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Summary: Cognitive Science, Third Edition, Chapters 7-13

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  • Course
  • Inleiding tot de Cognitiewetenschap
  • Institution
  • Universiteit Utrecht (UU)
  • Book
  • Cognitive Science

A summary of chapters 7-13 of the book 'Cognitive Science, an introduction to the study of mind', Third Edition. Part of the course Artificial Intelligence at Utrecht University. The Network Approach - The Evolutionary Approach - The Linguistic Approach - The Emotional Approach - The Social Approac...

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  • No
  • 7, 8, 9, 10, 11, 12, 13
  • October 28, 2017
  • 31
  • 2017/2018
  • Summary

Subjects

  • artificial intelligence
  • cognitive science
  • network
  • evolutionary
  • linguistic
  • emotional
  • social
  • agents
  • robots
  • intelligence
  • cognition
  • utrecht
  • jay friedenberg
  • gordon silv
  • ai
  • introduction to cognitive science

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  • Summary Cognitive Science - Introduction to Cognitive Science
  • Cognitive Science: An Introduction to the Study of Mind (Friedenberg, Silverman) C1-5
  • Cognitive Science, Third Edition, Hoofdstuk 2 - 6
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  • Kunstmatige Intelligentie
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7.​ ​The​ ​Network​ ​Approach

Influenced​ ​by​ ​the​ ​principles​ ​of​ ​operation​ ​and​ ​organization​ ​of​ ​real-world​ ​brains.
Connectionism​​ ​tries​ ​to​ ​understand​ ​how​ ​the​ ​mind​ ​performs​ ​certain​ ​kinds​ ​of​ ​operations​ ​via
the​ ​construction​ ​of​ ​an​ ​artificial​ ​neural​ ​network​ ​(ANN)​​ ​-​ ​a​ ​computer​ ​simulation​ ​of​ ​how
populations​ ​of​ ​actual​ ​neurons​ ​perform​ ​tasks.

Artificial​ ​Neural​ ​Networks
Traditional​ ​computers​ ​are​ ​serial​ ​processors​:​ ​perform​ ​one​ ​computation​ ​at​ ​a​ ​time.​ ​The​ ​brain,
as​ ​well​ ​as​ ​ANNs,​ ​are​ ​parallel​ ​distributed​ ​processors​:​ ​large​ ​numbers​ ​of​ ​computing​ ​units
perform​ ​their​ ​calculations​ ​in​ ​parallel.​ ​Knowledge-based​ ​approach​:​ ​One​ ​conceptualizes​ ​the
problem​ ​and​ ​its​ ​solution​ ​in​ ​terms​ ​of​ ​symbols​ ​and​ ​transformations​ ​on​ ​the​ ​symbols​ ​(used​ ​a​ ​lot
in​ ​AI).​ ​Behavior-based​ ​approach​:​ ​A​ ​network​ ​is​ ​allowed​ ​to​ ​produce​ ​a​ ​solution​ ​on​ ​its​ ​own.
This​ ​does​ ​not​ ​involve​ ​the​ ​use​ ​of​ ​symbols​ ​(ANNs).​ ​Representations​ ​are​ ​inherent​ ​in​ ​ANNs​ ​but
do​ ​not​ ​exist​ ​in​ ​them​ ​in​ ​the​ ​form​ ​of​ ​symbols.​ ​They​ ​exist​ ​in​ ​most​ ​networks​ ​as​ ​a​ ​pattern​ ​of
activation​ ​among​ ​the​ ​network’s​ ​elements​ ​-​ ​distributed​ ​representation​.​ ​Local
representation​:​ ​in​ ​the​ ​form​ ​of​ ​activation​ ​in​ ​a​ ​single​ ​node​ ​in​ ​a​ ​network.
Pro:​​ ​They​ ​are​ ​capable​ ​of​ ​learning​ ​—>​ ​adaptively​ ​change​ ​their​ ​responses​ ​over​ ​time​ ​as​ ​they
are​ ​presented​ ​with​ ​new​ ​information​ ​(but​ ​also​ ​possible​ ​in​ ​machines​ ​that​ ​use​ ​symbolic
methods).

Characteristics​ ​of​ ​ANNs:
● Only​ ​exist​ ​as​ ​software​ ​simulations​ ​that​ ​are​ ​run​ ​on​ ​a​ ​computer
● Each​ ​neuron​ ​is​ ​represented​ ​as​ ​a​ ​node​,​ ​and​ ​the​ ​connections​ ​between​ ​nodes​ ​are
represented​ ​as​ ​links​.
● Signal​ ​node:​ ​activation​ ​value​ ​—>​ ​runs​ ​along​ ​the​ ​link​ ​that​ ​connects​ ​it​ ​to​ ​another
node(s)
● Input​ ​>​ ​threshold​ ​value​ ​—>​ ​fire
● Links​ ​have​ ​weights​:​ ​specify​ ​the​ ​strength​ ​of​ ​a​ ​link.​ ​Higher​ ​value,​ ​higher​ ​weight.

Early​ ​conceptions​ ​of​ ​Neural​ ​Networks
First​ ​researchers​ ​to​ ​propose​ ​how​ ​biological​ ​networks​ ​might​ ​function:​ ​McCulloch​ ​and​ ​Pitts,
1943.​ ​They​ ​assumed​ ​each​ ​neuron​ ​had​ ​a​ ​binary​ ​output,​ ​it​ ​could​ ​either​ ​send​ ​out​ ​a​ ​signal​ ​or
not​ ​send​ ​out​ ​a​ ​signal.​ ​Donald​ ​O.​ ​Hebb​ ​(1949)​ ​was​ ​the​ ​first​ ​to​ ​propose​ ​how​ ​changes​ ​among
neurons​ ​might​ ​explain​ ​learning​ ​—>​ ​Hebb​ ​rule:​ ​when​ ​one​ ​cell​ ​repeatedly​ ​activates​ ​another,
the​ ​strength​ ​of​ ​the​ ​connection​ ​between​ ​two​ ​cells​ ​is​ ​increased.​ ​He​ ​defined​ ​2​ ​types​ ​of​ ​cell
groupings:
1. Cell​ ​assembly:​​ ​a​ ​small​ ​group​ ​of​ ​neurons​ ​that​ ​repeatedly​ ​stimulate​ ​one​ ​another
2. Phase​ ​sequence:​​ ​a​ ​group​ ​of​ ​connected​ ​cell​ ​assemblies​ ​that​ ​fire​ ​synchronously​ ​or
nearly​ ​synchronously
Rosenblatt​ ​introduced​ ​in​ ​1958​ ​the​ ​perceptron​:​ ​neural​ ​nets​ ​designed​ ​to​ ​detect​ ​and​ ​recognize
patterned​ ​information​ ​about​ ​the​ ​world,​ ​store​ ​this​ ​information,​ ​and​ ​use​ ​it​ ​in​ ​some​ ​fashion.
They​ ​also​ ​learn​ ​from​ ​experience:​ ​can​ ​modify​ ​their​ ​connection​ ​strengths​ ​by​ ​comparing​ ​their
actual​ ​output​ ​with​ ​a​ ​desired​ ​output​ ​called​ ​the​ ​teacher​.

Back​ ​Propagation​ ​and​ ​Convergent​ ​Dynamics

,Three​ ​layer​ ​network:
1. Input​ ​layer​​ ​-​ ​a​ ​representation​ ​of​ ​the​ ​stimulus​ ​is​ ​presented
2. Hidden​ ​layer​​ ​-​ ​feeds​ ​activation​ ​energy​ ​to​ ​an​ ​output​ ​layer
3. Output​ ​layer​​ ​-​ ​generates​ ​a​ ​representation​ ​of​ ​the​ ​response
Error​ ​signal:​​ ​the​ ​difference​ ​between​ ​the​ ​actual​ ​and​ ​the​ ​desired​ ​outputs.​ ​The​ ​network​ ​uses
the​ ​error​ ​signal​ ​to​ ​modify​ ​the​ ​weights​ ​of​ ​the​ ​links.​ ​The​ ​kind​ ​of​ ​training​ ​based​ ​on​ ​error
feedback​ ​is​ ​called​ ​the​ ​generalized​ ​delta​ ​rule​​ ​or​ ​the​ ​back-propagation​​ ​learning​ ​model.

NETtalk​​ ​Is​ ​an​ ​ANN​ ​designed​ ​to​ ​read​ ​written​ ​English.​ ​Presented​ ​written​ ​letters​ ​—>
pronounces​ ​them​ ​—>​ ​fed​ ​to​ ​a​ ​speech​ ​synthesizer​ ​for​ ​the​ ​production​ ​of​ ​the​ ​sounds.​ ​System
consists​ ​of​ ​3​ ​layers.

Connectionist​ ​Approach:
Pro​:
● The​ ​similarity​ ​between​ ​network​ ​models​ ​and​ ​real-life​ ​neural​ ​networks:​ b ​ iological
plausibility​.
○ Artificial​ ​Networks​ ​share​ ​general​ ​structural​ ​and​ ​functional​ ​correlates​ ​with
biological​ ​networks
○ Artificial​ ​networks​ ​are​ ​capable​ ​of​ ​learning
○ Artificial​ ​networks​ ​react​ ​to​ ​damage​ ​in​ ​the​ ​same​ ​way​ ​that​ ​human​ ​brains​ ​do:
neural​ ​networks​ ​demonstrate​ ​graceful​ ​degradation​​ ​-​ ​gradual​ ​decrease​ ​in
performance​ ​with​ ​increased​ ​damage​ ​to​ ​the​ ​network.​ ​Small​ ​amounts​ ​of
damage​ ​—>​ ​small​ ​reductions​ ​in​ ​performance.
● Displays​ ​interference​​ ​(2​ ​sets​ ​of​ ​information​ ​are​ ​similar​ ​in​ ​content​ ​and​ ​interfere​ ​with
each​ ​other)​ ​and​ ​generalization​​ ​(represented​ ​by​ ​the​ ​ability​ ​to​ ​apply​ ​a​ ​learned​ ​rule​ ​to
a​ ​novel​ ​situation)
Con​:
● Biological​ ​plausibility​ ​should​ ​also​ ​be​ ​viewed​ ​as​ ​problematic
○ Real​ ​neurons​ ​are​ ​massively​ ​parallel,​ ​it​ ​is​ ​not​ ​yet​ ​possible​ ​to​ ​simulate​ ​parallel
processing​ ​of​ ​this​ ​magnitude.
○ Most​ ​networks​ ​show​ ​a​ ​convergent​ ​dynamics​​ ​approach,​ ​the​ ​activity​ ​of​ ​such​ ​a
network​ ​eventually​ ​dies​ ​down​ ​and​ ​reaches​ ​a​ ​stable​ ​state.​ ​This​ ​is​ ​not​ ​the​ ​case
for​ ​brain​ ​activity.​ ​Real​ ​neural​ ​networks​ ​are​ ​oscillatory​ ​and​ ​chaotic.
● Networks​ ​may​ ​have​ ​inadequate​ ​learning​ ​rules
○ Stability-plasticity​ ​dilemma​:​ ​states​ ​that​ ​a​ ​network​ ​should​ ​be​ ​plastic​ ​enough
to​ ​store​ ​novel​ ​input​ ​patterns;​ ​at​ ​the​ ​same​ ​time,​ ​it​ ​should​ ​be​ ​stable​ ​enough​ ​to
prevent​ ​previously​ ​encoded​ ​patterns​ ​form​ ​being​ ​erased.​ ​The​ ​fact​ ​that​ ​ANNs
show​ ​evidence​ ​of​ ​being​ ​caught​ ​in​ ​this​ ​dilemma​ ​is​ ​useful​ ​because​ ​it​ ​may​ ​offer
some​ ​insights​ ​into​ ​human​ ​interference.
○ Catastrophic​ ​interference:​​ ​occurs​ ​in​ ​instances​ ​in​ ​which​ ​a​ ​network​ ​has
learned​ ​to​ ​recognize​ ​a​ ​set​ ​of​ ​patterns​ ​and​ ​then​ ​is​ ​called​ ​on​ ​to​ ​learn​ ​a​ ​new​ ​set.
The​ ​newly​ ​learned​ ​patterns​ ​suddenly​ ​and​ ​completely​ ​erase​ ​the​ ​network’s
memory​ ​of​ ​the​ ​original​ ​patterns.
○ In​ ​supervised​ ​networks​,​ ​a​ ​“teacher”​ ​is​ ​necessary​ ​for​ ​the​ ​network​ ​to​ ​learn.
But​ ​where​ ​does​ ​this​ ​teacher​ ​come​ ​from?

,Semantic​ ​Networks
In​ ​semantic​ ​networks​​ ​each​ ​node​ ​has​ ​a​ ​specific​ ​meaning​ ​and,​ ​therefore,​ ​employs​ ​local
representation​ ​of​ ​concepts.​ ​Has​ ​been​ ​adopted​ ​by​ ​cognitive​ ​psychologists​ ​as​ ​a​ ​way​ ​to
explain​ ​the​ ​organization​ ​and​ ​retrieval​ ​of​ ​information​ ​in​ ​long-term​ ​memory.

Characteristics​ ​of​ ​Semantic​ ​Networks:
● A​ ​node’s​ ​activity​ ​can​ ​spread​ ​outward​ ​along​ ​links​ ​to​ ​activate​ ​other​ ​nodes,​ ​these​ ​nodes
can​ ​then​ ​activate​ ​still​ ​others:​ ​spreading​ ​activation​.​ ​Is​ ​thought​ ​to​ ​underlie​ ​retrieval​ ​of
information​ ​from​ ​long-term​ ​memory.​ ​Alternate​ ​associations​ ​that​ ​facilitate​ ​recall​ ​are
also​ ​called​ ​retrieval​ ​cues​.
● The​ ​distance​ ​between​ ​two​ ​nodes​ ​is​ ​determined​ ​by​ ​their​ ​degree​ ​of​ ​relatedness.
● Priming​:​ ​the​ ​processing​ ​of​ ​a​ ​stimulus​ ​is​ ​facilitated​ ​by​ ​the​ ​network’s​ ​prior​ ​exposure​ ​to
a​ ​related​ ​stimulus.

Hierarchical​ ​Semantic​ ​Network
Study​ ​by​ ​Collins​ ​and​ ​Quillian​ ​suggests​ ​that​ ​semantic​ ​networks​ ​may​ ​have​ ​a​ h ​ ierarchical
organization​,​ ​with​ ​different​ ​levels​ ​representing​ ​concepts​ ​ranging​ ​from​ ​the​ ​most​ ​abstract
down​ ​to​ ​the​ ​most​ ​concrete.​ ​They​ ​used​ ​a​ ​sentence​ ​verification​​ ​task.
1. Superordinate​​ ​category:​ ​animals​ ​—>​ ​eat​ ​food,​ ​breathe
2. Ordinate​​ ​categories:​ ​birds,​ ​cats​ ​—>​ ​can​ ​fly,​ ​purr
3. Subordinate​​ ​categories:​ ​Canary,​ ​Alleycat​ ​—>​ ​can​ ​sing,​ ​is​ ​yellow
A​ ​canary​ ​is​ ​an​ ​animal​ ​—>​ ​longer​ ​reaction​ ​time​ ​than​ ​‘A​ ​canary​ ​is​ ​a​ ​bird/a​ ​canary’
“isa”​ ​and​ ​“has​ ​a”​ ​link,​​ ​bird​ ​“isa”​ ​animal,​ ​bird​ ​“hasa"​ ​feathers
Con​:
● Concepts​ ​may​ ​be​ ​represented​ ​by​ ​prototypes​​ ​that​ ​represent​ ​generic​ ​or​ ​idealized
versions​ ​of​ ​those​ ​concepts.
● Principle​ ​of​ ​cognitive​ ​economy​:​ ​nodes​ ​should​ ​not​ ​have​ ​to​ ​be​ ​coded​ ​for​ ​more​ ​times
than​ ​is​ ​necessary.​ ​Seems​ ​to​ ​work​ ​better​ ​in​ ​principle​ ​than​ ​in​ ​reality.

Propositional​ ​Semantic​ ​Networks
ACT*​ ​is​ ​a​ ​hybrid​ ​model​:​ ​it​ ​specifies​ ​how​ ​multiple​ ​memory​ ​systems​ ​interact​ ​and​ ​how​ ​explicit
knowledge​ ​is​ ​represented.​ ​A​ ​proposition​ ​is​ ​the​ ​smallest​ ​unit​ ​of​ ​knowledge​ ​that​ ​can​ ​be
verified.​ ​Propositional​ ​networks​ ​allow​ ​for​ ​a​ ​greater​ ​variety​ ​of​ ​relationships​ ​among​ ​concepts.
An​ ​agent​ ​link​​ ​specifies​ ​the​ ​subject​ ​of​ ​the​ ​proposition,​ ​an​ ​object​ ​link​​ ​denotes​ ​the​ ​object​ ​or
thing​ ​to​ ​which​ ​the​ ​action​ ​is​ ​directed.​ ​The​ ​relation​ ​link​​ ​characterizes​ ​the​ ​relation​ ​between​ ​the
agent​ ​and​ ​the​ ​object.​ ​Anderson’s​ ​ACT*​ ​model​ ​can​ ​also​ ​account​ ​for​ ​the​ ​specific​ ​memories
each​ ​of​ ​us​ ​has​ ​as​ ​part​ ​of​ ​our​ ​experience.​ ​His​ ​model​ ​does​ ​this​ ​via​ ​its​ ​creation​ ​of​ ​2​ ​classes​ ​of
nodes:​ ​type​​ ​node;​ ​corresponds​ ​to​ ​an​ ​entire​ ​category​ ​(‘dogs’),​ t​ oken​​ ​nodes;​ ​correspond​ ​to
specific​ ​instances​ ​or​ ​specific​ ​items​ ​within​ ​a​ ​category​ ​(“Fido”).

Semantic​ ​Networks​ ​Evaluation:
Con​:
● T.O.T.​ ​phenomenon​:​ ​‘tip​ ​of​ ​the​ ​tongue’.​ ​Semantic​ ​Networks​ ​cannot​ ​easily​ ​explain
these​ ​sort​ ​of​ ​retrieval​ ​blocks.
● The​ ​opposite;​ ​the​ ​situation​ ​in​ ​which​ ​we​ ​can​ ​successfully​ ​retrieve​ ​an​ ​item​ ​from
memory​ ​despite​ ​the​ ​face​ ​that​ ​there​ ​are​ ​no​ ​close​ ​connections​ ​between​ ​retrieval​ ​cues

, and​ ​the​ ​target.​ ​Multiple​ ​links​ ​that​ ​radiate​ ​outward​ ​toward​ ​other​ ​nodes​ ​-​ ​a​ ​high​ d
​ egree
of​ ​fan​​ ​(eg​ ​water).
● Excessive​ ​activation​ ​—>​ ​solution:​ ​implementation​ ​of​ ​an​ ​inhibitory​ ​network.
● Reconstructive​ ​memory​:​ ​constitutes​ ​a​ ​separate​ ​process​ ​of​ ​retrieving​ ​items​ ​-​ ​one
that​ ​does​ ​not​ ​rely​ ​on​ ​spreading​ ​activation​ ​and​ ​the​ ​inherent,​ ​automatic​ ​characteristics
of​ ​the​ ​network.​ ​Guided​ ​search​​ ​-​ ​one​ ​governed​ ​by​ ​intelligence​ ​and​ ​reasoning​ ​(‘What
did​ ​you​ ​do​ ​on​ ​your​ ​birthday​ ​last​ ​year?’).

Network​ ​Science
Network​ ​science​:​ ​to​ ​explore​ ​the​ ​way​ ​in​ ​which​ ​complex​ ​networks​ ​operate.​ ​A​ ​network​ ​is
considered​ ​as​ ​any​ ​collection​ ​of​ ​interconnected​ ​and​ ​interacting​ ​parts.​ ​It’s​ ​interdisciplinary.
Contemporary​ ​network​ ​scientists​ ​additionally​ ​consider​ ​networks​ ​as​ ​dynamical​ ​systems​ ​that
are​ ​doing​ ​things.​ ​All​ ​networks​ ​share​ ​some​ ​universal​ ​mechanism​ ​of​ ​action.

Centrality
Issue​ ​of​ ​centrality​:​ ​how​ ​a​ ​network​ ​coordinates​ ​information.​ ​This​ ​can​ ​be​ ​accomplished
through​ ​a​ ​“leader”​ ​that​ ​receives​ ​information,​ ​evaluates​ ​it,​ ​and​ ​issues​ ​commands.​ ​Computers,
armies​ ​etc​ ​are​ ​systems​ ​of​ ​this​ ​kind.​ ​But​ ​the​ ​interesting​ ​case​ ​is​ ​how​ ​networks​ ​without​ ​any
such​ ​center​ ​achieve​ ​this​ ​kind​ ​of​ ​coordinated​ ​action.​ ​This​ ​question​ ​has​ ​particular​ ​relevance
for​ ​the​ ​human​ ​mind​ ​—>​ ​Cartesian​ ​theater​ ​and​ ​the​ ​homunculus​ ​problem.​ ​If​ ​we​ ​could​ ​figure
out​ ​the​ ​centrality​ ​issue,​ ​we​ ​might​ ​also​ ​determine​ ​the​ ​answer​ ​to​ ​the​ ​mystery​ ​of
consciousness.​ ​In​ ​some​ ​networks,​ ​coordinated​ ​global​ ​activity​ ​happens​ ​simply​ ​as​ ​a​ ​function
of​ ​spreading​ ​activation​ ​that​ ​disperses​ ​throughout​ ​the​ ​system​ ​quickly​ ​but​ ​which​ ​can​ ​arise
from​ ​any​ ​part​ ​of​ ​it.

Hierarchical​ ​Networks​ ​and​ ​the​ ​Brain
Connections​ ​in​ ​hierarchical​ ​networks​​ ​are​ ​organized​ ​in​ ​different​ ​levels.
1. Simple​ ​cells​:​ ​cells​ ​in​ ​the​ ​primary​ ​visual​ ​cortex​ ​that​ ​code​ ​for​ ​oriented​ ​line​ ​segments
2. Complex​ ​cells​:​ ​cells​ ​in​ ​the​ ​visual​ ​system​ ​that​ ​code​ ​for​ ​an​ ​oriented​ ​line​ ​segment
moving​ ​in​ ​a​ ​particular​ ​direction
3. Hypercomplex​ ​cells​:​ ​cells​ ​in​ ​the​ ​visual​ ​system​ ​that​ ​code​ ​for​ ​angles​ ​(two​ ​conjoined
oriented​ ​line​ ​segments)​ ​moving​ ​in​ ​a​ ​particular​ ​direction
If​ ​we​ ​extrapolate​ ​up​ ​in​ ​the​ ​hierarchy,​ ​we​ ​end​ ​up​ ​with​ ​cells​ ​in​ ​the​ ​highest​ ​layers​ ​that​ ​code​ ​for
large​ ​complex​ ​objects​ ​(“grandmother​ ​cells”).​ ​The​ ​hierarchy​ ​allows​ ​the​ ​visual​ ​system​ ​to
employ​ ​a​ ​“divide-and-conquer”​ ​strategy​ ​where​ ​it​ ​breaks​ ​down​ ​the​ ​complex​ ​visual​ ​image​ ​into
microscopic​ ​features​ ​and​ ​then​ ​assembles​ ​these​ ​features​ ​into​ ​parts​ ​and​ ​then​ ​wholes​ ​that​ ​can
be​ ​recognized.​ ​Communication​ ​between​ ​levels​ ​in​ ​hierarchies​ ​can​ ​allow​ ​for​ ​the​ ​resolution​ ​of
ambiguity​ ​in​ ​visual​ ​perception.​ ​Information​ ​in​ ​the​ ​visual​ ​system​ ​appears​ ​to​ ​travel​ ​in​ ​2
directions.​ ​It​ ​goes​ ​nog​ ​only​ ​in​ ​a​ ​feed-forward​ ​direction​ ​from​ ​the​ ​eye​ ​to​ ​the​ ​brain​ ​but​ ​also​ ​in​ ​a
feedback​ ​direction​ ​from​ ​higher​ ​brain​ ​centers​ ​to​ ​lower​ ​centers.

Small-World​ ​Networks:​​ ​We​ ​can​ ​define​ ​a​ ​small-world​ ​network​​ ​as​ ​any​ ​network​ ​where​ ​one
can​ ​get​ ​from​ ​any​ ​single​ ​point​ ​to​ ​any​ ​other​ ​point​ ​in​ ​only​ ​a​ ​small​ ​number​ ​of​ ​steps​ ​even​ ​though
the​ ​total​ ​number​ ​of​ ​elements​ ​may​ ​be​ ​exceedingly​ ​large.
Ordered​ ​and​ ​Random​ ​Connections:​ ​Random​ ​networks​​ ​are​ ​networks​ ​where​ ​the​ ​connections
are​ ​entirely​ ​local​ ​and​ ​can,​ ​therefore,​ ​be​ ​both​ ​short​ ​and​ ​long​ ​distance.​ ​In​ ​an​ o ​ rdered

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