Table 1 Summary of model fits

From: Heterogeneity in strategy use during arbitration between experiential and observational learning

  

Study 1

Study 2

Model

Npar

AIC

OOS acc

Frequency

Nbest (%tot)

AIC

OOS acc

Frequency

Nbest (%tot)

Baseline

4

215.7

0.521

0.205

25 (19.8)

219.8

0.511

0.159

83 (16.8)

Experiential learning

3

207.7

0.539

0.147

21 (16.7)

204.9

0.552

0.060

24 (4.9)

Observational learning

2

197.2

0.569

0.311

40 (31.8)

194.8

0.575

0.190

95 (19.3)

Fixed mixture

6

191.0

0.593

0.115

14 (11.1)

186.0

0.607

0.324

160 (32.5)

Dynamic arbitration

6

191.2

0.595

0.222

26 (20.6)

187.1

0.608

0.267

131 (26.6)

  1. Each of the five models (Npar = number of parameters) was fitted to participants data first using Matlab’s cbm toolbox. Using individual model-fitting, we computed the mean AIC as well as mean out-of-sample accuracy (OOS acc) across participants. OOS accuracy was calculated for each individual by fitting the model on 7 task blocks and using the best-fitting parameters to calculate the likelihood of predicting the participant’s choices in the remaining block (then iterating across all 8 blocks). We then used cbm’s hierarchical Bayesian inference fitting across all five models to compute model frequency. Selecting the best-fitting model for each individual participant (highest model responsibility), we then calculated the number and proportion of participants for whom each model explains their data best (Nbest column).
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