The parsimony model comes from the idea of Occam’s razor: We choose the simple model that has more explanatory power.

The instance theory is a good model to explain the lexical decision task but it is not the only one. However, it simply makes it popular.

## What is a Good Model?

A good model should be presumably

• plausibility
• balance of parsimony and goodness-of-fit
• coherence of the underlying assumptions
• easy to understand when it breaks down
• consistency with known results
• especially with the simple and basic phenomena
• ability to explain rather than describe data
• extent to which model predictions can be falsified through experiments.

## How to choose a model?

It takes some thinking and calculations to choose a model.

## Compare Models

Many methods deals with the balance between parsimony and goodness-of-fit

• Information criteria: AIC and BIC
• Minumum description length
• Bayes factors

### Information Criteria: IC

We calculate the IC of all the models at hand, and specify the delta

$$\Delta _i = \mathrm{IC}_i - \operatorname{min} \mathrm{IC}$$

calculate the weights of models

$$w_i = \frac{ \exp{-\Delta_i/2} }{ \sum_{m=1}^M \exp{-\Delta_m/2} }$$

We prefer the model with larger weight $w_i$.

If we use AIC for IC in the formula, this weight $w_i$ is called Akaike weight; If we use BIC, the weight $w_i$ is called Schwarz weight.

### MDL

Fisher Information Approximation is one of the methods to determine the minimum description length.

Bayes factors

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