Fechner's law in metacognition: A quantitative model of visual working memory confidence

Ronald van den Berg, Aspen H. Yoo, Wei Ji Ma

Research output: Contribution to journalArticle

Abstract

Although visual working memory (VWM) has been studied extensively, it is unknown how people form confidence judgments about their memories. Peirce (1878) speculated that Fechner's law-which states that sensation is proportional to the logarithm of stimulus intensity-might apply to confidence reports. Based on this idea, we hypothesize that humans map the precision of their VWM contents to a confidence rating through Fechner's law. We incorporate this hypothesis into the best available model of VWM encoding and fit it to data from a delayed-estimation experiment. The model provides an excellent account of human confidence rating distributions as well as the relation between performance and confidence. Moreover, the best-fitting mapping in a model with a highly flexible mapping closely resembles the logarithmic mapping, suggesting that no alternative mapping exists that accounts better for the data than Fechner's law. We propose a neural implementation of the model and find that this model also fits the behavioral data well. Furthermore, we find that jointly fitting memory errors and confidence ratings boosts the power to distinguish previously proposed VWM encoding models by a factor of 5.99 compared to fitting only memory errors. Finally, we show that Fechner's law also accounts for metacognitive judgments in a word recognition memory task, which is a first indication that it may be a general law in metacognition. Our work presents the first model to jointly account for errors and confidence ratings in VWM and could lay the groundwork for understanding the computational mechanisms of metacognition.

Original languageEnglish (US)
Pages (from-to)197-214
Number of pages18
JournalPsychological Review
Volume124
Issue number2
DOIs
StatePublished - Mar 1 2017

Fingerprint

Short-Term Memory
Metacognition

Keywords

  • Confidence
  • Fechner's law
  • Metacognition
  • Recognition memory
  • Working memory

ASJC Scopus subject areas

  • Psychology(all)

Cite this

Fechner's law in metacognition : A quantitative model of visual working memory confidence. / van den Berg, Ronald; Yoo, Aspen H.; Ma, Wei Ji.

In: Psychological Review, Vol. 124, No. 2, 01.03.2017, p. 197-214.

Research output: Contribution to journalArticle

van den Berg, Ronald ; Yoo, Aspen H. ; Ma, Wei Ji. / Fechner's law in metacognition : A quantitative model of visual working memory confidence. In: Psychological Review. 2017 ; Vol. 124, No. 2. pp. 197-214.
@article{25e361451eb9450c9c1e33f1223765ae,
title = "Fechner's law in metacognition: A quantitative model of visual working memory confidence",
abstract = "Although visual working memory (VWM) has been studied extensively, it is unknown how people form confidence judgments about their memories. Peirce (1878) speculated that Fechner's law-which states that sensation is proportional to the logarithm of stimulus intensity-might apply to confidence reports. Based on this idea, we hypothesize that humans map the precision of their VWM contents to a confidence rating through Fechner's law. We incorporate this hypothesis into the best available model of VWM encoding and fit it to data from a delayed-estimation experiment. The model provides an excellent account of human confidence rating distributions as well as the relation between performance and confidence. Moreover, the best-fitting mapping in a model with a highly flexible mapping closely resembles the logarithmic mapping, suggesting that no alternative mapping exists that accounts better for the data than Fechner's law. We propose a neural implementation of the model and find that this model also fits the behavioral data well. Furthermore, we find that jointly fitting memory errors and confidence ratings boosts the power to distinguish previously proposed VWM encoding models by a factor of 5.99 compared to fitting only memory errors. Finally, we show that Fechner's law also accounts for metacognitive judgments in a word recognition memory task, which is a first indication that it may be a general law in metacognition. Our work presents the first model to jointly account for errors and confidence ratings in VWM and could lay the groundwork for understanding the computational mechanisms of metacognition.",
keywords = "Confidence, Fechner's law, Metacognition, Recognition memory, Working memory",
author = "{van den Berg}, Ronald and Yoo, {Aspen H.} and Ma, {Wei Ji}",
year = "2017",
month = "3",
day = "1",
doi = "10.1037/rev0000060",
language = "English (US)",
volume = "124",
pages = "197--214",
journal = "Psychological Review",
issn = "0033-295X",
publisher = "American Psychological Association Inc.",
number = "2",

}

TY - JOUR

T1 - Fechner's law in metacognition

T2 - A quantitative model of visual working memory confidence

AU - van den Berg, Ronald

AU - Yoo, Aspen H.

AU - Ma, Wei Ji

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Although visual working memory (VWM) has been studied extensively, it is unknown how people form confidence judgments about their memories. Peirce (1878) speculated that Fechner's law-which states that sensation is proportional to the logarithm of stimulus intensity-might apply to confidence reports. Based on this idea, we hypothesize that humans map the precision of their VWM contents to a confidence rating through Fechner's law. We incorporate this hypothesis into the best available model of VWM encoding and fit it to data from a delayed-estimation experiment. The model provides an excellent account of human confidence rating distributions as well as the relation between performance and confidence. Moreover, the best-fitting mapping in a model with a highly flexible mapping closely resembles the logarithmic mapping, suggesting that no alternative mapping exists that accounts better for the data than Fechner's law. We propose a neural implementation of the model and find that this model also fits the behavioral data well. Furthermore, we find that jointly fitting memory errors and confidence ratings boosts the power to distinguish previously proposed VWM encoding models by a factor of 5.99 compared to fitting only memory errors. Finally, we show that Fechner's law also accounts for metacognitive judgments in a word recognition memory task, which is a first indication that it may be a general law in metacognition. Our work presents the first model to jointly account for errors and confidence ratings in VWM and could lay the groundwork for understanding the computational mechanisms of metacognition.

AB - Although visual working memory (VWM) has been studied extensively, it is unknown how people form confidence judgments about their memories. Peirce (1878) speculated that Fechner's law-which states that sensation is proportional to the logarithm of stimulus intensity-might apply to confidence reports. Based on this idea, we hypothesize that humans map the precision of their VWM contents to a confidence rating through Fechner's law. We incorporate this hypothesis into the best available model of VWM encoding and fit it to data from a delayed-estimation experiment. The model provides an excellent account of human confidence rating distributions as well as the relation between performance and confidence. Moreover, the best-fitting mapping in a model with a highly flexible mapping closely resembles the logarithmic mapping, suggesting that no alternative mapping exists that accounts better for the data than Fechner's law. We propose a neural implementation of the model and find that this model also fits the behavioral data well. Furthermore, we find that jointly fitting memory errors and confidence ratings boosts the power to distinguish previously proposed VWM encoding models by a factor of 5.99 compared to fitting only memory errors. Finally, we show that Fechner's law also accounts for metacognitive judgments in a word recognition memory task, which is a first indication that it may be a general law in metacognition. Our work presents the first model to jointly account for errors and confidence ratings in VWM and could lay the groundwork for understanding the computational mechanisms of metacognition.

KW - Confidence

KW - Fechner's law

KW - Metacognition

KW - Recognition memory

KW - Working memory

UR - http://www.scopus.com/inward/record.url?scp=85013408553&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013408553&partnerID=8YFLogxK

U2 - 10.1037/rev0000060

DO - 10.1037/rev0000060

M3 - Article

VL - 124

SP - 197

EP - 214

JO - Psychological Review

JF - Psychological Review

SN - 0033-295X

IS - 2

ER -