Hi, I’m Carrie Anne, and welcome to Crash Course Computer Science!
So, over the course of this series, we’ve focused almost exclusively on computers – the
circuits and algorithms that make them tick.
Because...this is Crash Course Computer Science.
But ultimately, computers are tools employed by people.
And humans are… well… messy.
We haven’t been designed by human engineers from the ground up with known performance
specifications.
We can be logical one moment and irrational the next.
Have you ever gotten angry at your navigation system? Surfed wikipedia aimlessly?
Begged your internet browser to load faster?
Nicknamed your roomba?
These behaviors are quintessentially human!
To build computer systems that are useful, usable and enjoyable, we need to understand
the strengths and weaknesses of both computers and humans.
And for this reason, when good system designers are creating software, they employ social,
cognitive, behavioral, and perceptual psychology principles.
INTRO
No doubt you’ve encountered a physical or computer interface that was frustrating to
use, impeding your progress.
Maybe it was so badly designed that you couldn’t figure it out and just gave up.
That interface had poor usability.
Usability is the degree to which a human-made artifact – like software – can be used
to achieve an objective effectively and efficiently.
To facilitate human work, we need to understand humans - from how they see and think, to how
they react and interact.
For instance, the human visual system has been well studied by Psychologists.
Like, we know that people are good at ordering intensities of colors.
Here are three.
Can you arrange these from lightest to darkest?
You probably don’t have to think too much about it.
Because of this innate ability, color intensity is a great choice for displaying data with
continuous values.
On the other hand, humans are terrible at ordering colors.
Here’s another example for you to put in order… is orange before blue, or after blue?
Where does green go?
You might be thinking we could order this by wavelength of light, like a rainbow, but
that’s a lot more to think about.
Most people are going to be much slower and error-prone at ordering.
Because of this innate ineptitude of your visual system, displaying continuous data
using colors can be a disastrous design choice.
You’ll find yourself constantly referring back to a color legend to compare items.
However, colors are perfect for when the data is discrete with no ordering, like categorical
data.
This might seem obvious, but you’d be amazed at how many interfaces get basic things like
this wrong.
Beyond visual perception, understanding human cognition helps us design interfaces that
align with how the mind works.
Like, humans can read, remember and process information more effectively when it’s chunked
– that is, when items are put together into small, meaningful groups.
Humans can generally juggle seven items, plus-or-minus two, in short-term memory.
To be conservative, we typically see groupings of five or less.
That’s why telephone numbers are broken into chunks, like 317, 555, 3897.
Instead of being ten individual digits that we’d likely forget, it’s three chunks,
which we can handle better.
From a computer's standpoint, this needlessly takes more time and space, so it’s less
efficient.
But, it’s way more efficient for us humans – a tradeoff we almost always make in our
favor, since we’re the ones running the show...for now.
Chunking has been applied to computer interfaces for things like drop-down menu items and menu
bars with buttons.
It’d be more efficient for computers to just pack all those together, edge to edge
– it’s wasted memory and screen real estate.
But designing interfaces in this way makes them much easier to visually scan, remember
and access.
Another central concept used in interface design is affordances.
According to Don Norman, who popularized the term in computing, “affordances provide
strong clues to the operations of things.
Plates are for pushing.
Knobs are for turning.
Slots are for inserting things into.
[...] When affordances are taken advantage of, the user knows what to do just by looking:
no picture, label, or instruction needed.”
If you’ve ever tried to pull a door handle, only to realize that you have to push it open,
you’ve discovered a broken affordance.
On the other hand, a door plate is a better design because it only gives you the option
to push.
Doors are pretty straightforward – if you need to put written instructions on them,
you should probably go back to the drawing board.
Affordances are used extensively in graphical user interfaces, which we discussed in episode
26.
It’s one of the reasons why computers became so much easier to use than with command lines.
You don’t have to guess what things on-screen are clickable, because they look like buttons.
They pop out, just waiting for you to press them!
One of my favorite affordances, which suggests to users that an on-screen element is draggable,
is knurling – that texture added to objects to improve grip and show you where to best
grab them.
This idea and pattern was borrowed from real world physical tools.
Related to the concept of affordances is the psychology of recognition vs recall.
You know this effect well from tests – it’s why multiple choice questions are easier than
fill-in-the-blank ones.
In general, human memory is much better when it’s triggered by a sensory cue, like a
word, picture or sound.
That’s why interfaces use icons – pictorial representations of functions – like a trash
can for where files go to be deleted.
We don’t have to recall what that icon does, we just have to recognise the icon.
This was also a huge improvement over command line interfaces, where you had to rely on
your memory for what commands to use.
Do I have to type “delete”, or “remove”, or... “trash”, or… shoot, it could be anything!
It’s actually “rm” in linux, but anyway, making everything easy to discover and learn
sometimes means slow to access, which conflicts with another psychology concept: expertise.
As you gain experience with interfaces, you get faster, building mental models of how
to do things efficiently.
So, good interfaces should offer multiple paths to accomplish goals.
A great example of this is copy and paste, which can be found in the edit dropdown menu
of word processors, and is also triggered with keyboard shortcuts.
One approach caters to novices, while the other caters to experts, slowing down neither.
So, you can have your cake and eat it too!
In addition to making humans more efficient, we’d also like computers to be emotionally
intelligent – adapting their behavior to respond appropriately to their users’ emotional
state – also called affect.
That could make experiences more empathetic, enjoyable, or even delightful.
This vision was articulated by Rosalind Picard in her 1995 paper on Affective Computing,
which kickstarted an interdisciplinary field combining aspects of psychology, social and
computer sciences.
It spurred work on computing systems that could recognize, interpret, simulate and alter
human affect.
This was a huge deal, because we know emotion influences cognition and perception in everyday
tasks like learning, communication, and decision making.
Affect-aware systems use sensors, sometimes worn, that capture things like speech and
video of the face, as well as biometrics, like sweatiness and heart rate.
This multimodal sensor data is used in conjunction with computational models that represent how
people develop and express affective states, like happiness and frustration, and social
states, like friendship and trust.
These models estimate the likelihood of a user being in a particular state, and figure
out how to best respond to that state, in order to achieve the goals of the system.
This might be to calm the user down, build trust, or help them get their homework done.
A study, looking at user affect, was conducted by Facebook in 2012.
For one week, data scientists altered the content on hundreds of thousands of users’
feeds.
Some people were shown more items with positive content, while others were presented with
more negative content.
The researchers analyzed people's posts during that week, and found that users who were shown
more positive content, tended to also post more positive content.
On the other hand, users who saw more negative content, tended to have more negative posts.
Clearly, what Facebook and other services show you can absolutely have an affect on
you.
As gatekeepers of content, that’s a huge opportunity and responsibility.
Which is why this study ended up being pretty controversial.
Also, it raises some interesting questions about how computer programs should respond
to human communication.
If the user is being negative, maybe the computer shouldn’t be annoying by responding in a
cheery, upbeat manner.
Or, maybe the computer should attempt to evoke a positive response, even if it’s a bit
awkward.
The “correct” behavior is very much an open research question.
Speaking of Facebook, it’s a great example of computer-mediated communication, or CMC,
another large field of research.
This includes synchronous communication – like video calls, where all participants are online
simultaneously – as well as asynchronous communication – like tweets, emails, and
text messages, where people respond whenever they can or want.
Researchers study things like the use of emoticons, rules such as turn-taking, and language used
in different communication channels.
One interesting finding is that people exhibit higher levels of self-disclosure – that
is, reveal personal information – in computer-mediated conversations, as opposed to face-to-face
interactions.
So if you want to build a system that knows how many hours a user truly spent watching
The Great British Bakeoff, it might be better to build a chatbot than a virtual agent with
a face.
Psychology research has also demonstrated that eye gaze is extremely important in persuading,
teaching and getting people's attention.
Looking at others while talking is called mutual gaze.
This has been shown to boost engagement and help achieve the goals of a conversation,
whether that’s learning, making a friend, or closing a business deal.
In settings like a videotaped lecture, the instructor rarely, if ever, looks into the
camera, and instead generally looks at the students who are physically present.
That’s ok for them, but it means people who watch the lectures online have reduced
engagement.
In response, researchers have developed computer vision and graphics software that can warp
the head and eyes, making it appear as though the instructor is looking into the camera
– right at the remote viewer.
This technique is called augmented gaze.
Similar techniques have also been applied to video conference calls, to correct for
the placement of webcams, which are almost always located above screens.
Since you’re typically looking at the video of your conversation partner, rather than
directly into the webcam, you’ll always appear to them as though you’re looking
downwards – breaking mutual gaze – which can create all kinds of unfortunate social
side effects, like a power imbalance.
Fortunately, this can be corrected digitally, and appear to participants as though you’re
lovingly gazing into their eyes.
Humans also love anthropomorphizing objects, and computers are no exception, especially
if they move, like our Robots from last episode.
Beyond industrial uses that prevailed over the last century, robots are used increasingly
in medical, education, and entertainment settings, where they frequently interact with humans.
Human-Robot Interaction – or HRI – is a field dedicated to studying these interactions,
like how people perceive different robots behaviors and forms, or how robots can interpret
human social cues to blend in and not be super awkward.
As we discussed last episode, there’s an ongoing quest to make robots as human-like
in their appearance and interactions as possible.
When engineers first made robots in the 1940s and 50s, they didn’t look very human at all.
They were almost exclusively industrial machines with no human-likeness.
Over time, engineers got better and better at making human-like robots – they gained
heads and walked around on two legs, but… they couldn’t exactly go to restaurants
and masquerade as humans.
As people pushed closer and closer to human likeness, replacing cameras with artificial
eyeballs, and covering metal chassis with synthetic flesh, things started to get a bit...
uncanny... eliciting an eerie and unsettling feeling.
This dip in realism between almost-human and actually-human became known as the uncanny valley.
There’s debate over whether robots should act like humans too.
Lots of evidence already suggests that even if robots don’t act like us, people will
treat them as though they know our social conventions.
And when they violate these rules – such as not apologizing if they cut in front of
you or roll over your foot – people get really mad!
Without a doubt, psychology and computer science are a potent combination, and have tremendous
potential to affect our everyday lives.
Which leaves us with a lot of question like you might lie to your laptop, but should your
laptop lie to you?
What if it makes you more efficient or happy?
Or should social media companies curate the content they show you to make you stay on
their site longer to make you buy more products?
They do by the way.
These types of ethical considerations aren’t easy to answer, but psychology can at least
help us understand the effects and implications of design choices in our computing systems.
But, on the positive side, understanding the psychology behind design might lead to increased
accessibility.
A greater number of people can understand and use computers now that they're more intuitive
than ever.
Conference calls and virtual classrooms are becoming more agreeable experiences.
As robot technology continues to improve, the population will grow more comfortable
in those interactions.
Plus, thanks to psychology, we can all bond over our love of knurling.
I’ll see you next week.