Category Archives: Human Performance Modeling

What Is The Automation Doing? Mode Awareness Problems Catch Tesla By Surprise

Computer Systems, Human Performance Modeling, Product Design, Safety, Surface Transportation, Uncategorized
June 29, 2016 Guest Author 19 Comments

One of the most compelling arguments for introducing autonomous driving vehicles is the potential reduction in injuries and fatalities caused primarily by human error. According to the National Highway Transportation Safety Administration, human error accounts for 94% of fatal crashes, and advanced safety technologies can be expected to reduce these numbers substantially.

However, at the end of April this year, there was an incident in which a Tesla Model S, from a parked position, collided with a trailer parked in front of it after the “Summon” autopark feature was activated. With Summon, the driver can exit the car and make it roll slowly forward or backward into a tight parking space using the key or an app. The incident caused only about $700 in damage to the luxury car’s windshield, but this incident might be a symptom of a greater problem.

Marketing Technology

Computer Systems, Human Performance Modeling, Individual Differences, Internet, Macroergonomics, Product Design
February 18, 2016 Marc Resnick Leave a comment

Gartner, the IT research company, published its 2016 list of predictions for marketing technology a few weeks ago. As I was reading it, I was struck with how easily the list could be aligned with the user experience of the typical purchase process. Not the complete buyer’s journey; but at least the transactions steps in the middle.

In less than three years, advances in marketing technology will move beyond human intervention to streamlining and scaling activities that currently require manual interactions with audiences. Intelligent technologies will do more than automate repetitive operations — they will investigate, evaluate and make decisions on behalf of both marketers and customers.Marketing technology will soon become so intelligent that it will perform tasks that have always required direct human involvement.

Strawman Fallacy

Cognitive Engineering and Decision Making, Human Performance Modeling, Macroergonomics, Perception and Performance
February 8, 2016 Marc Resnick Leave a comment

The latest episode of the You Are Not So Smart podcast touches on a great example of self-delusion that we haven’t covered here yet. In this episode, David McRaney interviews Jesse Richardson of “Your Logical Fallacy Is”, a site that I am definitely going to have to check out about the strawman fallacy.

Person A: Soup is delicious
Person B: I tried soup once. It was terrible. Therefore you are wrong.

AI and Humor

Computer Systems, Human Performance Modeling, Training, Uncategorized
February 3, 2016 Marc Resnick 2 Comments

I can’t decide if this is a triumph for analytics and algorithms or if it is one of those gaps that is ripe for human attention.

Arjun Chandrasekaran from Virginia Tech and pals say they’ve trained a machine-learning algorithm to recognize humorous scenes and even to create them. They say their machine can accurately predict when a scene is funny and when it is not, even though it knows nothing of the social context of what it is seeing.

Risk Preference and the NAcc

Cognitive Engineering and Decision Making, Human Performance Modeling, Individual Differences, Safety
January 18, 2016 Marc Resnick 1 Comment

This study in Brain came from a team from the UK and Spain. They are studying the link between risk preference and the nucleus accumbens (NAcc). Even given the limitations in associating brain activity and human behavior that I acknowledged above, there is reasonable evidence that the NAcc is linked to risk preference. This study is remarkable in that they did a controlled study and the participants were blind to the intervention. This is rare in neuropsych studies where confounds and mediators are hard to control for.

Short-lived phasic electrical stimulation of the region of the nucleus accumbens dynamically altered risk behaviour, transiently shifting the psychometric function towards more risky decisions only for the duration of stimulation. A critical, on-line role of human nucleus accumbens in dynamic risk control is thereby established.

Sense of Direction

Cognitive Engineering and Decision Making, Health Care, Human Performance Modeling, Individual Differences
January 11, 2016 Marc Resnick Leave a comment

I recently came across this article describing a study from a research team at the Norwegian University of Science and Technology. Their hypothesis was that the difference emerges from hormonal differences, specifically testosterone. Not brain wiring.

Using fMRI, the researchers saw that men in the study took several shortcuts, oriented themselves more using cardinal directions and used a different part of the brain than the women in the study. But when women got a drop of testosterone under their tongue, several of them were able to orient themselves better in the four cardinal directions.

Cognitive Dissonance

Cognitive Engineering and Decision Making, Human Performance Modeling, Individual Differences, Perception and Performance
January 5, 2016 Marc Resnick 1 Comment

Cognitive dissonance is when you have two conflicting ideas and yet you have reasons to believe both. There are many many many reasons that appealing ideas can conflict, so we are constantly facing the prospect of cognitive dissonance, or more importantly how to resolve it.

What is the neural explanation for this common type of psychological stress? Thanks to advances in imaging methods, especially functional MRI, researchers have recently identified key brain regions linked to cognitive dissonance. The area implicated most consistently is the posterior part of the medial frontal cortex (pMFC), known to play an important role in avoiding aversive outcomes, a powerful built-in survival instinct. In fMRI studies, when subjects lie to a peer despite knowing that lying is wrong—a task that puts their actions and beliefs in conflict—the pMFC lights up.

Curiosity Redux

Education, Human Performance Modeling, Individual Differences
December 10, 2015 Marc Resnick Leave a comment

Defining curiosity is the first challenge because so many experts from the fields of education, philosophy, psychology, and neuroscience define it differently. The simplest, and the one that I personally prefer, is the one used by Celeste Kidd and Benjamin Hayden at the U of Rochester “a drive state for information.” This is independent of any tangible reward. The information is the desired reward.

Philosopher Thomas Hobbes called it “the lust of the mind.” Former first lady Eleanor Roosevelt said it was “the most useful gift.” And, yes, we all know what killed the cat. But ask a group of scientists to define curiosity and you’ll get a rousing debate, and a lot of unanswered questions about its biology. No more, argue two University of Rochester researchers in a review of curiosity science published November 4 in Neuron. They propose that it’s time for researchers to organize and focus on curiosity’s function, evolution, mechanism, and development.

a person and a computer with doves representing peace

Intentional Intuition

Cognitive Engineering and Decision Making, Human Performance Modeling, Macroergonomics, Product Design
December 1, 2015 Marc Resnick 2 Comments

In the cognitive sciences, intuition is described as a way of processing information based on automatic, affective and personal standards, but it is not the opposite of rationality. Designers generate solutions to daily issues, which forces them to make decisions that cannot be always understood rationally. Designing for experiences is a delicate practice in a rational perspective, since the designer’s interpretation on how to trigger particular experiences can be highly influenced by intuition.

Ergonomics in Design

The Quarterly of Human Factors Applications

Category Archives: Human Performance Modeling

What Is The Automation Doing? Mode Awareness Problems Catch Tesla By Surprise

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Marketing Technology

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Strawman Fallacy

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AI and Humor

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Risk Preference and the NAcc

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Sense of Direction

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Cognitive Dissonance

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Curiosity Redux

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Intentional Intuition

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