Decision from models: Generalizing probability information to novel tasks

Hang Zhang, Jacienta T. Paily, Laurence Maloney

Research output: Contribution to journalArticle

Abstract

We investigate a new type of decision under risk where-to succeed-participants must generalize their experience in one set of tasks to a novel set of tasks. We asked participants to trade distance for reward in a virtual minefield where each successive step incurred the same fixed probability of failure (referred to as hazard). With constant hazard, the probability of success (the survival function) decreases exponentially with path length. On each trial, participants chose between a shorter path with smaller reward and a longer (more dangerous) path with larger reward. They received feedback in 160 training trials: encountering a mine along their chosen path resulted in zero reward and successful completion of the path led to the reward associated with the path chosen. They then completed 600 no-feedback test trials with novel combinations of path length and rewards. To maximize expected gain, participants had to learn the correct exponential model in training and generalize it to the test conditions. We compared how participants discounted reward with increasing path length to the predictions of 9 choice models including the correct exponential model. The choices of a majority of the participants were best accounted for by a model of the correct exponential form although with marked overestimation of the hazard rate. The decision- from-models paradigm differs from experience-based decision paradigms such as decision-from-sampling in the importance assigned to generalizing experience-based information to novel tasks. The task itself is representative of everyday tasks involving repeated decisions in stochastically invariant environments.

Original languageEnglish (US)
Pages (from-to)39-53
Number of pages15
JournalDecision
Volume2
Issue number1
DOIs
StatePublished - Jan 1 2015

Fingerprint

Probability Model
Reward
Path Length
Path
Exponential Model
Hazard
Paradigm
Choice Models
Generalise
Hazard Rate
Survival Function
Probability model
Shortest path
Completion
Choose
Maximise
Decrease
Invariant
Prediction
Zero

Keywords

  • Constant hazard rate
  • Decision from experience
  • Decision under risk
  • Exponential survival function
  • Generalization

ASJC Scopus subject areas

  • Social Psychology
  • Neuropsychology and Physiological Psychology
  • Applied Psychology
  • Statistics, Probability and Uncertainty

Cite this

Decision from models : Generalizing probability information to novel tasks. / Zhang, Hang; Paily, Jacienta T.; Maloney, Laurence.

In: Decision, Vol. 2, No. 1, 01.01.2015, p. 39-53.

Research output: Contribution to journalArticle

Zhang, Hang ; Paily, Jacienta T. ; Maloney, Laurence. / Decision from models : Generalizing probability information to novel tasks. In: Decision. 2015 ; Vol. 2, No. 1. pp. 39-53.
@article{399cd2bd0879435f88c8b21b33e0d9a1,
title = "Decision from models: Generalizing probability information to novel tasks",
abstract = "We investigate a new type of decision under risk where-to succeed-participants must generalize their experience in one set of tasks to a novel set of tasks. We asked participants to trade distance for reward in a virtual minefield where each successive step incurred the same fixed probability of failure (referred to as hazard). With constant hazard, the probability of success (the survival function) decreases exponentially with path length. On each trial, participants chose between a shorter path with smaller reward and a longer (more dangerous) path with larger reward. They received feedback in 160 training trials: encountering a mine along their chosen path resulted in zero reward and successful completion of the path led to the reward associated with the path chosen. They then completed 600 no-feedback test trials with novel combinations of path length and rewards. To maximize expected gain, participants had to learn the correct exponential model in training and generalize it to the test conditions. We compared how participants discounted reward with increasing path length to the predictions of 9 choice models including the correct exponential model. The choices of a majority of the participants were best accounted for by a model of the correct exponential form although with marked overestimation of the hazard rate. The decision- from-models paradigm differs from experience-based decision paradigms such as decision-from-sampling in the importance assigned to generalizing experience-based information to novel tasks. The task itself is representative of everyday tasks involving repeated decisions in stochastically invariant environments.",
keywords = "Constant hazard rate, Decision from experience, Decision under risk, Exponential survival function, Generalization",
author = "Hang Zhang and Paily, {Jacienta T.} and Laurence Maloney",
year = "2015",
month = "1",
day = "1",
doi = "10.1037/dec0000022",
language = "English (US)",
volume = "2",
pages = "39--53",
journal = "Decision",
issn = "2325-9965",
publisher = "American Psychological Association Inc.",
number = "1",

}

TY - JOUR

T1 - Decision from models

T2 - Generalizing probability information to novel tasks

AU - Zhang, Hang

AU - Paily, Jacienta T.

AU - Maloney, Laurence

PY - 2015/1/1

Y1 - 2015/1/1

N2 - We investigate a new type of decision under risk where-to succeed-participants must generalize their experience in one set of tasks to a novel set of tasks. We asked participants to trade distance for reward in a virtual minefield where each successive step incurred the same fixed probability of failure (referred to as hazard). With constant hazard, the probability of success (the survival function) decreases exponentially with path length. On each trial, participants chose between a shorter path with smaller reward and a longer (more dangerous) path with larger reward. They received feedback in 160 training trials: encountering a mine along their chosen path resulted in zero reward and successful completion of the path led to the reward associated with the path chosen. They then completed 600 no-feedback test trials with novel combinations of path length and rewards. To maximize expected gain, participants had to learn the correct exponential model in training and generalize it to the test conditions. We compared how participants discounted reward with increasing path length to the predictions of 9 choice models including the correct exponential model. The choices of a majority of the participants were best accounted for by a model of the correct exponential form although with marked overestimation of the hazard rate. The decision- from-models paradigm differs from experience-based decision paradigms such as decision-from-sampling in the importance assigned to generalizing experience-based information to novel tasks. The task itself is representative of everyday tasks involving repeated decisions in stochastically invariant environments.

AB - We investigate a new type of decision under risk where-to succeed-participants must generalize their experience in one set of tasks to a novel set of tasks. We asked participants to trade distance for reward in a virtual minefield where each successive step incurred the same fixed probability of failure (referred to as hazard). With constant hazard, the probability of success (the survival function) decreases exponentially with path length. On each trial, participants chose between a shorter path with smaller reward and a longer (more dangerous) path with larger reward. They received feedback in 160 training trials: encountering a mine along their chosen path resulted in zero reward and successful completion of the path led to the reward associated with the path chosen. They then completed 600 no-feedback test trials with novel combinations of path length and rewards. To maximize expected gain, participants had to learn the correct exponential model in training and generalize it to the test conditions. We compared how participants discounted reward with increasing path length to the predictions of 9 choice models including the correct exponential model. The choices of a majority of the participants were best accounted for by a model of the correct exponential form although with marked overestimation of the hazard rate. The decision- from-models paradigm differs from experience-based decision paradigms such as decision-from-sampling in the importance assigned to generalizing experience-based information to novel tasks. The task itself is representative of everyday tasks involving repeated decisions in stochastically invariant environments.

KW - Constant hazard rate

KW - Decision from experience

KW - Decision under risk

KW - Exponential survival function

KW - Generalization

UR - http://www.scopus.com/inward/record.url?scp=84935487726&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84935487726&partnerID=8YFLogxK

U2 - 10.1037/dec0000022

DO - 10.1037/dec0000022

M3 - Article

AN - SCOPUS:84935487726

VL - 2

SP - 39

EP - 53

JO - Decision

JF - Decision

SN - 2325-9965

IS - 1

ER -