Presentation on theme: "Internal models, adaptation, and uncertainty Reza Shadmehr Johns Hopkins School of Medicine Ali Ghazizadeh Maurice Smith Konrad Koerding Siavash VaziriJoern."— Presentation transcript:
Internal models, adaptation, and uncertainty Reza Shadmehr Johns Hopkins School of Medicine Ali Ghazizadeh Maurice Smith Konrad Koerding Siavash VaziriJoern Diedrichsen
Duhamel, Colby, & Goldberg Science 255, 90-92 (1992) Internal models predict the sensory consequences of motor commands
musclesMotor commands force Body part State change Sensory system Proprioception Vision Audition Measured sensory consequences Forward model Predicted sensory consequences Integration Bayesian mixture
Vaziri, Diedrichsen, Shadmehr, J Neurosci 2006 Reach endpoints with respect to target Time (msec)
Vaziri, Diedrichsen, Shadmehr, J Neurosci 2006 Variance in reach errors indicates an integration of the predicted and actual sensory consequence of oculomotor commands Motor commands Sensory system Measured sensory input Forward model Predicted sensory consequences Integration Estimate of target location
Motor commands muscles force Body part State change Sensory system Proprioception Vision Audition Measured sensory consequences Forward model Predicted sensory consequences Integration Bayesian mixture What are internal models good for? Improve ability to sense the world. By predicting the sensory consequences of motor commands, and then integrating it with the actual sensory feedback, the brain arrives at an estimate that is better than is possible from sensation alone.
Equivalent to muscles being too strong McLaughlin 1967 Target Eye X 30% Saccadic target jump experiments: gain reduction
Kojima et al. (2004) J Neurosci 24:7531. Result 1: After changes in gain, monkeys exhibit recall despite behavioral evidence for washout. + _ + _ + _ Savings: when adaptation is followed by de-adaptation, motor system still exhibits recall Saccade gain = Target displacement Eye displacement
Result 2: Following changes in gain and a period of darkness, monkeys exhibit a jump in memory. + _ + Offline learning: with passage of time and without explicit training, the motor system still appears to learn Kojima et al. (2004) J Neurosci 24:7531.
Motor adaptation as concurrent learning in two systems: A fast learning system that forgets quickly A slow learning system that hardly forgets Smith, Ghazizadeh, Shadmehr PLOS Biology, 2006 prediction Prediction error Learning
Savings: de-adaptation may not erase adaptation Task reversal period re-adaptation Trial number Smith, Ghazizadeh, Shadmehr PLOS Biology, 2006
A Hidden states Context perturbation Slow change fast change The Bayesian learners interpretation of prediction error
Offline learning: Passage of time has asymmetric affects on the fast and slow systems Smith, Ghazizadeh, Shadmehr PLOS Biology, 2006 Task reversal period dark period re-adaptation Trial number Slow state Fast state -
1. Perturbations that can affect the motor plant have multiple time scales. Some perturbations are fast: muscles recover from fatigue quickly. Some perturbations are slow: recovery from disease may be slow. 2.Faster perturbations are more variable (have more noise). 3.Disturbances result in error, which can be observed, but with sensory noise. 4.The problem of learning is one of credit assignment: when I observe a disturbance, what is the time-scale of this perturbation? 5.To solve this problem, the brain must keep a measure of uncertainty about each possible timescale of perturbation. The learners view about the cause of motor errors Koerding, Tenenbaum, Shadmehr, unpublished
Savings: de-adaptation does not washout the adapted system Simulation Koerding, Tenenbaum, Shadmehr, unpublished Spontaneous recovery
Model 1 (Smith et al.): Error causes changes in multiple adaptive processes. Fast adaptive processes are highly responsive to error, but quickly forget. Slowly adaptive processes respond poorly to error, but retain their changes. Prediction: When actions are performed with zero error, states of the adaptive processes decay, but at different rates. Model 2 (Koerding et al.): Motor system is disturbed by processes that have various timescale (fatigue vs. disease). Credit assignment of error depends on uncertainty regarding what is the timescale of the disturbance. Prediction: When there are actions but the sensory consequences cannot be observed, states decay at various rates, but uncertainty grows. Increased uncertainty encourages learning. What prediction dissociates the two models?
Trial number Slow state Fast state Task reversal period dark period re-adaptation - Model 1: After a period of darkness, there will be spontaneous recovery, but rate of re-adaptation will be the same as initial learning. Adapting without uncertainty Smith, Ghazizadeh, Shadmehr PLOS Biology 2006
Model 2: After a period of darkness, there will be spontaneous recovery, but the rate of re-adaptation will be faster than initial learning. Adapting with uncertainty Monkey data from Kojima et al. (2004). Simulations from Koerding, Tenenbaum, Shadmehr, unpublished Bayesian learner
1100020003000 Saccade number Darkness Robinson et al. J Neurophysiol, in press Sensory deprivation may increase uncertainty, resulting in faster learning Monkeys were trained each day, but between training sessions they put on dark goggles, reducing their ability to sense consequences of their own motor commands. Darkness
Adapting with uncertainty: some predictions Sensory deprivation Faster subsequent rate of learning. Example: A subject that spends a bit of time in the dark will subsequently learn faster than a subject that spends that time with the lights on. Why: In the dark, uncertainty about state of the motor system increases. Longer inter-stimulus interval Better retention. Example: A subject that trains on n trials with long ITI will show less forgetting than one that trains on the same n trials with short ITI. Why: events that take place spaced in time will be interpreted as having a long timescale.
Ali Ghazizadeh Maurice Smith Konrad KoerdingSiavash VaziriJoern Diedrichsen By combining the predictions of internal models with sensory measurements, the brain ends up with less noisy estimates of the environment than is possible with either source of information alone. Fast and slow adaptive processes arose because disturbances to the motor system have various timescales (fatigue vs. disease). When faced with error, the brain faces a credit assignment problem: what is the timescale of the disturbance? To solve this problem, the brain likely keeps a measure of uncertainty about the timescales. A prediction error causes changes in multiple adaptive systems. Some are highly responsive to error, but rapidly forget. Others are poorly responsive to error but have high retention. This explains savings and spontaneous recovery. Summary