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Lecture notes week 1-8

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Lecture notes/collegeaantekeningen improving human performance in practice.

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  • January 18, 2023
  • 23
  • 2022/2023
  • Class notes
  • Francesco walker (coordinator)
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Improving human performance in practice – lectures

Week 1: work and task analysis

Initial focus of ACP  engineering and human factors/cognitive ergonomics in for example
military and aviation.

Cognition: Current fields:
 Selective attention  Knowledge work
 Vigilance  Creativity and sports
 Interference  Industry
 Capacity  High tech and robotics
 Identification  Education
 Decision making  Traffic
 Government

Task analysis: looking at a non-optimal situation, find the cause and give advice

Drunk  car crash  advice: don’t drink and drive

However, other possible factors: distraction, fatigue, insufficient training, road design,
fogged windshield  interactions between causes.

Resilience to imperfection: aim to ensure that imperfections in people and situations do not
lead to disaster.

Hindsight bias cannot prevent problems


Towards a system analysis (team is more than the sum of its parts)

,Task analysis (light)  cognitive psychology allows you to already identify limiting factors in
a not too complicated job:
 Situation awareness
 Attention/concentration
 Working memory load
 Knowledge
 Availability of information

Week 2 – Safety

Past to present
Human error  human performance variability
Motivation training to not pass a red signal  analysis of why a signal is being ignored

Railway accidents  high availability bias

The “Swiss cheese model”:
 Between an accident and a cause are different barriers
 An accident occurs when the “holes” in barriers line up
 Solution: decrease the size of the holes or add more barriers
 The arrow represents the human factor: Why, What and when.

Very often, there is no malicious intent and things “happen”

Unintentional behavior:
Slips  commission
Lapses  omission  increasing problem

Intentional behavior:
Mistakes  strong but wrong
Violations  breaking rules

, SPAD  signal passed at danger from minimal to grave passings
Target was not realized, but there was a significant decrease in SPADS

Variation exist and does not have to be entirely eliminated, however we could implement
thresholds to avoid too high or too low result.

Variation leaves to improvement and competition

Utrecht central station: Opportunities for change: less crossings and more information
processing  how would this increase safety?
 Unfortunately  can’t be just “tried” and has a high cost/high risk nature
 The outcome can only be explained after the experiment

How to reduce the risk of SPADS  Risk is exposure X probability X effect:
 Exposure: influence the number of signals/crossings  flexibility lost
 Probability (behavior): given that there is a red signal, influence the probability to
passing that signal:
- Using simulators (waiting for a SPAD is too time-consuming)
- Proxy indicator  max breaking capacity to SPAD
 Effect: Reduce the consequences

Innovative way of improving safety:
 There were no indications that negative mBtSPAD were caused by extraordinary
factors
 Negative SPADS were extreme outcomes of the accepted way of working  initially
not safety related
 With a focus on optimizing the primary process, not only safety but also punctuality
and energy consumption.
 To what extent is there unnecessary variation in the way organizations operate

Conclusions:
 Two approaches that are complementary to each other:
- Eliminate causes of incidents
- Eliminate variation. Change the normal way of operating in such a way that
extreme outcomes are less frequent
 Positive surprises are as worrying as negative ones
 Safety is a control issue and implies predictability of performance in all aspects
 Look for sources for variation in human performance

Week 3 – Education

Predictors of school success  70% of variance depends on gender, SES and IQ
14 years of education to influence the other 30%

What is the goal of school? Is school the optimal mean to achieve the goal  applies also to
company training.
Thorndike  transfer is only possible from the trained activity to identical elements.

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