Neural Networks and Cognitive Control Flashcards
3 goals of science
- description: what are we observing
- prediction: what will we observe next
- Explanation: why is that what we observe
what was the early explanation of planetary motion
ancient greece: helios driving his chariot- when ever he was in the mood
- not a goo d prediction
3 models of planetary motion after helios
- ptolemaic geocentric model
- copernican heliocentric model
- keplers laws of planetary motion
keplers law of plantetary motion accounted for all: (3)
- description: observed data
- Prediction: extremely accurate predictions
- explanation: ‘simple’ elegant framework
- -> the things we want in our science
why do we have quantitative models? (7)
- data requires a model to be understood and explained
- verbal theorizing alone cannot substitute for quantitative analysis
- there are always several alternative models that vie for explanation of data, and we must compare those alternatives
- model comparisons rests on both quantitative evaluation and intellectual and scholary judgement
- even seemingly intuitive verbal theories can turn out to be incoherent or ill specified
- only instantiation in a quantitative model ensures that all assumptions of a theory have been identified and test
what is the fundemental trade off with models
- more detailed = more accurate but hard to understand
- have to make it specific to what you are looking for
- –> simplicity & elegance versus complexity & accuracy
what is the goal of modelling
maximize explanatory power while minimizing complexity
what types of cognitive models are there? (4)
- mathematical models (fitts law)
- symbolic models
- dynamic systems models
- hybrid models
ways you can wire neurons is called
topology
4 types of topology models
- feedforward
- simple recurrent (elman)
- self organizing map (kohonen)
- fully recurrent
adjusts weights based on difference bewtween actual output and correct output (shown an object and you say what it is an dif you are right you are told and if wrong you are told, you can adjust)
supervised learning
adjust weights based on correlations in input “neurons that fire together wire together” (keep exposing it to lots of input and it learns what things go together)
unsupervised learning
adjusts weights based on difference between actual reward and expected reward
reinforcement learning
a feedforward model is what type of learning? how does it learn weights?
supervised
- back propagation
a single layer hopfield network is what type of learning
unsupervised learning (unsupervised recurrent networks)