notes | modelling

# modelling process:

1. pre-model: design experiment, build models, simulation, params/model recovery.

2. modelling: fitting real data, model caparison, model validation.

3. post-modelling: latent variable analysis, reports.

# attention:

1. ultimate goal should be: what a model can/cannot tell us about the mind?

# notes:

1. triangle - computational psychiatry

disease - model - data

disease: psychiatric, neurologicol, etc. scientific goal. related to clinical practice

model: or theory, hypothesis, assumption. e.g. connectivity, graph theory, free energy, stochastic process(reinforcement), etc.

data: behavioural, cognition, emotion, motor, sensory, decision making, etc. observable via tasks/self-report. supplementary data from genome/mri.