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Human Machine System Exam and Principles of Ecological Optics, Exams of Computer Science

University of BathComputer Science

The reasons for automation, ironies of automation, human performance issues caused by automation, complexity of systems, function allocation models, slips vs lapses vs mistakes, error shaping factors, limitations of Swiss Cheese, probabilistic risk assessment, figure-ground organization effect, object-centered and observer-centered cues, ecological optics, optical invariants, and experimental design. It also explains the factors affecting judgments of self-motion and ways to determine mental workload. useful for students studying human-machine systems, automation, and cognitive psychology.

Typology: Exams

2022/2023

Available from 02/09/2024

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Human machine system exam
Why do we want/need Automation? (4) -
1. Increasing performance and productivity
2. Controlling dangerous or impossible processes
3. Controlling difficult or unpleasant processes
4. Extending human capabilities
5 Ironies of Automation -
1. Designer assumes automation is more reliable than the human, but what the designer cannot
automate is left to the human
2. Humans are left to monitor, a task they perform worst
3. Automation takes away opportunity to practice skills
4. Knowledge of processes is best obtained by active participation (automation works faster than human
and makes decisions human doesn't understand making supervising impossible)
5. System + Automation is more complex than just the system (automation can hide system
degradation)
Human Performance Issues caused by automation (3) -
1. Complacency (over-reliance)
2. Vigilance (reduced awareness)
3. Skill degradation and transient workload peaks
What makes a system complex? (Open vs Closed systems) -
-Complexity = dimensionality x interdependence
-open systems - probabilistic
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Human machine system exam

Why do we want/need Automation? (4) -

  1. Increasing performance and productivity
  2. Controlling dangerous or impossible processes
  3. Controlling difficult or unpleasant processes
  4. Extending human capabilities 5 Ironies of Automation -
  5. Designer assumes automation is more reliable than the human, but what the designer cannot automate is left to the human
  6. Humans are left to monitor, a task they perform worst
  7. Automation takes away opportunity to practice skills
  8. Knowledge of processes is best obtained by active participation (automation works faster than human and makes decisions human doesn't understand making supervising impossible)
  9. System + Automation is more complex than just the system (automation can hide system degradation) Human Performance Issues caused by automation (3) -
  10. Complacency (over-reliance)
  11. Vigilance (reduced awareness)
  12. Skill degradation and transient workload peaks What makes a system complex? (Open vs Closed systems) - -Complexity = dimensionality x interdependence -open systems - probabilistic

-closed systems - deterministic Properties of complex environments (6) -

  1. Long time constants
  2. High degree of risks
  3. Subsystems highly coupled
  4. Highly automated
  5. Data uncertain
  6. Humans deal with fault situations Function Allocation Models (3) -
  7. HABA MABA
  8. Level of Automation LOA
  9. Adaptive & Adaptable Automation Humans Are Better At - -Learning -Pattern recognition -Dealing with uncertainties (flexibility) -Common sense -Creativity -Multi-functionality Machines Are Better At - -Computing speed and accuracy
  1. differences between subsequent levels is often too subtle to have practical importance
  2. need for a certain level of automation may not be fixed, but might vary over 'me warranted by situational demands! Adaptive & Adaptable Automation -
  3. Adaptive - infer the need for LOA reallocation on basiss of measured operator response (e.g. external conditions, performance measures, mental load) or some mission-based criteria
  4. Adaptable - operator-triggered reallocation for the decision of timing Adaptive & Adaptable Automation Critique (4) -
  5. Humans do not always easily deal with rapid changing configurations (inconsistency)
  6. Computers may be good a taking control, but are not always good at giving back control
  7. Adaptive systems triggered by operator workload or physiological responses may be operator-dependent and thus require careful calibration for each individual
  8. Danger of operator-triggered reallocations is that an operator may fail to recognize hazardous situations and thus may select inappropriate levels of automation for a certain task Human-Centered Automation (HCA) Requirements -
  9. Automated systems must be comprehensible
  10. Primary objective of automation is to maintain and enhance situation awareness.
  1. Automation must never be permitted to perform, or fail, silently
  2. Automation should provide the human operator with an appropriate range of control and management options
  3. Automation should be designed both for maximum error resistance and tolerance HCA rationale - -Because systems are fallible, humans bear the ultimate responsibility for the safety of operations. -HCA helps determine WHAT automation needs to offer us and WHY, but lacks to explain HOW Slips vs Lapses vs Mistakes - Slip, lapse - Intention correct, action wrong --Slip - executing wrongly --Lapse - omitting/forgetting Mistake - Intention wrong (planning failure) Active vs Latent Errors -
  4. Active - nearly immediate consequences
  5. Latent - designed or left error-prone conditions in system, becoming apparent only in certain circumstances Reasons for: Skill-based slip/lapse (5) - -inexperience -inattention, fatigue -overattention

-Selectivity -Working memory overload -Memory cueing/reasoning by analogy -Matching bias (as search stopper) -Incorrect/incomplete mental model Bad Apple Theory - The error is due to the human - why wasn't this bad apple fired before they made a mistake Limitations of Swiss Cheese (5) - -model is static and normative -it assumes the defenses are always there -it assumes the "alignment" of the individual holes is independent from each other -Safety layers that are never used or tested have a large probability of failure when needed -Safety layers that make daily work practices difficult have a large probability of being circumvented Probabilistic Risk Assessment (PRA) (2) - -Model the system and the system failures -Calculate the probability of success or failure for a system mission

Dynamic PRA (3) -

  1. The system state changes over time
  2. Events (failures) can occur at different time
  3. Events influence the change of system state PRA problems (3) -
  4. Assumption that the events that turn up in incident analysis are the same as those responsible for accidents. We don't know
  5. Assumption that we can find, model and foresee all relevant accident scenarios. We cannot
  6. Scale problems. Normally we can only consider a part of a system in a part of its activities Etic vs Emic -
  7. Etic - See the context and events as an outsider, with full knowledge of the outcome and knowledge of what operators "should have done"
  8. Emic - Try to see the context and events as they unfold, as a participant, and figure out what actions are "locally rational" from that viewpoint To achieve 10^-7 safety (3) -
  9. Crew verification
  10. Equipment certification

-Continuation Figure-Ground organization effect - Sometimes the brain has difficulty deciding what is the figure and what is the ground behind it. Once it accepts one part as the figure, the other part is condemned to the ground. Thus, you cannot see both figures at the same time Emergent Features - Global property of the set of stimuli (e.g. displays) that is not evident as each is seen in isolation (e.g. vertical alignment of gauges) Object-centered (Pictorial) cues (8) - -linear perspective -interposition -height in plane -light and shadow -relative size -textural gradients -relative motion gradient (parallax) -aerial perspective Observer-centered (Depth) cues (3) -

  1. Accommodation - How much change in lens shape the out-of-focus image triggers gives idea of object distance
  2. Binocular disparity - Difference in image location as seen by each eye (parallax)
  3. Convergence - Amount of inward rotation ("cross-eyedness")

Ecological Optics - Ecological optics is a theory proposed by J.J. Gibson that attempts to escape the reductionism of physical and geometrical optics by introducing the concept of ambient light to enable the study of biological vision. He suggests that light must be regarded not of distinct rays with just 2 specific variables (wavelength, intensity), but rather a series of optical arrays. Optical arrays are pencils of light where the boundaries between the rays hold the information essential to perception Principles of Ecological Optics (3) -

  1. There is a set of optic arrays in every habitable environment when it is illuminated
  2. Some correspondence exists between the structure of a local optic array and the structure of the local environment, and also between a set of arrays and the whole environment
  3. The variables of structure in an array and a set of arrays are potential stimuli for an ocular system When is an optical source of information called an optical invariant -
  4. When there is a change in form and texture due to a transformation, some features of the optical array structure remain the same (invariants). This provides a basis for the impression of a constant object or rigid surface. Why is the horizon an optical invariant - The horizon never moves even when every other structure in the light is changing. This stationary circle is that to which all optical motions have reference. It is neither subjective nor objective; it expresses the reciprocity of observer and environment. Main factors affecting judgments of self-motion (2) -
  5. Gradients (relative 'amount') of optical flow - determines relative velocities
  6. Pattern (relative 'direction') of optical flow is cue for direction of relative motion with respect to a surface Optical Invariants (7) -
  1. Invest in sufficient training for subjects (training/learning effects spread data/reduce reliability)
  2. Always be on watch for confounding factors Components of a research question - -It should be focused and detailed enough such that they are not answered with a 'yes or no' -Should contain clues for what will be investigated and how to find the answer 2 types of experimentation to test hypotheses -
  3. Mensurative - comparing existing things
  4. Manipulative 2 types of confounds -
  5. Operational - e.g. a measure designed to assess a particular construct inadvertently measures something else
  6. Procedural - e.g. mistakenly allow another variable to change along with the IV Type I and II errors - I - falsely reject null hypothesis II - falsely retain null hypothesis Mitigating Type I and II errors - I - do not set alpha value too high (0.05-0.01 is acceptable) II - reduce overlap between distributions How to reduce overlap of distributions (or spread in the data) (4) -
  7. Use large sample sizes
  1. Select homogeneous participant group
  2. Provide good instructions and sufficient training
  3. Use accurate sensors for collecting measurements Assumptions for using Parametric statistical tests -
  4. Interval or ratio data
  5. Approximately normally distributed
  6. Homogeneity of variance Task Demand Load (TDL) - = F(task, system type)
  • The mental effort required to accomplish the task as follows from system properties and task definitions (Task) Mental Load ((T)ML) - = F(operator(emotion), support system, training)
  • The amount of mental effort as experienced by the human operator Willing-to-Spend Capacity - = F(operator)
  • Base level of sustainable/acceptable mental workload Ways to determine TDL (4) -
  1. Task Analysis - can either be hierarchical, time-line based (required/available), or considering complexity/conflict between competing task resources (visual, auditory, kinesthetic, cognitive, psychomotor)
  2. Model Based Analysis - determining required amount of pilot compensation in Crossover Model
  1. Automation - Change the perceived complexity of tasks
  2. Display Augmentation - Better displays improve situation awareness, reduce time to process information, and minimize visual search (T)ML - Subjective Measures - -Human asked for direct assessment with Rating Scale -Most sensitive, most transferable, and least intrusive -Generally consistent and easy to use EXAMPLES: 1-D: (Modified) Cooper-Harper Multi-D: NASA TLX (Task Load indeX) (Modified) Cooper-Harper - -Begin by asking if task can be reliably accomplished (impossible = Level 10) -Subsequently break down difficulty level by error frequency/magnitude, workload, and then effort to find 1-10 rating NASA TLX (Task Load indeX) - -6 subscales applied in 3 steps Subscales:
  3. Mental demand
  4. Physical demand
  5. Temporal demand
  6. Effort
  7. Performance
  8. Frustration Level

Steps:

  1. Weight each subscale (pairwise comparison)
  2. Rate tasks on each subscale
  3. Combine to get weighted rating (T)ML - Secondary (Dual-) Task Measures - -Subjects required to perform 2 tasks, one with priority. performance of the second task can then be directly compared. EXAMPLES:
  4. Rythmic tapping
  5. Random Number Generation (degree of randomness decreases = ML increase)
  6. Probe Reaction Time
  7. Time Estimation
  8. Critical Instability Tracking Task (T)ML - Physiological Measures - EXAMPLES:
  9. Pupil diameter
  10. Heart-rate variability
  11. Evoked Brain Potentials (EBP) Others - Muscle tension, skin respiration Disadvantages - Can be quite intrusive Relationship between Workload and Performance - -Inverted 'U' graph. Optimum performance happens at 'medium' workload, aka the willing-to-spend capacity.
  1. Co-contraction of muscles -increased stiffness and viscosity -effective for large range of frequencies-costs a lot of energy
  2. Proprioceptive feedback -length, velocity, and force feedback -only effective for low frequencies due to time-delays in nervous system -energy efficient (only active when perturbations present) 2 types of Motor Control -
  3. Feed-forward -Requires good internal model of interaction -Most used with no perturbations for fast goal-directed movements
  4. Feedback -Requires sensory information -Most used for disturbance rejection Force-Feedback systems (3) -
  5. Haptic Feedback System - user is provided feedback through the sense of touch.
  6. Exoskeleton Force Feedback - external mechanical 'skeleton' is used. This method of feedback allows for a multi-dimensional input, thus increasing the number of possibilities to control the system.
  7. Master-Slave Feedback - Human controls the master machine. This machine then sends the control signals to a slave machine. This slave machine measures its environment and sends its feedback back to the master. Types of Muscle Neurons (3) -
  1. Motor neurons (activate muscles): (α-neurons) which transfer quickly to muscle fibers, and (γ-neurons) which transfer slower to muscle spindles.
  2. Sensory neuron Antagonistic Pairs - What and Why - -Muscles are mostly made out of 'muscle fibers' called myofibrils. Due to the chemical structure of the myofibrils, muscles can only contract. -To enable bones to move in multiple directions, antagonistic pairs of muscles are often present in the human body. When the flexor contracts, tension on the bone increases. This allows rotation in one direction. When the extensor contracts, tension on the bone is released, enabling motion in the other direction. Draw a block diagram of a pilot controlling an aircraft, in a tracking task. - Use separate blocks for neuromuscular system and the manipulator (stick). Also include effects of the accelerating environment What is the effect of the following on the "sensitivity" of hand and stick position to acceleration -
  3. An increased mass of the stick
  4. And increased stiffness of the stick Phases of the cognitive process (3) (aka big words representing common sense) -
  5. Encoding of the real world situation into the representation offered by the artefact
  6. Generation of a solution in the representation of the artefact
  7. Translation of this solution from the artefact's representation into the real-world