# Simulations of the Autocorrelated Bayesian Sampler

This vignette provides a brief introduction of the Autocorrelated Bayesian Sampler (ABS; Zhu et al. (2024)) and the `R` scripts for running simulations of ABS using `samplr` package.

ABS is a sequential sampling model that assumes individuals draw autocorrelated samples from their memory of hypotheses based on their posterior beliefs, which is called “posterior of hypotheses”. These samples are subsequently integrated to perform various tasks: ABS is capable of generating estimates, confidence intervals, and response times for estimation tasks, as well as choices, confidence judgments, and response times for two-alternative force choice (2AFC) tasks. Notably, ABS employs different stopping rules depending on the type of tasks. In this vignette, we will outline the process of simulating ABS under both stopping rules. In addition, although ABS assume a normal distribution for the posterior of hypotheses, our package allows users to use custom distributions, which will also illustrated in this vignette.

## Fixed stopping rule

### A normal distribution for posterior of hypothesis

The fixed stopping rule means that a fixed number of samples are drawn to complete the tasks such as estimations and confidence intervals. This rule applies to tasks such as estimation tasks.

Estimation tasks involve participants providing estimates, such as the number of stimuli (e.g., dots) on a screen or offering confidence intervals of that counts at a specified level.

In this vignette, we will begin by generating several random numbers to represent the stimuli counts. We will assume the estimation task consists of 10 trials, wherein participants are tasked with estimating the number of dots displayed on the screen in each trial.

    require(samplr)
    #> Loading required package: samplr
    set.seed(123)

    trial_stim <- sample(20:25, 10, replace=TRUE)
    print(trial_stim)
    #>  [1] 22 25 22 21 21 25 22 24 23 25

ABS employs the [R6][R6::R6Class] object-oriented programming (OOP) system. Thus we need to construct a new object before running simulations. In the initialising step, we need to provide the values of the following arguments:

- the proposal width of the MC3 sampler, `width`;
- the number of chains of the MC3 sampler, `n_chains`;
- the lower bound of the non-decision time, `nd_time`;
- the variability of the non-decision time, `s_nd_time`;
- the rate parameter of the Erlang distribution for the response time, `lambda`.

In this section, we employ the normal posterior distribution, which requires specifying the values of `distr_name` and `distr_params`. The `distr_name` argument should be set to “norm” to indicate the normal distribution. The `distr_params` argument specifies the standard deviation of the normal distribution. This can be either a single numeric value, indicating a fixed standard deviation across all trials, or a numeric vector of the same length as the stimuli, specifying the standard deviation for each trial. It is important to note that ABS assumes the stimulus value is the mean of the normal distribution, thus there is no need to specify the mean separately.

    abs_model <- Zhu23ABS$new(
      width = 1, n_chains = 3, nd_time = 0.3,
      s_nd_time = 0.2, lambda = 10, distr_name = 'norm', distr_params = 1
    )

The simulation process is conducted using the `simulate` function, which requires two arguments: `stopping_rule` and `start_point`. Based on the assumptions of ABS, individuals employ a fixed stopping rule for the estimation task, meaning a predetermined number of samples are drawn. Therefore, `stopping_rule` is set to `'fixed'`.

The `start_point` argument is set to `NA` by default, indicating that the starting point of the first trial for each MC3 chain is randomly selected from the posterior of hypotheses, and the starting points of subsequent trials for each chain are set to the last sample of the same chain in the previous trial. Alternatively, users have the option to specify starting points for each trial, ensuring that the length of starting points matches the length of `trial_stim`. It is important to note that when specified, all chains share the same starting point in each trial, and it will break the dependency of samples between adjacent trials.

To demonstrate the usage of `start_point`, we will run the simulation twice: once with the default settings and again with specified starting points.

Additionally, when `stopping_rule = 'fixed'`, two further arguments are required:

- the fixed number of samples, `n_sample`;
- the stimuli of the estimate task, `trial_stim`;

    abs_model$simulate(stopping_rule = 'fixed',
                       n_sample = 5, trial_stim = trial_stim)

The results of the `simulate` method save in the field `sim_results`. Users can get access to the results by running `abs_model$sim_results`. It is important to note that ABS assumes the response time for each trial follows an Erlang distribution, with the shape parameter equal to the length of the samples that is determined by `n_sample` and the rate parameter `lambda` has been specified above. The response time value provided in the table is a random number drawn from the Erlang distribution.

The table below presents the simulation results, displaying only the samples from the cold chain of the MC3 sampler in the `samples` column. These samples will be used for generating the responses of ABS. With the default setting of `start_point = NA`, the starting point of the first trial was randomly drawn from the normal distribution `N(22, 1)`. From the second trial, the starting point was set to the last point of the previous trial. For instance, the starting point of the second trial was 23.19899 in this simulation. It is important to note that when `start_point = NA`, only the starting point of the first trial was included in the samples of ABS. The starting points of subsequent trials served merely as initializers for the sampler and were excluded from the ABS samples.

    knitr::kable(abs_model$sim_results)

trial | samples | stimulus | rt | point_est
--- | --- | --- | --- | ---
1 | 22.46092, 21.19585, 23.16227, 23.07685, 23.19899 | 22 | 0.6962331 | 23.19899
2 | 22.81852, 22.94063, 24.40037, 24.06716, 23.57613 | 25 | 0.5775903 | 23.57613
3 | 22.02688, 21.93149, 22.02688, 22.06456, 21.75045 | 22 | 0.8583808 | 21.75045
4 | 21.21486, 21.34514, 21.53813, 21.27593, 21.27593 | 21 | 0.8593033 | 21.27593
5 | 21.83892, 20.57877, 20.56013, 20.27643, 20.27643 | 21 | 0.8591766 | 20.27643
6 | 21.03049, 21.03049, 21.03049, 20.56761, 20.56761 | 25 | 0.6687111 | 20.56761
7 | 20.56761, 21.79455, 21.05602, 21.59945, 21.59945 | 22 | 0.7996729 | 21.59945
8 | 22.63067, 22.63067, 22.63067, 23.84692, 23.84692 | 24 | 0.8465396 | 23.84692
9 | 22.77392, 22.77392, 21.72141, 22.47818, 22.47818 | 23 | 1.026247 | 22.47818
10 | 22.47818, 22.47818, 22.47818, 24.14924, 24.35889 | 25 | 0.7949772 | 24.35889

In the upcoming simulations, we will run the simulation with specified `start_point`. To proceed, let us generate some starting points for the simulation.

    start_point <- runif(length(trial_stim), 20, 25)
    print(start_point)
    #>  [1] 23.15387 22.60421 23.29811 23.64680 22.43411 21.92228 20.03417 20.01842
    #>  [9] 24.97468 20.53943

It is worth noticing that before rerunning the simulation, users should either create a new object or reset the `sim_results` using the `reset_sim_results` method.

    abs_model$reset_sim_results()
    abs_model$simulate(stopping_rule = 'fixed',
                       start_point = start_point,
                       n_sample = 5,
                       trial_stim = trial_stim)

The following table shows the simulation results with specified starting points. We notice that the first sample of each trial follows the order of `start_point` and is included in the samples of ABS.

    knitr::kable(abs_model$sim_results)

trial | samples | stimulus | rt | point_est
--- | --- | --- | --- | ---
1 | 23.15387, 22.94857, 23.32674, 22.86275, 22.84844 | 22 | 0.9391273 | 22.84844
2 | 22.60421, 23.98278, 23.16726, 23.83018, 24.05747 | 25 | 0.960213 | 24.05747
3 | 23.29811, 23.29811, 23.25266, 21.96929, 22.97895 | 22 | 0.8303294 | 22.97895
4 | 23.64680, 23.64680, 20.38653, 20.90503, 20.90503 | 21 | 0.843562 | 20.90503
5 | 22.43411, 22.43411, 23.02500, 22.82066, 22.82066 | 21 | 0.7319466 | 22.82066
6 | 21.92228, 22.28465, 22.17285, 23.14012, 24.37679 | 25 | 0.7435707 | 24.37679
7 | 20.03417, 20.16216, 20.61667, 20.93413, 21.34805 | 22 | 0.6252088 | 21.34805
8 | 20.01842, 20.01842, 19.95175, 19.91607, 21.29672 | 24 | 0.7063606 | 21.29672
9 | 24.97468, 23.03316, 22.99983, 22.99983, 22.99983 | 23 | 0.5919077 | 22.99983
10 | 20.53943, 20.53943, 22.07215, 22.38586, 22.43730 | 25 | 0.5465358 | 22.43730

In addition to performing point estimation, ABS can also simulate confidence interval estimation by the `confidence_interval` method.

    abs_model$confidence_interval(0.5)

The following table shows the interval estimation on the level of 0.5. `conf_interval_l` and `conf_interval_u` represent the lower and the upper bounds.

    knitr::kable(abs_model$sim_results)

trial | samples | stimulus | rt | point_est | conf_interval_l | conf_interval_u
--- | --- | --- | --- | --- | --- | ---
1 | 23.15387, 22.94857, 23.32674, 22.86275, 22.84844 | 22 | 0.9391273 | 22.84844 | 22.86275 | 23.15387
2 | 22.60421, 23.98278, 23.16726, 23.83018, 24.05747 | 25 | 0.960213 | 24.05747 | 23.16726 | 23.98278
3 | 23.29811, 23.29811, 23.25266, 21.96929, 22.97895 | 22 | 0.8303294 | 22.97895 | 22.97895 | 23.29811
4 | 23.64680, 23.64680, 20.38653, 20.90503, 20.90503 | 21 | 0.843562 | 20.90503 | 20.90503 | 23.64680
5 | 22.43411, 22.43411, 23.02500, 22.82066, 22.82066 | 21 | 0.7319466 | 22.82066 | 22.43411 | 22.82066
6 | 21.92228, 22.28465, 22.17285, 23.14012, 24.37679 | 25 | 0.7435707 | 24.37679 | 22.17285 | 23.14012
7 | 20.03417, 20.16216, 20.61667, 20.93413, 21.34805 | 22 | 0.6252088 | 21.34805 | 20.16216 | 20.93413
8 | 20.01842, 20.01842, 19.95175, 19.91607, 21.29672 | 24 | 0.7063606 | 21.29672 | 19.95175 | 20.01842
9 | 24.97468, 23.03316, 22.99983, 22.99983, 22.99983 | 23 | 0.5919077 | 22.99983 | 22.99983 | 23.03316
10 | 20.53943, 20.53943, 22.07215, 22.38586, 22.43730 | 25 | 0.5465358 | 22.43730 | 20.53943 | 22.38586

An advantage of R6 is that it allows method chaining, which means that we can simulate the point and confidence interval estimation in one line of code.

    abs_model$reset_sim_results()
    abs_model$simulate(
      stopping_rule = 'fixed',
      n_sample = 5,
      trial_stim = trial_stim,
      start_point=start_point)$confidence_interval(0.5)
    knitr::kable(abs_model$sim_results)

trial | samples | stimulus | rt | point_est | conf_interval_l | conf_interval_u
--- | --- | --- | --- | --- | --- | ---
1 | 23.15387, 23.15387, 21.32755, 21.17221, 22.25114 | 22 | 0.7928794 | 22.25114 | 21.32755 | 23.15387
2 | 22.60421, 23.58632, 24.49342, 25.26377, 25.25655 | 25 | 0.9453281 | 25.25655 | 23.58632 | 25.25655
3 | 23.29811, 23.29811, 23.01077, 21.48146, 22.46782 | 22 | 0.9608318 | 22.46782 | 22.46782 | 23.29811
4 | 23.64680, 23.64680, 23.64680, 23.86076, 22.98811 | 21 | 1.022691 | 22.98811 | 23.64680 | 23.64680
5 | 22.43411, 21.75967, 21.57249, 20.92830, 20.56626 | 21 | 0.9489973 | 20.56626 | 20.92830 | 21.75967
6 | 21.92228, 23.13268, 24.05023, 23.89194, 22.83065 | 25 | 1.087054 | 22.83065 | 22.83065 | 23.89194
7 | 20.03417, 21.18261, 22.23694, 20.48587, 20.77174 | 22 | 1.15924 | 20.77174 | 20.48587 | 21.18261
8 | 20.01842, 20.41099, 20.70444, 20.39197, 21.92217 | 24 | 0.5480482 | 21.92217 | 20.39197 | 20.70444
9 | 24.97468, 24.97468, 24.96424, 24.19963, 23.86320 | 23 | 0.6626406 | 23.86320 | 24.19963 | 24.97468
10 | 20.53943, 20.97900, 20.93043, 24.20136, 24.15534 | 25 | 0.9091687 | 24.15534 | 20.93043 | 24.15534

### A custom posterior of hypotheses

In this section, we will illustrate how to employ custom distributions to ABS with a fixed stopping rule, using the same experimental scenario and the same stimuli. First, we specify our custom posterior function.

     custom_post_func <- function(x){
      if (x >= 19 & x < 22){
        return(0.3)
      } else if (x >= 22 & x < 24) {
        return(0.6)
      } else if (x >= 24 & x < 26) {
        return(0.1)
      } else {
        return(0)
      }
    }

To employ a custom posterior of hypotheses, two special arguments are required in the initialisation step: `custom_distr` and `custom_start`. The `custom_distr` argument accepts a list of custom posterior functions, one for each trial, matching the length of the stimuli. The `custom_start` argument specifies the first starting point of the Zhu23ABS sampler, i.e., the initial sample of the entire simulation.

It is important to distinguish `custom_start` from `start_point` in the `simulate()` function. `custom_start` is only necessary when providing a custom posterior of hypotheses, whereas `start_point` can be used for both Gaussian and custom posteriors. `custom_start` initializes the `Zhu23ABS` sampler without breaking the dependency of samples between trials. In contrast, `start_point` sets the starting point for each trial, thus breaking the dependency between samples of adjacent trials. Additionally, if users provide a vector of `start_point`, a value for `custom_start` is still required as a placeholder and will be overwritten by `start_point`.

    custom_func_list <- replicate(
      length(trial_stim), custom_post_func, simplify = FALSE
    )
    abs_model <- Zhu23ABS$new(
      width = 1, n_chains = 3, nd_time = 0.3,
      s_nd_time = 0.2, lambda = 10,
      custom_distr = custom_func_list, custom_start = 23
    )
    abs_model$simulate(
      stopping_rule = 'fixed',
      n_sample = 5,
      trial_stim = trial_stim
    )

The following table shows the simulation results with a custom posterior. We notice that the first sample of the first trial equals to the value of `custom_start`.

    knitr::kable(abs_model$sim_results)

trial | samples | stimulus | rt | point_est
--- | --- | --- | --- | ---
1 | 23.00000, 21.65776, 24.54276, 23.28013, 21.58476 | 22 | 0.939751 | 21.58476
2 | 20.19480, 20.98478, 22.94399, 23.09041, 23.87754 | 25 | 0.7334943 | 23.87754
3 | 22.45328, 23.07457, 20.38108, 20.66667, 21.56507 | 22 | 1.064708 | 21.56507
4 | 25.33200, 19.58683, 19.06603, 20.86223, 20.86223 | 21 | 0.7530445 | 20.86223
5 | 25.98513, 23.84136, 24.07200, 19.73874, 20.51456 | 21 | 0.621633 | 20.51456
6 | 23.48351, 21.85166, 20.35451, 21.06815, 19.97855 | 25 | 0.5564545 | 19.97855
7 | 19.64951, 19.10088, 19.21834, 19.15238, 20.56728 | 22 | 1.020738 | 20.56728
8 | 19.99142, 20.41566, 20.41566, 19.60917, 22.16439 | 24 | 1.090832 | 22.16439
9 | 21.46472, 22.29925, 23.05005, 22.20548, 22.20548 | 23 | 0.6287007 | 22.20548
10 | 22.75063, 22.26817, 20.41036, 20.14076, 20.76399 | 25 | 0.6757852 | 20.76399

## Relative stopping rule

### A normal distribution for posterior of hypothesis

The relative stopping rule means that the model counts the difference in evidence between the two hypotheses, and terminates the sampling process whenever the accumulated difference exceeds a threshold. This rule applies to tasks such as 2AFC.

2AFC is a cognitive task that asks participants to make judgments between two alternatives. For instance, in the random dot motion (RDM) task, participants are presented with a screen where most dots move coherently in either the left or right direction, and they’re asked to perceive the correct direction.

ABS is able to describe and simulate this cognitive process. Similarly, we will begin by randomly generating 10 directions from the set `c('left', 'right')` to represent the stimuli in the RDM task.

    trial_stim <- factor(sample(c('left', 'right'), 10, TRUE))

In 2AFC, ABS employs a sampling process and converts the samples into “evidence” supporting either the left or right responses. Specifically, if the sample falls below the decision boundary, it supports the first level in `trial_stim`, which in our example is “left”; otherwise, the sample will support the second level, which is “right”. According to the assumptions of ABS, it employs a ‘relative’ stopping rule: It counts the difference in evidence between the two responses, and terminates the sampling process whenever the accumulated difference exceeds a threshold.

To simulate the 2AFC of ABS, we need to initialize a new ABS model and then use the `simulate` method with `stopping_rule = 'relative'`. The posterior of hypotheses will be normal distribution. The following arguments are required:

- the threshold of the accumulated difference, `delta`;
- the decision boundary that split the posterior of hypotheses, `dec_bdry`;
- the discriminability, `discrim`, which determine the mean values of the distributions for the posterior of hypotheses. It is analogous to the concept of “sensitivity” in signal detection theory, representing the distance between the mean values of two distributions: one for “left” stimuli and the other for “right” stimuli in 2AFC. Under the assumption of ABS, these two distributions are symmetric around 0, so their mean values are `-discrim/2` and `discrim/2`, respectively.
- the stimuli, `trial_stim`;
- the prior on responses, `prior_on_resp`, which determines the prior preference for the stimuli. The default setting is `c(1, 1)`, representing an unbiased prior `Beta(1, 1)`. Users can modify this to reflect different prior preferences.
- the prior dependency, `prior_depend`, which controls whether the prior on responses changes based on the previous stimulus. The default setting is `TRUE`, which adjusts the prior by adding 1 to the `prior_on_resp` according to the previous stimulus.
- the maximum length of the MC3 sampler, `max_iterations`, which determines the maximum length of the MC3 sampler. The sampler will stop when the length of the samples exceeds this value, even if the samples have not met the relative stop rule. The default setting is 1000, suitable for most cases, but users can adjust it based on the discriminability and the relative stopping rule.

To demonstrate the usage of these arguments, we will also run the simulation twice: once with the default settings and again with some of the settings modified.

    abs_model2 <- Zhu23ABS$new(
      width=1, n_chains = 3, nd_time = 0.3, s_nd_time = 0.2,
      lambda = 10, distr_name = 'norm', distr_params = 1
    )
    abs_model2$simulate(
      stopping_rule = 'relative', delta = 4, dec_bdry = 0,
      discrim = 1, trial_stim = trial_stim
    )

The table below presents the simulation results, including the simulated response, response time, confidence, and point estimates. It is important to note that in the simulation of 2AFC, the length of the sample sequences may vary due to ABS utilizing a relative stopping rule. To illustrate its mechanism, let us examine the sequences in the first two trials as an example.

    knitr::kable(abs_model2$sim_results)

trial | samples | response | stimulus | accuracy | rt | confidence | point_est
--- | --- | --- | --- | --- | --- | --- | ---
1 | 0.4275131, 0.1622293, 0.3256617, 0.2739435 | right | right | 1 | 0.8842087 | 0.8333333 | 0.2739435
2 | -0.9458666, -1.9165161, -1.0475625, -1.4366053, -1.4366053 | left | left | 1 | 0.6003210 | 0.7500000 | -1.4366053
3 | -0.9621143, -0.9621143, -1.0762124 | left | left | 1 | 0.4214597 | 0.8333333 | -1.0762124
4 | -0.2486073, -0.2486073, -0.2486073 | left | right | 0 | 0.5587766 | 0.8333333 | -0.2486073
5 | 0.9018419, 1.6166903, 1.6817583 | right | right | 1 | 0.7195611 | 0.8333333 | 1.6817583
6 | 1.6817583, 1.9277007, -0.7676286, 2.3873046, 0.9870808 | right | right | 1 | 0.8551096 | 0.7500000 | 0.9870808
7 | 0.9870808, 0.9870808, 0.9870808 | right | right | 1 | 0.5159783 | 0.8333333 | 0.9870808
8 | -0.41717131, -0.70702914, 2.21601114, 1.39502444, 1.76113883, 1.76113883, -0.05608772, -1.00232885, -0.58802069, 0.52335972, 0.46143706, 0.52596694, 0.52596694 | right | right | 1 | 1.4518855 | 0.6250000 | 0.5259669
9 | 0.2868992, -0.9103864, -0.9103864, -0.9103864, -0.7006005, -0.9896238, -0.9896238 | left | left | 1 | 1.1940603 | 0.7000000 | -0.9896238
10 | -1.5721525, 0.7261261, 0.4822092, 0.4822092, -1.7215719, -1.7215719, -0.3113327, -0.2768050, -0.2768050 | left | left | 1 | 1.6029330 | 0.6666667 | -0.2768050

The prior on responses, set to `c(1, 1)`, corresponds to an unbiased Beta distribution `Beta(1, 1)`. Let us consider the first trial: the initial sample, 0.4275131, falls above the decision boundary of 0, supporting “right”. Consequently, the posterior on responses shifts to `Beta(1, 2)`. With a relative difference of 1 between the amounts of evidence supporting both stimuli, which is lower than the relative stopping rule, the sampling process continues. Subsequent samples are analysed similarly: the posterior adjusts according to the samples until the relative difference meets the stopping rule. In this case, the last sample supporting “right” results in a posterior of `Beta(1, 5)`, satisfying the stopping rule and prompting ABS to return a “right” response.

With the `prior_depend=TRUE` argument, the prior on responses for the second trial depends on the stimulus of the first trial. Given that “right” was the correct response in the first trial, the prior on responses for the second trial is `Beta(1, 2)`. Since no starting points are provided, ABS began with 0.2739435, the last sample of the first trial, but this sample is excluded from the samples and from the calculation of the posterior on responses. The process then proceeds similarly to the first trial. In this instance, after eight samples in total, the posterior reaches `Beta(6, 2)`, satisfying the stopping rule and resulting in a “left” response.

It is important to clarify two points. Firstly, the prior on responses is not accumulated when `prior_depend=TRUE`. In the example above, the prior on responses for the third trial is `Beta(2, 1)` rather than `Beta(2, 2)`. Secondly, it’s crucial to distinguish the starting points of ABS from those in the drift diffusion model (DDM; Ratcliff (1978), Ratcliff & McKoon (2008)). The bias of the starting points in ABS is independent from the bias of the responses, which is captured by the prior on responses.

In the upcoming simulations, we will run the simulation again with two arguments changed: `start_point` and `prior_depend`. To proceed, let us generate some starting points for the simulation.

    start_point <- runif(length(trial_stim), -3, 3)
    print(start_point)
    #>  [1]  2.49825567 -2.57474596  1.86057651 -0.08457317 -1.16859165  0.70724300
    #>  [7] -1.08447977  0.91402088  2.49508984  0.41447419

Next, we will put these starting points into the ABS model and rerun the simulation. It is worth noting that in this simulation, the starting point of each trial precisely matches what we provided, and all starting points are included in the calculation of the posterior of responses. Additionally, it is important to observe that the prior on responses resets to `Beta(1, 1)` at the start of every trial.

    abs_model2$reset_sim_results()
    abs_model2$simulate(
      stopping_rule = 'relative', delta = 4, dec_bdry = 0,
      discrim = 1, trial_stim = trial_stim, start_point = start_point,
      prior_depend = FALSE
    )
    knitr::kable(abs_model2$sim_results)

trial | samples | response | stimulus | accuracy | rt | confidence | point_est
--- | --- | --- | --- | --- | --- | --- | ---
1 | 2.498256, 2.498256, 2.498256, 4.653175 | right | right | 1 | 0.5254800 | 0.8333333 | 4.6531752
2 | -2.574746, -1.481331, -1.887003, -2.847988 | left | left | 1 | 0.5513093 | 0.8333333 | -2.8479882
3 | 1.8605765, 0.8811842, 0.6171134, 0.0614605 | right | left | 0 | 0.7555300 | 0.8333333 | 0.0614605
4 | -0.08457317, 0.25636442, 0.69083272, -0.97940277, 0.96035322, 1.21691271, 1.21691271, 0.59006379 | right | right | 1 | 1.1302366 | 0.7000000 | 0.5900638
5 | -1.1685916, -0.9746845, -1.2742347, -0.3545086 | left | right | 0 | 0.9798226 | 0.8333333 | -0.3545086
6 | 0.7072430, 0.7072430, 0.6659027, 1.1382033 | right | right | 1 | 0.5360159 | 0.8333333 | 1.1382033
7 | -1.08447977, -0.29601230, 0.93947644, -0.06303025, -1.35547336, -1.35547336 | left | right | 0 | 0.7949616 | 0.7500000 | -1.3554734
8 | 0.9140209, 0.9140209, 0.9140209, 0.9140209 | right | right | 1 | 0.9697987 | 0.8333333 | 0.9140209
9 | 2.495090, 2.400942, 2.569325, 2.569325 | right | left | 0 | 0.9800438 | 0.8333333 | 2.5693248
10 | 0.4144742, -0.4003186, 0.9795133, -0.7381408, -0.7385936, -0.7385936, 2.2072473, -2.5076334, -2.5076334, -2.5076334 | left | left | 1 | 1.6487114 | 0.6666667 | -2.5076334

### A custom posterior of hypotheses

In this section, we will illustrate how to employ custom distributions to ABS with a relative stopping rule, using the RDM task with the same stimuli. First, we specify two custom posterior functions for the “left” and “right” stimuli, and create a list of the custom posterior function according to the stimuli.

    custom_post_left <- function(x){
      if (x >= -3 & x < -1){
        return(0.25 * x + 0.75)
      } else if (x >= -1 & x < 0) {
        return(-0.25 * x + 0.25)
      } else {
        return (0)
      }
    }

    custom_post_right <- function(x){
      if (x >= -1 & x < 1){
        return(0.25 * x + 0.25)
      } else if (x >= 1 & x < 3) {
        return(-0.25 * x + 0.75)
      } else {
        return (0)
      }
    }

    custom_func_list <- lapply(trial_stim, function(stim) ifelse(stim=='left', custom_post_left, custom_post_right))

Then, we initialise a `Zhu23ABS` object with a list of `custom_func_list` and a value of `custom_start`.

    abs_model2 <- Zhu23ABS$new(
      width=1, n_chains = 3, nd_time = 0.3, s_nd_time = 0.2,
      lambda = 10, custom_distr = custom_func_list, custom_start = -0.1
    )
    abs_model2$simulate(
      stopping_rule = 'relative', delta = 4, dec_bdry = 0,
      discrim = 1, trial_stim = trial_stim
    )

The following table shows the simulation results with a custom posterior. We notice that the first sample of the first trial equals to the value of `custom_start`.

    knitr::kable(abs_model2$sim_results)

trial | samples | response | stimulus | accuracy | rt | confidence | point_est
--- | --- | --- | --- | --- | --- | --- | ---
1 | -0.1000000, -0.1000000, 1.1016574, -0.4119108, 0.4092011, 0.1937227, 0.1727152, 0.7248535, 1.5414128, 1.4869701 | right | right | 1 | 1.3962562 | 0.6666667 | 1.4869701
2 | 1.48697, 1.48697, 1.48697 | right | left | 0 | 0.7224212 | 0.8333333 | 1.4869701
3 | 1.48697011, -0.06471386, -0.06471386, -0.06471386, -0.99778854 | left | left | 1 | 0.8641009 | 0.7500000 | -0.9977885
4 | -0.41446080, -0.03089549, 1.42896914, 1.42896914, 2.20108267, 1.42896914, 0.21296796, 1.49176136, -0.57370256, 0.88807227, 0.98585835 | right | right | 1 | 1.6384660 | 0.6428571 | 0.9858584
5 | 1.6889577, 1.8963487, 0.5086753 | right | right | 1 | 0.6274639 | 0.8333333 | 0.5086753
6 | 0.30462902, 1.41494998, 0.03257175 | right | right | 1 | 0.4416011 | 0.8333333 | 0.0325717
7 | -0.32957081, -0.16841672, 0.04310975, 0.48995349, 0.11556503, 0.16657204, 0.70833944 | right | right | 1 | 1.4056088 | 0.7000000 | 0.7083394
8 | 0.4987224, 0.5140892, 0.8615744 | right | right | 1 | 0.9689298 | 0.8333333 | 0.8615744
9 | 0.8615744, -0.5686874, -0.3232613, -1.6409327, -1.1384310, -1.1384310, -0.8294954 | left | left | 1 | 1.0342848 | 0.7000000 | -0.8294954
10 | -0.8531594, -1.2648573, -1.2744982 | left | left | 1 | 1.1686782 | 0.8333333 | -1.2744982

## References

Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https://doi.org/10.1162/neco.2008.12-06-420
Zhu, J.-Q., Sundh, J., Spicer, J., Chater, N., & Sanborn, A. N. (2024). The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times. Psychological Review, 131(2), 456–493. https://doi.org/10.1037/rev0000427