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model_parameters
model_parameters("RW1972")
## $name
## [1] "alphas" "betas_on" "betas_off" "lambdas"
##
## $default_value
## [1] 0.4 0.4 0.4 1.0
| 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 |
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
| 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 |
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
| 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 |
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
| 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 |
model_parameters("HDI2020")
## $name
## [1] "alphas"
##
## $default_value
## [1] 0.4
model_parameters("HD2022")
## $name
## [1] "alphas"
##
## $default_value
## [1] 0.4
| alphas |
\(\alpha\) |
Learning rate for presented stimulus |
## $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
| 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 |
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
| 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 |
## $name
## [1] "alphas"
##
## $default_value
## [1] 0.4
| alphas |
\(\alpha\) |
Placeholder; no meaning. |
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