Evolutionary robotics as a tool for studying human sensorimotor

dynamics

Marieke RohdeTalk: Multisensory Perception and

Action Group

Max Planck Institute for Biological

Cybernetics, Tübingen, 4.9.2007

Structure

1. My Background

2. Evolutionary Robotics

3. Perceptual Crossing and Delays

4. Future Research

1.) Background

My ideas on and interests in perception

Personal Background

• Studium Generale, Leibniz Kolleg, Tübingen

• BSc Cognitive Science, Osnabrück

• MSc Evolutionary and Adaptive Systems,

Sussex

• PhD Sussex

Interests:

• What can we know about the world?

(Constructivism)

• Brain/Body/Environment interaction – how

does it link to what we think?

Perception• Dynamical/Embodied turn in Cognitive Science

• Examples from

– Sensory Substitution

– Sensorimotor Adaptation

– Change Blindness

– …

• Sensorimotor constraints can be formalised.

• two “contacts” vs. permanent activity: Perception of an external object.

• Formalisation can be very difficult

2.) Evolutionary Robotics

ER

• Closed loop AI modelling

• Underspecify mechanism minimise/control prior assumptions

about behaviour generating mechanism

• Proof of concept, hypothesis generators, beyond cognitive limits,

against intuition

• Dynamical Model of brain, body, world interaction

• „Tool for Thinking“, „Opaque Thought Experiment“, „Intellectual

Warm-Up, mental gymnastics“, „Frictionless Brains“

• GA != model of evolution, CTRNN controller != model of brain

Beer 2003: Categorical Perception

• Circular Agent

• Recurrent Neural Network

Controller

• Move left and right, Distance

sensor array

• Catching Circles, avoiding

Diamonds

• (symmetry)

Beer 2003: Categorical Perception

• Foveate Object

• Scan Left and Right

• Circle: scanning

movements smaller

and smaller

• Diamond: large

avoidance movement

What the agent sees...

Clamp the inputs

„Psychophysics“

• Labeling and

Discrimination

• Discrimination Criteria

(Width!)

„Psychophysics“

• When is the

decision made?

Dynamical Explanations

• Hardcore Mathematics

• Dynamics of agent environment

system

What‘s the point?

• Criticism: No human or animal has ever done that

• Criticism: If at all, modelling limited to

– Insects

– Babys

– Low level behaviour

• Minimal sensorimotor approaches to perception – what if

you use the same simulation for agents and humans?

Di Paolo, Rohde & Iizuka 2007, exchange to GSP

3.) Multisensory Delays

Background

• Cunningham et al. (2001): Adaptation to

visual delays (200ms) in a reactive task.

• Negative aftereffect:

– performance breakdown if delay was

removed

– „The plane seemed to move before the

mouse did – effect appeared to come before

the cause.”

• None of these had been reported before or

after – apart from Libet‘s classical work?

No adaptation in earlier studies

• Smith and Smith 1962,

• Stetson et al. 2006 „it may be that shifts of 100

ms are beyond the hardware limitations of the

calibration mechanisms“

• Cunningham et al.: Time pressure

My Experiments in Compiegne

• Try to find minimal experimental conditions that produce this effect

and distinguish them from one‘s that don‘t

• Dynamically analyse sensorimotor behaviour and adaptation

• „Tactile pacman“

Thanks to:

Olivier Gapenne

Charles Lenay

Dominique Aubert

John Stewart

• Some variables more interesting than others

(# of crossings, time to stabilise)

• Some subjects more interesting than others

• Taxonomy based on some measures/visual

appearance of trajectories

How to formalise?

• Velocity, accelaration, crossings, direction,

distance, centering, object velocities, …

• …or any combination of these…

• …in any sub-group of subjects

Results

ER Model

• Evolve agents to solve the task

– With delay

– Without delay

The model is very close to the

experiment, i.e. the evolved

agents produce the same format

of data as the experimental

participants.

As in Di Paolo et al. 2007

Evolution

• Evolvability: Agents were

evolved with delays (DC) and

without delays (NDC)

• Does solving the task with

delays entail solving the task

without delay? This would

mean, negative after effects are

impossible.

• Velocity: Agents evolved without

delays moved on average 78%

faster. (experimental as well, but

not significantly)

Systematic Displacements

• An artifact of the simulation: There are adaptation effects (systematic displacements)

in both cases. However, the fitness function is not exact enough to capture these.

• The same criterion was used in the experiment. Maybe there was a negative

aftereffect, but it had not been registered?

• Test hypothesis in experimental data: displacements from different distributions?

• Introduce delay: p = 0.0125 [-0.6318 -0.0785],

• Remove delay: p = 0.0205 [0.0486 0.5658]

Reflex-like and reactive strategies

• The time window to catch an

object defined by the task is just

big enough for the execution of a

reflex.

• Average trajectories:

– MSE pre vs. post: much smaller

p = 0.0026

– MSE pre/delay adap/post: p =

0.7656/0.5723

• Predictors: Sensory state? Other

trajectoires? Sensory history?

• Velocity: slower but not significant

Conclusions

• Model before doing the experiment (trial and error is easier in simulation)

• Simplification: no history, no sound signals, object velocity....

• Find adaptation effects: systematic displacements, MSE from average

trajectories

• Distinction:

– reflex-like: Displacement

– Reactive: Increase in inertia

– Anticipatory: Delay

• Measures: other trajectories, momentary sensory state vs. time series, ...

4. Future Research

Replication and extension

• Cunningham et al.

2001

• Pros: feasible, simple,

strong

• Cons: vision, not fully

controlled

• Test time pressure:

vs. slower

Modality dependence

• Are adaptation effects linked to

particular/ established

sensorimotor couplings?

• Pros:

– No vision

– Conceptually strong

• Cons:

– Did not work too well in the past

Stimulus Ambiguity and Simultaneity Perception

• „Temporal Necker Cube“

• Delay vs. Inertia

• Delay vs. Displacement

• Delay vs. No delay

• Pros:

– Powerful testbed. Experience without introspection.

• Cons:

– Speculative, open ended, complicated.

Other free floating ideas

• Variable delays

– in my control, outside my control

– Variation of delay necessary for task

• @ WP2 (equilibrium):

– Redundant signals: which one will supersede?

...and evolutionary robotics?

... Will help with the „how“

– What is the relation between sensation, motion and

behaviour to start with?

– How does it change?

– Under which circumstances?

– Which strategy is prone to which change?

– Which strategy produces which systematic error?

– What are the dynamical subtleties?

Any questions?

References• Auvray, M., Lenay, C., & Stewart, J. (2006). The attribution of intentionality in a simulated

environment: the case of minimalist devices. In Tenth Meeting of the Association for the Scientific Study of Consciousnes, Oxford, UK, 23-26 June, 2006.

• Bach-y-Rita, P., M. E. Tyler, and K. A Kaczmarek: Seeing With the Brain. Int. J. Human-Computer Interaction 15(2) 2003. 285-295.

• Beer, R.D. (2003). The dynamics of active categorical perception in an evolved model agent (with commentary and response). Adaptive Behavior 11(4):209-243.

• Cunningham, D.W., A. Chatziastros, M. von der Heyde and H.H. Bülthoff: Driving in the future: Temporal visuomotor adaptation and generalization. Journal of Vision 1(2), 88-98 (2001)

• Cunningham, D.W., Billock, V.A. and Tsou, B.H.: Sensorimotor adaptation to violations of temporal contiguity. Psychological Science 12(6), 532-535 (2001)

• Di Paolo, E. A., Adaptive Systems lecture presentations, University of Sussex (UK), Spring Term 2006.

• Harvey, I., Di Paolo, E., Wood, R., Quinn, M. and Tuci, E. A. (2005). Evolutionary Robotics: A new scientific tool for studying cognition. Artificial Life, 11(1-2):79-98.

• Helbing et al. Nature, 388, pp 45 – 50, (1997). • Lenay C. (2003) Ignorance et suppléance : la question de l'espace, HDR 2002, Université de

Technologie de Compiègne• Smith K.U. and W. M. Smith: Perception and motion: an analysis of space-structured behavior.

Philadelphia, Saunders, 1962.• Stetson, C, X. Cui, P. R. Montague and D. M. Eagleman: Illusory temporal reversal of action and

effect reveals neural conflict response. Submitted to Neuron.

Appendix: Perceptual Crossing

c.f. Di Paolo et al. 2007

Perceptual Crossing (Auvray, Lenay and Stewart 2006)

• Two subjects move on a tape and have to indicate who is the other

by mouseclick

• Fixed object and shadow (identical motion) as „lures“

• 70% correct responses

• Stimulation-response ratio for shadow and other subject identical.

• Distinction emerges from the mutual search for each other.

Evolutionary Robotics Simulation

• Qualitatively similar results

• Confirmation of their conclusion:

– The distinction arises from the global dynamics of interaction

– No individual discriminatory capacity

Evolutionary Robotics Simulation

• Why does it take so long to evolve

avoidance of the static object?

• Looking back at data:

– 33% stimulation from static object

– 15% stimulation from shadow

• An evolutionary robotics model

predicts human behaviour:

Integrated sensory stimulation is a

good predictor for when subjects

click!

Extended Model (Hiro Iizuka)

• Question: Are these findings just

an artefact of the experimental

design?

• Interact with a recording, not with

a shadow

• Perceptual strategy:

– Induce breakdown to “test” the

interaction partner

– No individual recognition capacity

or ‘model of the other’

– just exploit noise/subtle timing

differences + interaction dynamics

Take home message

• If you take into consideration the interaction level and its dynamics

– Supposedly hard tasks become become simple (Is this another

intentional entity or just a mobile stimulus?)

– Supposedly simple tasks become difficult (Is this a static object?)

• For any particular social behaviour, probably, both interactional and

individual factors play a role

• Need for a methodology that accounts for both

• Simulation models to generate hypotheses and proof concepts

Reciprocally Causal Processes

Model by Helbing et al. Nature, 388, pp 45 – 50, (1997).

Many times, when you have complex, durable processes with circular or

reciprocal causality, you will observe the formation of some spontaneous

invariant organisation.

Reciprocally Causal Processes

• Sensorimotor coordination Can be

viewed as a process of reciprocal

causation (Between global and local

dynamics) – blind person with a cane.

• Fluid behaviour becomes less and less

conscious as it starts to rely on

coherences of sensorimotor

coordination.

• Surprise originates only when

sensorimotor coordination breaks

down. (Obstacles, perturbations,

distortions, etc.)

• Perceptual invariants:

– Size

– Shape

– Space

– Colour

– Simultaneity

– …