1. Neuroscience

Negative affect influences the computations underlying food choice in bulimia nervosa

  1. Blair RK Shevlin
  2. Loren Gianini
  3. Joanna Steinglass
  4. Karin Foerde
  5. E Caitlin Lloyd
  6. Kelsey Hagan
  7. Laura A Berner  Is a corresponding author
  1. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, United States
  2. Department of Psychiatry, Columbia University Medical Center, United States
  3. Department of Psychology, University of Amsterdam, Netherlands
  4. Department of Psychiatry, Virginia Commonwealth University, United States
https://doi.org/10.7554/eLife.105146.5
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Cite this article
  1. Blair RK Shevlin
  2. Loren Gianini
  3. Joanna Steinglass
  4. Karin Foerde
  5. E Caitlin Lloyd
  6. Kelsey Hagan
  7. Laura A Berner
(2026)
Negative affect influences the computations underlying food choice in bulimia nervosa
eLife 14:RP105146.
https://doi.org/10.7554/eLife.105146.5
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6 figures, 33 tables and 1 additional file

Figures

Figure 1 with 1 supplement
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Affect induction and task design.

(A) Study timeline. BN and HC participants completed the study tasks on two separate days. On session 1, participants were randomly assigned to the neutral mood or negative mood induction before completing the Food Choice Task. On session 2, participants experienced the alternative mood induction before completing another run of the Food Choice Task. The mood inductions involved combinations of music and autobiographical writing. (B) Food Choice Task. During the Food Choice Task, both BN and HC participants rated 43 food items across three phases. In the Tastiness Ratings and Healthiness phases, participants rated each item on a 5-point Likert scale from Bad to Good and Unhealthy to Healthy, respectively. In the Choice phase, participants indicated their strength of preference for a presented food item, compared to a personally tailored neutral reference item.

Figure 1—figure supplement 1
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Effectiveness of the affect induction across POMS subscales.

Change in each Profile of Mood States (POMS) subscale (Anger, Confusion, Depression, Fatigue, Tension, Vigor) following the neutral and negative affect inductions, shown separately for the healthy control (HC; n = 21) and bulimia nervosa (BN; n = 25) groups. Error bars indicate standard errors of the mean.

Figure 2
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Food Choice Task behavior.

(A) Choice behavior estimated from regression models. Re-analysis of the raw data excluding outlier response times (2.5% of trials n = 85) replicated original findings (Gianini et al., 2019). While both groups were less likely to choose high-fat foods (over the neutral reference item) than low-fat foods (over the neutral reference item), the BN group was even less likely than the HC group to choose high-fat food items. However, we did not identify any significant effects of the Affect Condition on choices. Error bars indicate 95% confidence intervals of the estimated effects. (B) Influence of health and taste ratings on food choice. Health ratings influenced food choice more in the BN group than in the HC group. Within the HC group, food choice was influenced more strongly by taste ratings than health ratings. Error bars indicate standard errors of the estimated coefficients. Note. Corresponding statistics are presented in Appendix 1—table 9. HC = healthy controls (n = 21); BN = bulimia nervosa (n = 25).

Figure 3
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Example evidence accumulation trajectories predicted by the starting time diffusion decision model (stDDM).

Each trajectory is a simulated agent that considers one attribute alone before beginning to consider both tastiness and healthiness attributes together. (A) Tastiness onset delay. The dashed, green trajectory represents a case where positive healthiness information about a low-fat food item quickly biases the agent toward the accept threshold before taste information has time to influence the evidence accumulation process. The solid purple trajectory illustrates a case where evidence related to a high-fat food is initially biased toward the reject threshold while the aversive healthiness information dominates the evidence accumulation process. Once information about the item’s appetitive tastiness comes online, the evidence accumulation changes its trajectory toward the accept threshold. (B) Healthiness onset delay. The dashed, orange trajectory illustrates a case where evidence related to the highly appetitive tastiness attribute dominates the evidence accumulation process, influencing the trajectory to terminate at the accept threshold of a high-fat food before the healthiness attribute is considered. For the solid brown trajectory, a less appetitive high-fat food item is ultimately rejected once aversive healthiness information enters the evidence accumulation process. (C, D) Hypothesized model of affect-induced binge-eating. Each trajectory is a simulated agent with BN making a decision involving a high-fat food. During neutral affect (gray solid line), the high-fat food is trending toward the accept threshold until the onset of aversive healthiness information biases the trajectory toward the reject threshold. (C) Attribute-weight hypothesis. During negative affect, either the attribute weight for taste information increases (red dashed line) or the attribute weight for healthiness information decreases (blue dashed line). In both cases, the trajectory is ultimately biased toward the accept threshold. (D) Attribute-onset hypothesis. During negative affect, the initial delay shifts and taste information is accumulated longer before healthiness information comes online. With a longer delay in the onset of healthiness information, the evidence accumulation for the high-fat food has enough time to reach the accept threshold.

Figure 4 with 1 supplement
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Parameter estimates.

Colors indicate diagnosis: purple = bulimia nervosa (BN; n = 25); green = healthy controls (HC; n = 21). Error bars represent the standard error of the mean. (A) Attribute onset. In the neutral condition, we observed a Group by Food Type cross-over effect: while the BN group showed a greater initial bias toward accumulating tastiness information of high-fat foods than low-fat foods, the HC group showed a greater tastiness information bias for low-fat foods than high-fat foods. After the negative affect induction, any Food Type-based distinctions disappeared, and both groups’ biases toward tastiness information increased, but this effect was more pronounced in the BN group. (B) Weight on tastiness information. The HC group put more weight on tastiness information than the BN group. The negative affect induction reduced tastiness weights for the HC group, but not the BN group. (C) Weight on healthiness information. The BN group put more weight on healthiness information than the HC group, especially for high-fat foods. The negative affect induction did not have significant effects on healthiness weights for either group.

Figure 4—figure supplement 1
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Results from the parameter recovery exercise.

Scatterplots relate parameters estimated from the empirical data (fit) to those recovered from data simulated with the winning model (rec), for boundary separation (α), healthiness and tastiness attribute weights (ωhealth, ωtaste), non-decision time (τND), relative starting time (τs), and starting point bias (z).

Figure 5
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Affect-induced changes in information onset were associated with more frequent subjective binge episodes.

In the negative affect condition (right facet), longer delays in accumulating healthiness information (i.e., reduced τs) for high-fat foods compared to low-fat foods were associated with more frequent subjective binge episodes. Line type and shape refer to Food Type: Solid lines and circles = low-fat foods; dashed lines and triangles = high-fat foods.

Figure 6
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Revised model of affect-induced binge-eating.

Each trajectory is a simulated individual with BN who considers the taste attribute alone (pink shaded sides of the panels) before considering both tastiness and healthiness attributes together (blue shaded sides of the panels). In both panels the solid purple trajectory illustrates a decision involving a low-fat food, where tastiness information has a weak, positive weight and healthiness information has a strong, positive weight. The dashed blue trajectory illustrates a decision involving a high-fat food, where tastiness information has a strong, positive weight and healthiness information has a strong, negative weight. (A) Low negative affect. During neutral affect, the high-fat food is trending toward the accept threshold until the onset of aversive healthiness information biases the trajectory toward the reject threshold. (B) High negative affect. During negative affect, the initial delay is shifted and tastiness information is accumulated longer before healthiness information comes online. With a longer delay in the onset of healthiness information, the evidence accumulation for the high-fat food has enough time to reach the accept threshold.

Tables

Appendix 1—table 1
Linear mixed-effects regression analyzing overall negative affect across Group, Affect Condition, and Timing.
PredictorsBSEtp
Intercept–7.147.35–0.970.335
Group55.029.975.52<0.001
Affect Condition–2.434.49–0.540.590
Timing0.054.490.010.992
Group × Affect Condition–7.696.09–1.260.209
Group × Timing–7.216.09–1.180.239
Affect Condition × Timing20.436.353.220.002
Group × Affect Condition × Timing7.098.620.820.412
Nsubject46   
Observations184   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 2
Linear mixed-effects regression analyzing attribute onset (τs) across Group, Affect Condition, and Food Type.
PredictorsBSEtp
Intercept–0.460.07–6.04<0.001
Group0.140.101.390.173
Affect Condition–0.160.07–2.160.032
Food Type0.140.091.540.125
Group × Affect Condition–0.230.10–2.260.026
Group × Food Type–0.290.12–2.330.022
Affect Condition × Food Type–0.120.09–1.450.150
Group × Affect Condition × Food Type0.280.122.360.020
Nsubject46   
Observations184   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 3
Simple effects analyses comparing groups on attribute onset (τs) difference scores.
ContrastEstimateSEdftp
Group difference in condition effect by food type
Condition effect for Low Fat foods–0.230.10132–2.260.026
Condition effect for High Fat foods0.050.101320.510.614
Group differences in food type effects by condition
Food type effects in Neutral condition–0.290.13132–2.330.022
Food type effects in Negative condition–0.010.03132–0.520.607
Food type effects within groups and condition
Low fat vs. High fat (HC Neutral)0.230.061324.05<0.001
Low fat vs. High fat (HC Negative)0.290.051325.64<0.001
Low fat vs. High fat (BN Neutral)0.220.061323.61<0.001
Low fat vs. High fat (BN Negative)0.380.051327.00<0.001

  1. Note. HC = healthy controls; BN = bulimia nervosa. Difference scores reflect food-type effects (high-fat minus low-fat) and condition effects (Negative minus Neutral). Bold p-values indicate p < 0.05.

Appendix 1—table 4
Linear mixed-effects regression analyzing tastiness attribute weights (ωtaste) across Group, Affect Condition, and Food Type.
PredictorsBSEtp
Intercept0.570.0413.55<0.001
Group–0.390.06–6.86<0.001
Affect Condition–0.160.05–2.960.004
Food Type–0.230.06–4.05<0.001
Group × Affect Condition0.180.072.460.015
Group × Food Type–0.060.08–0.830.410
Affect Condition × Food Type0.010.050.210.834
Group × Affect Condition × Food Type–0.100.07–1.470.144
Nsubject46   
Observations184   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 5
Simple effects analyses comparing groups on tastiness attribute weight (ωtaste) difference scores.
ContrastEstimateSEdftp
Group difference by condition interaction
Neutral vs. Negative (HC – BN)0.130.061321.950.053
Group differences within conditions
HC vs. BN (Neutral)0.420.04449.68<0.001
HC vs. BN (Negative)0.300.06445.17<0.001
Condition effects within groups
Neutral vs. Negative (HC)0.150.051323.200.002
Neutral vs. Negative (BN)0.030.041320.610.542
Food type effects within groups
Low-fat versus High-fat (HC)0.220.051324.29<0.001
Low-fat versus High-fat (BN)0.340.051327.12<0.001

  1. Note. HC = healthy controls; BN = bulimia nervosa. Bold p-values indicate p < 0.05.

Appendix 1—table 6
Linear mixed-effects regression analyzing healthiness attribute weights (ωhealth) across Group, Affect Condition, and Food Type.
PredictorsBSEtp
Intercept–0.510.04–12.14<0.001
Group0.380.066.73<0.001
Affect Condition0.090.051.820.071
Food Type–0.040.07–0.620.533
Group × Affect Condition–0.130.07–1.890.062
Group × Food Type0.280.093.090.002
Affect Condition × Food Type0.070.080.870.384
Group × Affect Condition × Food Type–0.040.11–0.340.735
Nsubject46   
Observations184   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 7
Simple effects analyses comparing groups on healthiness attribute weight (ωhealth) difference scores.
ContrastEstimateSEdftp
Group Differences within conditions
HC vs. BN (Neutral)–0.530.0444–12.02<0.001
HC vs. BN (Negative)–0.380.0644–6.42<0.001
Condition effects within groups
Neutral vs. Negative (HC)–0.130.05132–2.500.014
Neutral vs. Negative (BN)0.020.051320.460.645
Food type effects within groups
Low-fat versus High-fat (HC)0.010.061320.130.897
Low-fat versus High-fat (BN)–0.260.06132–4.40<0.001

  1. Note. HC = healthy controls; BN = bulimia nervosa. Bold p-values indicate p < 0.05.

Appendix 1—table 8
Zero-inflated negative binomial model of symptom severity.
OutcomePredictorsBSEZp
Subjective binge episodesCount modelIntercept–29.4512.30–2.400.017
τsNeuLF–0.210.60–0.360.722
τsNeuHF1.481.291.140.254
τsNegLF1.094.02–0.270.787
τsNegHF–46.3916.56–2.800.005
Zero-inflated modelIntercept–1.280.60–2.120.034
N subject25   
Observations25   
Objective binge episodesCount modelIntercept5.694.201.360.175
τsNeuLF0.470.281.690.091
τsNeuHF0.160.590.270.790
τsNegLF0.993.030.330.745
τsNegHF0.074.620.020.998
Zero-inflated modelIntercept–21.278306.430.000.998
N subject25
Observations25

  1. Note: Neu = Neutral affect condition; Neg = Negative affect condition; LF = Low-fat food; HF = High-fat food. Bold p-values indicate p < 0.05.

Appendix 1—table 9
Logistic mixed-effects regression analyzing food choice across Affect Condition and Food Type split by Group.
PredictorsBSEZp
Intercept–0.290.18–1.590.113
Group–0.480.18–2.630.009
Affect Condition–0.020.09–0.260.793
Food Type–0.620.13–4.58<0.001
Group × Affect Condition0.070.090.820.410
Group × Food Type–0.270.13–1.990.047
Affect Condition × Food Type–0.030.05–0.620.535
Group × Affect Condition × Food Type–0.100.05–1.910.056
Nsubject46   
Observations3271   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 10
Logistic mixed-effects regression analyzing choice across Group and Affect Condition using tastiness and healthiness attribute ratings.
PredictorsBSEZp
Intercept–0.600.24–2.460.014
Group–0.800.24–3.36<0.001
Affect Condition0.050.200.260.797
Taste Rating [z]2.930.2611.40<0.001
Health Rating [z]1.490.246.29<0.001
Group × Affect Condition0.240.191.210.225
Taste × Group–0.650.23–2.780.005
Health × Group0.640.232.770.006
Taste × Affect Condition–0.450.17–2.570.010
Health × Affect Condition–0.100.17–0.610.542
Taste × Group × Affect Condition–0.030.13–0.200.841
Health × Group × Affect Condition0.040.160.240.812
Nsubject46   
Observations3271   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 11
Logistic mixed-effects regression analyzing self-control across Group and Affect Condition.
PredictorsBSEZp
Intercept–0.300.18–1.680.093
Group0.380.182.140.033
Affect Condition0.080.120.700.486
Group × Affect Condition0.150.121.270.204
Nsubject46   
Observations1118   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 12
Linear mixed-effects regression analyzing log-transformed response times across Group and Affect Condition using tastiness and healthiness attribute ratings.
PredictorsBSEZp
Intercept0.530.0415.10<0.001
Group–0.020.04–0.700.487
Affect Condition–0.020.02–1.160.250
Food Type–0.030.01–2.070.044
Choice0.010.010.640.525
Group × Affect Condition–0.020.02–0.840.406
Group × Food Type–0.020.01–1.170.248
Affect Condition × Food Type0.010.010.640.527
Group × Choice0.030.012.440.015
Affect Condition × Choice0.000.01–0.500.959
Food Type × Choice0.030.012.700.007
Group x Affect Condition × Food Type–0.010.01–0.640.524
Group x Affect Condition × Choice–0.010.01–1.260.208
Group x Food Type × Choice–0.010.01–0.650.513
Affect Condition × Food Type × Choice0.010.010.750.455
Group x Affect Condition × Food Type × Choice–0.010.01–0.600.548
N subject46   
Observations3271 

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 13
Model fit metrics for alternative model specifications.

All models were estimated with the time-varying drift rate but differed in which parameters varied by Food Type.

ModelParameters per subjectGroupWAICParameters varying with Food Type
M07HC5104NaN
BN6510
M19HC4886ωhealth,ωtaste
BN6266
M28HC4926τs
BN6298
M310HC4874τs,ωhealth,ωtaste
BN6135

  1. Note. ωhealth=health coefficient,ωtaste=taste coefficient,τs=relative starting time.

Appendix 1—table 14
Confusion matrix indicating frequency with which each candidate model was selected for each simulated model.
Predicted
SimulatedModelM0M1M2M3
M01.000.000.000.00
M10.001.000.000.00
M20.000.000.001.00
M3*0.000.000.001.00
  1. *

    symbol indicates winning model from model comparison based on empirical data.

Appendix 1—table 15
Analysis of variance using mixed effects models to assess deviance of M0 predicted choice frequencies compared to empirical data.
TermsFDfp
Data source18.361, 308<0.001
Data source x Group0.141, 3080.709
Data source x Affect Condition0.551, 3080.458
Data source x Food Type16.761, 308<0.001
Data source x Group x Affect Condition0.061, 3080.808
Data source x Group x Food Type1.321, 3080.252
Data source x Affect Condition x Food Type0.001, 3080.964
Data source x Group x Affect Condition x Food Type0.011, 3080.919
Nsubject46  
Observations368  

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 16
Analysis of variance using mixed effects models to assess deviance of M1 predicted choice frequencies compared to empirical data.
TermsFDfp
Data source95.271, 308<0.001
Data source × Group4.731, 3080.030
Data source × Affect Condition0.101, 3080.754
Data source × Food Type67.781, 308<0.001
Data source × Group × Affect Condition0.891, 3080.346
Data source × Group × Food Type0.341, 3080.563
Data source × Affect Condition × Food Type0.181, 3080.670
Data source × Group × Affect Condition × Food Type0.601, 3080.441
Nsubject46  
Observations368  

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 17
Analysis of variance using mixed effects models to assess deviance of M2 predicted choice frequencies compared to empirical data.
TermsFDfp
Data source14.081, 308<0.001
Data source × Group0.051, 3080.827
Data source × Affect Condition0.061, 3080.803
Data source × Food Type6.611, 3080.011
Data source × Group × Affect Condition0.201, 3080.654
Data source × Group × Food Type1.491, 3080.223
Data source × Affect Condition × Food Type0.271, 3080.601
Data source × Group × Affect Condition × Food Type0.051, 3080.821
Nsubject46  
Observations368  

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 18
Analysis of variance using mixed effects models to assess deviance of M3 predicted choice frequencies compared to empirical data.
TermsFDfp
Data source8.841, 3080.003
Data source × Group0.001, 3080.944
Data source × Affect Condition0.921, 3080.339
Data source × Food Type1.701, 3080.193
Data source × Group × Affect Condition0.131, 3080.720
Data source × Group × Food Type0.701, 3080.404
Data source × Affect Condition × Food Type0.111, 3080.741
Data source × Group × Affect Condition × Food Type0.971, 3080.326
Nsubject46  
Observations368  

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 19
Logistic mixed-effects regression assessing choice predictions from Model M0.
PredictorsBSEZp
Intercept0.290.122.300.021
Group–0.340.12–2.740.006
Affect Condition–0.080.07–1.150.251
Food Type–0.010.07–0.190.848
Group × Affect Condition0.030.070.390.695
Group × Food Type–0.320.07–4.34<0.001
Affect Condition × Food Type–0.050.05–1.010.312
Group × Affect Condition × Food Type–0.060.05–1.270.205
Nsubject46   
Observations6542   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 20
Logistic mixed-effects regression assessing choice predictions from Model M1.
PredictorsBSEZp
Intercept0.890.156.12<0.001
Group–0.280.15–1.930.053
Affect Condition–0.080.07–1.030.303
Food Type0.420.076.35<0.001
Group × Affect Condition0.170.072.330.020
Group × Food Type–0.220.07–3.270.001
Affect Condition × Food Type–0.040.04–0.980.325
Group × Affect Condition × Food Type0.020.040.600.552
Nsubject46   
Observations6542   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 21
Logistic mixed-effects regression assessing choice predictions from Model M2.
PredictorsBSEZp
Intercept0.280.142.050.041
Group–0.420.14–3.080.002
Affect Condition–0.030.08–0.390.695
Food Type–0.160.11–1.520.128
Group × Affect Condition0.000.080.010.993
Group × Food Type–0.360.11–3.36<0.001
Affect Condition × Food Type–0.080.06–1.370.170
Group x Affect Condition × Food Type–0.070.06–1.300.195
Nsubject46   
Observations6542   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 22
Logistic mixed-effects regression assessing choice predictions from Model M3.
PredictorsBSEZp
Intercept0.130.130.940.347
Group–0.360.13–2.680.007
Affect Condition–0.110.07–1.500.133
Food Type–0.360.09–4.16<0.001
Group × Affect Condition0.020.070.280.777
Group × Food Type–0.270.09–3.110.002
Affect Condition × Food Type–0.090.04–1.960.050
Group × Affect Condition × Food Type0.030.040.580.562
Nsubject46   
Observations6542   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 23
Linear mixed-effects regression assessing non-decision time (τND) across Group and Affect Condition.
PredictorsBSEtp
Intercept0.510.0316.06<0.001
Group0.020.040.390.701
Affect Condition0.020.030.740.464
Group × Affect Condition–0.090.04–2.190.034
Nsubject46   
Observations92   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 24
Linear mixed-effects regression assessing boundary separation (α) across Group and Affect Condition.
PredictorsBSEtp
Intercept3.460.1033.55<0.001
Group–0.400.14–2.840.007
Affect Condition–0.370.09–4.26<0.001
Group × Affect Condition0.230.121.940.058
Nsubject46   
Observations92   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 25
Linear mixed-effects regression assessing starting point (z) across Group and Affect Condition.
PredictorsBSEtp
Intercept0.410.0220.13<0.001
Group–0.010.03–0.480.634
Affect Condition–0.020.02–1.010.317
Group × Affect Condition0.030.030.980.331
Nsubject46   
Observations92   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 26
Linear mixed-effects regression assessing the Anger POMS subscale as a function of Group, Affect Condition, and Timing.
PredictorsBSEtp
Intercept0.571.770.320.748
Group7.592.403.160.002
Affect Condition0.001.330.001.000
Timing0.001.330.001.000
Group × Affect Condition–2.281.80–1.260.208
Group × Timing–1.011.83–0.550.581
Affect Condition × Timing5.711.883.040.003
Group × Affect Condition × Timing0.662.570.260.798
Nsubject46   
Observations184   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 27
Linear mixed-effects regression assessing the Confusion POMS subscale as a function of Group, Affect Condition, and Timing.
PredictorsBSEtp
Intercept2.990.943.170.002
Group6.071.284.75<0.001
Affect Condition–0.740.68–1.080.282
Timing–0.840.68–1.220.224
Group × Affect Condition–0.440.92–0.480.630
Group × Timing1.140.931.230.221
Affect Condition × Timing2.300.952.420.017
Group x Affect Condition × Timing–0.921.30–0.710.482
Nsubject46   
Observations173   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 28
Linear mixed-effects regression assessing the Depression POMS subscale as a function of Group, Affect Condition, and Timing.
PredictorsBSEtp
Intercept0.822.570.320.750
Group16.063.474.63<0.001
Affect Condition–0.341.66–0.210.836
Timing–0.491.63–0.300.765
Group × Affect Condition–4.012.23–1.800.074
Group × Timing–0.202.23–0.090.928
Affect Condition × Timing5.582.292.440.016
Group × Affect Condition × Timing3.563.131.140.258
Nsubject46   
Observations174   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 29
Linear mixed-effects regression assessing the Fatigue POMS subscale as a function of Group, Affect Condition, and Timing.
PredictorsBSEtp
Intercept3.521.332.650.010
Group6.041.813.340.001
Affect Condition–1.260.95–1.340.183
Timing0.480.930.510.610
Group × Affect Condition1.821.271.430.154
Group × Timing–0.981.28–0.770.443
Affect Condition × Timing0.741.330.560.578
Group × Affect Condition × Timing–0.351.80–0.200.845
Nsubject46   
Observations181   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 30
Linear mixed-effects regression assessing the Tension POMS subscale as a function of Group, Affect Condition, and Timing.
PredictorsBSETp
Intercept2.761.471.880.066
Group8.762.004.38<0.001
Affect Condition0.050.790.060.952
Timing0.100.790.120.904
Group × Affect Condition–1.571.07–1.470.145
Group × Timing–2.501.07–2.340.021
Affect Condition × Timing1.861.111.670.098
Group × Affect Condition × Timing3.821.512.530.013
Nsubject46   
Observations184   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 31
Linear mixed-effects regression assessing the Vigor POMS subscale as a function of Group, Affect Condition, and Timing.
PredictorsBSEtp
Intercept17.521.3313.14<0.001
Group–10.401.82–5.73<0.001
Affect Condition0.880.980.900.372
Timing–0.570.96–0.590.554
Group × Affect Condition0.871.340.650.518
Group × Timing0.481.340.360.720
Affect Condition × Timing–4.931.37–3.58<0.001
Group × Affect Condition × Timing1.301.900.680.495
Nsubject46   
Observations176   

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 32
Linear regression models of negative urgency.
ParameterPredictorsBSEtp
Attribute onset
(τs)		Intercept–2.183.61–0.610.552
NeutralLF0.170.220.760.456
NeutralHF0.130.470.280.779
Negative LF1.122.520.450.661
Negative HF–8.374.15–2.020.057
N subject25  
Observations25  
Tastiness weight
(ωTaste)		Intercept2.920.1519.61<0.001
Neutral LF0.260.460.570.578
Neutral HF0.730.571.280.215
Negative LF–0.510.47–1.080.291
Negative HF–0.170.47–0.360.724
N subject25  
Observations25  
Healthiness weight
(ωHealth)		Intercept2.860.1126.01<0.001
Neutral LF–0.590.36–1.620.122
Neutral HF–0.420.31–1.380.184
Negative LF0.650.391.660.113
Negative HF0.270.271.000.330
N subject25  
Observations25  

  1. Bold p-values indicate p < 0.05.

Appendix 1—table 33
Linear regression models of the restraint subscale of EDE-Q.
ParameterPredictorsBSEtp
Attribute onset
(τs)		Intercept13.8114.000.990.336
Neutral LF0.580.870.660.515
Neutral HF1.501.830.820.424
Negative LF1.459.770.150.883
Negative HF11.8716.090.740.469
N subject25  
Observations25  
Tastiness weight
(ωTaste)		Intercept4.450.518.80<0.001
Neutral LF–2.671.55–1.720.101
Neutral HF1.461.930.760.457
Negative LF–1.281.61–0.790.437
Negative HF–0.281.58–0.170.863
N subject25  
Observations25  
Healthiness weight
(ωHealth)		Intercept3.990.429.42<0.001
Neutral LF1.821.401.300.209
Neutral HF0.081.190.070.949
Negative LF1.401.510.930.366
Negative HF0.451.030.440.668
N subject25  
Observations25  

  1. Bold p-values indicate p < 0.05.

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  1. Blair RK Shevlin
  2. Loren Gianini
  3. Joanna Steinglass
  4. Karin Foerde
  5. E Caitlin Lloyd
  6. Kelsey Hagan
  7. Laura A Berner
(2026)
Negative affect influences the computations underlying food choice in bulimia nervosa
eLife 14:RP105146.
https://doi.org/10.7554/eLife.105146.5