contingency

library(MinervaTime)

Model a simple contingency scenario. When A is presented for 20 time steps as the first event, C is followed at time point 50. When B is presented 20 time steps as the first event, D is followed at time point 50. E is the ever-present context. 5 trials of intermixed training.

trial_timesteps = 200
timeline <- rbind(make_trial(1,c("A","C","E"),c(1,50,1),c(20,20,199)),
                  make_trial(2,c("B","D","E"),c(1,50,1),c(20,20,199)),
                  make_trial(3,c("A","C","E"),c(1,50,1),c(20,20,199)),
                  make_trial(4,c("B","D","E"),c(1,50,1),c(20,20,199)),
                  make_trial(5,c("A","C","E"),c(1,50,1),c(20,20,199)),
                  make_trial(6,c("B","D","E"),c(1,50,1),c(20,20,199)),
                  make_trial(7,c("A","C","E"),c(1,50,1),c(20,20,199)),
                  make_trial(8,c("B","D","E"),c(1,50,1),c(20,20,199)),
                  make_trial(9,c("A","C","E"),c(1,50,1),c(20,20,199)),
                  make_trial(10,c("B","D","E"),c(1,50,1),c(20,20,199))
                  )

# convert timeline to environment vectors
temporal_vectors <- make_temporal_vectors(trial_timesteps, overlap = 2)
event_vectors <- make_event_vectors(LETTERS[1:10],10)

timeline_vectors <- timeline_to_vector(timeline,
                                       event_vectors,
                                       temporal_vectors,200)

Run model

## define basic model parameters

library(RsemanticLibrarian) # for cosine_x_to_m

# initialize memory with 5 noise vectors
environment_matrix <- timeline_vectors[[1]]
noise <- matrix(runif(5*dim(environment_matrix)[2],-1,1)*.05,
                 nrow = 5,
                 ncol = dim(environment_matrix)[2])

memory <- noise

tau <- 3

## Run the model for each trial

model_results <- list()

for(t in 1:length(timeline_vectors)) {
  environment_matrix <- timeline_vectors[[t]]
  
  save_echo <- matrix(0,
                      nrow=dim(environment_matrix)[1],
                      ncol=dim(environment_matrix)[2])
  
  # intra-trial encoding and retrieval
  for (i in 1:dim(environment_matrix)[1]){
    
    activations <- c(cosine_x_to_m(environment_matrix[i,],
                                   memory)^tau)
    echo <- colSums(memory*activations)
    save_echo[i,] <- echo
    memory <- rbind(memory,environment_matrix[i,])
  
  }
  
  A_expectation <- rowMeans(save_echo[,1:10])
  B_expectation <- rowMeans(save_echo[,11:20])
  C_expectation <- rowMeans(save_echo[,21:30])
  D_expectation <- rowMeans(save_echo[,31:40])

  
  model_results[[t]] <- list(A_expectation = A_expectation,
                             B_expectation = B_expectation,
                             C_expectation = C_expectation,
                             D_expectation = D_expectation,
                             save_echo = save_echo)

}

plot model results

Shows the model’s expectation for the B event across a trial window, after the 1st, 5th, and 10th trial.

# trial 9 was A <-> C
# trial 10 was B <->D

plot(model_results[[1]]$A_expectation)

plot(model_results[[1]]$B_expectation)

plot(model_results[[1]]$C_expectation)

plot(model_results[[1]]$D_expectation)

plot(model_results[[2]]$A_expectation)

plot(model_results[[2]]$B_expectation)

plot(model_results[[2]]$C_expectation)

plot(model_results[[2]]$D_expectation)

plot(model_results[[3]]$A_expectation)

plot(model_results[[3]]$B_expectation)

plot(model_results[[3]]$C_expectation)

plot(model_results[[3]]$D_expectation)

plot(model_results[[4]]$A_expectation)

plot(model_results[[4]]$B_expectation)

plot(model_results[[4]]$C_expectation)

plot(model_results[[4]]$D_expectation)

plot(model_results[[9]]$A_expectation)

plot(model_results[[9]]$B_expectation)

plot(model_results[[9]]$C_expectation)

plot(model_results[[9]]$D_expectation)

plot(model_results[[10]]$A_expectation)

plot(model_results[[10]]$B_expectation)

plot(model_results[[10]]$C_expectation)

plot(model_results[[10]]$D_expectation)