# model_parameters

#### RW1972

    model_parameters("RW1972")

    ## $name
    ## [1] "alphas"    "betas_on"  "betas_off" "lambdas"
    ##
    ## $default_value
    ## [1] 0.4 0.4 0.4 1.0

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Learning rate for presented stimulus
betas_on, betas_off | \(\beta_{on},\beta_{off}\) | Intensity of presented and absent target
lambdas | \(\lambda\) | Maximum learning supported by target

#### MAC1975

    model_parameters("MAC1975")

    ## $name
    ## [1] "alphas"     "min_alphas" "max_alphas" "betas_on"   "betas_off"
    ## [6] "lambdas"    "thetas"     "gammas"
    ##
    ## $default_value
    ## [1] 0.4 0.1 1.0 0.4 0.4 1.0 0.2 0.3

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Starting associability (learning rate) for presented stimulus
min_alphas, max_alphas | \(\alpha_{min}, \alpha_{max}\) | Minimum and maximum associability for stimulus
betas_on, betas_off | \(\beta_{on},\beta_{off}\) | Intensity of presented and absent target
lambdas | \(\lambda\) | Maximum learning supported by target
thetas | \(\theta\) | Attentional learning rate parameter for stimulus
gammas | \(\gamma\) | Attentional learning weight for stimulus

#### PKH1982

    model_parameters("PKH1982")

    ## $name
    ## [1] "alphas"     "min_alphas" "max_alphas" "betas_ex"   "betas_in"
    ## [6] "lambdas"    "thetas"     "gammas"
    ##
    ## $default_value
    ## [1] 0.4 0.1 1.0 0.4 0.3 1.0 1.0 0.3

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Learning rate for presented stimulus
min_alphas, max_alphas | \(\alpha_{min}, \alpha_{max}\) | Minimum and maximum associability for stimulus
betas_in, betas_ex | \(\beta_{in},\beta_{ex}\) | Learning rates for inhibitory and excitatory associations
lambdas | \(\lambda\) | Maximum learning supported by target
thetas | \(\theta\) | Decay/strengthening associability rate parameter for stimulus
gammas | \(\gamma\) | Attentional learning weight for stimulus

#### SM2007

    model_parameters("SM2007")

    ## $name
    ## [1] "alphas"  "lambdas" "omegas"  "rhos"    "gammas"  "taus"    "order"
    ##
    ## $default_value
    ## [1] 0.4 1.0 0.2 1.0 1.0 0.2 1.0

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Learning rate for presented stimulus
lambdas | \(\lambda\) | Maximum learning supported by target
omegas | \(\omega\) | Weakening rate for presented stimulus
rhos | \(\rho\) | Salience contribution for unconditioned activation of target
gammas | \(\gamma\) | Contribution of stimulus to comparison process
taus | \(\tau\) | Learning rate for operator switch
order | \(order\) | Order for the comparison process

#### HDI2020/HD2022

    model_parameters("HDI2020")

    ## $name
    ## [1] "alphas"
    ##
    ## $default_value
    ## [1] 0.4

    model_parameters("HD2022")

    ## $name
    ## [1] "alphas"
    ##
    ## $default_value
    ## [1] 0.4

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Learning rate for presented stimulus

#### TD

    model_parameters("TD")

    ## $name
    ## [1] "alphas"    "betas_on"  "betas_off" "lambdas"   "gamma"     "sigma"
    ##
    ## $default_value
    ## [1] 0.05 0.40 0.40 1.00 0.95 0.90

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Learning rate for presented stimulus
betas_on, betas_off | \(\beta_{on},\beta_{off}\) | Intensity of presented and absent target
lambdas | \(\lambda\) | Maximum learning supported by target
gamma | \(\gamma\) | Temporal discount parameter
sigma | \(\sigma\) | Rate of decay for eligibility traces

#### ANCCR

    model_parameters("ANCCR")

    ## $name
    ##  [1] "reward_magnitude"  "betas"             "cost"
    ##  [4] "temperature"       "threshold"         "k"
    ##  [7] "w"                 "minimum_rate"      "sampling_interval"
    ## [10] "use_exact_mean"    "t_ratio"           "t_constant"
    ## [13] "alpha"             "alpha_reward"      "use_timed_alpha"
    ## [16] "alpha_exponent"    "alpha_init"        "alpha_min"
    ## [19] "add_beta"          "jitter"
    ##
    ## $default_value
    ##  [1] 1.000 1.000 0.000 1.000 0.600 1.000 0.500 0.001 0.200 0.000 1.200    NA
    ## [13] 0.020 0.200 0.000 1.000 1.000 0.000 0.000 1.000

Name | Symbol | Description
--- | --- | ---
reward_magnitude | \(CW_{j,j}\) | Reward magnitude for target
betas | \(\beta\) | Unconditional value for target
cost | \(cost\) | Response cost
temperature | \(temperature\) | Temperature for softmax function
threshold | \(\theta\) | Threshold to become meaningful causal target/putative cause
k,alpha,alpha_reward | \(k,\alpha,\alpha_{reward}\) | Learning rates for predecessor representation, predecessor representation contingency, and causal weights.
w | \(w\) | Weight for net contingency computation
minimum_rate | \(minimum\_rate\) | Lower bound on perceivable event rates
sampling_interval | \(sampling\_interval\) | Time interval to update base rate calculations
use_exact_mean | \(use\_exact\_mean\) | Whether to use exact mean calculations for \(\alpha\)
t_ratio | \(t\_ratio\) | Ratio to calculate time constant
use_timed_alpha | \(use\_timed\_alpha\) | Whether to use exponential decay for \(\alpha\)
alpha_exponent, alpha_init, alpha_min | \(alpha\_exponent,alpha\_init, alpha\_min\) | Parameters for exponential decay of \(\alpha\)
add_beta | \(add\_beta\) | Whether to add \(\beta\) to dopaminergic activity
jitter | \(jitter\) | Magnitude of perceptual noise for simultaneous events

#### RAND

    model_parameters("RAND")

    ## $name
    ## [1] "alphas"
    ##
    ## $default_value
    ## [1] 0.4

Name | Symbol | Description
--- | --- | ---
alphas | \(\alpha\) | Placeholder; no meaning.