Lecture 7 Flashcards
Information processing view
Development is inherently information processing
Analyzes cognition down into basic processes
Formalizes by coming up with computational/mathematical models of cognitive processes
Mechanizes by creating simulations of cognition as a way of trying to explain cognitive processing
Selection problem
There is so much information in the environment, difficult to decide what is relevant
Encoding problem
What kind of code is used to transmit information, how do we pattern information so it can be reliably communicated?
Tradeoff relationship between resiliency and efficiency based on how much redundancy there is.
Statistical relevance
The more alternatives are ruled out, the more informative an event is.
Selectional relevance
Meaningful things that you learn/read.
Cognitive science
Solving problems is a central task to generate a scientific explanation of the processes that generate scientific explanation.
Naturalistic imperative
Redundancy
Signal and noise overlap creates an ambiguity of interpretation.
Increasing redundancy diminishes ambiguity.
The amount of redundancy depends on the environment, where a rich environment can afford a sacrifice of efficiency.
Resiliency
Fault tolerance: resistant to noise, and signal recovery.
Computers
Logical machine
Software: logical side
Logical organization of information, encoded into abstract symbolic propositions.
Abstract, the relation between symbols is just to each other either irrelevant or absent.
Hardware: machine side
Causal structure: premises allow us to get to a conclusion.
Isomorphic structure: causal structure exactly parallels the logical structure of an argument.
Computer metaphor
Hardware has no plasticity, separating learning from plasticity.
Development becomes a narrative history of how the hardware and software happened to come together.
Hardware is irrelevant to understanding the software.
Development as it is functional and it develops by functioning
Design stance
Making a machine that can do the same as humans can allow us to
- observe the mind
- avoid participants please/deceive on experiments
- knowledge of rules, principles, and strategies as they were programmed and could be applied by humans.
Computer metaphor: criticisms
Unrealistic claim that hardware is irrelevant to software
- constraints can be a very important part of cognition
- The body may create machinery/conditions to manage limitations
Computers are not brain-like, hardware is static: not capable of qualitative change
- Embodiment/plastic nature of the brain may be important for intelligence.
- Role of plasticity in Intelligence
- Hardware is not irrelevant to software.
Software is not realistic/intelligent.
- Learning by manipulation of abstract symbols is difficult and not relevant to relevance.
- Difficulty with relevance and salience
Differences between propositional and procedural knowledge.
- No mutual dependency between each.
Fodor: No logical manipulations can run on to get you to a stronger logic.
- Emergent functions allow logic from outside and create stronger logic that can’t be logically generated.
Flowcharts: criticisms
Flowing along the arrow is information but often equivocation in the use of the term information.
Process information that is statistically relevant, not ideas.
