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.