PSY
Maria Castro 
CogNotes3.pptxAttention
Langston, PSY 4040
Cognitive Psychology
Notes 3
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Some examples
http://baddesigns.com/shampoo.html
http://baddesigns.com/insulin.html
http://baddesigns.com/tlight1.html
What do these have in common?
You’re driving in a strange neighborhood looking for "Long" street. You accidentally turn on "Lone.”
You’re thinking about a quiz that’s coming up in your next class as you walk there. Someone calls your name, but you don’t hear them.
You arrive late at a party and try to find your friends.
You’re driving home and want to stop at the store. Suddenly you find yourself at home and you didn’t stop.
You’re trying to think about the research paper you’re working on, but you keep thinking of the great first date you had last night.
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What do these have in common?
Detection.
http://baddesigns.com/streetsn.html
Filtering and selection.
Search.
http://baddesigns.com/pushto.html
Automatic processing.
Concentration.
The common element is attention.
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Architecture
Recall our box model:
Sensory
Store
LTM
STM
Filter
Pattern
Recognition
Selection
Input
(Environment)
Response
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Attention
In this model, attention is:
The filter and selection boxes (note that there’s a bit of a cheat built in when you separate them).
The arrows.
In this model attention does:
Putting together information from various sources.
Processing in STM (sort of).
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Themes
Early or late? The answer to this question influences a lot of other cognitive stuff.
What is it?
Some sort of bottleneck or filter?
A capacity or resource (or several kinds)?
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Attention
Highlights parts of the environment and blocks other parts.
Primes a person for speedy reaction.
Helps you retain information.
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Attention
As you can see from my attempts to define it, attention is usually defined as what it does. As a result, we’re going to study it as five kinds of thing.
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Attention
1. Detection
2. Filtering
3. Search
4. Automatic Processing
5. Concentration
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Detection
Two kinds of thresholds:
Absolute: Minimum amount of stimulation required for detection.
Difference: Amount of change necessary for two stimuli to be perceived as different.
Basically, we can find the absolute threshold and then a series of difference thresholds to work out the scale for a given physical dimension.
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Detection
Thresholds:
Vision: One candle, on a mountain, perfectly dark, 30 miles.
Hearing: A watch ticking 20 feet away.
Smell: A single drop of perfume in a three room apartment.
Touch: The wing of a bee on your cheek.
Taste: One teaspoon of sugar in two gallons of water.
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Determining Thresholds
How to determine thresholds:
Method of limits:
Ascending: Start with a value below the threshold, increase, ask for detection, increase… At the point a person says “detect,” average that stimulus value with the value from the previous trial. Repeat to estimate threshold.
Descending: Same, but start above threshold and work down.
Combining results from both directions will give you an estimate of the threshold.
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Determining Thresholds
How to determine thresholds:
Encyclopedia of Optical Engineering (2003, p. 2183)
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Determining Thresholds
How to determine thresholds:
Method of constant stimuli:
Present a series of randomly selected stimulus values, ask for yes/no response for each. The value that’s detected 50% of the time is the threshold.
CogLab for Muller-Lyer Illusion: Which method was it?
These methods can be adapted to determine difference thresholds.
Problem: Observer biases can contaminate the results.
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Determining Thresholds
We want thresholds to work like a step function, but they don’t:
Encyclopedia of Optical Engineering (2003, p. 2177)
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Signal Detection
Can estimate detection (sensitivity) independent of bias.
Two kinds of trials:
Noise alone: Background noise only.
Signal + noise: Background noise with signal.
Two responses from observer:
Detect.
Don’t detect.
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Signal Detection: Four Situations
| State of the world | ||
| Response | Signal | Noise |
| Yes (Present) | Hit | False Alarm |
| No (Absent) | Miss | Correct Rejection |
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Signal Detection: Four Situations
From http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
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Signal Detection: Four Situations
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Criterion
Yes »
« No
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Signal Detection: Four Situations
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Criterion
Hit
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Signal Detection: Four Situations
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Criterion
Miss
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Signal Detection: Four Situations
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Criterion
False Alarm
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Signal Detection: Four Situations
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Criterion
Correct Rejection
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Signal Detection: Sensitivity and Bias
We can estimate two parameters from performance in this task:
Sensitivity: Ability to detect.
Good sensitivity = High hit rate + low false alarm rate.
Poor sensitivity = About the same hit and false alarm rates.
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Signal Detection: Sensitivity and Bias
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Sensitivity
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Signal Detection: Sensitivity and Bias
From http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
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Signal Detection: Sensitivity and Bias
We can estimate two parameters from performance in this task:
Response bias: Willingness to say you detect.
Can be liberal (too willing) or conservative (not willing enough).
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Signal Detection: Sensitivity and Bias
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Liberal Criterion
Hit
False Alarm
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Signal Detection: Sensitivity and Bias
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Conservative Criterion
Hit
False Alarm
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Signal Detection: Sensitivity and Bias
Computing bias:
The criterion is the point above which a person says “detect.” It can be unbiased (the point where the distributions cross; 1.0), liberally biased (< 1.0), or conservatively biased (> 1.0).
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Signal Detection: Sensitivity and Bias
Since sensitivity and bias are independent, you can measure the effect of different biases on responding to a particular value for detectability.
Influences on bias:
Instructions (only say “yes” if you’re absolutely sure).
Payoffs (big reward for hits, no penalty for false alarms).
Probability of signal (higher probability leads to more liberal bias).
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Signal Detection
We did a CogLab for signal detection, let’s check that now…
(Note my challenge choosing “signal detection” when there’s also one called “simple detection.”)
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Applications
Radiology examples: The following examples are all from Mullan (2003). More information is available at http://www.ceessentials.net/article17.html
Find the appendicitis…
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Applications
Spam detection:
Hey, what’s up? I’ve been just taking it easy the last few weeks. Work, etc. I am trying to plan a trip over the holidays. Juggling the dates can be tricky I am finding. I’m sure I’ll figure out something.
I had to laugh a few weeks ago. My buddy Jeff sent me a website of some stuff he picked up. Told me to try it out. Thought it was a joke. I ordered some for the hell of it. I know it’s sort of funny but I figured with the kind of person you are you might find it interesting. I actually tried it and holy crap, the stuff seemed to work awesome. Like twice a day I have energy to-do-it and the "outcome" is pretty huge. I still can’t stop laughing at what I am seeing. Anyways, check it out for fun, seems to be working for me.
By the way, let me know if you are going anywhere over the holidays as well. O ya, may get a new SUV as well. Maybe Navigator or Commander. Take care.
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Applications
Spam detection:
Forwarding Message Service by FMS
From: Mark
Subject: Dieting
This is not meant to be an insult or anything but people are talking at work about your weight. I thought you should know. I know it would upset you if you knew but I know some friends here and outside work that have used a program that worked within weeks. I am not pushing anything on you but thought it wouldn’t hurt if you looked at it. I also think I am doing you a favor as it’s always nice when people talk about how much better you look than how much you’ve been putting on. I hope I am not intruding, just trying to help out. My cousin & friend Mike used this and it helped a lot. Here is the site I know they got it from direct.
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Applications
Signal detection is a problem in a variety of contexts:
Mammography.
Baggage screeners.
Van Wert, Horowitz, & Wolfe (2009, p. 543)
Applications
When targets are rare, what effect does that have on performance?
Mammography 0.3% target prevalence (Wolfe, Horowitz, & Kenner, 2005).
Baggage screening: Low (not typically published).
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Applications
Wolfe, Horowitz, & Kenner (2005): Rare targets frequently missed in search tasks.
Blue bars rare (1% 20/2000),
Yellow bars low (10%),
Red bars common (50%), x-axis = size of search set
Wolfe, Horowitz, & Kenner (2005, p. 439)
Applications
The problem is criterion shift:
When targets are rare, users become increasingly conservative, increasing the number of miss errors.
Applications
Modified from http://www.cns.nyu.edu/~david/handouts/sdt/sdt.html (Heeger, 2007)
Noise
Signal + Noise
Conservative Criterion
Hit
Miss
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Applications
Corrections to improve performance (Wolfe, Horowitz, Van Wert, Kenner, Place, & Kibbi, 2007):
Rest: No. Not strictly a vigilance issue.
Changing the payoff matrix: Not likely. Given the magnitude of the effect, it probably can’t get big enough.
Being able to change your answer doesn’t help (Van Wert, Horowitz, & Wolfe, 2009).
Applications
Wolfe et al. (2007): Efforts to reduce the effect.
Two observers doesn’t help.
Working independently, their misses are highly correlated. This suggests that it isn’t carelessness (sensitivity stays high), and lapses of attention or motor errors can’t account for them being correlated.
Working together they do a little better (still a large prevalence effect and response time more than doubled).
Applications
Wolfe et al. (2007): Efforts to reduce the effect.
Making them respond more slowly doesn’t help. After forcing slower responses, no change in sensitivity (still high) or criterion.
Applications
Wolfe et al. (2007): Efforts to reduce the effect.
More yes responses helps some, but is fraught with difficulty.
Adding in a high prevalence target to search for along with a low prevalence target improves performance, but “satisfaction of search” problem. If there are two, one might be missed. Also methodological issues.
Applications
Wolfe et al. (2007): Efforts to reduce the effect.
More yes responses helps some, but is fraught with difficulty.
Having multiple targets with multiple frequencies from different categories returns them to previous performance even though they say “yes” 50% of the time. Closer to reality.
Applications
Wolfe et al. (2007):
Bursts of higher prevalence with feedback does seem to help. Suggests a regular “refreshment” procedure to move the criterion back.
Connection
Why do you turn on “Long” when you’re looking for “Lone?”
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Attention
1. Detection
2. Filtering
3. Search
4. Automatic Processing
5. Concentration
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Filtering
How do we choose what to attend to? Is the choice made early or late?
We’ll look at several versions of filter models and some of the evidence.
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Filtering
Early: Broadbent (1957). Place the filter between sensory store and pattern recognition. The selection is made on the basis of a crude physical analysis.
Sensory
Store
Filter
Pattern
Recognition
Input
Broadbent (1957, p. 206)
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Filtering
The filter is a flap. It normally sits in a neutral position. If you drop a ball in, it moves the filter and drops out as a response. If something comes in each side, it will either be a competition or jam.
The two arms of the ‘Y’ could be two ears, an ear and an eye, … (Broadbent, 1957, p. 206).
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Filtering
Why is this reasonable? (Broadbent, 1957):
Two things put in simultaneously can jam it, as happens in people. Instructions can bias the flap to one side and allow something in, as in people.
When two things compete, the first or the stronger can get an advantage, as in people.
There is a switching cost for going to different arms of the ‘Y.’
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Filtering
Evidence for early filtering:
Filter flapping: Two sets of numbers come in, one set in each ear.
Report by ear: Easy.
Report in order: Hard.
The argument is that the filter lets in all of one channel, then the other, no problem. To switch back and forth takes a lot of effort.
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Filtering
Evidence for early filtering:
Dichotic listening. Two messages, one to each ear, played simultaneously.
Shadowing: Repeat out loud everything in one ear. What do people (or what don’t people) notice in the unattended ear?
Miss change of speaker.
Miss change of language.
Miss change of direction.
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Filtering
Problem for early models:
People detect their name on the unattended channel (cocktail party phenomenon).
Treisman (1960): If a shadowed story switches ears, people follow it and then correct. They have to be attending to meaning to follow the story.
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Filtering
Problem for early models:
Example 1:
…I SAW THE GIRL/song was WISHING…
…me that bird/JUMPING in the street…
Example 2:
…AT A MAHOGANY/three POSSIBILITIES…
…look at these/TABLE with her head…
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Filtering
Attenuation model:
Everything in memory is active at some resting level. Some stuff that’s important has a high resting level, making it easier to respond to (e.g., your name).
Other stuff has a lower resting level, making it harder to respond to (e.g., artichoke).
As you think about something, you raise its resting level.
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Filtering
Attenuation model:
Memory
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Filtering
Attenuation model:
High resting threshold
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Filtering
Attenuation model:
High resting threshold
Your name
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Filtering
Attenuation model:
Low resting threshold
Memory
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Filtering
Attenuation model:
Low resting threshold
Artichoke
Memory
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Filtering
Attenuation model:
High resting threshold
Low resting threshold
Artichoke
Your name
Memory
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Filtering
Attenuation model:
High resting threshold
Low resting threshold
Artichoke
Your name
Relatively small amount of information to reach it
Memory
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Filtering
Attenuation model:
High resting threshold
Low resting threshold
Artichoke
Your name
Relatively large amount of information to reach it
Memory
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Filtering
Attenuation model:
High resting threshold
Low resting threshold
Artichoke
Your name
Relatively small amount of information to reach it
Relatively large amount of information to reach it
Memory
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Filtering
Attenuation model:
The unshadowed ear is attenuated (the volume is low). This little bit of attention can reach something with a high resting level (your name, a story you’re shadowing), but not some random bit of information.
So, no filter, just attenuation.
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Filtering
Capacity model:
You have a certain amount of attention, you can spread it around as needed. If you spend a lot on one task, then you have less for others.
Primary task: Do well on this no matter what (main focus of resources).
Secondary task: Also do this.
By manipulating the difficulty of the primary task and measuring the secondary task, we can see how attention allocation affects performance.
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Filtering
Capacity model:
For example, Johnston and Heinz (1978) had two tasks:
Primary: Shadow one ear. This can be based on gender or category.
Secondary: Detect a light.
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Filtering
| Primary | Secondary | |
| Shadow one list (control) | 1.4% error | 310 ms |
| Easy (gender) | 5.3% error | 370 ms |
| Hard (category) | 20.5% error | 482 ms |
Capacity model: Johnston and Heinz (1978)
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Filtering
Capacity model:
What this implies is that the filter can be early (gender) or late (category), the amount of your resources that you allocate to it determines where the filter is.
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Filtering
Note that this section has mostly been about selective attention: choosing between two things.
You can do it, and the thing you’re not attending to suffers as a consequence.
What about dividing attention?
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Filtering
Neisser and Becklen (1975) superimposed two games and had people monitor one, the other, or both.
For one game, people weren’t so bad (selective attention).
For monitoring both, errors went up a bunch (divided attention).
I have some videos on the main cognitive page to illustrate this.
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Filtering
Simons and Chablis (1999):
Watch one of two teams passing a ball
Easy: Count passes
Hard: Count air passes and bounce passes separately
An unexpected event happens, do they notice?
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Filtering
Simons and Chablis (1999):
Simons & Chablis (1999, p. 1067)
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Filtering
Simons and Chablis (1999):
Simons & Chablis (1999, p. 1068)
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Filtering
Simons and Chablis (1999):
Hard was harder
The umbrella woman was easier (looked more like the players they were already looking for?)
The gorilla was easier when monitoring the black team (matched the features they were looking for?)
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Application
Why is it bad to talk on the phone and drive? (It’s about the same as driving drunk, Strayer, Drews, & Crouch, 2006; hands-free or not.)
Kunar, Carter, Cohen, & Horowitz (2008; doi: 10.3758/PBR.15.6.1135): Central attention bottleneck.
Experiment 1: Talking vs. narrative.
Sustained attention task while talking or while listening.
Talking hurts performance more.
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Application
Experiment 1: Talking vs. narrative.
Kunar, Carter, Cohen, & Horowitz (2008, p. 1137)
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Application
Experiment 2: Talking vs. shadowing vs. generating.
Sustained attention task while talking, or while repeating, or while generating words (from the last letter).
Talking and generating hurt performance more.
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Application
Experiment 2: Talking vs. shadowing vs. generating.
Kunar, Carter, Cohen, & Horowitz (2008, p. 1138)
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Application
Questions:
Why doesn’t hands-free work?
What about listening to the radio?
Why can I talk to someone who is in the car with me?
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Application
Dunning-Kruger effect (Kruger & Dunning, 1999):
Sanbonmatsu, Strayer, Biondi, Behrends, & Moore (2015)
Control drivers: Correlation between serious driving errors and rated driving performance r(49) = -.37 (more errors, lower ratings)
Cell phone drivers: r(49) = .25 (more errors, higher ratings!)
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Application
Multitasking?
The evidence is that it’s not a thing (it’s task-switching instead; Srna, Schrift, & Zauberman, 2018)
But, what if you think you’re multitasking vs. not, even if you’re doing the same two tasks?
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Application
Multitasking?
Srna, Schrift, & Zauberman, 2018:
Do two tasks (e.g., learn and transcribe)
Describe it as multitasking or one task
People do better when they think it’s multitasking
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Application
Multitasking?
Srna, Schrift, & Zauberman (2018, p. 1950)
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Connection
You’re walking to class and thinking about a quiz that’s coming up. Someone calls your name, but you don’t hear them.
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Attention
1. Detection
2. Filtering
3. Search
4. Automatic Processing
5. Concentration
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Search
How do you use attention to locate items in a complicated array? Two kinds of search:
Feature search: A single feature allows you to find the item you are searching for.
Find the blue S.
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Search
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Search
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Search
How do you use attention to locate items in a complicated array? Two kinds of search:
Conjunction search: You have to combine features to find the item you are searching for. This should take attention and be more difficult (Treisman, 1988).
Find the green T.
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Search
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Search
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Properties of searches:
Feature searches:
Don’t require attention (pop-out).
No help from location cueing (don’t need it).
Conjunction searches:
Require attention.
Affected by the number of distracters.
Helped by cueing the location.
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Search
CogLab for change detection: It’s a more advanced version of search. What affects detection?
Could something like the flicker effect occur when you’re driving?
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Connection
How about this: http://www.psych.ubc.ca/~heine/MMMSwitch.wmv
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Connection
How does this affect finding your friends at a party?
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Attention
1. Detection
2. Filtering
3. Search
4. Automatic Processing
5. Concentration
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Automatic Processing
After practice, some tasks no longer require attention. Three criteria for automatic tasks:
Occur without intention.
No conscious awareness/Can’t be introspected.
Don’t interfere with other activities.
Fast.
You can tell how the process of automatization is going by doing dual task studies (primary and secondary).
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Automatic Processing
Try this: Count the ’F’s in this passage…
FINISHED FILES ARE THE RESULT OF YEARS OF SCIENTIFIC STUDY COMBINED WITH THE EXPERIENCE OF MANY YEARS.
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Automatic Processing
Shiffrin and Schneider (1977): After 2100 trials with consistent practice (always look for a t, r, or l in a background of d, s, or p), switch (reverse target and distracter sets). It takes 900 trials to get back to original performance, and 1,500 trials to get as good as before.
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Shiffrin & Schneider (1977, p. 132)
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Automatic Processing
Automaticity can be a great benefit because it reduces resource demands and allows an operator to do multiple things at the same time.
However, since it’s unconscious, it can be a burden to overcome when the task changes or you need to add a step.
The Stroop task illustrates both of these.
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Automatic Processing
CogLab results for Stroop Effect.
The interpretation is that you automatically read the word. If that’s the task, the color doesn’t interfere because you don’t automatically register that. If you’re supposed to name the color, automatic reading messes you up.
Try the digit Stroop as another example…
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Connection
How does this relate to the problem of driving home and not stopping at the store?
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Attention
1. Detection
2. Filtering
3. Search
4. Automatic Processing
5. Concentration
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Concentration
Our last topic has to do with the task of “paying attention.”
Sometimes you have to concentrate on something in which you have no interest.
Sometimes you have to not think about something in which you have an interest.
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Concentration
Wegner, Schneider, Carter, and White (1987).
Try not to think of a white bear.
Five minutes, measure the number of times people do it.
Or, try to think of it.
Both are hard, with less activity later on.
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Wegner, Schneider, Carter, & White (1987, p. 8)
Concentration
Wegner, Schneider, Carter, and White (1987).
After suppression, it’s easier to keep thinking about a white bear.
After expression, it’s still hard not to think of a white bear at first, but people adapt.
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Connection
How could this relate to your thinking about your big date?
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End of Attention Show
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