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Alpha oscillations and event-related potentials reflect distinct dynamics of attribute construction and evidence accumulation in dietary decision making

  1. Azadeh HajiHosseini  Is a corresponding author
  2. Cendri A Hutcherson
  1. Department of Psychology, University of Toronto Scarborough, Canada
  2. Department of Marketing, Rotman School of Management, University of Toronto, Canada
Research Article
Cite this article as: eLife 2021;10:e60874 doi: 10.7554/eLife.60874
9 figures and 1 additional file

Figures

Self-regulation choice task structure.

Trials occurred in interleaved blocks of NATURAL, HEALTH (designed to increase the influence of healthiness on choice), and DECREASE (designed to decrease the influence of tastiness on choice) conditions. Each block started with instructions related to NATURAL or regulation conditions (HEALTH or DECREASE) that remained on the screen for 5 s followed by 15 choice trials. Every food stimulus was preceded by a fixation cross that was colour-coded for the condition. On each trial, subjects made a response (Strong No, No, Yes, or Strong Yes) to indicate whether they wanted to eat the food presented on the screen. See Materials and methods for a detailed description of the task and conditions.

Figure 2 with 1 supplement
Attribute construction (AC) and evidence accumulation (EA) models.

(a) AC and EA representations based on the drift-diffusion model (DDM) structure; we assumed that taste attribute construction (taste-AC) and health attribute construction (health-AC) signals in each trial integrated based on hierarchical multi-attribute drift diffusion model (HDDM) drfit rates (drift (t) = wtastiness * tastiness (t) + whealthiness * healthiness (t) + ValConst) and created the EA signal based on other DDM parameters. Model 1 computes the association of individually perceived tastiness and healthiness of each food with each of the taste-AC, health-AC, and EA signals. (b) Examples of time course of taste-AC (red), health-AC (blue), and EA (black) signals in two trials where a tasty but unhealthy (trial 1, solid lines) and a healthy but non-tasty (trial 2, dotted lines) food were presented. RT1 and RT2 are the reaction times on trial 1 and trial 2. Parameters used for these simulation examples are wtastiness = 0.7, whealthiness = 0.1, ValConst = 0.5, threshold = 0.9, spbias = 0.45, nondec = 0.462 s. (c) Model 1 coefficients showing the association between food tastiness ratings and the simulated taste-AC signal (solid red line), food tastiness ratings and the simulated EA signal (dashed red line), food healthiness ratings and the simulated health-AC signal (solid blue line), and food healthiness ratings and the simulated EA signal (dashed blue line) using individual HDDM parameters in NATURAL, HEALTH, and DECREASE conditions. Coefficients are averaged across subjects. Average RT in each condition is shown on the x-axes (mean RT = 0.98 s, 1.06 s, 1.05 s for NATURAL, HEALTH, and DECREASE, respectively). See Figure 2—figure supplement 1 and Figure 2—source data 1 for fitted model parameters.

Figure 2—source data 1

Group hierarchical multi-attribute drift diffusion model (HDDM) parameter estimates for each condition.

https://cdn.elifesciences.org/articles/60874/elife-60874-fig2-data1-v2.docx
Figure 2—figure supplement 1
Behaviour and drift-diffusion model (DDM) parameters and distributions; averaged (a) acceptance rate and (b) reaction time (RT) are shown.

Averaged (c) DDM drift parameters and (d) threshold, nondecision time, and starting point bias are shown. (e) Group posterior distribution of RT using hierarchical multi-attribute drift diffusion model (HDDM) individually fitted parameters vs. data. RT for no responses is shown on negative x-axis.

Figure 3 with 1 supplement
Event-related potential (ERP)-correlate of tastiness.

(a) ERP coefficients for tastiness in Model 2 in NATURAL condition satisfy significance criteria (see Materials and methods) on fronto-central channels ~400–700 ms post-food. (b) The time course of ERP-correlate of tastiness (red line) is correlated with time course of contribution of tastiness to taste attribute construction (taste-AC; dashed black line) more than evidence accumulation (taste-EA; dotted black line) signal (Model 1, Figure 2c). (c) Scalp distribution of the ERP-correlate of tastiness in NATURAL, HEALTH, and DECREASE is shown. Shaded error bars show within-subject standard error of the mean. Horizontal dotted lines show significant time bins (p<0.05). See Figure 3—figure supplement 1 for the ERP-correlate of healthiness and the parietal component of the ERP-correlate of tastiness.

Figure 3—figure supplement 1
Event-related potential (ERP)-correlate of healthiness and parietal component of the ERP-correlate of tastiness.

In the exploratory (not model-based) analysis, coefficients for healthiness in Model 2 in HEALTH were significant on (a) occipital and (b) frontal channels ~200-400 ms post-food. (c) Scalp distribution of the ERP-correlate of healthiness (top) and its difference in the HEALTH vs. NATURAL (bottom left) and DECREASE vs. HEALTH (bottom right) is shown; panels (d) and (e) show the parietal component of the ERP-correlate of tastiness (from Figure 3) and its correlation with tastiness and EA signals for completeness. Shaded error bars show within-subject standard error of the mean. Horizontal dotted lines show time bins where ERP-correlates of healthiness or tastiness are significant (p<0.05).

Figure 4 with 4 supplements
Association of alpha power with tastiness and healthiness.

(a) Alpha-correlate of tastiness: alpha power coefficients for tastiness in Model 2 in NATURAL condition satisfy significance criteria (see Materials and methods) on frontal and occipital-parietal channels ~500–1000 ms post-food. (b) Alpha-correlate of healthiness: alpha power coefficients for healthiness in Model 2 in HEALTH condition satisfy significance criteria (see Materials and methods) on frontal and occipital channels ~500–1000 ms post-food. (c) Scalp distribution of the alpha-correlates of tastiness (top left) and healthiness (top right) and the difference in alpha-correlate of tastiness in the HEALTH vs. NATURAL (bottom left) and DECREASE vs. NATURAL (bottom right) are shown. (d) Alpha-correlate of tastiness predicts successful down-regulation of tastiness influence (wtastiness) across subjects. (e) Time course of the alpha-correlate of tastiness (red line) is correlated with time course of contribution of tastiness to taste attribute construction (taste-AC; dashed black line) and evidence accumulation (taste-EA dotted black line) signals (Model 1, Figure 2c). (f) Time course of alpha-correlate of healthiness (blue line) is correlated with time course of contribution of healthiness to health attribute construction (health-AC; dashed black line) and evidence accumulation (health-EA; dotted black line) signals (Model 1, Figure 2c); shaded error bars show within-subject standard error of the mean. Horizontal dotted lines show significant time bins (p<0.05). See Figure 4—figure supplements 13 for alpha-correlates of tastiness and healthiness calculated separately for faster and slower reaction times. Figure 4—figure supplement 4 also shows the time-frequency maps of averaged power for food- and response-locked data.

Figure 4—figure supplement 1
Alpha-correlates of tastiness and healthiness for food-locked and response-locked data for fast and slow trials; alpha-correlate of tastiness shown separately for trials with (a) fast and (b) slow responses in food-locked data.

The sensitivity of alpha to tastiness seems to extend during slower responses, indicating that alpha power carries an integration of taste attribute to the evidence accumulation as opposed to an independent representation of attribute construction. At channels where alpha-correlate of tastiness was found, alpha power was also significantly correlated with tastiness prior to the response for fast (c) and slow (d) trials. Alpha-correlate of healthiness shown separately for trials with (e) fast and (f) slow responses in food-locked data. The sensitivity of alpha to healthiness weakens in slower trials, suggesting that the construction of healthiness attribute is less consistent across slower trials. At channels where alpha-correlate of healthiness was found, alpha power was also significantly correlated with healthiness for prior to the response fast (g) and slow (h) trials. Shaded error bars show within-subject standard error of the mean. Horizontal dotted lines show significant time bins (p<0.05).

Figure 4—figure supplement 2
Time course of averaged (a) food-locked and (b) response-locked alpha power in fast, slow, and medium trials in the NATURAL condition.
Figure 4—figure supplement 3
Alpha-correlates of tastiness and healthiness (btastiness and bhealthiness) for food-locked and response-locked data in the taste-sensitive channels in NATURAL and health-sensitive channels in HEALTH condition shown separately for fast and slow trials.

Note that this plot depicts btastiness and bhealthiness in one graph for both taste- and health-sensitive channels, whereas in Figure 4—figure supplement 1, btastniess was only shown in taste-sensitive channels and bhealthiness was only shown in health-sensitive channels. Alpha correlation with tastiness and healthiness (Model 2) at taste-sensitive channels is shown for trials with (a) fast and (b) slow responses in food-locked data in NATURAL condition. Alpha correlation with tastiness and healthiness (Model 2) at health-sensitive channels is shown for trials with (c) fast and (d) slow responses in food-locked data in HEALTH condition. Alpha correlation with tastiness and healthiness (Model 2) at taste-sensitive channels is shown for trials with (e) fast and (f) slow responses in response-locked data in NATURAL condition. Alpha correlation with tastiness and healthiness (Model 2) at health-sensitive channels is shown for trials with (g) fast and (h) slow responses in response-locked data in HEALTH condition. Shaded error bars show within-subject standard error of the mean. Horizontal dotted lines show significant time bins (p<0.05).

Figure 4—figure supplement 4
Time-frequency plots of averaged channels (top), Fz (middle), and Pz (bottom) in (a) food- and (b) response-locked electroencephalogram.
Theta-correlate of tastiness.

(a) In the exploratory (not model-based) analysis, theta power coefficients for tastiness in Model 2 were significant on frontal and occipital channels in HEALTH and DECREASE conditions ~200–500 ms post-food.(b) Scalp distribution of the theta-correlate of tastiness in HEALTH (top left) and DECREASE (top right) and the difference in theta-correlate of tastiness in the HEALTH vs. NATURAL (bottom left) and DECREASE vs. NATURAL (bottom right) are shown. Theta-correlate of tastiness predicts (c) successful down-regulation of tastiness influence (wtastiness) and is correlated with (d) alpha-correlate of tastiness across subjects; shaded error bars show within-subject standard error of the mean. Horizontal dotted lines show significant time bins (p<0.05).

Author response image 1
Comparison between DDM parameters calculated through simple and hierarchical MCMC algorithms.
Author response image 2
Contribution of tastiness (left) and healthiness (right) to RT in all conditions.
Author response image 3
a) time course of contribution of tastiness to alpha slope of change (red line) plotted against time course of contribution of tastiness to slope of simulated tastiness attribute construction (AC, dashed black line) and slope of simulated evidence accumulation (EA, dotted black line) signals, b) time course of contribution of healthiness to alpha slope of change (blue line) plotted against time course of contribution of healthiness to slope of simulated healthiness attribute construction (AC, dashed black line) and slope of simulated evidence accumulation (EA, dotted black line) signal; Shaded error bars show within-subject standard error of the mean.
Author response image 4
Alpha-correlates of tastiness and healthiness in NATURAL condition for fast (a) and slow (b) trials; ERP-correlates of tastiness and healthiness for fast (c) and slow (d) trials.

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