Ana Ri a Secca da Fonseca
Disse a ion p esen ed o ob ain he
Ph.D deg ee in Biology | Neu oscience
Ins i u o de Tecnologia Química e Biológica An ónio Xa ie | Uni e sidade No a de Lisboa
Oei as,
May, 2016
Inse he e an image
wi h ounded co ne s
Pombal’s maze: a no el decision-
making pa adigm
Ana Ri a Secca da Fonseca
Disse a ion p esen ed o ob ain he Ph.D deg ee in Biology | Neu oscience
Ins i u o de Tecnologia Química e Biológica An ónio Xa ie | Uni e sidade No a de Lisboa
Oei as, May, 2016
Pombal’s maze: a no el decision-
making pa adigm
Resea ch wo k coo dina ed by:
Cá den o inquie ação, inquie ação
É só inquie ação, inquie ação
Po quê, não sei
Mas sei
É que não sei ainda
José Má io B anco, in “Inquie ação”
À memó ia de meu pai
de quem he dei a inquie ação
que me ouxe a é aqui
This wo k was de eloped in he con ex o he In e na ional Neu oscience
Doc o al P og amme (INDP) o he Champalimaud Resea ch P og amme,
Champalimaud Cen e o he Unknown, Lisbon, Po ugal. The p ojec
en i led “Pombal’s maze: a no el decision-making pa adigm” was ca ied
ou a Ins i u o Gulbenkian de Ciência, Oei as, Po ugal and a he
Champalimaud Resea ch P og amme, Champalimaud Cen e o he
Unknown, Lisbon, Po ugal, unde he scien i ic supe ision o Zacha y
Mainen, Ph.D, and unde he guidance o he Thesis Commi ee composed by
Ca los Ribei o, Ph.D, and Rui Cos a, Ph.D. This wo k was suppo ed by he
ellowship SFRH/BD/33944/2009 om Fundação pa a a Ciência e
Tecnologia, Po ugal.

i
ACKNOWLEDGEMENTS
I eel ex emely p i ileged o ha e been o e ed he possibili y o
unde ake such an inc edible in ellec ual endea o . Abo e all, I hank he
people who h oughou hese yea s ha e gene ously gi en me he
oppo uni y o become a be e pe son, owa ds he wo ld and owa ds
mysel
Zach as an example o ex a-senso ial in ui ion and in ellec ual eedom
Masa as an example o igo and endless gene osi y
Pa ícia o all he cogni i e ough and umble
Gil o silence
Ma ia o h owing ladde s a he moon
Bass as an example o making-i -happen
Cindy as an example o ho ough kindness owa ds Na u e
E ic as an example o scien i ic cul u e
Nico as an example o de ail
Sa a as an example o endu ance
Sam as an example o insu ec ion
Alex as an example o ebellion
Susana and Ma a as examples o se iously play ul science
Rui and Ca los o pu ing hings in o pe spec i e
INDP 2008 o all he un and suppo
CNP communi y as an example o di e si y and ac i e lis ening
wo ld wide web as an example o human coope a ion
Alexand a Elbakyan as example o open science
Ra s as eminde s o a uni e sal bond
Ana C. H. o helping me disco e new ways o look wi hin
ii
À minha amília xoné como exemplo de amizade e es upidez na u al
À minha amília de sangue po me e ensinado o be o e o iso
À a ó Augus a como exemplo de le eza
À a ó Luz como um exemplo do b inca
Ao i mão Filipe como exemplo de des emo
À mãe Ana como exemplo de esis ência
A Ma ilde como exemplo de impe manência
A F ede ico como exemplo de amo incondicional
iii
RESUMO
Os animais são cons a emen e con on ados com decisões: o que come ?
Onde encon a pa cei o sexual? Como alcança aquela go a de água?
Ac ualmen e, deco e um deba e den o do campo cujo o objec o de
es udo são as omadas de decisão, sob e quais são os mecanismos
subjacen es à selecção de acções: se á que os animais escolhem de en e
o que p e endem ob e - o e o no, ou escolhem de en e o que p ecisam
de aze pa a alcança esse e o no? Exis em dados compo amen ais e de
ac i idade neu onal que supo am a hipó ese de que há compe ição en e
as acções po enciais a acon ece em á eas elacionadas com a
componen e mo o a, enquan o os animais êm que decidi de en e
múl iplas opções qual a que le a ao objec i o. Mais especi icamen e, em
sido a ançada a hipó ese de que a selecção de acções depende dum
mecanismo de compe ição en e acções po enciais, onde um sinal
pola izado a o ece uma das acções, le ando à sua selecção. Po o ma a
melho comp eende em que medida a selecção de acções depende
e ec i amen e dum mecanismo de compe ição, é necessá io explo a
melho quais os ipos de in e acção en e acções po enciais,
simul aneamen e ao ní el da ac i idade neu onal e do compo amen o.
Uma das o mas em que es e objec i o pode á se a ingido é a a és da
in es igação da ac i idade neu onal e do compo amen o enquan o
animais decidem en e múl iplas acções em que odas le am ao objec i o.
Den o das neu ociências, exis em duas linhas de in es igação cujo um
dos objec i os é de e mina o que acon ece no cé eb o quando múl iplas
acções pe mi em a ingi o objec i o: o es udo de na egação espacial, que
po um lado, p e ende comp eende a selecção de acções pela pe spec i a
do p ocessamen o de in o mação espacial, ocando-se nos pon os do
i
espaço que ob igam a uma decisão - pon os de escolha- e
co esponden es escolhas disc e as, e o planeamen o mo o , que p e ende
comp eende a seleção de acções pela pe spec i a do con olo mo o ,
ocando-se nas dínâmicas con ínuas do mo imen o. Assim, desenhámos e
desen ol emos um no o pa adigma compo amen al- a que chamámos
de labi in o de Pombal- onde são o e ecidas múl iplas opções de acção
pa a chega a um objec i o. Es e pa adigma ap esen a a oedo es o
p oblema de escolhe en e caminhos in e connec ados quais os que lhe
pe mi em chega a uma ecompensa sinalizada po um sinal luminososo,
o que implica que es a a e a em simul aneamen e ca ac e ís icas de uma
a e a de na egação e de uma a e a de planeamen o mo o . O pa adigma
p opos o baseia-se num labi in o que ap esen a uma es u u a de uma
g elha com múl iplos pon os de ecompensa e múl iplas in e secções.
Es as in e secções inham o po encial pa a se o na em pon os de escolha,
dando a opo unidade aos animais de aze á ias decisões ao longo do
caminho. Foi obse ado que os oedo es adop am a es a égia óp ima de
escolhe os caminhos que são mais cu os pa a chega à ecompensa, na
maio ia das ezes. Den o do conjun o dos caminhos mais cu os, os
animais mos am endências pa a ce as opções baseando-se em
ca ac e ís icas geomé icas (e.g. o núme o de mudanças de di ecção).
Es as endências e lec em cus os in e nos implíci os associados a
di e en es ca ac e ís iscas dos caminhos que combinadas podem ge a
compe ição en e di e en es acções, pois podem se mu uamen e
exclusi as. Adicionalmen e, oi obse ado que os animais diminuiam a
elocidade quando se ap oxima am da in e secção, o que é indica i o de
que es es locais e am is os como sí ios onde ha ia a possibilidade de
decidi en e acções. Compa ando ajec ó ias de caminhos em
xi
Figu e 15 T ials ca ego ized acco ding o di e en c i e ia.. .................. 50
Figu e 16 Example o s a -goal pai o mul iple op ion analysis.. ........ 51
Figu e 17 Example o s a -goal pai o cen e a oidance analysis. ....... 52
Figu e 18 Numbe ing o each po .. ......................................................... 53
Figu e 19 Example s a -goal pai o maze expe ience.. ........................ 52
Figu e 20 Goal p obabili ies pe s a po .. ............................................. 53
Figu e 21 Me ics associa ed wi h di ec ion o mo emen . ..................... 54
Figu e 22 Animals a oid cen e pa hs.. ................................................... 56
Figu e 23 Animals p e e ed o he go back h ough he di ec ion o
a i al a he po . ..................................................................................... 57
Figu e 24 P e e ence o ype A is main ained ac oss he majo i y o
s a -goal pai s.. ........................................................................................ 59
Figu e 25 Ra s p e e pa hs ha minimize dis ance o goal as e .. ......... 58
Figu e 26 Ra s p e e pa hs ha go h ough he cen e , in a h ee op ions
condi ion.. ................................................................................................ 59
Figu e 27 Animals p e e o con inue in he di ec ion ha hey a i ed a
he po .. ................................................................................................... 60
Figu e 28 Animals p e e pa hs wi h one u n.. ....................................... 61
Figu e 29 Animals a e as e o each he goal when aking a pa h wi h
one u n. ................................................................................................... 62
Figu e 30 Di e en ac o s de ine di e en expec ed p opo ions o each
op ion. ...................................................................................................... 68
Figu e 31 T ial ype A minimizes numbe o u ns and ial ype C
minimizes Euclidean dis ance o he goal.. .............................................. 63
Figu e 32 Di e en ac o s de ine di e en expec ed p opo ions o each
op ion.. ..................................................................................................... 64

xii
Figu e 33 Ra s don' p e e pa hs ha minimize Euclidean dis ance o
goal.. ......................................................................................................... 65
Figu e 34 Animals p e e pa hs wi h one u n. ........................................ 66
Figu e 35 Mo emen ime is independen o numbe o u ns................. 67
Figu e 36 The p obabili y o choice o op ion A o op ion B should be
he same.. ................................................................................................. 72
Figu e 37 Window o in e es o beha io al analysis.). ......................... 77
Figu e 38 Linea ans o ma ion o ajec o ies o spa ial alignmen .. ... 78
Figu e 39 Raw eloci ies and co esponding ajec o ies.. ...................... 78
Figu e 40 Examples o binned ajec o ies and co esponding eloci ies..
.................................................................................................................. 79
Figu e 41 Example ial dynamics p o ile whe e one a le he choice
poin ROI and hen e-en e ed i . ............................................................. 79
Figu e 42 Pai ing o s a -goal pai s acco ding o spa ial o e lap.. ......... 80
Figu e 43 Animals a e as e o lea e s a po when hey a e abou o do
a sho es pa h.. ........................................................................................ 81
Figu e 44 Animals ake mo e ime o lea e he s a po ROI when mo e
op ions a e a ailable.. .............................................................................. 83
Figu e 45 Animals we e as e o lea e s a po when he goal was
close .. ...................................................................................................... 84
Figu e 46 Bell-shaped eloci y p o ile.. .................................................. 85
Figu e 47 Animals slow down while eaching he choice poin .. ............ 86
Figu e 48 Ex a non-op imal op ion does no a ec dec ease in eloci y,
when one op imal op ion is a ailable....................................................... 87
Figu e 49 Ex a non-op imal op ion does no a ec dec ease in eloci y,
when mo e han one op imal op ion is a ailable.. ................................... 89
xiii
Figu e 50 Animals slow down mo e when choice poin o e s wo
op imal pa h op ions.. ............................................................................... 90
Figu e 51 Dis ance o he ligh does no a ec eloci y dec ease a he
choice poin .............................................................................................. 92
Figu e 52 T ajec o ies om compe ing op ions in a h ee op ion condi ion
di e ge 20 cm a e animal has le he s a po ROI.. ........................... 94
Figu e 53 T ajec o ies om compe ing op ions in a six op ion condi ion
di e ge 20 cm a e animal has le he s a po ROI. ............................ 95
xi
AUTHORS CONTRIBUTION
Ana Ri a Fonseca (ARF) and Zacha y F ank Mainen (ZFM)
designed he expe imen s (Chap e 2). ARF conduc ed he
expe imen s and analyzed he da a (Chap e 3 and 4).
x
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ______________________________________ i
RESUMO _________________________________________________ iii
ABSTRACT ________________________________________________ i
ABREVIATION LIST _________________________________________ ix
FIGURE INDEX ______________________________________________ x
AUTHORS CONTRIBUTION __________________________________ xi
TABLE OF CONTENTS _______________________________________ x
1 GENERAL INTRODUCTION ________________________________ 1
1.1 The en ance… _________________________________________ 2
1.1.1 Wha _____________________________________________________ 3
1.1.2 Why ______________________________________________________ 3
1.1.3 How ______________________________________________________ 3
1.2 F aming decision-making p ocesses _________________________ 4
1.3 Ac ion selec ion mechanisms ______________________________ 6
1.3.1 Compe i ion be ween ac ions _________________________________ 8
1.4 Mul iple ac ions, one goal _______________________________ 11
1.4.1 Na iga ion and ac ion selec ion _______________________________ 12
1.4.2 Mo o planning and ac ion selec ion ___________________________ 16
1.5 Guide o he eade ____________________________________ 20
2 POMBAL’S MAZE ______________________________________ 22
2.1 In oduc ion __________________________________________ 23
2.2 Me hods _____________________________________________ 24
x i
2.2.1 Animal subjec s __________________________________________ 24
2.2.2 Expe imen al appa a us ___________________________________ 25
2.2.3 Pombal’s maze pa adigm __________________________________ 26
2.2.4 Spa ial dis ibu ion o ewa d _______________________________ 27
2.2.5 T aining _________________________________________________ 28
2.2.6 Video p ocessing _________________________________________ 29
2.2.7 T ajec o ies in o sequences ________________________________ 30
2.2.8 Video and BCon ol synch oniza ion ________________________ 32
2.2.9 Da a base _______________________________________________ 32
2.2.10 Valida ion c i e ia _______________________________________ 33
2.3 Resul s _______________________________________________ 35
2.3.1 P e e ence o sho es pa hs ______________________________ 35
2.3.2 S eng h o ligh -wa e pai ing ______________________________ 38
2.3.3 Biased choices ___________________________________________ 39
2.4 Discussion ____________________________________________ 44
3 DISCRETE CHOICES _____________________________________ 47
3.1 In oduc ion __________________________________________ 48
3.2 Me hods _____________________________________________ 48
3.2.1 T ial classi ica ion _________________________________________ 49
3.2.2 Mo emen ime ___________________________________________ 54
3.2.3 Di ec ion o mo emen ____________________________________ 54
3.2.4 Measu ing p e e ence _____________________________________ 55
3.3 Resul s _______________________________________________ 55
3.3.1 P e e ence o allocen ic ea u es: cen e e sus edge ________ 55
3.3.2 P e e ence o egocen ic ea u es: backwa ds e sus o wa d _ 56
3.3.3 P e e ence o geome ical ea u es: minimizing he Euclidean
dis ance o goal __________________________________________________ 57
3.3.4 P e e ence o geome ical ea u es: numbe o u ns _________ 61

x ii
3.3.5 Quali a i e amewo k _____________________________________ 67
3.3.6 Compe i ion be ween op ions ______________________________ 62
3.4 Discussion ____________________________________________ 67
4 DYNAMICS OF CHOICE __________________________________ 75
4.1 In oduc ion __________________________________________ 76
4.2 Me hods _____________________________________________ 76
4.2.1 Spa ial window o in e es __________________________________ 77
4.2.2 Spa ial da a alignmen and binning _________________________ 77
4.2.3 Veloci y exclusion c i e ion _________________________________ 79
4.2.4 ROC analysis ____________________________________________ 80
4.3 Resul s _______________________________________________ 81
4.3.1 La ency o lea e he s a po ______________________________ 81
4.3.2 Bell-shaped eloci y p o ile_________________________________ 84
4.3.3 Veloci y a he choice poin _________________________________ 86
4.3.4 T ajec o ies o compe ing pa hs ____________________________ 92
4.4 Discussion ____________________________________________ 95
5 GENERAL DISCUSSION _________________________________ 100
5.1 Concep ual aming ____________________________________ 101
5.2 O e iew o main empi ical indings ______________________ 101
5.3 C i ical poin s ________________________________________ 103
5.4 Pombal’s maze and ac ion selec ion ______________________ 105
5.5 Final ema ks and u u e di ec ions _______________________ 108
6 REFERENCES _________________________________________ 110
1 GENERAL
INTRODUCTION
I ge in
in wha , om he ou side, i ’s called he inside
sea ching o he pa h ha will ge o he cen e
lea ing longing and ea on he ou side
Sé gio Godinho, in “The maze”
(F eely ansla ed by ARF)
2
1.1 The en ance…
Na u e has p o ided humans wi h a lo o wonde abou . Fa away licke ing
poin s up abo e ou heads, sea ages, clamo ous ligh ning c uising da k
skies, apples ha pe sis o all and days ha pe sis o die. Bu maybe, and
jus maybe, he phenomenon ha elen lessly akes mo e o ou su p ise is
ou sel es, wha is wi hin us. And, wha e e ha migh be, i li es in an
enclosed ini e body which in e ac s wi h o he s – conspeci ics o no - and
wi h he en i onmen , h ough beha io .
E en hough he e is no consensus abou a de ini ion o beha io ,
neu oscien is s ag ee ha i is gene a ed by di e en ypes o biological
ma e (ne e cells, muscles) which wo k oge he o gene a e ac ions. The e
a e many pa s bu in he end he body is jus one. I a a goes on a ques o
ind wa e hen i s head, legs, ail and 200 000 000 neu ons ha e o ge he e,
in ull commi men wi h ha decision, oge he . Bu animals ha e o make
decisions cons an ly: wha o ea , whe e o ma e, how o each ha wa e
d op... The e a e always many op ions so how do animals selec a single
one?
The e is an ongoing deba e wi hin he decision-making ield abou he
unde lying mechanisms o ac ion selec ion: is ac ion selec ion achie ed by
i s making a choice be ween abs ac ep esen a ion o ou comes and hen
pe o ming a senso imo o ans o ma ion o ha abs ac ion o he ac ion
space o is i achie ed by e alua ing se e al po en ial ac ions while a he
same ime weighing he cos s and bene i s o each, be o e a i ing a a
decision?
3
1.1.1 Wha
One o he undamen al p oblems in decision-making is o unde s and how
ac ions a e selec ed (1.2 F aming decision-making p ocesses). Fo a long
ime i was hough ha ac ion selec ion elied mos ly on a cen al execu i e
sys em ha would hen send he selec ed abs ac ac ion o be implemen ed
by he mo o sys em. The e is cu en ly compelling e idence ha highe
mo o a eas a e in ol ed in he ac ion selec ion p ocess. I has been
hypo hesized ha ac ion selec ion is gene a ed by a compe i ion p ocess
be ween po en ial ac ions which unde lying a chi ec u e elies on mu ual
inhibi ion ha is ed by a alue-based bias signal (1.3 Ac ion selec ion
mechanisms).
1.1.2 Why
On one side, he e is a ai amoun o esea ch ha has b ough
unde s anding abou he gene a ion o he bias signal. On he o he side, he e
is bo h beha io and neu al da a ha suppo s he hypo hesis ha compe i ion
be ween po en ial ac ions is aking place in mo o - ela ed a eas while
animals a e deciding be ween mul iple op ions o each a goal (1.3.1
Compe i ion be ween ac ions). None heless, in o de o u he unde s and
o wha ex en ac ion selec ion elies on compe i ion, i is necessa y o u he
explo e he ypes o in e ac ions be ween po en ial ac ions bo h a he le el o
neu al ac i i y and beha io .
1.1.3 How
One way in which his subjec could be add essed is by in es iga ing
decision-making p oblems whe e mul iple op ions lead o a goal and he
animal can eely choose which op ion o ake (1.4 Mul iple ac ions, one
10
p esen ed o a monkey ha has o saccade o one o wo pa allel a ge s o
implici ly epo which mo ion di ec ion i pe cei ed, allowed us o
unde s and ha in la e al in apa ie al a ea (LIP) he e a e neu ons whose
ac i i y co ela es wi h he in eg a ion o in o ma ion. Ano he example is
he odo -mix u e ca ego iza ion ask, whe e a mix u e o wo odo s is
p esen ed o a a ha has o nose poke in o one o wo pa allel po s o
implici ly epo which odo iden i y i pe cei ed, allowed o unde s and ha
no only SC was ac i e be o e mo emen ini ia ion bu ha his ac i i y
a ied wi h senso y unce ain y and ask di icul y (Felsen & Mainen 2012).
Value-based decision-making pa adigms allowed answe ing ques ions like
whe e and how subjec i e expe ience in o ma ion is in eg a ed. Fo example,
in a ask whe e monkeys chose be ween wo ypes o juice o e ed in
di e en amoun s, OFC neu ons encode alue independen ly o isuospa ial
ac o s and mo o esponses (Padoa-Schioppa & Assad 2006). Ano he
example is a dynamic o aging ask, whe e a s chose eely be ween wo
spa ial goals ha deli e ed a ixed amoun o wa e ewa d wi h di e en
p obabili ies, allowed o unde s and ha neu ons in seconda y mo o co ex
(M2) encode alue o upcoming choice (Sul e al. 2012).
In summa y, using hese ca ego ies o pa adigms beha io al and neu al da a
was in es iga ed while senso ial and/o subjec i e in o ma ion was
manipula ed, c ea ing decision con lic s ha he subjec had o sol e. This
lead o be e unde s anding on how he b ain ep esen s and in eg a es
in o ma ion ha is aluable o decisions whe he his in o ma ion is
senso ial o de i ed om subjec i e expe ience (Glimche e al. 2009;
Sug ue e al. 2005). The p e iously desc ibed pa adigms con ibu ed o he
unde s anding abou how he bias is gene a ed bu i s ill emains o be
unde s ood i he e is a compe i ion ci cui ha compu es he ac ion selec ion

11
and ha would be ed wi h his bias signal. The e a e o he ela ed lines o
s udy ha ha e add essed hese ques ions mo e speci ically.
1.3.1.2 Rep esen a ion o po en ial ac ions
The hypo hesis ha ac ion selec ion is implemen ed h ough compe i ion
elies hea ily on he implici p emise ha po en ial ac ions unde selec ion
a e simul aneously ep esen ed while he e is an ongoing decision-making
(Cisek 2006; Tippe e al. 1998). The heo y ha pe cei ing and ac ing on
he wo ld depends on he simul aneous speci ica ion o mul iple po en ial
ac ions han can be a o ded by he en i onmen has been a ound o 40
yea s (Gibson 1979). Du ing ha ime con incing e idence has been
accumula ed showing ha when an animal is o e ed he possibili y o
choose be ween mul iple ac ions o each a goal hese po en ial ac ions a e
simul aneously ep esen ed in mo o - ela ed a eas (Cisek & Kalaska 2002;
Cisek & Kalaska 2005; Basso & Wu z 1998; Houk 1995).
In summa y, a leas some o he necessa y condi ions o he implemen a ion
o ac ion selec ion h ough compe i ion a e me , bo h o he exis ence o a
bias signal and o he simul aneous ep esen a ion o po en ial ac ions.
None heless, i is no ully unde s ood how and wha ype o in e ac ions
dynamically occu be ween po en ial ac ions, bo h a he le el o neu al
ac i i y and beha io . I is necessa y o u he explo e he ypes o
in e ac ions be ween po en ial ac ions bo h a he le el o neu al ac i i y and
beha io . One way in which his subjec could be add essed is by
in es iga ing neu al ac i i y and beha io while animals a e deciding
be ween mul iple op ions ha all lead o goal
1.4 Mul iple ac ions, one goal
12
Wi hin he decision-making ield, he e a e wo ypes o p oblems ha
equi e he subjec o make a decision abou which ac ion o ake, among
al e na i es, o each he same goal: mo o planning asks and spa ial
na iga ion asks. Bo h ypes o asks in ol e making choices abou which
mo emen o sequence o mo emen s o op imize in o de o each a goal
(Penne & Mizumo i 2012; Whi lock e al. 2008; Wolpe & Landy 2012),
being he main di e ence ha , on he i s case, he mo emen s unde choice
a e pe o med by one body pa (hand, a m o eye) and, in he second case,
he whole body. Also, he e is an impo an line o complemen a i y be ween
hese wo ields associa ed o he ype o ac ion selec ion co ela es hey
ocus on. I , on one side, in na iga ion pa adigms one o he key windows
o unde s anding ac ion selec ion is he choice poin , whe e disc e e choices
a e measu ed and in e p e ed acco ding o di e en s a egies (Tolman
1938); in he case o mo o planning, one o he key windows is he
mo emen be ween s a and goal posi ions, whe e he con inuous dynamics
o mo emen is assessed (Flash & Hogan 1985). I seems ha conside ing
bo h o hese wo pe spec i es can b ing a mo e comple e unde s anding
abou ac ion selec ion co ela es and may lead o a mo e de ailed
cha ac e iza ion o bo h he ype o choices a choice poin s and he
dynamics o hose choices. Consequen ly, his can con ibu e o a mo e
comple e comp ehension o how animals selec ac ions namely by exposing
co ela es o he in e ac ions be ween po en ial ac ions.
1.4.1 Na iga ion and ac ion selec ion
I has been p oposed ex ensi ely ha animals selec ac ions in na iga ion
con ex s using wo dis inc schemes de i ed om ins umen al condi ioning:
goal-di ec ed ac ion selec ion, d i en by esponse-ou come associa ions, and
13
habi ual ac ion-selec ion, d i en by s imulus- esponse associa ions
(Glimche e al. 2009; Penne & Mizumo i 2012; Ni e al. 2006; Balleine &
Os lund 2007; Daw & Shohamy 2008). Goal-di ec ed ac ion selec ion is
sensi i e o he con ingencies be ween ac ions and hei ou comes and o he
u ili ies o hese ou comes. I depends on ha ing a schema o how he wo ld
wo ks ha can be used o de e mine he consequences o ac ions (Redish
2016). This a gues o animals being capable o lea ning he casual
ela ionship be ween hei ac ions and he esul ing ou comes, allowing hem
con ol o e hei own ac ion based on hei desi e o a pa icula ou come
goal (Tolman 1948). This ype o schema con e s animals wi h he abili y o
plan ac ions o maximize expec ed ewa ds wi hin hei en i onmen and
lexibly in eg a e new in o ma ion like a no el s imuli and o a change in
ewa d con ingency. In ha sense, his sys em does no impose ha a speci ic
ac ion has o be selec ed bu animals can eso o i o guide beha io
(Redish 2016; Johnson e al. 2007). I is hough ha his lexibili y comes
wi h a ime cos because i in ol es delibe a ion. This sys em is c i ically
dependen on in ac hippocampal unc ion (Pold ack & Packa d 2003;
Packa d & McGauch 1996). On he o he side, habi ual ac ion selec ion is
sensi i e o he pai ing be ween an eceden s imuli wi hin he en i onmen
and ac ions wi hou ega d o hei consequen ial ou comes (Ni e al. 2006).
Animals au oma e hei beha io when ac ions eliably led o goals, by
de eloping s o ed ac ion chains ha can be eleased a app op ia e imes bu
ha , once ini ia ed, end o un o conclusion (Hull 1943; Dez ouli e al.
2014). The habi sys em allows o as ac ion selec ion bu also pe mi s
a bi a y s imulus-ac ion pai ings. Hence, i p o ides he basis o simple ,
non-in eg a ed na iga ion unc ions such as s imulus ecogni ion and
app oach and is c i ically dependen on in ac s ia al unc ion (Penne &
Mizumo i 2012).
14
F om he pe spec i e o ac ion selec ion, sol ing a na iga ion ask is also
sol ing an op imiza ion p oblem ha depends on he p ocessing o spa ial
con ex ual in o ma ion (Ni e al. 2006; Penne & Mizumo i 2012).
Rein o cemen lea ning is a heo e ical amewo k ha aims a desc ibing he
p ocess h ough which an o ganism lea ns o op imize beha io wi hin a
decision en i onmen . The decision-making en i onmen s in which
ein o cemen lea ning occu s is de ined by a se o s a es (Su on & Ba o
1998), which in he case o na iga ion, can be ep esen ed by loca ions on a
maze, such as a co ne o an in e sec ion; a se o possible ac ions ha he
decision-make can choose om, such as u n le o a el wes ; and, inally,
a se o ules ha he decision-make mus lea n abou he en i onmen
h ough pe sis en in e ac ion wi h i , such as whe e is ewa d loca ed and
when. In ein o cemen lea ning, ou comes such as ood o wa e ha e
nume ical u ili ies, and he impe a i e is o choose ac ions o maximize a
long- e m measu e o o al u ili y (Ni e al. 2006; Glimche e al. 2009). The
ac ions implemen ed by he decision-make implica e a change om one
s a e o ano he , which leads o di e en ou comes. Then he p oblem is o
decide which s a egy maximizes u ili y gi en many di e en possibili ies o
s a es. In he ein o cemen lea ning amewo k, on one side, using goal-
di ec ed sys em implica es ha , when a a choice poin , animals wo k ou he
ul ima e ou comes consequen on a sequence o hei ac ions by sea ching
h ough a ee o s a es, joined up acco ding o he ac ions ha lead be ween
hem, and choosing ac ions based on he ou comes’ cu en u ili ies; on he
o he side, habi ual ac ion selec ion sys em implica es ha , when a he
choice poin , animals chose an ac ion by compa ing hei ela i e s o ed
alues, a he han he ac ual iden i y o he ou comes consequen on
sequences o ac ions .
15
In na iga ional con ex s, he ou comes a e o en dependen on he
implemen a ion o whole sequences o ac ion choices dependen on he
spa ial con ex . Hence, unde s anding how animals selec which pa h o ake
o each a goal is also a ma e o spa ial cogni ion. This a ea o knowledge
ocus, o example, in in es iga ing how spa ial in o ma ion om he
en i onmen is p ocessed and ep esen ed in he b ain (Mose e al. 2008;
O’Kee e 1979), how in e nal cues a e used o na iga e he en i onmen
(Taube 2007), how animals in eg a e in o ma ion om a a elled pa h o
hen e u n o an ini ial posi ion (McNaugh on e al. 2009) o how animals
lea n o sol e a maze (Tolman & Pos man 1953). Among o he beha io al
pa adigms, mazes ha e been he mos popula se ing o unde s and how
animals na iga e he en i onmen (Knie im & Hamil on 2011; Sha ma e al.
2010). Usually, inside mazes, animals a e p esen ed wi h he p oblem o
na iga ing h ough a ne wo k o in e connec ing pa hs and dead ends whe e
only one o ew op ions lead o ewa d. Gene ally, he unde lying s a egy o
hese pa adigms is o le he animal o amilia ize i sel wi h he maze by
lea ning spa ial ea u es and ewa d con ingencies, and hen manipula e
ei he o bo h. These manipula ions a e designed so ha he obse ed
beha io al e ec s can be explained by di e en lea ning ac o s. Fo
ins ance, in he case o Tolman–Hull plus maze whe e a s a e ained o
collec ing ewa d acco ding wi h a ce ain ule - u n le om he sou h a m
o he wes a m- o es wha animals ha e lea ned, ials s a s in a di e en
a m and, hen depending on which s a egy he animal is ollowing, he
beha io al e ec should be di e en (Ga dne e al. 2013; Rup ech e al.
2014). Ano he example is he Mul iple-T ask whe e a s a e ained o un
h ough a cen al ack and o u n le o igh o ood (B own 1946;
Schmi ze -To be & Redish 2004). On each day he cen al ack has a

16
di e en shape and he side on which he inal ewa d is placed can change,
bu bo h a iables gene ally emain cons an wi hin a day. He e he aim is o
de e mine wha animals lea n by obse ing, o ins ance, when animals ake
w ong pa hs. Gi en ha one o he aims o his ype o s udies is o
unde s and how animals p ocess and in eg a e spa ial in o ma ion, he goal is
usually no cued o allow o he animals o e eal hei expec a ion o whe e
he goal is, which may b ing up how he animals is using spa ial in o ma ion.
An excep ion o his is he Cue-T ask whe e a s a e ained o u n le o
igh a a T in e sec ion whe e he co ec choice depends on a senso y cue
p o ided on he s em o he T (Johnson & Redish 2007; Tho n e al. 2010).
In his case, he aim can be o be e unde s and how animals lea n o
associa e s imulus wi h ewa d (Ba nes e al. 2005). O e all, hese pa adigms
allow o asking di e en ques ions om how is spa ial in o ma ion
p ocessed and lea ned and how possible choices map on o di e en
s a egies.
While sol ing a maze, he animal is aced wi h choice poin s whe e a
decision akes place. A he choice poin , animals should conside which
ac ion among al e na i es leads o ewa d and, depending on he unde lying
s a egy and on he decision a iables ha he animal is aking in o
conside a ion, he selec ed ac ion should be di e en . As in any o he
decision-making s udy, he expe imen al s a egy is o pose a p oblem o he
subjec whe e, gi en an op imiza ion p oblem, he di e en choices e eal
he unde lying p ocesses o ac ion selec ion.
1.4.2 Mo o planning and ac ion selec ion
In planning a mo emen , he b ain has o selec one o many possible
mo emen plans. De e mining whe he he mo o sys em na u ally p epa es
17
mul iple po en ial mo emen s when me ely p esen ed wi h al e na i e a ge s
o ac ion, one o which will subsequen ly be selec ed be o e a mo emen is
e en equi ed, is c i ical o he unde s anding o he unde lying mechanisms
by which he b ain ini ially ep esen s and makes decisions be ween
compe ing op ions in he en i onmen (Galli an e al. 2015; Mu akami &
Mainen 2015). As desc ibed be o e, i has been p oposed ha selec ion o
ac ions may be pe o med h ough compe i ion among neu ons p epa ing o
a ailable ac ions (Cisek 2007). This u he suppo s he iew ha mo o
sys ems do no passi ely e lec he esul o comple ed cogni i e p ocesses;
a he , i is c ucially linked o he dynamic decision-making p ocess i sel
(McKins y e al. 2008; O’Ho a e al. 2016; Hai h e al. 2015). Conside ing
he linkage be ween cogni i e and mo o p ocesses, and he concu en
p ocessing o mul iple mo o plans, compe ing cogni i e s a es a e likely o
p epa e co esponding plans in pa allel (Song & Nakayama 2009; Cisek
2012). Hence, he quali y o he in e ac ions be ween po en ial ac ions could,
in po en ial, spill o he mo emen execu ion dynamically modula ing
eac ion ime o ajec o y.
In he pa icula se ing o mo o planning pa adigms, o ins ance, i has
been shown ha when eaching o a a ge in he p esence o a dis ac o ,
whe e he dis ac o a o ded a mo emen wi h he o he hand, esul ed in
highe eac ion ime in compa ison when he dis ac o s a o ded esponses
o he same hand (Ray e al. 2014). Mo eo e , he e is e idence ha he
numbe o ac ions unde conside a ion o selec ion modula es eac ion ime
ha inc eases wi h he inc ease in he numbe o possible each a ge s
(Chu chland e al. 2008).
E idence o dynamical in e ac ions be ween ac ion plans has come om
kinema ic s udies o eye and a m mo emen s made o a ge s in he p esence
18
o dis ac o s in ‘go-be o e-you-know’ asks. I has been obse ed ha he
ajec o y o a each o a a ge is in luenced by he p esence and placemen
o dis ac e s (Welsh e al. 1999; Tippe e al. 1998), sugges ing ha mul iple
a ge and dis ac o ela ed di ec ional signals coexis and in e e e, in neu al
popula ions speci ying each di ec ion. When a a ge and dis ac o appea
in close p oximi y, he endpoin s o a ge -di ec ed eye and a m mo emen s
o en land be ween hem, an obse a ion which has been e med ‘spa ial
a e aging’ (Van De S igchel & Theeuwes 2006; Egge e al. 2002;
Theeuwes e al. 1998). In a case whe e he p esen ed a ge s co esponded o
po en ial each loca ions and no as dis ac o , i has been shown ha ,
ajec o ies a e spa ially sensi i e o he p obabilis ic dis ibu ion o a ge s
wi hin he display. Speci ically, when p esen ed wi h wo o h ee a ge
displays, subjec s ini ia e hei eaches owa d an in e media y o a e aged
loca ion be o e co ec ing he ajec o y o he mo emen owa ds he cued
a ge loca ion. This indica es ha he obse ed spa ial a e aging o he
eaching ajec o y depends no only on he spa ial posi ion o he s imulus in
he display, like in he p e iously desc ibed case o he dis ac o s, bu also
on he p obabili y ha hese s imulus a e a ge s ha a o d each mo emen s
(Chapman e al. 2010). The e has been some deba e abou whe he he
po en ial each a ge s we e being encoded in isual e sus mo o
coo dina es so ha he spa ial a e aging was a consequence o isual
a e aging bu , ecen ly, i was shown ha by in oducing an obs acle in some
ials while subjec s whe e sol ing a ‘go-be o e-you-know’ ask gene a ed a
co ec spa ial a e aged each ajec o y p edic ed by a e aging o mo o
a ge loca ions a he han a e aging o isual a ge loca ions (S ewa e al.
2014).
One o he hypo hesis ha has been pu o wa d o explain all hese
beha io al obse a ions is ha closely spaced isual s imuli ( a ge s o
19
dis ac o s) c ea e o e lapping hills o ac i i y in he co esponding mo o
maps o s uc u es in ol ed in mo emen s o he eye in s uc u es like SC
(McPeek 2003), and a m in s uc u es like mo o co ex (Geo gopoulos e al.
1988) wi h he inal mo emen ec o being de e mined by he a e aging o
hese signals (Tippe e al. 2001). Unde his iew, bo h a ge s and
dis ac o s a e he e o e hough o be ini ially ep esen ed as po en ial a ge s
by he isuo-mo o sys em (McPeek & Kelle 2004). In ag eemen wi h his
in e p e a ion, i was ound ha ac i i y in he SC appea s g ea es a an
in e media y loca ion when spa ial a e aging o eye mo emen occu s
(Glimche & Spa ks 1993) a guing o he simul aneous ini ia ion o mul iple
mo emen s and consequen a e aging o he esul an ac i i y.
On he o he side, i has also been shown ha in a cued- esponse delayed
ask, when wo a ge s o di e en alue a e p esen ed be o e he go signal
and, a e he goal signal, he less alued a ge is cued o each, he a e age
ajec o y ends o be ini ially de ia ed owa ds he highe alue a ge
(Pas o -Be nie e al. 2012). Cong uen ly wi h his iew, o ins ance, i has
been obse ed ha neu al ac i i y co ela ed wi h mo emen di ec ion is p e-
shaped by in o ma ion abou he a ailable mo o choices (Ku a a 1993). Fo
example, neu al ac i i y ela ed o an ac ion ends o be s onge i he ac ion
is mo e likely o yields highe ewa ds (Kable & Glimche 2009; Sug ue e
al. 2005). This obse a ion would again en o ce he in e p e a ion ha he e
is an unde lying ongoing compe i ion be ween he wo ep esen a ions o he
wo each mo emen s bu he one ha is mo e alued is winning be o e he
cue is p esen ed, and hen when he cued signal is p esen ed he subjec has
o upda e he alue o each a ge and ake he each ha leads o ewa d.
Mo e speci ically, he hypo hesis ha compe i ion is implemen ed h ough
mu ual inhibi ion inds i s s onge e idence in s udies ha show ha peaks
26
wo was 75 cm. The acks ha composed he g id we e 15 cm wide. The
ha d s uc u e o he maze was buil wi h me al ails and acks whe e plas ic
shee s pa s ha slid in o he ails sli . Mo eo e , ~2 cm away om each po
he e was a whi e isible led encapsula ed in a 10 cm heigh plas ic ube
which would signal o ewa d a ailabili y. A Poin G ey FL3-U3-13S2M
came a wi h lens Fujinon YV28x28SA-2 was placed a 1.5 me e s abo e he
cen e o he maze and ideo eco dings whe e pe o med a 73 ps wi h
esolu ion o 1280x960. Expe imen s we e pe o med unde dim ligh (~10
lux) o inc ease he con as be ween backg ound and ligh cue. The ideo
was eco ded on he in a ed spec um and he maze was illumina ed by
s anda d CCTV in a ed lamps.
2.2.3 Pombal’s maze pa adigm
Figu e 1 Top iew o he maze. The maze was an ele a ed g id s uc u e wi h nine wa e po s
embedded on he loo , wi h no walls. Each po was dis anced om he closes neighbo by 75 cm
and each ack was 15cm wide. Co-localized wi h each wa e po he e was a isible led
encapsula ed in a plas ic ube.

27
Each ial ini ia ed when one o he LED ligh s, co-localized wi h each po ,
was swi ched on. When he animal poked in ha goal po , ligh wen o and
wa e ewa d was eleased (Figu e 2). I he animal was ou o he po o
mo e han 0.5 s, hen a 100 ms in e - ial in e al (ITI) s a ed, a e which a
new ial s a ed. A e ligh was on, he animal could eely choose which
pa h o ake o each he goal (Figu e 3). The e was no penal y o no
choosing he sho es pa h. I he animal ook mo e han 90 s o poke in, ligh
wen o and a new ial s a ed.
2.2.4 Spa ial dis ibu ion o ewa d
Spa ial dis ibu ion o ewa d was de ined acco ding o a se o p obabilis ic
ules so ha i would be unlikely ha a s could p edic whe e he nex
Figu e 2 T ial s uc u e. Schema ic d awing o he succession o e en s in each s a e: ligh was
swi ched on in po loca ion A (o ange); a e a poked in ha po (blue ace), ligh wen o and
a poked in and ou , signaling ewa d consump ion. When he a was ou o he po o mo e han
500ms, an in e - ial in e al s a ed, las ing o 100ms (g ey).
Figu e 3 Example ial. Schema ic d awing o an example ial whe e he s a po co esponded
o he loca ion whe e ewa d was las ly a ailable (blue ci cle) and he goal po co esponded o he
loca ion whe e ligh was on (yellow shape). Bo h ewa d consump ion pe iod and ITI a e no
ep esen ed.
28
ewa d was going o be a ailable be o e ligh u ned on. The aim was o
make i cos ly o he a o decide which pa h o ake in he nex ial be o e
he ligh was on, and, hence, inc easing he p obabili y ha he decision was
happening in a de ined ime window be ween ligh is on and a lea ing s a
po . Ne e heless, a he same poin exposing he a s, exclusi ely, o a
uni o m dis ibu ion o loca ions implied ha all s a -goal pai s would be
equally ep esen ed in ou sample which would lead o he
o e ep esen a ion o ials ha we e no p opo ional in in e es o he
analysis. In o de o gene a e a bias, we de ined ca ego ies o in e es o he
beha io al analysis and biased he dis ibu ion o ewa d loca ion
acco dingly. These ca ego ies and gene a ed biases will be explained in
dep h in Chap e 3, Sec ion 3.2.1.
2.2.5 T aining
The aining sequence consis ed o : (I) handling (5 sessions); (II) 20 minu es
ee explo a ion o he maze (1 session); (III) nose poking wa e associa ion
whe e ligh was on o six andom po loca ions simul aneously so ha i
animal poked in ei he o hem ewa d would be eleased (2 sessions); (IV)
ligh wa e associa ion whe e numbe o ligh s pe ial would dec ease wi h
inc ease in numbe o ials; (V) when only one ligh was on pe ial he ull
session. This p og ession happened ac oss se e al days and i was
indi idualized o each a . The a ionale was ha as long as he animals
would con inue unning a e ewa ds he exposu e o he ligh -wa e pai ing
would happen. Hence, he c i e ion o aining p og ess was he numbe o
ials pe day (Figu e 4). Animals signi ican ly do mo e ials in he las
session han in he i s session (one-sided, pai ed - es , p=0.0020, n=8).
A e his s age, animals we e conside ed o be su icien ly exposed o he
maze and o he ligh -wa e pai ing.
29
Ra s ook on a e age 9±0.5 sessions (mean±S.E.M.) om i s session o
s age III o he las session o s age IV o each his s age.
O e all, each a pe o med ~5000 alid ials, ha is, ials whe e he animal
s a ed he ial on he co ec po and inished in he co ec goal po and
whe e no inco ec pokes we e pe o med.
O e all, each a pe o med ~5000 alid ials, ha is, ials whe e he animal
s a ed he ial on he co ec po and inished in he co ec goal po and
whe e no inco ec pokes we e pe o med.
2.2.6 Video p ocessing
Video acquisi ion and online analysis we e pe o med wi h Bonsai (Lopes e
al. 2015), whe e x and y posi ion, body cen oid and o ien a ion o he animal
we e ex ac ed om each ame using backg ound sub ac ion and bina y
egion analysis by ellipsoidal app oxima ion. A ea occupied by he a
co esponded o ~3500 pixels. I was es ima ed ha , om he body cen e o
Figu e 4 Numbe o ials inc eases wi h expe ience. Compa ison be ween he mean numbe o
ials in he i s aining session and mean numbe o ials in las aining session. E o ba s a e
mean±S.E.M.
30
he ip o he snou he e was a ~5 cm dis ance. Gi en ha he e was ish eye
like image dis o ion, we applied a ans o ma ion ma ix o he image using
a s anda d image co ec ion p ocedu e. This ans o ma ion ma ix was
ob ained a e image calib a ion using a checke boa d. A e he
ans o ma ion, he e o was equal o 0.9 cm in he x di ec ion, and equal o
0.8 cm in y di ec ion. Smoo hing was applied o x, y alues o cen e o mass
in o de o educe noise om di e en sou ces such as ideo ji e o body
wobble (Hen e al. 2004). We used a gene alized e sion o he local
weigh ed eg ession (LOWESS) echnique (Cle eland 1979) called LOESS.
In he la e , each smoo hed alue is gi en by a weigh ed quad a ic leas
squa es eg ession ins ead o a weigh ed linea leas squa es eg ession as in
he o me . We used a lag o 35 poin s, co esponding o ~ 0.5 s window
(Figu e 5). This smoo hing echnique was applied using MATLAB Cu e
Fi ing Toolbox. Veloci y o e x and eloci y o e y we e de i ed om
cen e o mass posi ion using a s anda d nume ical me hod and hen he
module 𝑣 = 𝑠𝑞𝑟𝑡(𝑣𝑥^2 + 𝑣𝑦^2) was calcula ed. Video ame a e ji e was
on a e age 3% (da a no shown).
2.2.7 T ajec o ies in o sequences
Figu e 5 Example o eloci y ace o e y dimension. In g ey is ep esen ed he ace be o e
smoo hing y as a unc ion o ime and in black is ep esen ed he ace a e smoo hing, o a
window o ~25s.
31
A e smoo hing, we ans o med x,y coo dina es in o a sequence o numbe s
co esponding o po loca ion. This p ocedu e was pe o med acco ding o
he ollowing s eps:
1) x,y coo dina es in pixels whe e ans o med in o x,y coo dina es in
he eal wo ld o cm, acco ding o a linea ela ion ex ac ed ia a s anda d
calib a ion p ocedu e.
2) The cen e o mass posi ion was con e ed in o a sequence o
loca ions in an o line p ocedu e. The loca ion o each po was manually
de ined by ex ac ing x,y coo dina es ou o a dis o ion-co ec ed ame.
Regions o In e es (ROI) whe e de ined by a squa e cen e ed in he x, y
coo dina es o each po cen e and side size equal o 13 cm (Figu e 6). Each
ROI was assigned he co esponding poke numbe be ween 1 and 9 and
whene e he animal occupied one o he 9 a eas, x,y coo dina es whe e
a ibu ed he co esponding numbe . When he x,y coo dina es did no
ma ch any o he ROIS, 0 was he assigned alue. Finally, all consecu i e
equal numbe s whe e educe o one elemen such ha sequence loses
Figu e 6 Regions o in e es cen e ed on wa e po s. Example session whe e x,y posi ion
(black) is de ec ed in he di e en ROIs associa ed wi h each po (colo ).

32
in o ma ion on how long he animal s ayed in he same ROI and all ze os
co esponding o mo emen in be ween ROIs we e emo ed. Hence,
in o ma ion abou een e ing ROIs was p ese ed.
2.2.8 Video and BCon ol synch oniza ion
Video and nose poke da a whe e aligned using he led isible ligh associa ed
o each po so ha in each ial he ime o onse o ligh was gi en bo h by
he clock om he BCon ol and he de ec ion o ligh change in he ideo
ame. The ime lag be ween he BCon ol da a and he ideo da a was on ~
17ms.
2.2.9 Da a base
All he analysis was pe o med in Ma lab® 2014b (8.4.0.150421, The
Ma hWo ks, Inc). Pe each a and each session, we au oma ically gene a ed
a ma ix whe e ows co esponded o ials and columns co esponded o
di e en ields o wo ypes:
 Raw in o ma ion such as ame numbe when ial s a ed, ame
numbe when i ended, ime in session when ial s a ed, ime in session
when ial ended, s a po , goal po .
 P ocessed a iables: sho es pa h leng h, a pa h leng h, sho es
pa h {0 – non sho es pa h, 1 – sho es pa h}, u n {0 – s aigh , 1 -
u n}, di ec ion o mo emen a a i al o po {Sou h, Wes , No h,
Eas }, di ec ion o mo emen a depa u e, among o he s.
Each ial was agged wi h he co esponding s a e o he a iable in he
co esponding column. Fo example, o selec ing ials whe e s a po was
1, goal po was 3 and a did sho es pa h - 1 we would apply simply a
conjunc ion logic ule ex ac ing he ows o which he columns would mee
hese c i e ia. All he analysis was pe o med using as basic uni s s a -goal
33
pai s, which implici ly means ha we aligned da a on he s a po nose poke
e en un il goal po nose poke e en .
2.2.10 Valida ion c i e ia
Fo he alida ing he pa adigm, we e alua ed beha io acco ding o h ee
c i e ia:
I. Maze amilia iza ion and isual disc imina ion o cue, whe e we
ocused on choices be ween sho es pa hs and de ou s. Mo e
speci ically we measu ed:
a. Median la ency o lea e s a po in ials whe e a s did sho es
pa hs and whe e a s did a de ou . La ency o lea e s a po was
de ined has he in e al be ween ial s a ed, ha is, ligh u ned
on and he ime ha a ook o lea e ROI o he s a po . Fo
each s a -goal pai we calcula ed he median la ency o lea e s a
po , a e aged ac oss s a -goal pai s o each a and de e mined
he a e age ac oss he popula ion.
b. Popula ion median p opo ion o sho es pa h ials in compa ison
wi h a null model ha selec ed andomly a each choice poin om
he se o closes geome ical neighbo s, ha is, a s ochas ic choice
model. This sampling was un i e a i ely un il he goal was inally
me . Fi s ly, his agen was un eigh imes, one o each a , whe e
i was p esen ed wi h he same s a -goal pai s as each o he a s.
A e de e mining he median p opo ion o sho es -pa h ials o
each s a -goal pai o each un, we calcula ed he median o
each un and, inally, he median o medians ac oss he eigh uns.
Finally, we un his p ocedu e 1000 imes and calcula ed he 95%
con idence in e al.
34
c. Popula ion median p opo ion o sho es pa h ials in compa ison
wi h a s ochas ic choice model, as a unc ion o Manha an
dis ance o goal. We spli he da a in b. in o ials acco ding o
Manha an dis ance o he goal and de e mine median p opo ion
o sho es pa h ials ac oss dis ance o goal.
d. Popula ion median p opo ion o sho es pa h ials ac oss
sessions, a e aining had inished. A e de e mining he median
p opo ion o sho es pa h ials o each s a -goal pai o each
a , we de e mined he median ac oss s a -goal pai s o each a
and, inally, he median o medians ac oss he popula ion. This
measu e was calcula ed o each o he i s i e sessions a e
aining had ended.
II. S eng h o ligh wa e pai ing, whe e we ocused on choices be ween
poking o no in non-cued po s. Mo e speci ically, we measu ed he
p opo ion o ials whe e animals poked a leas once in a po whe e
ligh was no on. We sepa a ed ials we e animals did sho es pa hs
and hen quan i ied he median p opo ion o hese whe e animals did
inco ec poking. We used he median gi en ha he he e was a high
p obabili y ha dis ibu ion ac oss a s would be close o ze o as ime
goes by. Hence, we selec ed he i s 5 sessions, a e animals we e
conside ed o be ained and om hese we selec ed ials whe e
animals did sho es pa hs. We excluded de ou s om his analysis
because he main conce n was ha a s would use ligh as di ec ional
cue o sho es pa h choice bu hen would be s opping a each wa e
po o check o ewa d.
III. Biased beha io , whe e we ocused on choices be ween di e en
op ions wi hin each se o possible sho es pa hs. Mo e speci ically,
we sepa a ed ials in o h ee condi ions: ials whe e animals could
35
selec be ween wo sho es pa h op ions, h ee sho es pa h op ions o
six sho es pa h op ions. Fo each s a -goal pai , we a bi a ily
ca ego ized each o he op ions (op ion A, op ion B, e c) and
de e mined he a e age p opo ion o ials consis en ly ac oss a s
co esponding o each op ion. We a e aged o e all s a -goal pai s o
each condi ion pe a and hen a e aged ac oss a s. In he condi ion
whe e wo sho es pa hs we e a ailable, he compa ison o de e mine a
bias was made wi h a shu ling o he da a, so ha in each ial o each
s a -goal pai o each a he choice was sampled om a binomial
dis ibu ion. Mo eo e , we epea ed he analysis bu sepa a ing da a
in o h ee ba ches: he i s 1/3 o he sessions, he second 1/3 o he
sessions and las 1/3 o he sessions. The da a was spli in o ba ches
ins ead o doing session by session analysis because he e was no
enough s a is ical powe o ha . Las ly, he o e all biases we e
compa ed be ween he h ee ba ches co esponding o h ee expe ience
epochs.
2.3 Resul s
2.3.1 P e e ence o sho es pa hs
Fi s ly, in o de o unde s and i animals we e ollowing an op imal s a egy,
we compa ed he choices o he animals wi h a null model ha did no ake
in o conside a ion any in o ma ion abou he goal, ha is, a each choice
poin i selec ed andomly which pa h o ake. Hence, we compa ed he
median p opo ion o sho es pa h ials, ac oss he popula ion, wi h a
s ochas ic choice model simula ion which sampled andomly a each choice
poin (Figu e 7). Animals chose sho es pa hs mo e o en han a s ochas ic
choice model simula ion (pe mu a ion es , p<0.002, n=8). Mo e speci ically,
42
The e was no signi ican di e en o e he h ee epochs, o all he h ee
Figu e 13 The e is a bias owa ds one o he op ions, in a h ee op ion condi ion. (i) Example
s a -goal pai whe e six sho es pa h op ions we e a ailable. Only h ee o he six sho es pa h
op ions a e depic ed (A, B and C) (ii) Mean p opo ion o ials co esponding o each o he six
op ions in he se o sho es pa hs o one a ac oss all s a -goal pai s ma ching he condi ion. D,
E and F a e he co esponding diagonally symme ic pa hs o A, B and C (iii)
Mean p opo ion o
ials co esponding o each o he 6 op ions in he se o sho es pa hs (one-way ANOVA, p
<0.001, n=8). E o ba s a e mean±SEM.

43
condi ions wi h associa ed numbe o op ions. The e was no signi ican
di e ence ac oss he h ee ime epochs o all he h ee condi ions (one way
Figu e 14 Biases owa ds ce ain op ions do no change ac oss ime. (i) Mean p opo ion o ials
whe e op ion A was chosen ou o wo possibili ies ac oss a s (ho izon al line) o e session ba ches
(ligh e g ey – i s ba ch, middle g ey –second ba ch, da ke g ey hi d ba ch) (one way ANOVA
p=0.6759, n=8). (ii) Mean p opo ion o ials whe e op ions A, B and C whe e chosen ou o h ee
possibili ies ac oss a s (ho izon al line) o e session ba ches (ligh e g ey – i s ba ch, middle g ey
–second ba ch, da ke g ey hi d ba ch) ( wo way ANOVA, in e ac ion op ion x ime, p=0.1789,
n=8). (iii) Mean p opo ion o ials whe e op ions A, B, C, D, E and F whe e chosen ou o six
possibili ies ac oss a s (ho izon al line) o e session ba ches (ligh e g ey – i s ba ch, middle g ey
– second ba ch and da ke g ey hi d ba ch) ( wo way ANOVA, in e ac ion op ion x epoch p=0.1228,
n=8). E o ba s a e mean±SEM.
44
ANOVA, p=0.6759, n=8), h ee op ions ( wo way ANOVA in e ac ion
op ion x epoch, p=0.1579, n=8) and, inally, six op ions ( wo way ANOVA
in e ac ion op ion x epoch p=0.8322, n=8). This obse a ion indica ed ha
p e e ences o ce ain op ions we e obus ac oss ime which possibly mean
ha hese biases con e ed an ad an age o he beha io al s a egy ha
animals we e using o sol e his ask.
2.4 Discussion
Gi en ou in e es in gaining a be e unde s anding abou ac ion selec ion
phenomenon, we aimed a de eloping a no el decision-making pa adigm
which would ha e simul aneously na iga ion and mo o planning ea u es.
Hence, we designed and de eloped wha we e med Pombal’s maze
pa adigm whe e a s chose om mul iple op ions in a g id-like maze which
pa h o ake o each a wa e ewa d signaled by a isual cue, on a ial-by-
ial basis.
In o de o alida e he pa adigm p emises, beha io was cha ac e ized and
alida ed using di e en c i e ia. Animals we e able o lea n he ask bo h
because hey selec ed sho es pa hs mos o he ials and because hey
mos ly checked o ewa d in he po s whe e ligh was on.
Addi ionally, he p opo ion o sho es pa hs inc eased wi h expe ience o
he maze. This could be explained by he need o lea n o isually
disc imina e he ligh posi ion being ha , gi en he maze symme ies, he
goal could ha e been in one o i e possible loca ions, and/o he need o
lea n he maze geome y, being ha i imposed cons ain s on which ac ion
sequences could be pe o med (diagonals ac oss po s we e no an op ion)
and, consequen ly, which pa hs could be aken o each he goal and om
hose which we e a he sho es dis ance.
45
In compa ison wi h a s ochas ic choice model simula ion, animals chose
sho es pa hs mo e o en which en o ced ou hypo hesis ha animals we e
ollowing an op imal s a egy o using isual in o ma ion abou he goal o
selec he sho es pa h. P edic ably, in o de o he s ochas ic choice model
o gene a e choices ha would ma ch he op imal s a egy i would jus
equi e he in oduc ion o he cons ain whe e op ions ha would no
minimize dis ance o he ligh would ha e ze o p obabili y. This would
co espond o an ideal subjec wi h a sho es pa h p opo ion equal o one.
Animals can pe o m close o wha would be expec ed by an ideal subjec so
i was concluded ha animals we e using isual in o ma ion o choose he
op imal pa hs o each he cued goal. E en hough he lea ning p ocesses ha
a s go h ough while being ained we e no subjec o in es iga ion in his
p ojec , he po en ial in e es o doing so will be alluded o in Chap e 5.
Pombal’s is a wo dimensional maze ha was designed so ha he e we e
mul iple pa hs o each he goal. The solu ion ound was inspi ed by mo o
planning asks whe e ypically he s a posi ion and he goal posi ion a e he
same o all possible ac ions. Acco dingly, all pa hs we e a he same
Manha an dis ance o he goal. Gi en he wo dimensional na u e o he
maze, his necessa ily implica ed ha hei geome y had o be di e en and,
hence, he necessa y ac ion sequences o a el hose pa hs would also be
di e en . Hence, we conside ed ha e en hough he e we e di e en ac ion
sequences o a el he same dis ance and o each he same goal, i could be
a gued ha he di e ence in cos ac oss ac ion sequences was p obably
esidual. Ne e heless, igo ously, he e we e di e ences be ween hese
ac ion sequences. In ha sense i was impo an o quan i y i animals
showed biases owa ds some op ions o e o he s, o ins ance, i would ha e
been possible o choose always he same sho es pa h wi hin a gi en s a -
goal pai . We obse ed ha animals consis en ly show biases owa ds ce ain
46
op ions wi hin he se o sho es pa hs. Hence, he obse ed pa e ns o
choice seemed an indica ion ha animals we e aking in o conside a ion
o he decision a iables besides dis ance o goal. Possibly, hese we e also
decision a iables ele an o he op imiza ion p oblem ha animals we e
sol ing and hence po en ially e ealing o he unde lying decision-making
p ocesses. Ne e heless, he p oblem ha animals we e ying o sol e was
also a na iga ion p oblem whe e he goal did no ha e o be in e ed om
landma ks o did no ha e o be emembe ed, bu s ill in o ma ion abou he
goal posi ion had o be in eg a ed wi h in o ma ion abou maze geome y,
somehow. Pombal’s maze is also a cued na iga ion pa adigm.
In conclusion, he s a egy ha animals we e using o sol e his op imiza ion
p oblem emains o be unde s ood. Speci ically, i is necessa y o unde s and
in g ea e dep h how animals we e using:
 goal isual in o ma ion. Did animals disc imina e he exac loca ion
o he goal among i e possibili ies and only hen hey selec ed he
sho es pa h op ion among all possible pa hs? Did animals
disc imina e he di ec ion o he ligh in ela ion o hei head posi ion
and hen ollowed h ough he pa h ha seemed o be close o he
ligh ?
 maze geome y in o ma ion. Which a iables a e animals weighing
when hey choose be ween di e en op ions?
Answe ing hese ques ions will add o a be e unde s anding o he s a egy
ha animals used o sol e he ask and i will also allow us o de e mine
which pool o ac ions was being conside ed a each ial, hence, be e
de ining he ac ion selec ion laye o he decision-making p oblem. These
ma e s will be subjec o u he in es iga ion in he ollowing chap e s.
47
3 DISCRETE CHOICES
and nea
he cen e o mysel he e is an hal open ga e
and I ha e ahead he unco e ed mons e
he beas ha I gua d and ha gua ds wha li es wi hin
Sé gio Godinho, “The maze”
(F eely ansla ed by ARF)

48
3.1 In oduc ion
In he p e ious chap e , we in oduced Pombal’s maze pa adigm and
discussed he op imal s a egy o sol e he unde lying decision-making
p oblem.
In his chap e , we will in oduce he easoning we used o ame beha io al
analysis o he decision-making pa adigm desc ibed in Chap e 2, and, hen,
we will summa ize he beha io o a s ained on ha pa adigm. On a i s
le el, in Pombal’s maze, animals could eely choose which pa h o ake o
each he goal. As long as he animal poked in he po whe e ligh was on, i
would ge ewa d, so, in ha sense, his ask did no implica e a o ced
choice. Ne e heless, gi en he wa e dep i a ion s a e o he animal,
a ionally, he op imal s a egy would be o selec pa hs ha minimize
dis ance o he goal, so ha , animals would ge o goal as as as possible,
maximizing he ewa d a e. On a second le el, o ials whe e mul iple
sho es pa hs op ions we e a ailable, he e we e geome ical ea u es which
dis inguished hese pa hs (e.g. numbe o u ns). Bu he e may ha e been
o he ea u es ha we e dependen on in e nal biases o he animals, o
ins ance, p e e ence o mo ing in a gi en di ec ion. Such ea u es may ha e
imposed a cos o a bene i o he s a egy o he animal. Mo e speci ically,
we in es iga ed which decision a iables he a s we e aking in o
conside a ion, by desc ibing e ealed p e e ences o gi en ypes o pa hs.
Ou app oach was based on selec ing and compa ing ial condi ions ha
could isola e only one po en ial decision a iable. O e all, we we e aiming
in de ining he unde lying na iga ion p oblem.
3.2 Me hods
49
3.2.1 T ial classi ica ion
T ials we e collapsed depending on di e en classi ica ion c i e ia:
A. Manha an dis ance o he goal: whe e ials we e g ouped acco ding
o dis ance in maze segmen s be ween po loca ion and goal po
(Figu e 15i);
B. Euclidean dis ance o he goal: whe e ials we e g ouped acco ding
o Euclidean dis ance in segmen s be ween po loca ion and goal po
(Figu e 15ii);
C. Numbe o pa h op ions: whe e ials we e g ouped acco ding o
numbe o sho es pa h op ions o he goal (Figu e 15iii);
D. Numbe o u ns: whe e ials we e g ouped acco ding o he numbe
o u ns ac oss sho es pa h op ions (Figu e 15i );
E. Allocen ic ea u es: whe e ials we e g ouped acco ding o whe e
he pa hs goes h ough in he maze (Figu e 15 );
F. Egocen ic ea u es: whe e ials we e g ouped acco ding o ea u es
ha depend on body posi ion (Figu e 15 i);
50
Figu e 15 T ials ca ego ized acco ding o di e en c i e ia. (i) Schema ic d awings o examples
o ou s a -goal pai s whe e he a had o a el be ween s a and goal one maze segmen , wo
maze segmen s, h ee maze segmen s o six maze segmen s. (ii) Schema ic d awings o i e
examples o s a -goal pai s whe e a would ha e o a el be ween s a and goal he Euclidean
dis ance co esponding o one maze segmen ,
𝟐 maze segmen s, wo maze segmen s,
𝟓 maze
segmen s o 2
𝟐
maze segmen s. (iii) Schema ic d awing o ou examples o s a -goal pai s whe e
he e is a se o sho es pa hs wi h one op ion, wo op ions, h ee op ions o six op ions. (i )
Schema ic d awings o h ee examples o s a -goal pai s whe e pa h equi ed one u n, wo u ns o
h ee u ns. ( ) Schema ic d awing o an example s a -goal pai whe e he e we e only wo sho es
pa hs and whe e one would equi e going h ough he edge and he al e na i e would equi e going
h ough he cen e .
Schema ic d awing o an example o a s a -goal pai whe e a could selec one
o wo sho es pa h op ions whe e i would equi e o go backwa ds in he opposi e di ec ion in
ela ion he di ec ion o a i al.
51
In Chap e 2, we men ioned ha spa ial loca ion o ewa d was p obabilis ic
bu subjec ed o a bias ac o (Sec ion 2.2.4). He e we explain in mo e de ail
wha his en ailed. The e we e 72 possible s a -goal pai s bu , o example,
32 pai s co esponded o ials whe e only one sho es pa h op ion was
a ailable. So, in o de o ge a mo e e enly dis ibu ion o ial ypes we
p esen ed a s ~7 session days whe e all s a -goal pai s we e equally
p obable (uni o m dis ibu ion) and ~15 session days whe e he e was a bias.
These biases whe e de ined o maximize he numbe o ials associa ed o
di e en ca ego ies o ial ypes. We conside ed h ee main ca ego ies o
ials: cen e a oidance ials, which aim was o de e mine i animals showed
a oidance o he cen e ; mul iple op ion ials, which aim was o analyze
beha io when mo e han wo op ions we e a ailable and, inally, expe ience
ials, which aim was allowing he animals o expe ience enough ewa ds in
he cen e . Spli ing ials in o hese di e en condi ions allowed o a mo e
de ailed in es iga ion o he p e e ences o ce ain pa hs. Fo his analysis
only sho es pa h ials we e conside ed. Acco ding o his ame, we
de ined h ee main ca ego ies o ials:
1. Mul iple op ion ials, which aim was o analyze beha io when mo e
han 2 op ions we e a ailable (Figu e 16). As a a ge , we alloca ed
45% o ials.
Figu e 16 Example o s a -goal pai o mul iple op ion analysis. Schema ic d awing o one
example s a -goal pai whe e mo e han 2 sho es pa h op ions we e a ailable.
58
ials ac oss a s whe e pa hs ha would minimize Euclidean dis ance o he
ligh would be selec ed (Figu e 24).
Ra s showed a signi ican p e e ence o pa hs ha minimize Euclidean
dis ance o goal (pe mu a ion es , p=0<0.002, n=8).
In o de o es o obus ness o his obse a ion, we spli he da a in o s a -
goal pai s and measu ed he mean p opo ion o ials o ype A ac oss a s in
compa ison wi h sampling om a binomial dis ibu ion (Figu e 25). I was
obse ed ha 10/16 s a -goal pai s show means abo e he 95% con idence
in e al (pe mu a ion es , p<0.002, n=8). This indica es ha p e e ence o
ype A pa hs is obus ac oss di e en loca ions in he maze and ac oss a s,
en o cing he hypo hesis ha aking his pa hs is pa o s a egy. Secondly,
we analyzed mean p opo ion o ials whe e animals selec ed ype A bu
sepa a ed by s a -goal pai s ha go h ough he cen e s h ough he edge
and compa ed hese means in o de o unde s and i choosing A was
dependen on loca ion in he maze (Figu e 26).
Figu e 24 Ra s p e e pa hs ha minimize dis ance o goal as e . (a) Schema ic d awing o an
example s a -goal pai whe e, a e i s segmen o pa h, a ype A pa h would ake he a close o
he ligh posi ion in e ms o Euclidean dis ance and a ype B pa h would ake a u he away om
he ligh posi ion in e ms o Euclidean dis ance. (b) Mean p opo ion o ials whe e a s choose ype
A pa hs and he expec ed le el o chance sampled om a binomial dis ibu ion (pe mu a ion- es
p<0.002, n=8). E o ba s o a s a e mean±SEM. E o ba s o binomial sampling a e 95%
con idence.

59
The mean p opo ion o ype A ials ha go h ough he cen e is
Figu e 25 Ra s p e e pa hs ha go h ough he cen e , in a h ee op ions condi ion. (i)
Schema ic d awing o example s a -goal pai s whe e he pa hs go h ough he edge (solid line) o
h ough he cen e (dashed line). (ii) Mean p opo ion o ials whe e a s choose op ion cen e and
he expec ed le el o chance sampled om a binomial dis ibu ion (pe mu a ion- es p<0.002, n=8).
E o ba s o a s a e mean±SEM. E o ba s o binomial sampling a e 95% con idence.
Figu e 26 P e e ence o ype A is main ained ac oss he majo i y o s a -goal pai s. (i)
Schema ic d awing o he numbe ing o each po loca ion. (ii) Mean p opo ion o ials whe e ype
A pa hs we e selec ed ac oss a s o each s a -goal pai (whi e ba s) in compa ison wi h and he
expec ed le el o chance sampled om a binomial dis ibu ion sampling om a binomial dis ibu ion.
E o ba s a e 95% con idence.
60
signi ican ly highe han he mean p opo ion o ype A ials ha go h ough
he edge (one sided, pai ed - es , p < 0.002).
Mo eo e , gi en ha we, p e iously, obse ed a p e e ence o selec ing
pa hs ha go in he opposi e di ec ion o po a i al, in wo op ion condi ion,
we epea ed ha analysis o he h ee op ion condi ion. We compa ed mean
p opo ion o ials ha a s chose pa hs ha go on he opposi e di ec ion o
a i al a he po (Figu e 27) wi h sampling om a binomial dis ibu ion.
Animals showed p e e ence o going in he same di ec ion, in he h ee
op ion case (pe mu a ion es , p<0.002, n=8). Fo he condi ion whe e six
op ions we e a ailable, analyzing he p e e ence o minimizing dis ance o
ligh was p oblema ic because he e was he insepa able con ound o he
numbe o u ns.
Figu e 27 Animals p e e o con inue in he di ec ion ha hey a i ed a he po . Compa ison
be ween mean p opo ion o ials ha a s le s a po in he opposi e di ec ion o po a i al wi h
sampling om a binomial dis ibu ion (pe mu a ion es , p=0<0.002, n=8). E o ba s o a s a e
mean±SEM. E o ba s o binomial sampling a e 95% con idence.
61
3.3.4 P e e ence o geome ical ea u es: numbe o u ns
Ano he ac o ha migh ha e played a ole in p e e ences o ce ain
op ions o e o he s was he numbe o u ns. I he aim o he animal was o
each goal as as as possible, making a u n physically implied a educ ion in
eloci y which could in oduce a cos o ce ain op ions. In o de o
in es iga e i animals had p e e ence o doing less u ns, we analyzed ials
whe e h ee sho es pa h op ions we e a ailable and whe e animals selec ed
be ween pa hs ha ake one o wo u ns. Wi hin ials ha equi e one u n,
we selec ed only he ials whe e he e was a di e gence poin be ween he
pa h wi h one u n and he pa h wi h wo u ns. We obse ed ha a s
p e e ed pa hs ha implica e less u ns, bo h in he ials whe e h ee op ions
we e a ailable (Figu e 28) (pe mu a ion es , p<0.002, n=8).
Figu e 28 Animals p e e pa hs wi h one u n. (i) In h ee op ion ials, he e we e wo ypes o
condi ions: wo pa hs ha equi e one u n and one pa h ha equi ed wo u ns .(ii) Compa ison
be ween mean p opo ion o ials ac oss he popula ion whe e op ion wi h one u n was selec ed
and he expec ed le el o chance sampled om a binomial dis ibu ion (pe mu a ion es , p<0.002,
n=8). E o ba s o a s a e mean±SEM. E o ba s o binomial sampling a e 95% con idence.
62
Addi ionally, we quan i ied i he e was a di e ence be ween he in e als o
ime ha a s ook o a el h ough pa hs wi h di e en numbe o u ns. We
calcula ed he mean mo emen ime o each op ion ype (Figu e 29). The
mean mo emen ime o one u n was signi ican ly lowe han o pa hs
whe e a s ook wo u ns (one-sided, pai ed - es , p<0.001, n=8). As
p e iously, o he condi ion whe e six op ions we e a ailable, analyzing he
p e e ence o numbe o u ns was p oblema ic because he e was he
insepa able con ound o minimizing dis ance o ligh .
3.3.5 Compe i ion be ween op ions
In he condi ion whe e h ee op ions we e a ailable, be ween he wo pa hs
ha minimized he Euclidean dis ance o he ligh he e was one which had
less u ns, hence, we would no expec ha hose would be compe ing
choices. None heless, in he condi ion whe e six op ions we e a ailable
animals we e p esen ed wi h ials whe e, he e may ha e been compe i ion
be ween op ions. In he six op ions condi ion, he p e e ence o minimizing
Euclidean dis ance o he ligh and he p e e ence o minimizing numbe o
Figu e 29 Animals a e as e o each he goal when aking a pa h wi h one u n. Compa ing
mean mo emen ime ac oss popula ion be ween ials whe e animals ook a one u n pa h and ials
whe e animals ook a wo u n pa h (pai ed - es , p<0.001, n=8). E o ba is SEM.
63
u ns we e compe ing because he pa hs ha bes minimize he Euclidean
dis ance o ligh a e he ones wi h mo e u ns (Figu e 30).
A his poin we could pu o wa d a se o p edic ions o p e e ences in he
six op ion condi ion gi en hese minimiza ion ac o s and hen check i hese
would ma ch a ’s beha io (Figu e 31), seemingly o wha was p e iously
obse ed o he h ee op ion condi ion (3.3.6).
We obse ed ha he popula ion da a did no ma ch he p edic ions
associa ed o each o he wo minimiza ion ac o s. Fo ins ance, acco ding o
he minimiza ion Euclidean dis ance, pa hs A and B should ne e be chosen,
which animals did, simila ly, acco ding o he minimiza ion o numbe o
u ns, pa hs B and F should ne e be chosen.
Figu e 30 T ial ype A minimizes numbe o u ns and ial ype C minimizes Euclidean
dis ance o he goal. Schema ic d awing o wo example pa hs, in he six op ion condi ion, whe e A
would minimize he numbe o u ns bu no Euclidean dis ance o goal and whe e C would
minimize Euclidean dis ance o goal bu no he numbe o u ns.

64
In he end, he pa h ha was he bes comp omise be ween he wo ac o s
Figu e 31 Di e en ac o s de ine di e en expec ed p opo ions o each op ion. (i) Example
s a -goal whe e six sho es pa hs wi h di e en ia ed geome y (A, B and C and hei symme ical
pa hs D, E, F no ep esen ed) we e a ailable (le panel) and popula ion mean p opo ion o ials
co esponding o each op ion. (ii) Choice p obabili ies associa ed o each in e sec ion gi en he
c i e ion o minimiza ion o Euclidean dis ance (le panel) and expec ed p opo ions o each op ion,
gi en he de i ed condi ional p obabili ies ( igh panel). (iii) Choice p obabili ies associa ed o each
in e sec ion gi en he c i e ion o minimiza ion o numbe o u ns (le panel) and expec ed
p opo ions o each op ion, gi en he de i ed condi ional p obabili ies ( igh panel).
65
was pa h B which did no ma ch he mos p e e ed choice by he animals. In
o de o quan i y wi h mo e de ail he pa e ns o choices in he six op ion
condi ion, i s ly, we quan i ied he mean p opo ion o ials whe e a s
choose ype A ials, a e he ha ing c ossed he i s segmen o pa h
(Figu e 32). Symme ical ials we e collapsed so ha he op h ee op ions
we e mapped o he h ee bo om op ions. This case is he only symme y
axis ac oss op ions, ha is, he e is no pa h wi h same geome y ha could go
h ough he cen e o h ough he edge. The aim o his mapping was o make
i equi alen in condi ions o he analysis o h ee op ions.
Figu e 32 Ra s don' p e e pa hs ha minimize Euclidean dis ance o goal. (i) Schema ic
d awing o an example s a -goal pai whe e, a e he i s ini ial segmen , ype A pa h pu a u he
away om ligh and ype B pa h pu a u he away om ligh , in e ms o Euclidean dis ance. (ii)
Compa ison be ween mean p opo ion o ials whe e a s choose ype A ials and sampling om a
binomial dis ibu ion (pe mu a ion es , p <0.002, n=8). E o ba is SEM.
66
We obse ed ha a s p e e ed o ake ype A pa hs bellow chance
(pe mu a ion es , p <0.002, n=8). Fo he six op ion condi ion, animals did
no show p e e ence o s aying close o he ligh posi ion. Second, we
quan i ied he mean p opo ion o ials whe e a s could choose be ween
aking one, wo o h ee u ns pa hs. Animals p e e o ake pa hs wi h one
u n (Figu e 33) (one-way ANOVA, pos -hoc Tukey-K ame es , p < 0.001,
1 u n x 2 u ns, p <0.001, 1 u n x 3 u ns, p<0.001).
We also quan i ied mo emen ime as a unc ion o he numbe o u ns o
he pa h (Figu e 34) and he e was no a signi ican di e ence be ween he
mo emen imes o pa hs wi h di e en numbe o u ns (one-way ANOVA,
p= 0.4760, n=8).
Figu e 33 Animals p e e pa hs wi h one u n. (i) In six op ion ials, he e a e h ee ypes o
pa hs: pa hs ha equi e one u n, pa hs ha equi e wo u ns and pa hs ha equi e h ee u ns (ii)
Compa ison be ween median p opo ion o ials ac oss he popula ion o each op ion wi h
associa ed di e en numbe o u ns (one-way ANOVA, pos -hoc Tukey-K ame , p= 2.2743e-06, 1
u n x 2 u ns, p< 0.001, 1 u n x 3 u ns, p<0.001, n=8). E o ba is SEM.
67
Going back o he ques ion o wha animals would do i he e was a con lic
be ween minimizing Euclidean dis ance and he numbe o u ns, i seemed
ha animals p e e ed o ake pa hs wi h less u ns o e minimizing he
Euclidean dis ance o he ligh , e en hough mo emen ime was no
di e en o pa hs wi h di e en numbe o u ns.
3.3.6 Quali a i e amewo k
We iden i ied wha we e wo po en ially key ea u es o beha io . I seems
ha animals p e e ed o choose pa hs ha minimized Euclidean dis ance o
he goal and pa hs ha minimize he numbe o u ns. On ha accoun , we
could ask: how well can hese p emises explain all he obse ed choices? In
o de o answe his ques ion we de i ed analy ically he expec ed
p obabili ies associa ed o di e en biases and compa ed hose wi h he
beha io al esul s. Fo each in e sec ion, depending on each bias ac o , we
de ined he p obabili y o choosing one o he di e gen segmen s acco ding
o each minimiza ion ac o and hen we calcula ed he espec i e expec ed
Figu e 34 Mo emen ime is independen o numbe o u ns. Compa ing mean mo emen ime
ac oss popula ion be ween ials whe e animals ook one u n pa hs, wo u n pa hs and ials whe e
animals ook h ee u n pa hs (one-way ANOVA, p= 0.4760, n=8). E o ba is SEM.
74
A his poin , i emains o be be e unde s ood which o he abo e desc ibed
algo i hms a e being implemen ed. Do animals main ain a look up able
which hey consul a he beginning o each ial o conside he se o
po en ial ac ion sequences o each he goal? Al e na i ely, do animals make
sequen ial decisions along he pa h aking ad an age o each choice poin ?
O is a combina ion o hese wo algo i hmic solu ions, whe e animals lea e
he s a po wi h a pa h in mind bu hen hey ake he choice poin as a
decision a o dance o e-e alua e p e ious choice? Ce ainly, in es iga ing
beha io al co ela es o choice dynamics could help shed ligh in o hese
ques ions, as i has been shown by he s udy o eaching and eye ajec o ies
in equi alen con ex s such as mo o planning (Ray e al. 2014; Kau man e
al. 2015; Cos e al. 2014; Song & Nakayama 2008).

75
4 DYNAMICS OF
CHOICE
I eel
ha he mind is almos like a maze
whe e each s ee has un undis inguishable name
whe e each choice is made a he las momen
Se gio Godinho, in “The maze”
(F eely ansla ed by ARF)
76
4.1 In oduc ion
In he p e ious chap e , we in es iga ed disc e e choices in o de o
unde s and which s a egies animals we e using o sol e Pombal’s maze
pa adigm and discussed which decision a iables we e animals aking in o
accoun .
In his chap e , we in es iga ed dynamics o ac ion such as la ency o s a
mo emen , eloci y and ajec o y ac oss ypes o pa hs wi h he aim o
ela e hese dynamics wi h decision-making p ocesses. Mo e speci ically, we
wan ed o de e mine i ajec o y and/o eloci y p o iles could e eal
co ela ions wi h con lic be ween po en ial ac ions o commi men wi h a
choice. The aim was o gain a be e unde s anding o he unde lying s a egy
o decision.
Ou hypo hesis was ha i animals we e weighing op ions sequen ially
ac oss choice poin s we would no be able o sepa a e ajec o ies o di e en
ypes o pa hs un il animals eached choice poin s, simila ly wi h he
phenomenon o spa ial a e aging desc ibed in mo o planning Van De
S igchel & Theeuwes 2006; Egge e al. 2002; Theeuwes e al. 1998;
addi ionally, we hypo hesized ha i animals we e deciding sequen ially, we
would expec a eloci y dec ease while eaching a choice poin , simila ly
wi h wha was obse ed in a ask whe e eac ion ime inc eased when las
minu e choice was made a ailable (Kau man e al. 2015). Fi s , we will
summa ize he dynamics o he animal’ beha io in di e en ial ypes.
Secondly, we will in es iga e he hypo heses abou dynamics o choice
abo e desc ibed.
4.2 Me hods
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4.2.1 Spa ial window o in e es
Fo his chap e , we mainly ocused on a speci ic window o in e es o
analyze beha io (Figu e 37). Mainly, he aim was o s udy speci ically wha
happened jus be o e he i s choice poin . In e ms o condi ions, he ocus
was he case o h ee op ions whe e, a he i s choice poin , he e a e only
wo possible symme ical pa hs di e ging.
4.2.2 Spa ial da a alignmen and binning
In o de o allow o compa ison be ween di e en s a -goal pai s,
ajec o ies we e linea ly ans o med o be aligned o he x,y coo dina es o
he cen e o po 1 (le mos bo om po ), o always go upwa ds and o he
le (Figu e 38). A e wa ds, aw ajec o ies and eloci ies (Figu e 39)
whe e binned in he y di ec ion (Figu e 40).
Figu e 37 Window o in e es o beha io al analysis. Schema ic d awing o he spa ial window
used o analysis ( ed box) which co esponds o he space be ween lea ing he s a po and he i s
in e sec ion (g ey dashed box).
78
I was inhe en o he binning p ocess he educ ion o in o ma ion o e he
chosen dimension (y in his case) so i mean ha o analyzing pa s o he
ajec o y whe e he dominan dimension o mo emen is along x dimension,
his binning was no app op ia e.
Figu e 38 Linea ans o ma ion o ajec o ies o spa ial alignmen . i) Two example ials whe e
ajec o ies was linea ly ans o med o ge he ajec o ies all aligned ii) T ans o med ajec o ies,
aligned on he same posi ion.
Figu e 39 Raw eloci ies and co esponding ajec o ies. (i) T ajec o y p o ile o h ee di e en
ials, whe e each colo co esponds o a ial. (ii) Veloci y p o iles co esponden o ajec o ies in
(i) wi h same colo ma ch.
79
4.2.3 Veloci y exclusion c i e ion
In some ials, ajec o ies show ha a s le he choice poin ROI and hen
e-en e ed i (Figu e 41). This c i e ion was de ined as a ial exclusion ac o
Figu e 41 Example ial dynamics p o ile whe e one a le he choice poin ROI and hen e-
en e ed i . (i) T ajec o y co esponding o a ial whe e a le he in e sec ion ROI and e-en e ed i
(ii) de i ed eloci y.
Figu e 40 Examples o binned ajec o ies and co esponding eloci ies. (i) T ajec o ies binned
o e y o all ials o one s a -goal pai o he same a (g ey aces) wi h associa ed mean (ligh
blue) (ii) Co esponding eloci ies wi h he mean ace (ligh blue).

80
because hese ials could be, po en ially, beha io al co ela es o changes o
he decision (Bu k e al. 2014; Song e al. 2008). These ials ep esen ed less
han 1% o o e all ials pe s a -goal pai (da a no shown).
4.2.4 ROC analysis
In o de o de e mine disc iminabili y be ween wo di e en ypes o pa hs
we used ecei e ope an cha ac e is ic cu e and he espec i e measu e o
disc iminabili y, a ea unde he cu e (AUC) (Ma ill & Elec onics 1956).
This analysis was used bo h o disc imina ing ajec o ies o eloci ies.
Mo e speci ically, i s , we sepa a ed ials o each s a -goal pai by pa h
ype; second, we pe o med he ROC analysis o e he wo ypes o pa h pe
5cm bin o y; hi d, we de e mined he mean AUC as a unc ion y ac oss
s a -goal pai s o each a and, inally, we a e aged ac oss a s. Each s a -
goal pai had o ha e a leas 7 ials o each o he wo condi ions and bo h
dis ibu ions had o ha e he same size. Fo ha , we de e mined he smalles
sample size be ween he wo condi ions and used he n samples om he
dis ibu ion wi h mo e samples, whe e n co esponded o he size o he
smalles dis ibu ion. Gi en ha his ype o analysis is pe o med as a
unc ion o spa ial loca ion, o he compa ison be ween o e lapping pa h
ypes we pai ed each s a -goal pai o one o he pa h ypes wi h he
Figu e 42 Pai ing o s a -goal pai s acco ding o spa ial o e lap. (i) Example compa ison
be ween wo o e lapping pa h ypes whe e pa hs s a in he s a po and comple ely o e lap in
space (ii) Two examples o pai ing be ween wo ial ypes whe e pa hs om he wo di e en ypes
whe e o e lapping in space.
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co esponding o e lapping s a -goal pai o he o he pa h ype (Figu e 42).
This was o accoun o po en ial e ec s wi h image dis o ion e o s (0).
4.3 Resul s
Fo decision-making p ocesses i has been shown ha ime ela ed measu es
such as eac ion ime and mo emen ime ha e p o en e y in o ma i e
beha io al co ela es (Ba ack & Gold 2016). Seemingly, we conside ed
la ency o lea e s a po ROI, a e ligh was on, as a po en ial decision-
making co ela e.
4.3.1 La ency o lea e he s a po
We s a ed by compa ing he median la encies o lea e s a po in ials
whe e animals selec ed sho es pa h in compa ison wi h ials we e animals
did de ou s (Figu e 43). The hypo hesis was ha i , in gene al, animals we e
aiming a eaching he goal as as as possible, i ials whe e animals did
de ou s we e di e en al eady a beginning o he ial, indica ing an explici
Figu e 43 Animals a e as e o lea e s a po when hey a e abou o do a sho es pa h.
Mean la ency o lea e s a po o ials whe e a s choose sho es pa hs o de ou s (pai ed es ,
one-sided, p < 0.001, n=8). E o ba s a e SEM.
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disengagemen in hose ials. Ra s we e, on a e age, signi ican ly as e o
lea e he s a po in sho es pa h ials, in compa ison wi h ials whe e a s
chose a de ou pa h (pai ed es , one-sided, p<0.001, n=8). This could be
in e p e ed as i animals we e less mo i a ed o pu sue ewa d, om he
beginning o he ial, when hey we e abou o choose a de ou .
Al e na i ely, i could be hough ha his we e cases o ials we e isual
s imulus was no eadily de ec ed, o ins ance, due o animal being ben o e
he edge o maze; we conside ed ha , as soon as he animal le he s a po ,
independen ly o he posi ion, ligh would be isible so aking a sho es pa h
would s ill be possible.
Wi hin ials whe e animals selec ed sho es pa hs, we conside ed ha
la ency o lea e s a po was po en ially associa ed wi h he di icul y o
making a decision when mo e op ions we e in ol ed (Chu chland e al.
2008). Hence, we hypo hesized ha , i he e was an associa ed cogni i e
weigh o conside ing mul iple op ions han la ency o lea e s a po would
inc ease wi h he numbe o op imal op ions. Acco dingly, we compa ed
mean la ency o ini ia e mo emen as a unc ion o he numbe o op ions
(Figu e 44). Mul iple compa isons be ween pai s o numbe o op ions show
signi ican di e ences excep be ween wo op ions and h ee op ions
condi ion ( wo-way ANOVA, ac o numbe o op ions, p<0.001, pos -hoc
Tukey K ame , *p<0.05, **p<0.01 n=8).
Ne e heless, he numbe o op ions is co ela ed wi h dis ance o goal so we
sepa a ed ials whe e only one op ion was a ailable bu dis ance o goal was
di e en (Figu e 45i). The aim was o de e mine i , independen ly om he
numbe o op ions, he e was an e ec associa ed o dis ance o he goal.
Mo e speci ically, we hypo hesized ha la ency o lea e s a po would
inc ease wi h he Euclidean dis ance o he ligh .
83
We compa ed mean la encies be ween ials whe e, in one case, Euclidean
dis ance o he goal was one maze segmen and, in he o he case, Euclidean
dis ance o he goal was wo maze segmen s bu bo h condi ions only
comp ised one sho es pa h o he goal (Figu e 45ii). We obse ed ha
animals we e signi ican ly as e o lea e s a po when he goal was close
(pai ed - es , p= 0.0059, n=8). This would a gue o he hypo hesis ha he
la ency o ini ia e mo emen was co ela ed wi h di icul y in disc imina ing
o ligh posi ion.
One al e na i e in e p e a ion o his obse a ion was ha animals we e
mo e mo i a ed o pu sue an easie goal (Reppe e al. 2015).
Figu e 44 Animals ake mo e ime o lea e he s a po ROI when mo e op ions a e a ailable.
(i) Schema ic d awing o examples o s a -goal pai s whe e one, wo, h ee o six op ions a e
a ailable o each he goal (ii) Compa ison be ween mean la encies o lea e s a po as a unc ion o
numbe o op ions ( wo-way ANOVA, ac o numbe o op ions , p<0.001, pos -hoc Tukey K ame ,
*p<0.05, **p<0.01 n=8). E o ba is SEM.
90
(Figu e 50i ). The disc iminabili y p og essi ely inc eased un il a maximum
a 45 cm, ha is, 9.5 cm away om he choice poin (pe mu a ion es ,
*p<0.01). On one way, i seems ha a i ing a he choice poin when i
o e ed mo e han one op imal op ion seemed o maximally dec ease eloci y
which a gued o conside ing his obse a ion as a beha io al co ela e o
compe ion be ween di e en op ions. Mo eo e , eloci y associa ed o ials
Figu e 50 Animals slow down mo e when choice poin o e s wo op imal pa h op ions. (i)
Schema ic d awing o an example ype A pa h and ype B, whe e hese wo pa h ypes a e mu ually
exclusi e in he same ial. (ii) Mean eloci y o ype A pa h (solid line) and o ype B pa h
(dashed line) as a unc ion o posi ion, o one a ac oss all s a -goal pai s (n=16). The shaded a ea
is SD (iii) Mean eloci y o ype A pa h (solid line) and o ype B pa h (dashed line) as a unc ion
o posi ion ac oss he popula ion (n=8). The shaded a ea is SEM. (i ) ROC a ea unde he cu e as a
unc ion o y posi ion be ween dis ibu ion o eloci ies o ype A pa h and dis ibu ion o eloci ies
o ype B pa h ac oss popula ion (n=8) compa ed wi h shu led da a (ligh g ey shade) (pe mu a ion
es , *p< 0.01). Shaded da k a ea is SEM.

91
whe e choice poin only o e ed one op imal op ion was signi ican ly highe
as soon as he a le he s a po ROI (pe mu a ion es , *p<0.01, n=8).
This could be in e p e ed as an inc eased mo i a ion o pu sue a close goal
o an e ece o he ligh posi ion being close and hence, easie o
disc imina e. To y o disen angle i dec ease in eloci y was a ligh
disc imina ion co ela e, we compa ed pa h ypes whe e he i s choice poin
o e ed mo e han one non-op imal op ion bu he dis ance o ligh was
di e en (Figu e 51).
Hence, we would expec ha animals would slow down mo e while eaching
he choice poin i he ligh was u he away as in being ha de o
disc imina e. The e is no signi ican di e ence be ween he eloci y p o iles
o he wo ypes o pa hs which makes i less p obable ha he dec ease in
eloci y is due o isual disc imina ion. Be o e, i was also conside ed he
hypo hesis ha his dec ease in eloci y could be ela e o he physical
p esence o he po on he loo . Agains his hypo hesis is he obse a ion
ha he dec ease in eloci y a he a i al o he choice poin was bigge
when he e we e mo e op imal pa h op ions. O e all, i could be a gued ha
he obse ed dec ease in eloci y is ela ed o a delibe a ion p ocess ha
akes place due o he p esence o choice poin which a o ds a decision.
92
4.3.4 T ajec o ies o compe ing pa hs
I ha is he case hen we would expec ha he ajec o ies o compe ing
pa h op ions would only become sepa able a e he delibe a ion p ocess a
he a i al o he choice poin as a co ela e o esolu ion o he decision-
making p ocess. This means ha i was expec ed o ind signi ican
Figu e 51 Dis ance o he ligh does no a ec eloci y dec ease a he choice poin (i) Schema ic
d awing o an example ype A pa h and ype B. (ii) Mean eloci y o ype A pa h (solid line) and o
ype B pa h (dashed line) as a unc ion o posi ion, o one a ac oss all s a -goal pai s (n=16). The
shaded a ea is SD (iii) Mean eloci y o ype A pa h (solid line) and o ype B pa h (dashed line) as
a unc ion o posi ion ac oss he popula ion (n=8). The shaded a ea is SEM. (i ) ROC a ea unde he
cu e as a unc ion o y posi ion be ween dis ibu ion o eloci ies o ype A pa h and dis ibu ion o
eloci ies o ype B pa h ac oss popula ion (n=8) compa ed wi h shu led da a (ligh g ey shade)
(pe mu a ion es , *p< 0.05, n=8). Shaded da k a ea is SEM.
93
disc iminabili y be ween wo ypes o ajec o ies only a he a i al o he
choice poin . Hence, we quan i ied a which poin in space was possible o
disc imina e be ween ajec o ies o wo concu en pa h ypes. The a ionale
was such ha i i could be de e mined a poin whe e di e en pa h ypes
could be sepa a ed using ajec o y, his was an indica ion ha a choice had
been made. Mo eo e , we would expec ha conside ing he idea o spa ial
a e aging (Van De S igchel & Theeuwes 2006; Egge e al. 2002;
Theeuwes e al. 1998) ha he ajec o ies would o e lap un il a di e gence
o one o he a ge s. Mo e speci ically, we compa ed wo po en ially
compe ing pa hs ypes in a condi ion whe e h ee pa h op ions whe e
a ailable, each pa h was mu ually exclusi e op ion in a condi ion such ha
each ype would o e lap and geome ically di e ge a a choice poin (Figu e
52). Mean disc iminabili y be ween wo ajec o ies was i s signi ican 40
cm a e animals le he s a po ROI (pe mu a ion es , *p<0.01, n=8)
which co esponds o 15.5 cm be o e a i al a he choice poin . Gi en ha
he dis ance be ween he limi o he s a po ROI and he limi o he
choice poin is 54.5 cm, his means ha animals a eled almos wo hi ds o
he o he ack, mo e p ecisely 73% un il i was possible o an icipa e which
pa h hey we e going o ake.
Fu he mo e, we did he same analysis o de e mine he di e gence poin o
ajec o ies bu o wo compe ing pa hs om he condi ion whe e six op ions
a e a ailable (Figu e 53i). Disc iminabili y be ween ajec o ies was i s
signi ican 40 cm a e lea ing he s a po ROI. In bo h condi ions o h ee
and six op ions, he poin in space whe e wo compe ing ajec o ies di e ge
is he same.
94
This esul is unexpec ed because i seems ha ajec o y di e gence, which
we hypo hesized as a beha io al co ela e o commi men o a gi en choice,
was aking place 5 cm be o e he maximum dec ease o eloci y a he
choice poin , which we hypo hesized as being a beha io al co ela e o
compe i ion be ween po en ial ac ions a he choice poin .
Figu e 52 T ajec o ies om compe ing op ions in a h ee op ion condi ion di e ge 20 cm a e
animal has le he s a po ROI. (i) Schema ic d awing o an example ype A pa h and ype B,
whe e hese wo pa h ypes a e mu ually exclusi e in he same ial. (ii) Mean ajec o y o ype A
pa h (solid line) and o ype B pa h (dashed line) as a unc ion o posi ion, o one a ac oss all s a -
goal pai s (n=16). The shaded a ea is SD (iii) Mean ajec o y o ype A pa h (solid line) and o ype
B pa h (dashed line) as a unc ion o posi ion ac oss he popula ion (n=8). The shaded a ea is SEM.
(i ) ROC a ea unde he cu e as a unc ion o y posi ion be ween dis ibu ion o ajec o ies o ype A
pa h and dis ibu ion o ajec o ies o ype B pa h ac oss popula ion (n=8) compa ed wi h shu led
da a (ligh g ey shade) (pe mu a ion es , *p< 0.01). Shaded da k a ea is SEM.
95
4.4 Discussion
In his chap e , we in es iga ed dynamics o ac ion such as la ency o s a
mo emen , eloci y and ajec o y ac oss ypes o pa hs wi h he aim o
ela e hese dynamics wi h decision-making p ocesses. Mo e speci ically, we
wan ed o de e mine i ajec o y and/o eloci y p o iles could e eal
co ela ions wi h con lic be ween po en ial ac ions o commi men wi h a
choice. The main indings a e summa ized below:
Figu e 53 T ajec o ies om compe ing op ions in a six op ion condi ion di e ge 20 cm a e
animal has le he s a po ROI. (i) Schema ic d awing o an example ype A pa h and ype B,
whe e hese wo pa h ypes a e mu ually exclusi e in he same ial. (ii) Mean ajec o y o ype A
pa h (solid line) and o ype B pa h (dashed line) as a unc ion o posi ion, o one a ac oss all
s a -goal pai s (n=16). The shaded a ea is SD (iii) Mean ajec o y o ype A pa h (solid line) and
o ype B pa h (dashed line) as a unc ion o posi ion ac oss he popula ion (n=8). The shaded a ea is
SEM. (i ) ROC a ea unde he cu e as a unc ion o y posi ion be ween dis ibu ion o ajec o ies
o ype A pa h and dis ibu ion o ajec o ies o ype B pa h ac oss popula ion (n=8) compa ed wi h
shu led da a (ligh g ey shade) (pe mu a ion es , *p< 0.01). Shaded da k a ea is SEM.

96
 La ency o lea e s a po – we ound ha ha animals le he s a
po as e i hey we e abou o do a sho es pa h han i hey we e
abou o do a de ou . We in e p e ed his obse a ion as a mo i a ion
co ela e such ha , a he beginning o he ial, animals had al eady
decided o ake a sho es pa h o no . On he o he hand, he
di icul y in disc imina ing he ligh a he beginning o he ial due
o body posi ion and o ien a ion could only explain a di e ence in
la ency o lea e s a po be ween sho es pa hs and de ou s on he
o he di ec ion. I a s would ake less ime o lea e s a po when
abou o do a de ou , i could be a gued ha hey le he s a po
wi hou enough in o ma ion abou he goal and, hence, choose a non-
op imal pa h a he beginning ha comp omised he o e all sho es
pa h. Wi hin sho es pa h ials, we ound ha animals ake mo e
ime o lea e s a po in ials whe e six op ions a e a ailable which
could indica e ha he numbe o op ions a ailable could be
inc easing he ime o delibe a e a he s a po . Ne e heless, he e
is no di e ence in la ency be ween ials whe e wo op ions o h ee
op ions a e a ailable. Gi en ha he numbe o op ions also inc eased
wi h he dis ance o he goal which in oduced a po en ial con ound.
Acco dingly, in ials whe e he dis ance o he goal is di e en and
he e is only one op ion animals ake mo e ime o lea e he s a po
which could be ei he associa ed o mo i a ion o di icul y in
disc imina ing goal loca ion when he goal is u he away.
 Bell-shaped eloci y – we ound ha animals exhibi a bell-shaped
eloci y p o ile when a elling be ween wo po s sepa a ed by one
maze segmen , as expec ed om mo o planning li e a u e (Flash &
Hogan 1985).
97
 Cen e a oidance – we ound ha animals each a highe eloci y
peak in ials ha ake hem h ough he cen e which is cong uen
wi h da a obse ed in he open ield (Lipkind e al. 2004) when he
dis ance o goal is o wo segmen s and he e is only one op imal
op ion. This esul did no hold h ough o he case whe e h ee
op ions we e a ailable. One possible explana ion o his ype o
incong uence was ha he cen e a oidance ac o was being
supp essed by o he ac o s such minimizing Euclidean ligh .
 Veloci y a he choice poin - animals slowed down while a i ing a
he choice poin e en hough he e was only one a ailable op imal
op ion a ha poin . This obse a ion could be in e p e ed as a
consequence o he need o check o he ligh posi ion, gi en he
oppo uni y o ee alua e a p e ious decision. Ne e heless, we
obse ed no di e ence be ween eloci y p o iles in e ms o dec ease
a he choice poin o wo pa h ypes which goals we e a di e en
dis ances so we would a gue ha his obse a ion is no explained by
isual disc imina ion e ec . Mo eo e , he dec ease in eloci y did
no depend on he numbe o non-op imal choices (cen e s edge)
which a gues o a gene al e ec o being p esen ed wi h a choice
poin , as i he p esence o a choice poin a o ded a decision which
in e e ed wi h he cu en mo o plan (Kau man e al. 2015). Adding
o his, animals slowed down mo e i he choice poin a o ded mo e
han one op imal op ion. A bigge dec ease in eloci y could indica e
ha animals ake mo e ime o conside be ween wo op ions when
bo h o hem a e op imal han when one is op imal and he o he is
no . This could be in e p e ed as i he o e o an i ele an op ion o
he op imal s a egy was in e e ing wi h he decision o ake he
op imal pa h, equi alen ly as in mo o planning asks whe e
98
dis ac o s in e e e wi h he eaches (Welsh e al. 1999; Tippe e al.
1998). Hence, his obse a ion could be a co ela e o s ong
compe i ion be ween wo op imal ac ions o a weak compe i ion
be ween an op imal ac ion and a non-op imal ac ion (‘dis ac o ’). I
canno be excluded ha , he dec ease in eloci y while a i ing a he
choice poin does no ha e a componen associa ed o a
biomechanical adap ion (Va e al. 1996) associa ed o he physical
p esence o he po on he loo .
 T ajec o y di e gence – we ound ha ajec o ies o compe ing
pa hs di e ge a 15.5 cm be o e a i al a he choice poin . This could
be in e p e ed as a co ela e ha he decision abou he choice a he
choice poin was aken a ha loca ion in space, and consequen
execu ion o he chosen op ion s a ed. In a way, his would be
equi alen o he spa ial a e aging phenomenon obse ed in ‘go-
be o e-you-know’ asks (Welsh e al. 1999; Tippe e al. 1998) o in
asks whe e each ajec o y was used o epo bina y choices
(McKins y e al. 2008), whe e in he la e case he cue o which
a ge o mo e o was no de ined ex e nally by he expe imen e bu
by in e nal p ocesses, simila ly o Pombal´s maze. Al e na i ely, i
could be a gued ha animals had al eady decided which pa h o ake
a he beginning o he ial and we e wi hholding he ac ion
execu ion un il i was biomechanically necessa y. This seems a less
p obable possibili y because i would implica e ha animals would
ha e o main ain he memo y o he ou come o he decision un il i
was biomechanically necessa y o execu e i . Ye ano he possibili y,
is ha he di e gence happened be o e and ha he po en ially
insu icien spa ial esolu ion o he ideo ame oge he wi h he
small wid h o he ack did no allow o measu able sepa a ion o
99
he ajec o ies. Finally, i canno be excluded ha hese co ela es
co espond o a bias o he animals, no e lec ing he commi men
owa ds a decision.
In conclusion, i seems ha bo h mo i a ion and Euclidean dis ance o he
goal may in luence la ency o lea e s a po . Cen e a oidance seems o
ha e di e en weigh on he beha io depending on he numbe o op ions
and co ela ed Euclidean dis ance o he goal. Addi ionally, he e is e idence
ha animals ake choice poin s as a place ha a o ds a decision, whe he
he e a e only op imal pa h op ions o no . In ha sense, i could be ha e en
hough he e is an ini ial decision abou wha pa h o ake a he choice poin ,
he o e o he choice poin may lead animals o econside . The hypo hesis
o econside a ion is en o ced by he possibili y o de e mine be o e he
choice poin which pa h a e animals going o ake om a se o wo
compe ing op ions. O e all, i seems ha animals may be making decisions
be o e he choice poin bu gi en he o e ed oppo uni y o change minds,
animals ee alua e hei p e ious decision.
106
aims a unde s anding ac ion selec ion om he pe spec i e o spa ial
p ocessing ocusing on choice poin s and associa ed disc e e choices; and
mo o planning, which aims a unde s anding ac ion selec ion om he
pe spec i e o mo o con ol ocusing on he con inuous dynamics o
mo emen .
Pombal’s maze is a cued na iga ion ask whe e animals ha e o ga he
spa ial in o ma ion abou he goal, h ough isual s imulus, and choose one
pa h o ge he e. F om he pe spec i e o an ac ion selec ion p oblem,
animals a e equi ed o choose be ween di e en ac ions – going o wa d,
u ning igh o le . I we would conside a s ochas ic choice model ha
selec s andomly be ween po en ial ac ions a each choice poin , as i was
desc ibed in Chap e 2, i would no mimic he a s’ beha io . In o de o
his model o mimic he a beha io , a each choice poin he po en ial
ac ions should be conside ed bu hen di e en ype o in o ma ion would
bias he choice p obabili y o each ac ion: p e e ence o less u ns,
p e e ence o a oiding he cen e , p e e ence o s aying close o he ligh
as possible. A bias associa ed o u n a oidance can be gene a ed on he spo
a he choice poin by lea ning om p e ious expe ience ha u ning educes
eloci y o in oduces ene ge ic cos s – in a s imulus- esponse manne
(Penne & Mizumo i 2012; Ni e al. 2006). Ne e heless, a bias associa ed
o minimizing Euclidean dis ance would equi e an ex a s ep o compu a ion
o e alua ing how much each po en ial ac ion would lead o s aying close o
he ligh , in he u u e. Fo de e mining hese ela ionships be ween u u e
ac ions and spa ial ela ion o he ligh , we would a gue would be acqui ed
h ough a goal-di ec ed s a egy. I could be expec ed ha a e being o e
ained in he ask he animals would become habi ual and use a s imulus-
ou come associa ion as a ule (Penne & Mizumo i 2012; Ni e al. 2006).
Ne e heless, his would esul in o he animal ha ing o emembe a

107
possibly ex ensi e lis o s imulus-ou come associa ions. Ano he aspec ha
should bias he p obabili y o choice o a po en ial ac ion is he ac o i he
animal is al eady mo ing o no , ha is, i he choice poin coincides wi h he
s a loca ion o no , in which, o ins ance, making a u n would ha e a
di e en associa ed cos om when he animal is al eady mo ing. O e all, in
Pombal’s maze animals a e con on ed wi h he selec ion o ac ions whe e
di e en biases change he p obabili y o choice o each o he po en ial
ac ions. O e all, in o de o a s ochas ic choice model o mimic a ’s
beha io i would ha e o weigh o e he cos and bene i o he di e en
ea u es o he po en ial ac ions a he choice poin , engaging in a sequen ial
choice s a egy. Ne e heless, his ma hema ical desc ip ion o he ac ion
selec ion p oblem associa ed o sol ing Pombal’s maze would equi e he
in eg a ion o dynamical p ope ies o p o ide wi h a mo e accu a e
desc ip ion o beha io . Hence, in be ween closes neighbo s eloci y would
be modelled by a bell-shaped unc ion wi h a modi ica ion because animals
do no ully s op a choice poin s, hey educe eloci y and, in ac he
educ ion seems o be modula ed by he numbe o op imal choices. Las ly,
conside ing a componen ela ed o ajec o ies, he e is an obse a ion ha
seems incong uen wi h he hypo hesis ha animals make sequen ial choices
along he pa h, which is he esul ha posi ion on he ack an icipa es which
choice animals a e going o ake a he choice poin . I is no comple ely clea
i his di e gence o pa hs is as a co ela e o commi men owa ds a ce ain
choice o a bias. I i is he case ha ajec o y di e gence co ela es wi h
commi men his could implica e ha decision-making p ocesses a e no
exclusi e om choice poin s bu a e ongoing while animals a e mo ing,
which has been a gued is he case in mo o planning ypical asks (Song &
Nakayama 2009) so ha he po en ial ac ions a e ep esen ed and in e ac ing
dynamically, allowing o lexible adap ion o new po en ial ac ions o
108
changes in plan (Cisek 2012; Song & Nakayama 2009). This hypo hesis
would p edic ha e en a e decision has been aken and ac ion is unde
execu ion, he e is s ill he possibili y o econside ing (Kau man e al. 2015)
which could be seen as in e e ence be ween po en ial ac ions. One
obse a ion ha seems o go on his di ec ion is he exis ence o change in
mind ype o ials, whe e animals le he choice poin ollowing one
di ec ion and hen e u ned o he choice poin o go in he o he di ec ion. I
seems, o e all, ha he choice poin and inhe en possibili y i o e s he
animal o make choices gene a es an in e e ence wi h decisions p e iously
made. This ype o dynamical in e ac ion be ween po en ial ac ions seems o
be a he co e o unde s anding ac ion selec ion and he unde lying
compe i ion mechanisms.
5.5 Final ema ks and u u e di ec ions
The ocus o his hesis was o design and de elop a no el decision-making
pa adigm o s udying beha io while animals a e selec ing be ween mul iple
ac ions. The pa adigm, due o i s cha ac e is ics o being based on a maze
and on a ial s uc u e whe e mul iple op ions lead o goal, s ands bo h in he
ields o na iga ion and mo o planning. Hence, we aimed a aming he
p oblem ha animals we e sol ing conside ing hese wo amewo ks. On
one side, i was necessa y o cha ac e ize which p oblem we e he animals
sol ing in e ms o op imiza ion and decision a iables, in o de o de e mine
which ype o ac ions we e po en ially compe ing o be selec ed. On he o he
side, i was necessa y o be e cha ac e ize he dynamics o mo emen wi h
he aim o ind beha io al co ela es o ac ion selec ion ha could be
in eg a ed wi h he pa e ns o disc e e choices. In he end, in eg a ing choice
pa e ns wi h dynamical co ela es led o he unde s anding ha choice poin s
109
can a o d changes in mind, whe e he p e ious decision compe es wi h new
possibili ies. This seems like a pa icula ly ele an window o s udying
how po en ial ac ions dynamically in e ac and, hence, gaining a be e
unde s anding abou he unde lying mechanisms o ac ion selec ion.
In u u e di ec ions, we sugges o do elec ophysiological eco dings o
neu al ac i i y in mo o - ela ed a eas while animals a e sol ing Pombal’s
maze. Mo e speci ically, we conside ha he window be ween he loca ion
whe e di e gence o ajec o ies occu s, un il he loca ion whe e he animal
lea es he choice poin , is pa icula ly ele an o sea ching o neu al
co ela es o ac ion selec ion. We conside ha one o he b ain a eas which
could be a good candida e o ecoding neu al ac i i y is M2 which is an a ea
associa ed wi h ac ion planning and spon aneous ac ion ini ia ion (Sul e al.
2012; Mu akami e al. 2014; E lich e al. 2011), conside ed o be
homologous o p ima e supplemen a y mo o a eas (Reep e al. 1990; Reep e
al. 1986). We would expec o ind ac i i y co ela ed wi h simul aneous
ep esen a ion o po en ial ac ions, simila ly wi h wha has been ound in
PMd (Cisek & Kalaska 2005). Mo e speci ically, we would expec o
cha ac e ize he in e ac ions be ween po en ial ep esen a ions, om he
momen whe e animals show ajec o y di e gence onwa ds. These
obse a ions could help o disambigua e i ajec o y di e gence is associa ed
o commi men owa ds a decision o no . I ha is he case, i would be
ele an o see i he e a e no maliza ion e ec s be ween ep esen a ions o
di e en ac ions, which would a gue o a compe i ion mechanism aking
place be ween po en ial ac ions. Hence, we belie e ha hese obse a ions
would con ibu e o gaining a deepe unde s anding abou ac ion selec ion
mechanisms and espec i e unde lying ci cui y.
110
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