↓ Skip to main content

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, September 2013
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

news
4 news outlets
blogs
1 blog
policy
1 policy source
twitter
14 X users
googleplus
1 Google+ user

Readers on

mendeley
356 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Simultaneous modeling of visual saliency and value computation improves predictions of economic choice
Published in
Proceedings of the National Academy of Sciences of the United States of America, September 2013
DOI 10.1073/pnas.1304429110
Pubmed ID
Authors
Abstract

Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a drift-diffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 356 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 13 4%
France 3 <1%
United Kingdom 2 <1%
Germany 2 <1%
Sweden 1 <1%
Netherlands 1 <1%
Iran, Islamic Republic of 1 <1%
Hungary 1 <1%
Greece 1 <1%
Other 2 <1%
Unknown 329 92%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 100 28%
Researcher 67 19%
Student > Master 37 10%
Student > Bachelor 22 6%
Professor 22 6%
Other 51 14%
Unknown 57 16%
Readers by discipline
Readers by discipline Count As %
Psychology 114 32%
Neuroscience 47 13%
Agricultural and Biological Sciences 29 8%
Economics, Econometrics and Finance 20 6%
Engineering 14 4%
Other 65 18%
Unknown 67 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 53. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 August 2022.
All research outputs
#1,006,351
of 34,358,242 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#14,984
of 118,924 outputs
Outputs of similar age
#7,117
of 242,505 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#169
of 905 outputs
Altmetric has tracked 34,358,242 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 118,924 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.1. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 242,505 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 905 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.