Author: Victor Navarro

# heidi_similarity

#### Victor Navarro

    library(calmr)
    library(ggplot2)
    library(data.table)

# Simulating similarity effects

In Honey and Dwyer (2022), the authors propose that the similarity between the retrieved and nominal saliencies of stimulus representations modulates some quantities in the combination rule. Retrieved saliencies are exclusively for absent stimuli, and are the result of the existing associations between stimuli (see Eq. 8 in the model’s vignette). In contrast, nominal saliencies denote the intensity of stimulus representations when stimuli are presented on a trial (\(\alpha\)).

An intuitive example of the effect that saliency similarity has over responding refers to the effect that weakly retrieved representations have over behavior. The low similarity between a weakly retrieved representation and its nominal representation should result in a reduced effect of the former over behavior. For example, in a typical Pavlovian inhibition paradigm \[A(US)/AX\], an inhibitor (e.g., X) that has a strong effect on behavior when presented will only have a weak effect on behavior if weakly retrieved by a stimulus that has a strong association with it (e.g., A).

Yet, the inspiration for proposing such a general rule was fairly specific. It was an attempt to explain why the introduction of a delay between CS and US stimuli in higher-order conditioning experiments could sometimes enhance responding to the stimulus that was never paired with the US (e.g., A after AX/X(US) or X(US)/AX).

## Reproducing the simulation presented in the paper

In the paper, the authors plot the similarity between the retrieved saliencies and the nominal saliencies for stimulus X during a sensory preconditioning experiment in which a short delay between X and the US was used (group Reduced) or not (group Same). The effect of introducing a delay was simulated as X having a saliency of .36; otherwise, its saliency was .40. The saliencies of A and the US were fixed to .30 and .50, respectively, for both conditions.

    df <- data.frame(
      Group = c("Same", "Reduced"),
      P1 = c("10A(X_a)", "10A(X_a)"),
      R1 = c(FALSE, FALSE),
      P2 = c("10(X_a)(US)", "10(X_b)(US)"),
      R2 = c(FALSE, FALSE)
    )
    params <- get_parameters(df, model = "HD2022")
    params$alphas[] <- c(.30, .40, .50, .36)
    model <- run_experiment(df,
      model = "HD2022",
      parameters = params
    )

### Plotting the similarity between saliencies

This plot is not currently supported in the package, but it can be easily generated by passing the \(A \rightarrow X\) association as one of the alphas for the internal function used to calculate the similarity `calmr:::.alphaSim`.

    vs <- results(model)$vs[s1 == "A" & s2 == "X" & phase == "P1"]
    vs[
      , nominal_alpha := ifelse(group == "Reduced", mean(.36, .40), .40)
    ][
      ,
      similarity := calmr:::.alphaSim(value, nominal_alpha)
    ]

    vs |>
      ggplot(aes(x = trial, y = similarity, linetype = group)) +
      geom_line() +
      theme_bw() +
      labs(x = "Trial", y = "Similarity", linetype = "Group")

### Plotting the distribution of responding

This one is a little bit trickier because the figure in the manuscript effectively contains many experiments varying the number of XA trials before starting the first-order conditioning phase. To address this, we run multiple simulations with different experimental designs.

    ntrials <- 1:10
    df <- data.frame(
      Group = c(paste0("S", ntrials), paste0("R", ntrials)),
      P1 = rep(paste0(ntrials, "A(X_a)"), 2),
      R1 = FALSE,
      P2 = rep(c("10(X_a)>(US)", "10(X_b)>(US)"), each = 10),
      R2 = FALSE,
      P3 = "1A#",
      R3 = FALSE
    )
    head(df)
    #>   Group      P1    R1           P2    R2  P3    R3
    #> 1    S1 1A(X_a) FALSE 10(X_a)>(US) FALSE 1A# FALSE
    #> 2    S2 2A(X_a) FALSE 10(X_a)>(US) FALSE 1A# FALSE
    #> 3    S3 3A(X_a) FALSE 10(X_a)>(US) FALSE 1A# FALSE
    #> 4    S4 4A(X_a) FALSE 10(X_a)>(US) FALSE 1A# FALSE
    #> 5    S5 5A(X_a) FALSE 10(X_a)>(US) FALSE 1A# FALSE
    #> 6    S6 6A(X_a) FALSE 10(X_a)>(US) FALSE 1A# FALSE

Run the model.

    model <- run_experiment(df,
      model = "HD2022",
      parameters = params
    )

And now we can manually plot the distribution of responding among stimuli `model$rs`.

    rs <- results(model)$rs[phase == "P3" & s2 == "US"]
    rs[, `:=`(
      trial = trial - 11,
      group_lab = ifelse(substr(group, 1, 1) == "R", "Reduced", "Same")
    )]

    rs |>
      ggplot(aes(x = trial, y = value, colour = s1, linetype = group_lab)) +
      geom_line() +
      theme_bw() +
      labs(x = "Trial", y = "R-value", colour = "stimulus", linetype = "Group") +
      facet_wrap(~s2)

### Some final notes

In their paper, Honey and Dwyer do not completely specify the rules for choosing a reference value for the similarity calculation whenever more than one nominal stimulus has been experienced. For example, in the above simulation, we use two nominal versions of the X stimulus (X_a and X_b), and so, whenever the model has to compute the similarity between retrieved (i.e., \(A \rightarrow X\)) and conditioned saliency values, we encounter the problem of having to choose among at least two conditioned values (X_a or X_b). Although the authors of the paper chose the saliency for the nominal X that was conditioned with the US (i.e., X_b), they do not specify how this choice should be made in other, less intuitive situations. As a way to avoid solving this issue, the current implementation of the similarity rule uses the average of the nominal stimuli as a reference value for the similarity calculation. For this specific simulation case, this implementation reduces the effect of similarity over the distribution of responding.