## $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 |
## $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 |
## $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 |
## $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 |
## $name
## [1] "alphas"
##
## $default_value
## [1] 0.4## $name
## [1] "alphas"
##
## $default_value
## [1] 0.4| Name | Symbol | Description |
|---|---|---|
| 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| 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 |
## $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 |
## $name
## [1] "alphas"
##
## $default_value
## [1] 0.4| Name | Symbol | Description |
|---|---|---|
| alphas | \(\alpha\) | Placeholder; no meaning. |