Description Usage Arguments Details References Examples

Modeling Judgments of Frequency with TODAM 2

1 |

`x` |
input handled by TODAM 2. Normal distributed inputs with mean = 0 and sd = 1 / n are allowed. This representation enables discrimination and similarity between different items. See vignette for details. |

`y` |
another input handled by TODAM 2. At least two inputs are needed for the simulation. |

`...` |
other inputs handled by TODAM 2. |

`sqc` |
sequence of the different objects. Each input gets
an ascending number. |

`gamma` |
is the atttention- or learningparameter. Values
between 0 and 1 are allowed. 1 represents perfect learning.
If |

`alpha` |
represents the decay. If |

In the original publication TODAM 2 is more complex
and has more parameters. Especially the design for the input
is a concatenation between item and context. The normal
distributed input has a *mean = 0* and *sd = 1/n*.
A pragmatic solution to make the models input comparable is
to use a binary input like in PASS. There is no explicit argument
for noise.

Convolution:

* F_{i}^{2} = ∑_{i=1} f_{i} * f_{m-i+1} and m = 2n - 1*

Memory:

*M_{t} = α M_{t-1} + γ F_{t}^{2} *

Correlation

*R_{m} = ∑_{(i;j)\in S(m)} F_{t}^{2} there
S(m)(i;j)| -(n-1)/2 ≤ i,j ≤ (n-1)/2 and i-j = m *

Murdock, B. B., Smith, D., & Bai, J. (2001). Judgments of frequency
and recency in a distributed memory model. *Journal of
Mathematical Psychology, 45,* 564–602.
https://doi.org/10.1006/jmps.2000.1339

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