PSYCH 354- in class notes

Titchner

school of psych

• unified psychology through its methodology

• invented new techniques to turn experiments to uncover properties of mind

what was the crisis in psychology

no one knew what psychology was, there was too many schools of thought

• it made up of different perspectives and ideas rather than a singe concept

how did psyhology get furhter fragmented

people emphasized content therefoe deemphasized the foundational concepts that tied psychology together

• heavy focus on isolated facts miss out on foundational elements that unite the discipline

difference between cognitive science and psychology

cognitive science is more interdiciplinary and requires research from all discipines involved, whereas psychology is more focused on studying human behaviour and the metnl processes behind those behvaiours

information processing

a feedback loop originating in cybernetics

• symbolic processing within a computer

cybernetics

the idea that an agent acts in the world there is a consequence that acts back on that agent from the world

how is information processing used in cognitive science

it is used as an abstract notion of information processing that glues togetehr the disciplines

• acts as a common language allowing them to communcate across disciplines

• by using the analogy that the mind is a computer it turns the mind into a inaminate abstract object into a physical componet an mkaes it easier to talk about

the information processing hypothesis

the human mind is a complex system that receives, stores, retrieves, transforms, and transmits information

• or cognition is computation

• or cognition is information processing

how deos the brain take on connectionist foundations

the brain process information but there are no rules or symbols but instead symbolic interactions within the brain

• processors send signals to other processors and so on…

essentially what is the main idea of embodied cognitive science

by acting on the world, manipulating the world

• the world ofeeres potential actions u can take part in (ka affordances)

Man as Machine → Rene Descartes

believed there was competition between physical vs non physical account of the mind- that are interrelated

• moved towards mehcnaizing thought

logicism → george boole

philosphy and a mathematical perspective into what human thought might be like if explained mechanically

• introduced mathematical rules for combining and evaluating symbols

• human thinking is the same as logical operations defined in his algebra

20th century truth tables

evolved boolean logic

• logical operations over 2 symbols, that can either be true or false → assigns truth values, while lgogical expressions assign truth value based on telationship between variables

• 2 varaibles alwyas has 4 possible states

what was Claude Elwood Shannon’s contribution

used boolean algebra to test the circuit before building it→ more efficient and cost effecitve

• translated mathematical logic into physical, electrical switches

• write circutis based on boolean algebta to create physical circuits→ output is the sam even if physical input is diffrent

2 levles of description

physically or describe the funciton its computing

algorithmic level

the basic components- the order of the sequence can change the outcome of the algorithim

• doesnt ask what or why, but rather answer the how question → what stteps are carried out in what order to permit the output of this combinationn lock

architecture

the univeral builing blocks, required to bring sequence of steps in algorithim to life

• symbolic account

• primitive information processing components- built into computer

functional analysis within the architecture

what each component does (funciton) → physical nature ignored

fucnitonal analysis

define a big task that an information process is carrying out → collecting experimental evidence to create a sequence of more basic/simpler functions ta if carried out in order bring the big function to life

• tote umit→ pure info processing, purely funcitonal

• test → treated as information, being either true or false

ryles regress

functional not physical

• keep breaking down funcitons into simpler parts, decompostion further and further in a never ending loop

problem with ryles regress

if theories only use functional terms u are unable to explain anything

• explaining something leads into making more functions

how did robert cummins explain how to escaepe ryles regress

introduced the the idea of subsumption

• after going through steps of ryles regress (definition, analysis) there comes a point where breaking down any further is redundant and must use subsumption

causal subsumption

building a simple machine to carry out a simple function (ex. neuron- either fires or it doesnt- simple)

• brings our simple fucntions to life, and fucnitonal description becomes a dunctional explnation

multiple realization

different physical things can bring the same funciton to life

• shows that physical properties dont matter as long as theyre on the same architectural level

• ex. a relay, vacuum tube, and transistor all perform the same funciton but all look differently

what are the multiple levels of investigation

• computational

• algorithmic

• fucntional architecture

• implementational

*levels ask diff questions, methodologies, angague, and diff types of training. (one discipline is more suited for certain levels)

computational level

define, typically with math

• asks what infromation processing problme is being solved

• answered with formal proofs

algorithmic level

brings fucntion to life

• asks the question of what sequence of steps- what algorithim- is being used to solve the information processing problem

• answered with experimentla studies of behaviour

• more than 1 algoirithim can perform at 1 computation

  • specifying an algoithim or program - infer the underlying algorithim

architectural level

asks what primitive operations is the systems algorihtim constructed→ decomposed algorithim into basic primitive info wired in the device

• answering this question involves a combinaiton of bevaioural observations and the methods of cognitive neuroscience

• what wired inprocesses are avaialbel to carrry out the algorithim

implementation levle

asks what physical porperties are responsible for birnging the archtectures primitives into being?

• answeing this question appeals to the narueal sciences

the explanatory goal

computational → algorithmic → architectura → implementational

• goal is to finf the 1 architecture for the 1 participant carrying out the algorihtim carrying out the computation we are studying

• connectivism approach

classical approach

rule governed manipulation of symbols

disembodied mind

claim that the mind is completely distinct from physical world

• words like mind, soul, spirit are synonymous in thhat theyre completley separate from physical world

mechanizing the infinite

the menral is infinite while the physical is not

• this difference cuases the possible seperation between the 2

the turing machine

device was central to making digital computers

• question answering machine

• physical device that does the rule governed manpulation of symbols

implication of the universal machine

can do anything that can be computed

• any algorithim perceived a turing machine is able to carry it out

physical symbol systems

the idea that finite physical device that can generate an infinite variety of behaviour

• brings to life idea of information processing

• rules in machine head

• device can be used to explain human cognition → brain as a physical symbol system that does cognition, perception, etc.

recursion

concept from math that states how finite things are able to produce infinite amounts of behavior

context-free grammar

rewrites rules

• can generate inifinite choices since its recursive

comparitive cognitive science

the comparison between theory/model with the participant

Eliza and the turing test

demonstrated weakness of turing test

• since it was able to pass even though it was designed specifically to not understand language

weak equivalence

2 systems are weakly equivalent if they produce the exact same input-output mapping

• they solve the same overall information-processing problem or compute the same function

• established strictly at the computational level

strong equivalence

when 2 systems are strongly equivalent if they compute the same input-output funcition and do so using the same algorithim grounded in the same architecture

• to prove this researchers would collect specific behavioural artifacts and avidence

• requiewa identity across multiple levels of investigation→ computational, algorithmic, and architectural

why do they run experiments in cog sci

to establish strong equivalence