Psychology of Computing: Crash Course Computer Science #38

0:03 Hi, I’m Carrie Anne, and welcome to Crash Course Computer Science!

0:05 So, over the course of this series, we’ve focused almost exclusively on computers – the

0:10 circuits and algorithms that make them tick.

0:12 Because...this is Crash Course Computer Science.

0:15 But ultimately, computers are tools employed by people.

0:17 And humans are… well… messy.

0:19 We haven’t been designed by human engineers from the ground up with known performance

0:23 specifications.

0:24 We can be logical one moment and irrational the next.

0:27 Have you ever gotten angry at your navigation system? Surfed wikipedia aimlessly?

0:31 Begged your internet browser to load faster?

0:34 Nicknamed your roomba?

0:35 These behaviors are quintessentially human!

0:37 To build computer systems that are useful, usable and enjoyable, we need to understand

0:42 the strengths and weaknesses of both computers and humans.

0:45 And for this reason, when good system designers are creating software, they employ social,

0:50 cognitive, behavioral, and perceptual psychology principles.

0:53 INTRO

1:02 No doubt you’ve encountered a physical or computer interface that was frustrating to

1:06 use, impeding your progress.

1:08 Maybe it was so badly designed that you couldn’t figure it out and just gave up.

1:12 That interface had poor usability.

1:14 Usability is the degree to which a human-made artifact – like software – can be used

1:18 to achieve an objective effectively and efficiently.

1:21 To facilitate human work, we need to understand humans - from how they see and think, to how

1:26 they react and interact.

1:27 For instance, the human visual system has been well studied by Psychologists.

1:31 Like, we know that people are good at ordering intensities of colors.

1:34 Here are three.

1:35 Can you arrange these from lightest to darkest?

1:38 You probably don’t have to think too much about it.

1:40 Because of this innate ability, color intensity is a great choice for displaying data with

1:44 continuous values.

1:45 On the other hand, humans are terrible at ordering colors.

1:48 Here’s another example for you to put in order… is orange before blue, or after blue?

1:53 Where does green go?

1:54 You might be thinking we could order this by wavelength of light, like a rainbow, but

1:57 that’s a lot more to think about.

1:59 Most people are going to be much slower and error-prone at ordering.

2:03 Because of this innate ineptitude of your visual system, displaying continuous data

2:06 using colors can be a disastrous design choice.

2:09 You’ll find yourself constantly referring back to a color legend to compare items.

2:14 However, colors are perfect for when the data is discrete with no ordering, like categorical

2:18 data.

2:19 This might seem obvious, but you’d be amazed at how many interfaces get basic things like

2:23 this wrong.

2:23 Beyond visual perception, understanding human cognition helps us design interfaces that

2:28 align with how the mind works.

2:30 Like, humans can read, remember and process information more effectively when it’s chunked

2:35 – that is, when items are put together into small, meaningful groups.

2:38 Humans can generally juggle seven items, plus-or-minus two, in short-term memory.

2:43 To be conservative, we typically see groupings of five or less.

2:46 That’s why telephone numbers are broken into chunks, like 317, 555, 3897.

2:51 Instead of being ten individual digits that we’d likely forget, it’s three chunks,

2:55 which we can handle better.

2:56 From a computer's standpoint, this needlessly takes more time and space, so it’s less

3:00 efficient.

3:01 But, it’s way more efficient for us humans – a tradeoff we almost always make in our

3:06 favor, since we’re the ones running the show...for now.

3:09 Chunking has been applied to computer interfaces for things like drop-down menu items and menu

3:12 bars with buttons.

3:14 It’d be more efficient for computers to just pack all those together, edge to edge

3:18 – it’s wasted memory and screen real estate.

3:20 But designing interfaces in this way makes them much easier to visually scan, remember

3:24 and access.

3:25 Another central concept used in interface design is affordances.

3:29 According to Don Norman, who popularized the term in computing, “affordances provide

3:33 strong clues to the operations of things.

3:36 Plates are for pushing.

3:37 Knobs are for turning.

3:39 Slots are for inserting things into.

3:41 [...] When affordances are taken advantage of, the user knows what to do just by looking:

3:45 no picture, label, or instruction needed.”

3:48 If you’ve ever tried to pull a door handle, only to realize that you have to push it open,

3:52 you’ve discovered a broken affordance.

3:54 On the other hand, a door plate is a better design because it only gives you the option

3:57 to push.

3:58 Doors are pretty straightforward – if you need to put written instructions on them,

4:01 you should probably go back to the drawing board.

4:04 Affordances are used extensively in graphical user interfaces, which we discussed in episode

4:08 26.

4:09 It’s one of the reasons why computers became so much easier to use than with command lines.

4:13 You don’t have to guess what things on-screen are clickable, because they look like buttons.

4:18 They pop out, just waiting for you to press them!

4:20 One of my favorite affordances, which suggests to users that an on-screen element is draggable,

4:24 is knurling – that texture added to objects to improve grip and show you where to best

4:29 grab them.

4:30 This idea and pattern was borrowed from real world physical tools.

4:33 Related to the concept of affordances is the psychology of recognition vs recall.

4:38 You know this effect well from tests – it’s why multiple choice questions are easier than

4:42 fill-in-the-blank ones.

4:43 In general, human memory is much better when it’s triggered by a sensory cue, like a

4:47 word, picture or sound.

4:48 That’s why interfaces use icons – pictorial representations of functions – like a trash

4:53 can for where files go to be deleted.

4:55 We don’t have to recall what that icon does, we just have to recognise the icon.

4:59 This was also a huge improvement over command line interfaces, where you had to rely on

5:03 your memory for what commands to use.

5:05 Do I have to type “delete”, or “remove”, or... “trash”, or… shoot, it could be anything!

5:10 It’s actually “rm” in linux, but anyway, making everything easy to discover and learn

5:14 sometimes means slow to access, which conflicts with another psychology concept: expertise.

5:19 As you gain experience with interfaces, you get faster, building mental models of how

5:23 to do things efficiently.

5:25 So, good interfaces should offer multiple paths to accomplish goals.

5:28 A great example of this is copy and paste, which can be found in the edit dropdown menu

5:32 of word processors, and is also triggered with keyboard shortcuts.

5:36 One approach caters to novices, while the other caters to experts, slowing down neither.

5:41 So, you can have your cake and eat it too!

5:43 In addition to making humans more efficient, we’d also like computers to be emotionally

5:47 intelligent – adapting their behavior to respond appropriately to their users’ emotional

5:52 state – also called affect.

5:54 That could make experiences more empathetic, enjoyable, or even delightful.

5:58 This vision was articulated by Rosalind Picard in her 1995 paper on Affective Computing,

6:03 which kickstarted an interdisciplinary field combining aspects of psychology, social and

6:08 computer sciences.

6:09 It spurred work on computing systems that could recognize, interpret, simulate and alter

6:13 human affect.

6:14 This was a huge deal, because we know emotion influences cognition and perception in everyday

6:19 tasks like learning, communication, and decision making.

6:23 Affect-aware systems use sensors, sometimes worn, that capture things like speech and

6:26 video of the face, as well as biometrics, like sweatiness and heart rate.

6:31 This multimodal sensor data is used in conjunction with computational models that represent how

6:35 people develop and express affective states, like happiness and frustration, and social

6:40 states, like friendship and trust.

6:42 These models estimate the likelihood of a user being in a particular state, and figure

6:46 out how to best respond to that state, in order to achieve the goals of the system.

6:50 This might be to calm the user down, build trust, or help them get their homework done.

6:55 A study, looking at user affect, was conducted by Facebook in 2012.

6:59 For one week, data scientists altered the content on hundreds of thousands of users’

7:03 feeds.

7:03 Some people were shown more items with positive content, while others were presented with

7:07 more negative content.

7:08 The researchers analyzed people's posts during that week, and found that users who were shown

7:12 more positive content, tended to also post more positive content.

7:16 On the other hand, users who saw more negative content, tended to have more negative posts.

7:21 Clearly, what Facebook and other services show you can absolutely have an affect on

7:25 you.

7:25 As gatekeepers of content, that’s a huge opportunity and responsibility.

7:29 Which is why this study ended up being pretty controversial.

7:32 Also, it raises some interesting questions about how computer programs should respond

7:35 to human communication.

7:37 If the user is being negative, maybe the computer shouldn’t be annoying by responding in a

7:41 cheery, upbeat manner.

7:43 Or, maybe the computer should attempt to evoke a positive response, even if it’s a bit

7:47 awkward.

7:48 The “correct” behavior is very much an open research question.

7:51 Speaking of Facebook, it’s a great example of computer-mediated communication, or CMC,

7:56 another large field of research.

7:57 This includes synchronous communication – like video calls, where all participants are online

8:01 simultaneously – as well as asynchronous communication – like tweets, emails, and

8:06 text messages, where people respond whenever they can or want.

8:09 Researchers study things like the use of emoticons, rules such as turn-taking, and language used

8:13 in different communication channels.

8:15 One interesting finding is that people exhibit higher levels of self-disclosure – that

8:19 is, reveal personal information – in computer-mediated conversations, as opposed to face-to-face

8:24 interactions.

8:25 So if you want to build a system that knows how many hours a user truly spent watching

8:29 The Great British Bakeoff, it might be better to build a chatbot than a virtual agent with

8:33 a face.

8:34 Psychology research has also demonstrated that eye gaze is extremely important in persuading,

8:38 teaching and getting people's attention.

8:40 Looking at others while talking is called mutual gaze.

8:43 This has been shown to boost engagement and help achieve the goals of a conversation,

8:47 whether that’s learning, making a friend, or closing a business deal.

8:50 In settings like a videotaped lecture, the instructor rarely, if ever, looks into the

8:54 camera, and instead generally looks at the students who are physically present.

8:58 That’s ok for them, but it means people who watch the lectures online have reduced

9:02 engagement.

9:03 In response, researchers have developed computer vision and graphics software that can warp

9:07 the head and eyes, making it appear as though the instructor is looking into the camera

9:10 – right at the remote viewer.

9:12 This technique is called augmented gaze.

9:15 Similar techniques have also been applied to video conference calls, to correct for

9:18 the placement of webcams, which are almost always located above screens.

9:21 Since you’re typically looking at the video of your conversation partner, rather than

9:25 directly into the webcam, you’ll always appear to them as though you’re looking

9:28 downwards – breaking mutual gaze – which can create all kinds of unfortunate social

9:33 side effects, like a power imbalance.

9:35 Fortunately, this can be corrected digitally, and appear to participants as though you’re

9:39 lovingly gazing into their eyes.

9:41 Humans also love anthropomorphizing objects, and computers are no exception, especially

9:46 if they move, like our Robots from last episode.

9:49 Beyond industrial uses that prevailed over the last century, robots are used increasingly

9:53 in medical, education, and entertainment settings, where they frequently interact with humans.

9:58 Human-Robot Interaction – or HRI – is a field dedicated to studying these interactions,

10:03 like how people perceive different robots behaviors and forms, or how robots can interpret

10:08 human social cues to blend in and not be super awkward.

10:11 As we discussed last episode, there’s an ongoing quest to make robots as human-like

10:15 in their appearance and interactions as possible.

10:17 When engineers first made robots in the 1940s and 50s, they didn’t look very human at all.

10:22 They were almost exclusively industrial machines with no human-likeness.

10:25 Over time, engineers got better and better at making human-like robots – they gained

10:30 heads and walked around on two legs, but… they couldn’t exactly go to restaurants

10:34 and masquerade as humans.

10:35 As people pushed closer and closer to human likeness, replacing cameras with artificial

10:39 eyeballs, and covering metal chassis with synthetic flesh, things started to get a bit...

10:44 uncanny... eliciting an eerie and unsettling feeling.

10:47 This dip in realism between almost-human and actually-human became known as the uncanny valley.

10:53 There’s debate over whether robots should act like humans too.

10:56 Lots of evidence already suggests that even if robots don’t act like us, people will

11:00 treat them as though they know our social conventions.

11:02 And when they violate these rules – such as not apologizing if they cut in front of

11:06 you or roll over your foot – people get really mad!

11:09 Without a doubt, psychology and computer science are a potent combination, and have tremendous

11:13 potential to affect our everyday lives.

11:16 Which leaves us with a lot of question like you might lie to your laptop, but should your

11:20 laptop lie to you?

11:21 What if it makes you more efficient or happy?

11:23 Or should social media companies curate the content they show you to make you stay on

11:27 their site longer to make you buy more products?

11:29 They do by the way.

11:31 These types of ethical considerations aren’t easy to answer, but psychology can at least

11:35 help us understand the effects and implications of design choices in our computing systems.

11:40 But, on the positive side, understanding the psychology behind design might lead to increased

11:45 accessibility.

11:46 A greater number of people can understand and use computers now that they're more intuitive

11:50 than ever.

11:51 Conference calls and virtual classrooms are becoming more agreeable experiences.

11:55 As robot technology continues to improve, the population will grow more comfortable

11:59 in those interactions.

12:00 Plus, thanks to psychology, we can all bond over our love of knurling.

12:04 I’ll see you next week.