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,

0:07 we’ve focused almost exclusively on computers–

0:09 the 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

0:21 from the ground up with known performance specifications.

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

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

0:30 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,

0:40 we need to understand the strengths and weaknesses of both computers and humans.

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

0:49 they employ social, cognitive, behavioral, and perceptual psychology principles.

0:53 INTRO No doubt you’ve encountered a physical

1:04 or computer interface that was frustrating to 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

1:17 software– can be used to achieve an objective effectively and efficiently.

1:21 To facilitate human work,

1:22 we need to understand humans- from how they see and think,

1:25 to how 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,

1:41 color intensity is a great choice for displaying data with continuous values.

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

1:48 Here’s another example for you to put

1:50 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,

1:56 like a rainbow, but 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,

2:05 displaying continuous data using colors can be a disastrous design choice.

2:09 You’ll find yourself constantly referring back

2:11 to a color legend to compare items.

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

2:17 like categorical data.

2:19 This might seem obvious,

2:20 but you’d be amazed at how many interfaces get basic things like this wrong.

2:23 Beyond visual perception,

2:25 understanding human cognition helps us design interfaces

2:27 that align with how the mind works.

2:30 Like, humans can read,

2:31 remember and process information more effectively when it’s chunked– that is,

2:36 when items are put together into small, meaningful groups.

2:38 Humans can generally juggle seven items,

2:41 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,

2:54 it’s three chunks, which we can handle better.

2:56 From a computer's standpoint,

2:58 this needlessly takes more time and space, so it’s less efficient.

3:01 But, it’s way more efficient for us humans–

3:04 a tradeoff we almost always make in our favor,

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

3:09 Chunking has been applied to computer interfaces for things

3:11 like drop-down menu items and menu bars with buttons.

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

3:17 edge to edge– it’s wasted memory and screen real estate.

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

3:24 remember 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,

3:33 “affordances provide 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

3:44 what to do just by looking: no picture, label,

3:47 or instruction needed.” If you’ve ever tried to pull a door handle,

3:50 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

3:56 design because it only gives you the option to push.

3:58 Doors are pretty straightforward– if you

4:00 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,

4:06 which we discussed in episode 26.

4:09 It’s one of the reasons why computers became

4:11 so much easier to use than with command lines.

4:13 You don’t have to guess what things on-screen are clickable,

4:16 because they look like buttons.

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

4:20 One of my favorite affordances,

4:21 which suggests to users that an on-screen element is draggable,

4:24 is knurling– that texture added to objects to improve

4:27 grip and show you where to best grab them.

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

4:33 Related to the concept of affordances

4:35 is the psychology of recognition vs recall.

4:38 You know this effect well from tests– it’s

4:40 why multiple choice questions are easier than fill-in-the-blank ones.

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

4:47 like a word, picture or sound.

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

4:52 a trash 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,

5:02 where you had to rely on your memory for what commands to use.

5:05 Do I have to type “delete”, or “remove”, or...

5:08 “trash”, or… shoot, it could be anything!

5:10 It’s actually “rm” in linux, but anyway,

5:12 making everything easy to discover and learn sometimes means slow to access,

5:16 which conflicts with another psychology concept: expertise.

5:19 As you gain experience with interfaces, you get faster,

5:22 building mental models of how 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,

5:30 which can be found in the edit dropdown menu of word processors,

5:33 and is also triggered with keyboard shortcuts.

5:36 One approach caters to novices,

5:38 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,

5:45 we’d also like computers to be emotionally intelligent– adapting their behavior

5:49 to respond appropriately to their users’ emotional 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

6:01 in her 1995 paper on Affective Computing,

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

6:07 social and computer sciences.

6:09 It spurred work on computing systems that could recognize,

6:12 interpret, simulate and alter human affect.

6:14 This was a huge deal, because we know emotion influences cognition

6:18 and perception in everyday tasks like learning,

6:21 communication, and decision making.

6:23 Affect-aware systems use sensors, sometimes worn,

6:25 that capture things like speech and video of the face,

6:28 as well as biometrics, like sweatiness and heart rate.

6:31 This multimodal sensor data is used in conjunction with computational

6:34 models that represent how people develop and express affective states,

6:38 like happiness and frustration, and social states, like friendship and trust.

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

6:46 and figure out how to best respond to that state,

6:48 in order to achieve the goals of the system.

6:50 This might be to calm the user down,

6:52 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

7:01 on hundreds of thousands of users’ feeds.

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

7:06 while others were presented with more negative content.

7:08 The researchers analyzed people's posts during that week,

7:10 and found that users who were shown more positive content,

7:13 tended to also post more positive content.

7:16 On the other hand, users who saw more negative content,

7:19 tended to have more negative posts.

7:21 Clearly, what Facebook and other services show

7:23 you can absolutely have an affect on 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

7:34 how computer programs should respond to human communication.

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

7:40 by responding in a cheery, upbeat manner.

7:43 Or, maybe the computer should attempt to evoke a positive response,

7:45 even if it’s a bit 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,

7:55 or CMC, another large field of research.

7:57 This includes synchronous communication– like video calls,

8:00 where all participants are online simultaneously–

8:02 as well as asynchronous communication– like tweets,

8:05 emails, and 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,

8:13 and language used in different communication channels.

8:15 One interesting finding is that people

8:17 exhibit higher levels of self-disclosure– that is,

8:19 reveal personal information– in computer-mediated conversations,

8:23 as opposed to face-to-face interactions.

8:25 So if you want to build a system that knows how

8:27 many hours a user truly spent watching The Great British Bakeoff,

8:30 it might be better to build a chatbot than a virtual agent with a face.

8:34 Psychology research has also demonstrated that eye

8:36 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

8:45 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,

8:53 looks into the camera,

8:55 and instead generally looks at the students who are physically present.

8:58 That’s ok for them, but it means people

9:00 who watch the lectures online have reduced engagement.

9:03 In response, researchers have developed computer vision and graphics

9:05 software that can warp the head and eyes,

9:08 making it appear as though the instructor is

9:09 looking into the camera– 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,

9:17 to correct for the placement of webcams,

9:19 which are almost always located above screens.

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

9:24 rather than directly into the webcam,

9:26 you’ll always appear to them as though you’re looking downwards– breaking

9:29 mutual gaze– which can create all kinds of unfortunate social side effects,

9:33 like a power imbalance.

9:35 Fortunately, this can be corrected digitally,

9:37 and appear to participants as though you’re lovingly gazing into their eyes.

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

9:45 especially if they move, like our Robots from last episode.

9:49 Beyond industrial uses that prevailed over the last century,

9:52 robots are used increasingly in medical, education, and entertainment settings,

9:56 where they frequently interact with humans.

9:58 Human-Robot Interaction– or HRI– is

10:01 a field dedicated to studying these interactions,

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

10:06 or how robots can interpret human social cues

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

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

10:14 as human-like in their appearance and interactions as possible.

10:17 When engineers first made robots in the 1940s and 50s,

10:20 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

10:28 robots– they gained heads and walked around on two legs,

10:32 but… they couldn’t exactly go to restaurants and masquerade as humans.

10:35 As people pushed closer and closer to human likeness,

10:38 replacing cameras with artificial eyeballs,

10:40 and covering metal chassis with synthetic flesh, things started to get a bit...

10:44 uncanny...

10:44 eliciting an eerie and unsettling feeling.

10:47 This dip in realism between almost-human

10:50 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,

10:59 people will treat them as though they know our social conventions.

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

11:05 in front of you or roll over your foot– people get really mad!

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

11:12 and have tremendous potential to affect our everyday lives.

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

11:19 but should your 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

11:26 you stay on 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,

11:34 but psychology can at least help us understand the effects

11:37 and implications of design choices in our computing systems.

11:40 But, on the positive side, understanding the psychology behind design

11:43 might lead to increased accessibility.

11:46 A greater number of people can understand and use

11:48 computers now that they're more intuitive than ever.

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

11:55 As robot technology continues to improve,

11:57 the population will grow more comfortable 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.