructuring for creative problem solving in robots. Robotics and Autonomous Systems, Special Issue on Robotics and Creativity, 86: 162-173.

Kralik, J. D., Shi, D., El-Shroa, O. A., and Ray, L. E. (2016). From low to high cognition: A multi-level model of behavioral control in the primate brain. Proceedings of the Annual Meeting of the Cognitive Science Society. Selected for oral presentation.

Kralik, J. D. (2017). Architectural design of mind & brain from an evolutionary perspective. Proceedings of the AAAI 2017 Fall Symposium: A Standard Model of the Mind.

Kralik, J. D., Muldrew, D. B. C., Gunasekaran, D., and Lange, R. D. (2017). Cognitive control for goal-directed reaching in a humanoid robot. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO). Selected for oral presentation.

Ray, T. H. and Kralik, J. D. (2017). Seeking true intelligence from the ground up: Evolutionary origins of cognition. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO). Selected for oral presentation and session chair/moderator (Kralik).

Kralik, J. D. (2018). Core High-Level Cognitive Abilities Derived from Hunter-Gatherer Shelter Building. In I. Juvina, J. Houpt, & C. Myers (Eds.), Proceedings of the 16th International Conference on Cognitive Modeling (pp. 49-54). Madison, WI: University of Wisconsin.

Kralik, J. D., Lee, J. H., Rosenbloom, P. S., Jackson, Jr., P. C., Epstein, S. L., Romero, O. J. , Sanz, R., Larue, O., Schmidtke, H. R., Lee, S. W., McGreggor, K. (2018). Metacognition for a Common Model of Cognition. Procedia Computer Science: 145, 730–739.

Lee, J., Kralik, J. D.*, and Jeong, J.* (2018). A Sociocognitive-Neuroeconomic Model of Social Information Communication: To Speak Directly or To Gossip. Proceedings of the Annual Meeting of the Cognitive Science Society. *Co-corresponding authors

Lee, J., Kralik, J. D.*, and Jeong, J.* (2018). A General Architecture for Social Intelligence in the Human Mind and Brain. Procedia Computer Science: 145, 747–756. *Co-corresponding authors

References

–Associative Learning II

–Associative Learning I

– Innate Systems

– Causal Reasoning I

– Causal Reasoning II

– Reductionism I

– Reductionism II

– Reductionism III

– Reductionism IV

Co

ntro

l Circu

it Lev

els

– Defense– Mating– Social

– Foraging & Ingestion

– Physical Environment

Action Control Circuit

{Stimuli}

Content Domains

Type 4:Abstract,

Reasoning-Based

Type 2:Content-Specific,

Associative-Based


Innate Systems


Reasoning-Based

Type 4:Arbitration (Control & Monitoring)

Cortex & Basal Ganglia (Nac-Shell)

Hypothalamus/Midbrain Complex

Cortex & Basal Ganglia (Nac-Core)

Neocortex: Agranular PFC

1st Granular PFC, PPC, TC

2nd Order PFC, PPC, TC

3rd Order PFC, PPC, TC

4th Order PFC, PPC, TC


ACC, Lateral PFC

Type 5:Metacognition

(Monitoring, Modulation & Control)

Error

Long-

Term

Memory

Representation Decision-Making Action{SEI} Outcome

Restructuring

Affective/

Goal

Gate

Feedback

(for Learning)

Example Representation: A Grid World

Initial State

Goal State

* Gray squares are obstacles

We Evolved to Act

–Associative Learning II

–Associative Learning I

– Innate Systems

– Causal Reasoning I

– Causal Reasoning II

– Reductionism I

– Reductionism II

– Reductionism III

– Reductionism IV

Co

ntro

l Circu

it Lev

els

– Defense– Mating– Social

– Foraging & Ingestion

– Physical Environment

Action Control Circuit

{Stimuli}

Content Domains

Type 4:Abstract,

Reasoning-Based


Associative-Based


Innate Systems


Reasoning-Based

Type 4:Arbitration (Control & Monitoring)

Cortex & Basal Ganglia (Nac-Shell)

Hypothalamus/Midbrain Complex

Cortex & Basal Ganglia (Nac-Core)

Neocortex: Agranular PFC

1st Granular PFC, PPC, TC

2nd Order PFC, PPC, TC

3rd Order PFC, PPC, TC



ACC, Lateral PFC

Type 5:Metacognition

(Monitoring, Modulation & Control)

Cross branches for beam reinforcements (walls)

Larger branches (boughs) as foundation beams

Smaller branches

Inserted in ground holes

Twine to tie down structural elementsLeaves or Grass exterior

Ground cleared of brush & debris

Entrance

Simple…right?

Core High-Level Cognitive Abilities Derived from

Hunter-Gatherer Shelter Building

Core High-Level Cognitive Abilities

Key Features that Pop

• Metacognition

• Social cognition

• Emotion

• Causality

• Content-specific (expert) vs Content-free (general)

• More

Jerald D. Kralik1, Jee Hang Lee2, Paul S. Rosenbloom3, Philip C.

Jackson, Jr.4, Susan L. Epstein5, Oscar J. Romero6 , Ricardo

Sanz7 , Othalia Larue8,

Hedda R. Schmidtke9, Sang Wan Lee1,2, Keith McGreggor10

1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology

(KAIST), Daejeon, 34141, South Korea2KI for Health Science and Technology, Korea Advanced Institute of Science and Technology

(KAIST), Daejeon, 34141, South Korea3Institute for Creative Technologies & Department of Computer Science, University of Southern

California, Los Angeles, CA, USA4TalaMind LLC, PMB #363, 55 E. Long Lake Rd., Troy, MI, USA

5Department of Computer Science, Hunter College and The Graduate Center of the City

University of New York, New York, NY, USA6Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA

7Autonomous Systems Laboratory, Universidad Politécnica de Madrid, Spain8Department of Psychology, Wright State University, Dayton, Ohio, USA

9Department of Geography, University of Oregon, Eugene, OR, USA10College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

Metacognition Working Group Members

Metacognition — Why? • Metacognition:

– cognition about cognition

• Addresses what the system knows

• The importance of what is known

• Including what has been remembered and what is worth

remembering or forgetting

• Advantages include

– Management/Orchestration/Arbitration of competing or

complementary functions

– Modulation to help finetune other cognitive processes

– Safeguards against confusion and errors from lower cognitive

processes

• especially those designed for efficiency and specialization

– Data management to reduce inefficiencies

• e.g., removal of obsolete information by forgetting

• A process is metacognitive if and only if:

• it receives input from

• sends output to

• or both receives from and sends to the same process type

• A process type is perception, decision, or action

• Cognition captures the entire perception-decision-action cycle

• Decision is broadly construed

• Including, for example: Reasoning, Planning, Attention

Metacognition Defined

• A process is metacognitive if and only if:

• it receives input from

• sends output to

• or both receives from and sends to the same process type

• A process type is perception, decision, or action

• Decision is broadly construed

• Including, for example: Reasoning, Planning, Attention

Metacognition Defined

DecisionDecision

Decision

• Category 0: cognition itself, with primary input from perception and primary output to action control

• Categories 1, 2, and 3 comprise metacognition

• Category 1: Primary input and output from and to decision processes

• Category 2: Primary input from decision processes; primary output feeds forward to action control

• Category 3: Input from perception; primary output to decision processes

General Categories of Metacognition

Examples of Each Category

• Category 1:

– Arbitration of Model-Free vs Model-Based Reinforcement Learning

– Self-Representation

– Reflection and Self Improvement

– Self-control

• Category 2:

– Social cognition

• Category 3:

– Context and abstract task relevant information

• Organizing

• Maintaining

• Allocating (cognitive resources)

• Regulating

• Modulating

• Modifying

• Replacing

• Configuring (e.g., parameters, goals,

reward functions)

• Healing

• Orchestrating

• Coordinating

• Arbitrating

• Broadcasting

• Recruiting

Components of Metacognition

• Understanding

• Awareness

• Reflection

• Explaining

• Debugging

• Generating

• Adapting

Thinking General Management (of single processes) Control of multiple processes

Eventually Consciousness (?)

• Artificial consciousness

• Emotions too!

Conclusions about Metacognition

• Indeed is occurring organically

• With multiple current approaches

• Mind/brain analysis shows need for explicit understanding and

framework

– Plus provides specifics to help

guide, anticipate

Why social per se?

Why not just smarter, to take over and offline?

• Answer:

– As intelligence of things increases, trying to do more for us will require:

• Anticipating needs

• More interactive assistance

• Examples:

– Personal assistant

– Home

– Work

– Integration with us

• We need to help it as well

• Dynamic, complex, novel activities

Social AI: Theoretically

• F1(Person, F2(AI, Goal))

• F2 = F2.1 & F2.2

• F1(Person, F2.1(AI-affordance, F2.2(AI-action, Goal)))

• AI-affordance = interface with us properly

• And yet we and our world are dynamic and complex!

• Therefore, as artificial intelligence and its prevalence (like IoT)

increases sociality increases

• Or put differently, if sociality capacity of system increases,

value, functionality increases

Why is Sociality so Hard?

Beliefs/Knowledge

Preferences

Goals

Rules/norms

Intentions (action policy)

Plus:

• Social rules, conventions

• Perspective-taking

• Culture

Beliefs/ Knowledge

Preferences

Goals

Rules/norms


Mind-reading, Perspective-taking, Theory of Mind

Beliefs/Knowledge

Preferences

Goals

Rules/norms


Plus:

• Social rules, conventions

• Perspective-taking

• Culture

Beliefs/ Knowledge

Preferences

Goals

Rules/norms


Our Social Intelligence Model

Figure 4: The complete model of social information communication. Brain region abbreviations: fusiform gyrus (FG), posterior superior temporal sulcus

(pSTS), medial prefrontal cortex (MPFC), temporoparietal junction (TPJ), amygdala (AMG), insula, right temporal lobe (rTL), interparietal sulcus (IPS),

anterior paracingulate cortex (aPCC), medial precuneus (med. precuneus), anterior insula (ant. insula), anterior cingulate cortex (ACC), orbitofrontal cortex

(OFC), ventral premotor cortex (vPMC), dorsolateral prefrontal cortex (dlPFC), posterior parietal cortex (PPC), posterior superior temporal cortex (pSTC),

caudate nucleus (CD), ventral striatum (VS), mesolimbic dopamine (DA) system, ventral tegmental area (VTA), premotor cortex (PMC), basal ganglia (BG).

Our Social Intelligence Model v2.0

• With Significant metacognition organically

• Lee, J., Kralik, J. D.*, and Jeong, J.* (2018). A General Architecture for Social Intelligence in the Human Mind and Brain. Procedia Computer Science: 145, 747–756. *Co-corresponding authors

. . .

P

C V

PC V

PC V

Actual

Model

PC V

Individually driven factor

Political

Factor P

C VP

C V

Shorter-Term

Aspect

Mood

General Cognitive Infrastruct

ure

Mind/Brain

Architecture

PC V

Cultural/Societal Factor

PC V

Social Contracts

Moral/Social

Dimension

A1-A2 A2-

A1

A1-A3

A3-A1

...Care/Har

m

General Social

...

...

CareHarm

... ...

Longer-Term

Aspect

Personal backgro

und

General affect,

Cognitive ability and

balance

Model-self:Content domain

Mating/Sexual

Social

Individually driven

Political

Cultural/

Societal

Social Contrac

ts

Moral/Social

Dimension

A2-A1

A1-A3

A3-A1

...Care/Harm

General

Social

...

...

Care

Harm

... ...

A1-A2

Self

P

C V

PC V

PC V

SelfOthe

r

Self-Actual-Model (SAM)

Self representation tree (SAM tree) Sel

fOther

Self

Other

AReceiv

er (A2)ATarget

(A3)

. . .

. . .

. . .


Tree

Model of Mind Tree

PC V

PC V

PC V

• Self & Others

• Self:

– Actual

– Model of Self

• Others:

– Receiver AR

• AR self & others

– Target AT

• AT self & others

Models of Minds

1. For relationship type (ingroup, outgroup, celebrity) more gossiping should

spread about ingroup compared to outgroup, and in some cases ingroup over

celebrities

2. Status effects: certain types of scenarios should generate more gossiping

about celebrities as compared to the other groups

3. Positive scenarios will show gossip spreading rates more comparable to

negative ones

4. Greater spreading of positively valenced scenarios with ingroup targets

5. Greater spreading of negativity about celebrities

6. Negativity should be reduced for ingroup targets

7. Dimensions of morality are predicted to generate the most gossip, especially

those involving more egregious threats, like harm and cheating

8. Differences should be found among the morality domains themselves

9. Even simple social activities should generate higher spreading rates for

ingroup targets, in order to maintain accurate detailed knowledge about

ingroup members

9 Predictions about ‘Gossip’

. . .

P

C V

PC V

PC V

Actual

Model

PC V

Individually driven factor

Political

Factor P

C VP

C V

Shorter-Term

Aspect

Mood

General Cognitive Infrastruct

ure

Mind/Brain

Architecture

PC V

Cultural/Societal Factor

PC V

Social Contracts

Moral/Social

Dimension

A1-A2 A2-

A1

A1-A3

A3-A1

...Care/Har

m

General Social

...

...

CareHarm

... ...

Longer-Term

Aspect

Personal backgro

und

General affect,

Cognitive ability and

balance

Model-self:Content domain

Mating/Sexual

Social

Individually driven

Political

Cultural/

Societal

Social Contrac

ts

Moral/Social

Dimension

A2-A1

A1-A3

A3-A1

...Care/Harm

General

Social

...

...

Care

Harm

... ...

A1-A2

Self

P

C V

PC V

PC V

SelfOthe

r


Self representation tree (SAM tree) Sel

fOther

Self

Other

AReceiv

er (A2)ATarget

(A3)

. . .

. . .

. . .


Tree

Model of Mind Tree

PC V

PC V

PC V

• How scale?

– Family Strangers

• Accuracy

– Genes

– Personal history

– Identity

– Society

• Big data & AI

– Privacy

Issues

Social Cognition Conclusions

• As intelligence increases, need for sociality increases

• Reading minds

– Complex, dynamic, enigmatic (hidden, uncertain)

• Thus, why it is needed

Conclusions

• Bottom-up task driven approaches are fine, valuable

• But lacking overarching theoretical understanding

– to capture complete system as whole

• Theoretical examination of the brain — especially high-level

human abilities — critical for AI

• Exposes and explicates critical abilities

– social and meta cognition

Thank you for this nice social

interaction!

why metacognition and social cognition will become critical … · 2019. 5. 23. · •solutions...

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