post ambiguity slowing
Creators: name: danieli, gal
name: Meiran, Nachshon
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: **this is an updated pre-registration of https://osf.io/zychv. The analysis will be performed on previously collected data, from several experiments, resulting in 145 subjects: experiments 1&amp;2 from Givon et al. (2019), the baseline blocks from https://osf.io/tphr2 and the behavioral data from https://osf.io/z8xqp. To check the feasibility of the proposed analysis, we ran the preliminary steps on one subject, in order to verify we can extract reliable estimates per trial. Data cleaning and preparation for modeling: For NAPS (emotion task) analysis • We planned to remove participants with accuracy lower than 0.5 (chance-level)- all participant were over chance. • 26 NAPS images had poor agreement with the norm (suggesting that the norm was inappropriate for this population) because they did not reach norm adherence rate of over 65% (slightly below chi-square significance) and were removed. • All reaction times (RT) were re-calculated from ms to sec, error responses were recoded from "0" to "2" • Mean RT and standard deviation (SD) were calculated individually per participant • RT's lower than 0.2 sec or higher than 3.5 SDs per participant per condition were removed Resulting with 22,663 trials from 145 participants All the exclusion criteria are routinely used in model fittings and will be implemented for the faces task as well. Model fit: We used hierarchal modeling for the linear ballistic accumulation (Brown &amp; Heathcote, 2008) fit using the 'ggdmc' package (Lin &amp; Heathcote, 2019) in R (R core team, 2021). Prior distributions parameters were set to: pop.mean&lt;-c(2,2,.3,2,.5) pop.scale&lt;-c(.6,.6,.1,.8,.8) prior.beta &lt;- c(1,1,1,1,1) and the model was set with two independent accumulators' one for correct responses and one for error responses. The standard deviation of both drift-rates was set to 1. burn in-period was set with 2000 MCMC samples and model fit was set with 8000 samples and thinning length of 12. We aimed for Gelman Rubin (PSRF) values closest to 1 as possible. Gelman Rubin values for the emotions task range between 1.00008 to 1.003. For the Faces task, we will use pop.mean priors that are either taken from prior works in which LBA was used, a 2-choice task was used, and mean RT/error rate are as-close as -possible to those which we observed. Alternatively, we will try to adjust the pop.mean priors which we use for the Emotion task so that they will match the RT/PE in the faces task. To achieve that, we will assume a similar nondecision time (meaning that pop.mean for t0 will be the same as in the Emotion task) while mean_v.true will be set so that it will yield a predicted mean RT similar to that in the Faces task. Specifically, RT-correct minus t0=B/mean_v.true (B= b-A, as usual). LOTO analysis: To achieve per-trial estimates of the drift-rate parameters for each participant, we will use the 'Leave One Trial Out' methodology (Gluth &amp; Meiran, 2019). In this analysis, we take a specific data set, remove one trial, and fit the LBA model to the new data, with all parameters fixed to those of the complete data, except the parameter of interest. Then we return this trial, remove the next trial, and fit the model again, and so on until we fitted the model with all trials removed. This allows us to obtain an estimation of the parameter of interest per trial, such that a trial with a higher parameter estimation, represents a lower then average "real" parameter and vice-versa (for rationale see Gluth &amp; Meiran, 2019). For each participant, we will create 10 different groups of size 6 participants (the participant whose data are estimated, and 5 other randomly selected participants). We will run the LOTO in each of these groups, with both drift rate parameters (mean_v.true, mean_v.false) free (and all the other parameters fixed at their maximal-posterior estimates from the ggdmc), but only for the trials of our subject-of-interest (the subject included in all 10 groups). Given that we now fit using hill-climbing-maximum-likelihood, we need starting values. These starting values for the drift rates will be the ggdmc parameter estimates across all participants. All the other LBA parameters will be taken from the individual-participant-estimates of the ggdmc. This will result with 20 parameter estimations per trial for each participant, 10 for the correct response drift rate and 10 for the error response drift rate. It is possible that the estimation process for some trials will not converge. Trials showing an excessive rate of convergence failures (&gt;4) will be removed from the pipeline of upcoming pre-processing. It is also possible that the estimation in some groups will not converge successfully, such that all trial will have the same LOTO estimation. If this will be the case in 1-2 groups for the same participant, further pre-processing will be only for the 8-9 groups for whom estimation successfully converged. Parameter's stability validation: In one attempt to verify the validity of the estimates, we employed the following procedure: To ensure the stability and reliability of the parameter's estimation, we will compute the Pearson correlation between all the 10 runs of parameter estimates. The assumption is that if the across-trial fluctuations in the parameter estimates reflect estimation noise rather than a real signal, the correlations across estimations should be zero. However, if the fluctuations in the parameters across trials are real, the correlations between estimations will be positive, thus showing that we capture real variations and not noise. This method is very different than that proposed by Gluth and Meiran, but it seems to provide a more direct (and intuitive) validation, and it is computationally MUCH lighter. Participants with more than 5% (i.e. 2-3) of correlations being &lt;=0 will be removed from all upcoming analysis because in these participants, the trial-fluctuations may not be real. To get a single parameter estimate per trial, we will average across the estimates (one estimate per randomly selected group) of that trial. This analysis was performed for one subject in the emotions task to verify its feasibility. All 90 correlations (45 for mean_v.true and 45 for mean_v.false) were positive and high (i.e. 0.98-0.99). We will perform this analysis separately for each parameter, per participant from the 145 subjects, in both tasks. ** unfortunately, this analysis did not allow to verify that the LOTO estimates truly capture real variance in the parameters. Therefor we will another analysis for both tasks, similar to that proposed by Gluth &amp; Meiran (2019): • We will use hierarchical modeling for the LBA, with the same prior distributions parameters as described above. We will calculate per participant BIC and sum over participants for full model BIC. • We will run the LOTO analysis, for each participant separately using its own parameters. we choose to run the LOTO analysis this way since we are interested in the variance in the parameters within each participant, theoretically we could have set the rest of the participant's parameters (including the mean.v_true and mean.v_false) to their own parameter's estimations obtained using the ggdmc. In such case their presence will not affect the change in estimation for the participant we are interested in (i.e., the participant for which we are performing the LOTO analysis). this lack of influence on the parameters will be consistent through all trial, thus not affecting the changes in the model's likelihood function. As a result, only the parameters of interest of the specific participant will be free. • We will calculate per trial BIC and ΔBIC (i.e., the difference between the full model's BIC and the specific trial's BIC). This will be the measure for the LOTO-base improvement in the goodness of fit of the model due to the exclusion of one trial from the fitting. • We will simulate multiple (&gt;50) data sets using the participant's parameters and the full model specifications, with one difference, sv will be set at 0. • We will run model fit on the simulated data with the full model specifications, calculate per participant BIC and sum over participants for full model BIC. • We will run the LOTO analysis on the simulated data, as described above (for the real data). • We will calculate per trial BIC and ΔBIC. • We will examine if the LOTO-based improvement in fit (ΔBIC) for the empirical data is greater than 95% of the improvements in the simulations that were generated with sv=0 (the null hypothesis). If so, we will move on with the analysis. Post ambiguity slowing: We plan to perform an analysis similar to post error slowing (PES), using ambiguity (reflected in a small gap between the two drift rates, evident in either low drift to the correct response, low mean_v.true in Trial n-1, or high drift to the error response, high mean_v.false in Trial n-1. Note that the LOTO estimates are reversed, implying that high=low and vice versa) as the independent variable in the model predicting instead of correctness of the response. Given the possible time-series drifts, due to autoregressive and moving average effects, it is not possible to conduct a regression, simply. We will therefore adjust all the variables before conducting the multiple regression analyses. Different adjustments are made for the DV and IV. For the IV (mean_v.true/false), values will be adjusted as follows: For each Trial n, the deviation of this trial from the average of Trials n-2, n-1, n+2 and n+3 will be computed (n+1 is omitted because this is the trial from which we take the DV). For the DV, the deviation of each Trial n's RT/accuracy from Trials n-3, n-2, n+1 and n+2 will be computed (n-1 is omitted because this is where the IV is taken). We will then regress the DVs in Trial n on the IVs in Trial n-1. We predict an effect for the RT's (with prior belief of 2:1 that there will be an effect) but will also explore its existence for the accuracy. Given the nature of the LOTO, these regressions will be computed within each subject, using multilevel modeling, assuming a normal distribution of regression weights in the population. All analysis will be performed for correct trials only! Such that error in trial n will be removed prior to calculating the DV and IV. Hypothesis: We hypothesize that trials following ambiguity (either a low mean_v.true or a high mean_v.false and effectively the discrepancy between the two) would either show RT slowing (if accurate) or would be relatively error-prone than trials following trials with lesser ambiguity. In other words, we predict that there will be a difference in the predicted direction in at least one dependent variable (RT, accuracy). Specifically, we predict that a high mean_v.false for a given trial (reflected in a low LOTO estimation for this parameter) will have a negative weight in the regression. We also predict (with less certainty) that a low mean_v.true (reflected in a high LOTO estimation for this parameter) will have a positive weight in the regression. This reversal is due to the revers nature of the LOTO estimations. Bayesian t-tests as well as null-hypothesis significance t-tests for the regression weights will be perform using the JASP (or similar) program. Previous work in our lab found PES evoked by both errors in the faces task and non-normative responses in the emotions task, but the slowing was mostly evident in the RT's of the faces task (there was also a slowing evident in the emotions task for positive valanced images, when trials were analyzed separately for positive and negative valanced images. see Givon et al.,2022). We use these post-error analysis to predict that we will find that ambiguity would result in slowing in the next face trial, but we will also explore this post ambiguity slowing in the next emotion trial, separately for positive and negative images. Similarly, while post-error effect in the literature are mostly seen in RT, our predictions regard RT as DV, while the prediction for error rate/accuracy are more tentative in nature. ** Additional analyses: For analysis significant analysis we will preform: • An additional hierarchical multiple-regression (multi-level) analysis on LOTO estimates, this time including B in the IV to check whether that the predictive power of the given ambiguity parameter does not result from variance in the B parameter that has "leaked" to the ambiguity parameter. • If the parameter\s will remain significantly predictive of the RT in N+1 when adding B to the regression, we will perform another multi-level regression, with similar pre-processing to the previous one. This time we will predict B at trial N with the ambiguity estimates of trial N-1. The purpose of this analysis is to identify a possible link between the change in the RT to change in the B. We predict that high ambiguity would be associated with a high B in the next trial, explaining RT prolongation in that trial by an increase in caution (high B=low LOTO estimate of B). Brown, S. D., &amp; Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57(3), 153–178. Givon, E., Itzhak-Raz, A., Karmon-Presser, A., Danieli, G., &amp; Meiran, N. (2019). How does the emotional experience evolve? Feeling generation as evidence accumulation. Emotion. Givon, E., Udelsman-Danieli, G., Almagor, O., Fekete, T., Shriki, O., &amp; Meiran, N. (2022). Can Feelings “Feel” Wrong? Similarities between Counter-Normative Emotion Reports and Perceptual Errors. Psychological Science. Gluth, S., &amp; Meiran, N. (2019). Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data. ELife, 8, e42607. Lin, Y.-S., &amp; Heathcote, A. (2019). ggdmc: Cognitive Models (0.2.6.0) [R package]. https://CRAN.R-project.org/package=ggdmc R core team. (2021). R: A language and environment for statistical computing. https://www.R-project.org/