Skip to main content

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Journal of Neuroscience
  • Log in
  • My Cart
Journal of Neuroscience

Advanced Search

Submit a Manuscript
  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE
PreviousNext
Research Articles, Behavioral/Cognitive

Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others

Alison Harris, John A. Clithero and Cendri A. Hutcherson
Journal of Neuroscience 12 September 2018, 38 (37) 7952-7968; DOI: https://doi.org/10.1523/JNEUROSCI.3327-17.2018
Alison Harris
1Department of Psychology, Claremont McKenna College, Claremont, California 91711,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alison Harris
John A. Clithero
2Lundquist College of Business, University of Oregon, Eugene, Oregon 97403,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for John A. Clithero
Cendri A. Hutcherson
3Department of Psychology, University of Toronto Scarborough, Toronto, Ontario, Canada M1C 1A4, and
4Department of Marketing, Rotman School of Management, Toronto, Ontario, Canada M5S 3E6
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Cendri A. Hutcherson
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1.

    Social food choice task. A, Experimental session. In Part 1, hungry participants with unrestricted diets were introduced to 2 partners, and provided taste and health ratings for the foods used in the experiment. Then, in Part 2, participants made food choices for themselves and the partners while their brain activity was measured with EEG. Participants knew that their choices mattered because a single trial was randomly selected and implemented for each recipient in Part 3. B, Sample statements from the Similar (top) and Dissimilar (bottom) partners in the experiment. C, Sample stimuli and screens from the decision task.

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2.

    Behavioral results. A, Percentage of healthy choices for each recipient. B, Average observed RT by recipient. C–E, Observed RT for trials separated on the basis of whether, given the same food option, the decision maker chose the same for (C) Own versus Similar, (D) Own versus Dissimilar, and (E) Dissimilar versus Similar. Thus, Own-Similar:Same Response reflects RT for trials in which the choice was for Own self, and the response was the same as for the Similar other, whereas Own-Similar:Different Response reflects RTs on those trials where the response was different from the comparable choice for the Similar other; and likewise for the other comparisons. *p < 0.05, **p < 0.01.

  • Figure 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3.

    ERP analysis of stimulus value integration by recipient. A, Heat map of significant p values associated with stimulus value, showing significant effects from 500 to 650 ms (green box) and 700 to 850 ms (blue box) after stimulus onset. B, Source reconstructions from 500 to 650 ms (green) and 700 to 850 ms (blue) overlaid on a representative brain image. Particularly during the earlier 500–650 ms window, stimulus value activity was localized to regions including VMPFC (circled). Inset, Top, Spherical masks based on peak coordinates from three neuroimaging studies: blue (Plassmann et al., 2007), magenta (Hare et al., 2009), and cyan (Litt et al., 2011). Inset, Bottom, Source localization of stimulus value from ∼450 to 600 ms after stimulus onset in two ERP studies: red (Harris et al., 2013) and yellow (Harris and Lim, 2016). C, Stimulus value integration by recipient. Topographic scalp distribution (left) and average waveforms for the linear ordering of stimulus value (red represents Strong No; orange represents Weak No; cyan represents Weak Yes; green represents Strong Yes) in the 500–650 ms window (shaded green box) and 700–850 ms window (shaded blue box), plotted separately for each recipient. Solid line indicates Own. Dashed line indicates Similar. Dotted line indicates Dissimilar. D, Comparison of scalp topography at 550 ms (left) and 800 ms (right) after stimulus for the main effect of stimulus value (top), the interaction of stimulus value with Similar recipient (middle), and the interaction of stimulus value with Dissimilar recipient (bottom) revealed significant reductions in stimulus value signals for the 2 other recipients during the late window, in line with average waveform data in C.

  • Figure 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4.

    ERP analysis of attribute-weighting by recipient. A, Scalp topography during time WOI from 500 to 650 ms after stimulus revealed differences in neural weighting of taste ratings (left) and health ratings (middle) by recipient. SOIs were identified by taking the conjunction of sensors showing significant ERP activity for Similar and Dissimilar in the Taste versus Health comparison (black box, right). B, Average waveforms associated with the linear ordering of taste (left) and health (right) plotted separately for each recipient. Solid line indicates Own. Dashed line indicates Similar. Dotted line indicates Dissimilar. Orange represents taste. Green represents health. C, D, Linear contrast weights for taste (C) and health (D) as a function of recipient.

  • Figure 5.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 5.

    Differences in average ERP waveforms by recipient identity. A, Heat map of significant p values showing stimulus-locked regression results for the main effects of Similar (left) and Dissimilar (right). Significant effects were visible for the Dissimilar recipient, but not the Similar recipient, at time windows from 350–400 ms after stimulus onset (green box). B, Scalp topography (left) and average waveforms (right) associated with choice recipient identity (cyan represents Own self; blue represents Similar other; orange represents Dissimilar other) 350–400 ms after stimulus onset. Inset, Source reconstruction from 350 to 400 ms after stimulus overlaid on a representative brain image localized this response to regions including the TPJ and STS. C, Heat map of significant p values showing response-locked regression results for the main effects of Similar (left) and Dissimilar (right). Significant effects were visible for the Similar recipient, but not Dissimilar recipient, ∼300 ms to 180 ms before the response (green box). Later effects in the Dissimilar condition coincide with motor activity related to initiation of the key press response. D, Scalp topography (left) and average waveforms (right) associated with choice recipient identity. Cyan represents Own self. Blue represents Similar other. Orange represents Dissimilar other. Inset, Source reconstruction from 300 to 180 ms before the response localized this differential response to regions including the IPS.

  • Figure 6.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 6.

    Hierarchical Bayesian parameter estimates of the influence of neural response on model parameters. A–C, Trial-by-trial fluctuations in the ERP in SOIs for the value-related ERP from 500 to 650 ms (A) were linked with significantly greater influence on the drift rate v of the taste attribute when choosing for Own self and Similar other (B) and the health attribute when choosing for the Dissimilar other (C), suggesting that this response relates to evidence accumulation in the computational model. D–F, Trial-by-trial fluctuations in the response-locked ERP component (D) differentiated choices for the Similar other to a greater extent than Dissimilar or Own choices, consistent with computational model-fitting of the behavioral barrier parameter (E), and likewise demonstrated a significant influence on the barrier for response (F).

  • Figure 7.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 7.

    DDM simulations. A, Simulations (N = 10,000) of RT data for same versus different choice using best-fitting specification M2. Trials are separated based on the same criteria described for observed data in Figure 2, with observed data (left) plotted next to predicted model RTs (right) for each combination of recipient. B, C, Predicted (B) and observed (C) relationship between drift rates and percentage of different choices when comparing choices for 2 recipients, simulated separately for each of the 16 possible combinations of health and taste rating. Proximity of the dots to the diagonal reflects similarity of food values for 2 recipients, and size and color of dots are coded according to the percentage of different choices.

Tables

  • Figures
    • View popup
    Table 1.

    DDM specifications summarya

    Parameter with trial typeModel no.DICMSE choiceMSE RT yesMSE RT no
    vM146384.20.01370.05370.0694
    v, aM246318.60.01350.05320.0689
    v, zM346522.10.01370.05590.0655
    v, a, zM446415.60.01370.05380.0682
    • ↵av, Drift rate; a, threshold; z, starting point.

    • View popup
    Table 2.

    DDM posterior estimates and convergence for Model M2a

    ParameterMeanSD
    Drift (v), Intercept−1.9820.055
    Drift (v), HealthOwn0.0620.011
    Drift (v), HealthSimilar0.1830.010
    Drift (v), HealthDissimilar0.6600.011
    Drift (v), TasteOwn0.7220.011
    Drift (v), TasteSimilar0.6650.010
    Drift (v), TasteDissimilar0.0070.011
    Barrier (a), Intercept2.0060.047
    Barrier (a), Similar0.1250.015
    Barrier (a), Dissimilar0.0490.015
    Starting point (z)0.4930.006
    NonDecisionTime0.3570.026
    Convergence
        R̂1.00020.0002
    • ↵aStatistics for parameter estimates reflect the mean ± SD of the N = 10,000 posterior samples of group-level parameters.

    • View popup
    Table 3.

    DDM posterior estimates and convergence for Model M2 with stimulus-locked ERP dataa

    ParameterMeanSD
    Drift (v), Intercept−2.0040.057
    Drift (v), ERP−0.0490.010
    Drift (v), ERP HealthOwn0.0000.005
    Drift (v), ERP HealthSimilar−0.0030.004
    Drift (v), ERP HealthDissimilar0.0170.004
    Drift (v), ERP TasteOwn0.0280.004
    Drift (v), ERP TasteSimilar0.0230.004
    Drift (v), ERP TasteDissimilar−0.0010.004
    Drift (v), HealthOwn0.0540.011
    Drift (v), HealthSimilar0.1790.011
    Drift (v), HealthDissimilar0.6720.011
    Drift (v), TasteOwn0.7340.011
    Drift (v), TasteSimilar0.6750.010
    Drift (v), TasteDissimilar−0.0060.010
    Barrier (a), Intercept2.0000.043
    Barrier (a), Similar0.1180.016
    Barrier (a), Dissimilar0.0430.016
    Starting point (z)0.4940.006
    NonDecisionTime0.3600.025
    Convergence
        R̂1.00060.0010
    • ↵aStatistics for parameter estimates reflect the mean ± SD of the N = 10,000 posterior samples of group-level parameters.

Back to top

In this issue

The Journal of Neuroscience: 38 (37)
Journal of Neuroscience
Vol. 38, Issue 37
12 Sep 2018
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Advertising (PDF)
  • Ed Board (PDF)
Email

Thank you for sharing this Journal of Neuroscience article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others
(Your Name) has forwarded a page to you from Journal of Neuroscience
(Your Name) thought you would be interested in this article in Journal of Neuroscience.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Citation Tools
Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others
Alison Harris, John A. Clithero, Cendri A. Hutcherson
Journal of Neuroscience 12 September 2018, 38 (37) 7952-7968; DOI: 10.1523/JNEUROSCI.3327-17.2018

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Request Permissions
Share
Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others
Alison Harris, John A. Clithero, Cendri A. Hutcherson
Journal of Neuroscience 12 September 2018, 38 (37) 7952-7968; DOI: 10.1523/JNEUROSCI.3327-17.2018
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF

Keywords

  • drift-diffusion model
  • event-related potentials
  • social decision making

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

Research Articles

  • Expectation cues and false percepts generate stimulus-specific activity in distinct layers of the early visual cortex Laminar profile of visual false percepts
  • Acute ethanol modulates synaptic inhibition in the basolateral amygdala via rapid NLRP3 inflammasome activation and regulates anxiety-like behavior in rats
  • Haploinsufficiency of Shank3 in mice selectively impairs target odor recognition in novel background odors
Show more Research Articles

Behavioral/Cognitive

  • Prostaglandin E2 induces long-lasting inhibition of noradrenergic neurons in the locus coeruleus and moderates the behavioral response to stressors
  • Detection of spatially-localized sounds is robust to saccades and concurrent eye movement-related eardrum oscillations (EMREOs)
  • Rewarding capacity of optogenetically activating a giant GABAergic central-brain interneuron in larval Drosophila
Show more Behavioral/Cognitive
  • Home
  • Alerts
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Issue Archive
  • Collections

Information

  • For Authors
  • For Advertisers
  • For the Media
  • For Subscribers

About

  • About the Journal
  • Editorial Board
  • Privacy Policy
  • Contact
(JNeurosci logo)
(SfN logo)

Copyright © 2023 by the Society for Neuroscience.
JNeurosci Online ISSN: 1529-2401

The ideas and opinions expressed in JNeurosci do not necessarily reflect those of SfN or the JNeurosci Editorial Board. Publication of an advertisement or other product mention in JNeurosci should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in JNeurosci.