The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide vari- ety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and eval- uate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Per- ception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

/pdf/the-emulation-theory-of-representation-motor-control-imagery-2cq16sb7pi.pdf

The emulation theory of representation: motor control, imagery, and perception.

The sections in this article are:


1
Neural Control of Postural Orientation and Equilibrium
1.1
Behavioral Goals

1.2
Biomechanical Principles

1.3
Postural Strategies




2
Postural Orientation
2.1
Stiffness and Tonic Muscle Activation

2.2
Controlling Postural Orientation

2.3
Internal Representation of Postural Orientation




3
Coordination of Postural Equilibrium
3.1
Triggered Reactions to External Disturbances

3.2
Anticipatory Postural Adjustments for Voluntary Movement

3.3
Modeling of Postural Coordination




4
Sensory Control of Postural Orientation and Equilibrium
4.1
Sensory Integration

4.2
Somatosensory System

4.3
Vestibular System

4.4
Visual System




5
Central Neural Control of Posture
5.1
Spinal Cord and Brainstem

5.2
Basal Ganglia

5.3
Cerebellum

5.4
Cerebral Cortex




6
Concluding Remarks

Postural Orientation and Equilibrium

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of air emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

/pdf/the-emulation-theory-of-representation-motor-control-imagery-1vydkap3vi.pdf

The emulation theory of representation: Motor control, imagery, and perception - eScholarship

This paper examines the effects of stress on sustained attention. With recognition of the task itself as the major source of cognitive stress, a dynamic model is presented that addresses the effects of stress on vigilance and, potentially, a wide variety of attention-demanding performance tasks.

/pdf/a-dynamic-model-of-stress-and-sustained-attention-2pj5ck9kp6.pdf

A dynamic model of stress, and sustained attention

Modern microprocessor technology and display systems make it entirely feasible to automate many of the flight-deck functions previously performed manually. There are many benefits to be derived from automation; the question today is not whether a function can be automated, but whether it should be, due to various human factors issues. It is highly questionable whether total system safety is always enhanced by allocating functions to automatic devices rather than human operators, and there is some reason to believe that flight-deck automation may have already passed its optimum point. This is an age-old question in the human factors profession, and there are few guidelines available to the system designer. This paper presents the state-of-the-art in human factors in flight-deck automation, identifies a number of critical problem areas, and offers broad design guidelines. Some automation-related aircraft accidents and incidents are discussed as examples of human factors problems in automated flight.

Flight-deck automation - Promises and problems

A model is presented to predict human dynamic spatial orientation in response to multisensory stimuli. Motion stimuli are first processed by dynamic models of the visual, vestibular, tactile, and proprioceptive sensors. Central nervous system function is modeled as a steady state Kalman filter that optimally blends information from the various sensors to form an estimate of spatial orientation. Where necessary, nonlinear elements preprocess inputs to the linear central estimator in order to reflect more accurately some nonlinear human response characteristics. Computer implementation of the model has shown agreement with several important qualitative characteristics of human spatial orientation.

Optimal Estimator Model for Human Spatial Orientation

A model is being developed to predict pilot dynamic spatial orientation in response to multisensory stimuli Motion stimuli are first processed by dynamic models of the visual, vestibular, tactile, and proprioceptive sensors Central nervous system function is then modeled as a steady-state Kalman filter which blends information from the various sensors to form an estimate of spatial orientation Where necessary, this linear central estimator has been augmented with nonlinear elements to reflect more accurately some highly nonlinear human response characteristics Computer implementation of the model has shown agreement with several important qualitative characteristics of human spatial orientation, and it is felt that with further modification and additional experimental data the model can be improved and extended Possible means are described for extending the model to better represent the active pilot with varying skill and work load levels

Optimal estimator model for human spatial orientation

The 'Inverted-U Hypothesis' states that for a given task, there is an optimal level of workload or demand that yields the highest level of performance. A departure in either direction will result in a monotonically lower performance level, hence an inverted-U-shaped relationship between task demand and quality of performance. Most studies to date have failed to demonstrate the left-hand branch of the curve, that is, the regime in which performance presumably rises as load increases. The purpose of this study was to explore whether low-level additional demand on the monitor would result in improved performance. Four groups of subjects performed a visual monitoring task for 48 min, then two of the four groups were given additional tasks, and a third had potentially distracting information on its display. Results indicated that the two groups with additional demand detected more signals than did the control group or the control-plus-distraction group. There were no significant differences in false alarms.

Vigilance and task load: in search of the inverted U

The effects of gust disturbances and the pilot's participation mode in the control task on his work-load level and failure detection performance during a simulated low visibility landing approach are examined. We found that the participation mode had a strong effect on the pilot's work load, the induced work load being lowest when the pilot acted as a monitoring element during a coupled approach and highest when the pilot was an active element in the control loop. The effects of differential work load and participation mode on failure detection were separated. The participation mode was shown to have a dominant effect on the failure detection performance, with a failure in a monitored (coupled) axis being detected significantly faster than a comparable failure in a manually controlled axis.

Detection by Pilots of System Failures during Instrument Landings

: Pilots use information from a variety of sensory mechanisms to determine their estimate of orientation and motion. An understanding of this process and a quantitative model are essential for development of effective simulator motion cueing devices. A multisensory model for dynamic spatial orientation is being developed for this purpose. Aircraft or simulator motion is translated into stimuli which are processed by dynamic models of the appropriate sensors (visual, vestibular, tactile, and proprioceptive), and are then fed to a central estimator which has been modeled as a linear optimal estimator, specifically a steady state Kalman Filter. In addition to the linear estimation process, some non-linear effects, such as the well documented delay in onset of visually induced motion, require non-linear additions to the model. Such additions have been kept to a minimum so as to retain the uniqueness and conceptual appeal of a linear optimization algorithm. The model has been implemented as a computer program and has predicted some of the important qualitative characteristics of human dynamic spatial orientation under combined wide field visual motion and platform motion. Several types of special tactile and proprioceptive cues are also being considered but have not been validated. The modeling effort has underscored the need for additional data in some areas and several experiments have been suggested to help fill these gaps. (Author)

Renwick E. Curry

Papers

Optimal Estimator Model for Human Spatial Orientation

Optimal estimator model for human spatial orientation

Vigilance and task load: in search of the inverted U

Detection by Pilots of System Failures during Instrument Landings

Sensory Mechanism Modeling.