Lecture 5: Sequential Multitasking

Dario Salvucci, Department of Computer Science, Drexel University. Copyright © 2011 All Rights Reserved. 1

Lecture 5:Sequential Multitasking

Dario Salvucci, Department of Computer Science, Drexel University. Copyright © 2011 All Rights Reserved.

The Study of Multitasking

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Dual choice, PRPSimple tracking

Game playingDriver distraction

Task switchingBasic interruption

AviationHealth care

Doing tasks at the same time:

Concurrent MultitaskingDoing one task after another:

Sequential Multitasking


Concurrent à Sequential Multitasking

There are many types of interference that make concurrent multitasking more sequential...– procedural interference– perceptual/motor interference – (declarative, problem-state interference)

In these cases, interference hinders the task,but the thread still continues to make progress.

What if a thread can’t make any progress at all?– e.g., no longer in front of computer keyboard– e.g., visual resource unavailable for too (?) long

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You were ordering a platypus,but a friend invited you for coffee.

Of course, you say “yes.” You still want to order the

platypus, so you suspend this task.

Later, you get back to your computer. “Now what was I doing?”

“Ah right, ordering a platypus...” And you resume the task...

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Problem State

Levels of interference in different situations...

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Problem State

Problem state = temporary information required during task execution– roughly speaking, a task’s “mental context”– in ACT-R: stored in the imaginal buffer

Example: Solving 3+4– encode “3”, then “+”, then “4”– all this is now held in the problem state / imaginal buffer– in this case, used to pass along information for retrieval– can also be used to remember new information

• i.e., associate 3, +, 4, and then 7 with one another

Example: Tracking– no problem state needed!

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Problem State

Example: Writing a paper– what do you need to keep in mind as you’re writing a...

• sentence?• paragraph?• section?

– where do you maintain the least amount of information? Example: Tracking, or Driving

– no problem state needed!

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Problem State

Borst, Taatgen, & van Rijn (2010)– hard w/ problem state vs. easy w/o problem state– task #1: subtraction

• hard (borrowing) vs. easy (no borrow)

– task #2: typing• hard (remember letter) vs. easy (show letter)

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Problem State

Borst, Taatgen, & van Rijn (2010)– response time

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Problem State

Borst, Taatgen, & van Rijn (2010)– accuracy

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Memory & Interruptions

This last experiment doesn’t (explicitly) require rehearsal of memory information.

But thinking about problem state, memory clearly plays an important role in interruptions.

We’ll take a closer look at this relationship in a few minutes.

First, let’s look at the details of the ACT-R theory of memory — well, as much as we need to discuss multitasking in this context.

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What are the (at least two) important features of human memory?– information strengthens with use– information decays over time

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Theory of Memory


Chunk Activation

Each chunk has a base level of activation– base-level activation B

The connections between chunks have varying strengths– strengths of association S

Each chunk also has a current weight W > 0 if in current focus of attention — i.e., in the goal chunk– context C

Each chunk has both permanent and transient noise– (not shown in equation)


Base-Level Learning

B = initial base-level (defaults to 0) k = each “presentation” tk = time since kth presentation d = decay parameter for each presentation


Presentations

Chunk Creation– add-dm, perceptual buffer, +goal>

Retrieval and Harvesting– when a retrieval is used in =retrieval>

Chunk Merging– a newly created chunk that’s the same as an old chunk

is merged with old chunk


Retrieval Time

F is a parameter giving the time scale(called the latency factor)– defaults to 1.0

A is the activation of the retrieved chunk Retrieval Threshold τ

– A ≥ τ , or else retrieval fails (causes an error)

– generally defaults to 0.0


Assuming F = 1...– high A -> low RT– low A -> high RT– (negative A -> RT is so high that it’s below threshold)

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Retrieval Time

AFeTime −=


Retrieval Probability

A is the activation of the retrieved chunk τ is the retrieval threshold s is the noise parameter


Theory of Memory

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Retrievalevery 3seconds...


Memory for Goals

Memory for goals theory (Altmann & Trafton, 2002)

+ ACT-R memory theory (Anderson et al., 2004)

– to suspend a task, people encode (rehearse) the current goal until it’s readily available in memory• in ACT-R, each retrieval boosts a chunk’s activation,

making it easier to recall• e.g., rehearse “I’m ordering a platypus” a few times

– to resume the task, people simply recall the goal• in ACT-R, associated cues can facilitate recall• e.g., seeing computer, or browser on platypus web page

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Memory for goals as threads...

Encoding

Retrieval

Encoding Retrieval

Memory for Goals

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Primary task Secondary task Primary task

ProceduralRehearsegoal

Continuerehearsal

Declarativeretrieving

goal

Rehearsegoal

Stoprehearsal

ProceduralRetrieve

goalResume

with goal

Declarativeretrieving

goal

Primarytask...


Memory for Goals

What is the “goal”?– goal = lightweight tag for what you’re doing– problem state = information associated with goal

• e.g., what am I ordering? -- platypus;which one do I want? -- medium stuffed

Process model in ACT-R– at interruption, store away goal + problem state– encode goal “sufficiently” (other work addresses this)

• rehearse goal for ~ 2.5 seconds• encoding & secondary task interleaved as threads

– after interruption, retrieve goal + problem state

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Memory for Goals

How many retrievals/rehearsals is a “good” number for a typical interruption?

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Interruption Study

Monk, Trafton, & Boehm-Davis (2008)– explored effects of interruption duration & demand

on primary-task resumption– primary task: programming a VCR– interruption duration: 3, 8, or 13 seconds– interrupting task

• no-task: just wait• track: manual tracking task• n-back: compare current and previous letters (<,>)

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Interruption Study

Model– model for primary task

• full model not needed!• specified declarative chunk to represent the goal• estimated time parameter for performing first action

– models for interrupting tasks• no-task: trivially waits• track: adapted from previous work (Salvucci & Taatgen, 2008)

• n-back: simplified from previous work (Juvina & Taatgen, 2007)

– uses declarative resource to retrieve last item!

– model for interruption process described earlier– but when exactly should encoding occur?

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Interruption Study

Encoding strategies– S1: Encode during the entire interruption– S2: Encode for n seconds, concurrently with secondary

task– S3: Encode until retrieval takes no more than n seconds,

concurrently with the secondary task– S4: Encode for n seconds prior to the interruption,

ending at the onset of the interruption– S5: Encode for a few (3) retrievals prior to the

interruption, ending at the onset of the interruption

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Interruption Study

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Monk et al. (2008)


Interruption Study

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Monk et al. (2008)Model – S1

(rehearse entire interruption)

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no effect of duration


Interruption Study

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duration effect too large;no n-back interaction


Interruption Study

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(rehearse for n sec before interrupt)

no n-back interaction

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Interruption Study

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(rehearse until retrieval < n sec)

strange n-back interaction(interference forces

too much encoding!)

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Interruption Study

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(rehearse for n sec after interrupt)

Yes!n-back interaction due todeclarative interference

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Interruption Study

ACT-R model for S2– interleaved with tracking

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Interruption Study

ACT-R model for S2– interleaved with the N-back task

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Interruption Study

Tracking error– data: slight effect for 3-sec interruption across three

experiments (albeit not conclusive)– model: S2 & S3 show this effect due to encoding

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Model



Summary: The threading in threaded cognition, when combined with a theory of memory,gives an account of suspension & resumptionfor task interruption.

How does this apply to realistic (non-lab) tasks? Next lecture...

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Lecture 5: Sequential Multitasking

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