<p>I am interested in the question of <em>how</em> people use/integrate previous experiences with instances of tasks or events to make predictions about the duration of future instances of tasks/events.</p>

<p>To illustrate:
I am a regular user of Google Calendar, and I use its drag-and-stretch time-interval interface. In doing so, I constantly <em>seem</em> to be making judgments such as "Event A has properties [a1,...,aN], ####.... it will probably last about two hours".
My question, then, is: what is happening during the "####" stage of this process?</p>

<p><em>*</em>*EDIT: To avoid confusion, I would like to make clear that I am interested in cases where an agent is facing an "instance" of a "type" of event (if you imagine that the agent has a set of "memory traces" related to instances of the event, then I am referring to a case in which there is a reasonably high amount of variance/uncertainty in duration across previous instances), that agent has certain knowledge/evidence, and the agent must make a guess and/or a decision based upon a guess.
I've used the calendar planning example as an illustration. However, I am asking this question more broadly, as it relates to time scales ranging from very small to very large.</p>

<hr>

<p>This question can be addressed at many levels of analysis (in Marr's sense). I am familiar with research related to this question, such as Griffiths and Tenenbaum's "Optimal Predictions in Everyday Cognition", Warren Meck's "Neuropharmacology of Timing and Time Perception", and empirical work on human planning behaviors/suboptimalities.</p>

<p>Can you recommend any further readings on this topic? I am particularly interested in research that treats this problem as an inductive reasoning task and investigates the question through the use of computational models.</p>

<p><strong>UPDATE:</strong> I have just begun finding some very relevant articles, such as these:</p>

<p>1) <a href="http://www.ncbi.nlm.nih.gov/pubmed/15716368" rel="nofollow">http://www.ncbi.nlm.nih.gov/pubmed/15716368</a> (link to abstract)</p>

<p>2) <a href="http://www.shadlen.org/pmwiki/uploads/Science/JazayeriNat%20Neurosci2010.pdf" rel="nofollow">http://www.shadlen.org/pmwiki/uploads/Science/JazayeriNat%20Neurosci2010.pdf</a></p>

<hr>

<blockquote>
  <p>Griffiths, T. L., &amp; Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767-773. <a href="http://www.dc.uba.ar/materias/incc/practicas/p1/Griffiths-Tenenbaum-PsychSci06.pdf" rel="nofollow">PDF</a></p>
  
  <p>Meck, W. H. (1996). Neuropharmacology of timing and time perception. Cognitive Brain Research, 3(3), 227-242. <a href="http://homepage.psy.utexas.edu/homePage/Class/Psy355/Gilden/meck.pdf" rel="nofollow">PDF</a></p>
</blockquote>

CC BY-SA 3.0
2013-04-08T17:13:24.147Z
Hi **what**, I didn't intend to limit the question's scope to the example of putting entries into a calendar. Addressing your question, though: it's possible that I am a bit peculiar in the way I use calendars... I tend to schedule every little event and continually modify my estimates of duration. You've presented one case (a scheduled class) in which there is essentially no uncertainty about duration. Your second case might not be the best example either for multiple reasons (e.g. you have control over the duration, and you've suggested an upper bound on duration rather than a prediction).
Mynah
CC BY-SA 3.0
2013-04-08T17:27:18.570Z
Examples at smaller temporal scales: 1) You are at a crosswalk. A car is coming your way at a certain speed S1. The "Do Not Walk" sign has been flashing for a certain amount of time T1. You must decide: "is it too late to run across the street, or can I still make it?".  2) Each morning, you get a coffee from a coffee shop. Getting your coffee never takes the same amount of time. In fact, you live in a tourist-y city, and there is a great deal of variance in waiting time because the length of the line varies. Before you enter the coffee shop, make your best guess about how long it will take.
Mynah
CC BY-SA 3.0
2013-04-08T18:41:26.813Z
**what**, you wrote: "...If you really want to do your best (because you need to catch that train) there is probably some kind of conscious logic/probabilistic reasoning going on, like "thumbnailing" the mean of remembered lengths, correcting this for what you can see (how many people are on the street today, so is the line going to be long) etc." <- Thanks for your comments relating to a two-process perspective. I would like to focus on the question of "What kind of probabilistic reasoning." I'm not sure why you specify that this reasoning would necessarily be "conscious" [continued]
Mynah
CC BY-SA 3.0
2013-04-08T18:46:28.773Z
[continued] as I would assume that much if not most of it would be unconscious. To further clarify my question: your example of "logic/probabilistic reasoning" involved some process of ""thumbnailing" the mean of remembered lengths.... correcting this". One question is: "if this is true, how does it work?". However, I remember once reading about a model that suggested people store in memory a finite set of previous experiences of durations of a type of event, and each time they need to make an estimate, one of these durations is randomly sampled (not averaged) from this finite set.
Mynah
CC BY-SA 3.0
2013-04-08T22:43:11.663Z
**what** I'm hoping to give a better, cleaner, simpler example of one case (which does not cover the scope of my question). Imagine a person playing a special game of Whack-A-Mole. The moles in this game do NOT come out of their holes at regular intervals (distribution over time-spacings is not uniform) but there are some statistical (not temporal-order dependent) regularities. Imagine that we are researchers measuring when the person's muscles begin to tighten in anticipation. An interesting question might be: does this person's average performance improve with experience? How? <- Decompose.
Mynah
<cognitive-modeling><computational-modeling><time>
Predicting the Duration of Future Events
Mynah
# Predicting the Duration of Future Events

(Asked by: username_0 on 2013-04-08)

I am interested in the question of *how* people use/integrate previous experiences with instances of tasks or events to make predictions about the duration of future instances of tasks/events.

To illustrate:
I am a regular user of Google Calendar, and I use its drag-and-stretch time-interval interface. In doing so, I constantly *seem* to be making judgments such as "Event A has properties [a1,...,aN], ####.... it will probably last about two hours".
My question, then, is: what is happening during the "####" stage of this process?

*\**\*EDIT: To avoid confusion, I would like to make clear that I am interested in cases where an agent is facing an "instance" of a "type" of event (if you imagine that the agent has a set of "memory traces" related to instances of the event, then I am referring to a case in which there is a reasonably high amount of variance/uncertainty in duration across previous instances), that agent has certain knowledge/evidence, and the agent must make a guess and/or a decision based upon a guess.
I've used the calendar planning example as an illustration. However, I am asking this question more broadly, as it relates to time scales ranging from very small to very large.

---

This question can be addressed at many levels of analysis (in Marr's sense). I am familiar with research related to this question, such as Griffiths and Tenenbaum's "Optimal Predictions in Everyday Cognition", Warren Meck's "Neuropharmacology of Timing and Time Perception", and empirical work on human planning behaviors/suboptimalities.

Can you recommend any further readings on this topic? I am particularly interested in research that treats this problem as an inductive reasoning task and investigates the question through the use of computational models.

**UPDATE:** I have just begun finding some very relevant articles, such as these:

1) <http://www.ncbi.nlm.nih.gov/pubmed/15716368> (link to abstract)

2) <http://www.shadlen.org/pmwiki/uploads/Science/JazayeriNat%20Neurosci2010.pdf>

---

> Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767-773. [PDF](http://www.dc.uba.ar/materias/incc/practicas/p1/Griffiths-Tenenbaum-PsychSci06.pdf)
>
> Meck, W. H. (1996). Neuropharmacology of timing and time perception. Cognitive Brain Research, 3(3), 227-242. [PDF](http://homepage.psy.utexas.edu/homePage/Class/Psy355/Gilden/meck.pdf)

**Tags:** cognitive-modeling, computational-modeling, time

> **Comments:**
> **username_0**: Hi **what**, I didn't intend to limit the question's scope to the example of putting entries into a calendar. Addressing your question, though: it's possible that I am a bit peculiar in the way I use calendars... I tend to schedule every little event and continually modify my estimates of duration. You've presented one case (a scheduled class) in which there is essentially no uncertainty about duration. Your second case might not be the best example either for multiple reasons (e.g. you have control over the duration, and you've suggested an upper bound on duration rather than a prediction).  
> **username_0**: Examples at smaller temporal scales: 1) You are at a crosswalk. A car is coming your way at a certain speed S1. The "Do Not Walk" sign has been flashing for a certain amount of time T1. You must decide: "is it too late to run across the street, or can I still make it?".  2) Each morning, you get a coffee from a coffee shop. Getting your coffee never takes the same amount of time. In fact, you live in a tourist-y city, and there is a great deal of variance in waiting time because the length of the line varies. Before you enter the coffee shop, make your best guess about how long it will take.  
> **username_0**: **what**, you wrote: "...If you really want to do your best (because you need to catch that train) there is probably some kind of conscious logic/probabilistic reasoning going on, like "thumbnailing" the mean of remembered lengths, correcting this for what you can see (how many people are on the street today, so is the line going to be long) etc." <- Thanks for your comments relating to a two-process perspective. I would like to focus on the question of "What kind of probabilistic reasoning." I'm not sure why you specify that this reasoning would necessarily be "conscious" [continued]  
> **username_0**: [continued] as I would assume that much if not most of it would be unconscious. To further clarify my question: your example of "logic/probabilistic reasoning" involved some process of ""thumbnailing" the mean of remembered lengths.... correcting this". One question is: "if this is true, how does it work?". However, I remember once reading about a model that suggested people store in memory a finite set of previous experiences of durations of a type of event, and each time they need to make an estimate, one of these durations is randomly sampled (not averaged) from this finite set.  
> **username_0**: **what** I'm hoping to give a better, cleaner, simpler example of one case (which does not cover the scope of my question). Imagine a person playing a special game of Whack-A-Mole. The moles in this game do NOT come out of their holes at regular intervals (distribution over time-spacings is not uniform) but there are some statistical (not temporal-order dependent) regularities. Imagine that we are researchers measuring when the person's muscles begin to tighten in anticipation. An interesting question might be: does this person's average performance improve with experience? How? <- Decompose.  

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## 0 answers

