6 • Great ape cognitive systemsANNE E. RUSSONPsychology Department, Glendon College of York University, Toronto

INTRODUCTION

This chapter considers cognition in great apes as inte-grated systems that orchestrate the many abilities thatgreat apes express, systems for which satisfactory char-acterizations remain elusive. In part, difficulties owe toresearch trends. Empirical studies have been guided bydiverse and sometimes contradictory models, questions,measures, tasks, and living conditions. Performancelevels have proven inconsistent across individuals, rear-ing conditions, and testing conditions, and evidence ispatchy across species for virtually any facet of cogni-tion. Evidence on wild great apes, the most importantfrom an evolutionary perspective, is especially patchybecause research has favored captives; much of whatis available was collected for other purposes, so it wasneither described nor analyzed with cognition in mind.The issues at stake are also hard-felt ones that touch onthe human–nonhuman boundary, so entrenched beliefsinfect how the literature is interpreted and even what ofit is read.

Attempts have none the less been made to developan integrated model of great ape cognition using avail-able evidence. They include both edited survey vol-umes (Matsuzawa 2001a; Parker, Mitchell & Miles 1999;Russon, Bard & Parker 1996) and integrative reviews,three of the latter as major books (Byrne 1995 (RWB),Parker & McKinney 1999 (P&M); Tomasello & Call1997 (T&C)) and others as articles (e.g., Byrne 1997;Suddendorf & Whiten 2001; Thompson & Oden 2000;Whiten & Byrne 1991). My aim is not to analyze thisterrain, yet again, in detail, but to offer a compact misea date to ground evolutionary reconstruction. Guidingquestions are “what, if anything, about great ape cog-nition requires evolutionary explanations beyond thosedeveloped for other nonhuman primates?”, and “how isgreat ape cognition best characterized with respect toevolutionary questions?”

CONCEPTS AND MODELS OFCOGNITION

Situating great ape cognition comparatively hinges onmental processes that support symbolism, notably rep-resentation, metarepresentation, and hierarchization.Weaker and stronger conceptualizations exist for eachand which is used affects assessments of great apes’ capa-bilities.

Weak meanings of symbolism include reference byarbitrary convention (Peirce 1932/1960), using inter-nal signs like mental images to stand for referentsrather than using direct sensations or motor actions,and solving problems mentally versus experientially. Inthe strong sense, symbolism refers to self-referring sys-tems wherein phenomena owe their significance andeven existence to other symbols in the system ratherthan to sensorimotor entities (e.g., Deacon 1997; Donald2000; Langer 2000). Representation can refer to anyform of mental coding that stands for entities, perceptualincluded (Perner 1991; Whiten 2000) or, more strongly,to recalling to mind or “re-presenting” mental codesfor entities and simple object relations in the absenceof normal sensorimotor cues (P&M; Whiten 2000).Meanings of metarepresentation range from represent-ing other representations (e.g., Leslie 1987; Matsuzawa1991; Whiten & Byrne 1991) to representing a represen-tation as a representation, i.e., an interpretation of a sit-uation (Perner 1991). Meanings of hierarchization spancreating new, higher-order cognitive structures fromlower-level ones (i.e., structures with superordinate–subordinate features: Byrne & Byrne 1991; Case 1985;Langer 1998) to generating cognitive structures thatshow embedding (e.g., classification showing nesting ofclasses: Langer 1998).

Developmentalists commonly consider weak andstrong forms to be related in humans, as basic andadvanced ontogenetic achievements of early and later

Copyright Anne Russon and David Begun 2004.

76

Great ape cognitive systems 77

childhood respectively (Table 6.1). Comparative pri-mate cognition often shares this view (P&M; Whiten2000). Insofar as symbols must be grounded in realworld referents at some point (Donald 1991) and weaksymbolism is the more likely in great apes, I considergreat ape cognition relative to weak symbolism and itsassociated processes (strong representation, weak hier-archization, weak metarepresentation). The terms sym-bolic, representation, hierarchization, and metarepre-sentation henceforth refer to these meanings.

The models guiding empirical studies of great apecognition also contribute to disparities because of theways in which they shape the generation of evidenceand the interpretive frameworks they impose. Severalimportant models are sketched below to suggest theirstrengths and limitations for understanding great apecognition.

Animal models designed for nonhuman mentalityhave been frequent frameworks for studies of great apecognition. They concentrate on the non-symbolic, asso-ciative processes presumed to govern nonhuman cog-nition, for example trial-and-error experiential learn-ing or behavior chains. This leaves them conceptuallyand methodologically impoverished concerning sym-bolic cognition (Anderson 1996; RWB; Rumbaugh 1970;T&C), quantification and logic being important excep-tions (e.g., Boysen & Hallberg 2000; Thompson & Oden2000), so relatively little of the evidence they have gener-ated helps determine whether great apes, or any species,are capable of symbolic cognition.

Generality–modularity models are potentiallyimportant because they concern cognitive architecture.In this view, favored by evolutionary psychologists andneo-nativists, there exist cognitive “modules,” problem-specific cognitive structures that represent innatelyspecified neurological systems and operate with rela-tive autonomy, as well as general purpose or centralprocesses that apply across problem types and affectsystem-wide properties (e.g., representation, executivecontrol structures, working memory). These modelshave influenced understandings of great ape cognitionwith their assumption that modular architecture char-acterizes nonhuman cognition and generality evolveduniquely in humans (e.g., Mithen 1996; T&C; Tooby& Cosmides 1992). Little if any empirical study hasexamined cognitive architecture in great apes, however.Studies of great ape cognition have typically assumedmodularity and have aimed for clean tests of individual

problem-specific structures – effectively eliminatingchances for detecting use of multiple or general purposeprocesses. Given the lack of relevant empirical evidence,these models remain speculative concerning great apes.

Cognitive science models portray the mind as adevice for processing, storing, integrating, and trans-forming information. Some of their concepts have beenincorporated into models of cognitive development (e.g.,Case 1985; Leslie 1987; Pascual-Leone 1987), othershave aided in detecting hierarchization in great ape cog-nition (Byrne & Byrne 1991; Byrne, Corp & Byrne 2001;Byrne & Russon 1998; P&M; Russon 1998). Limita-tions concern portraying cognition in static, mechanisticterms that may not apply to living beings.

Models of human cognitive development haveproven valuable for assessing primate cognition com-paratively because they provide conceptual and method-ological tools for assessing non-symbolic and symboliccognition within one unified framework and the gen-eration and structure of cognition. Piaget’s model sup-ported the first developmental studies of great ape cog-nition; among its greatest contributions is its portrayal ofcognition as constructed progressively during ontogenyand directly affected by interaction with the environ-ment. Early piagetian studies focused on sensorimotor(human infant) cognition, which relies on pre-symbolicprocesses similar to those portrayed in animal models, sosimilar limits apply. Recently, neo-piagetian models havesupported studies of the rudimentary symbolic range(for an overview, see P&M). Models inspired by Vygot-sky, which portray socio-cultural forces like apprentice-ship or enculturation as fundamental to cognitive devel-opment (e.g., Donald 2000; T&C), have spawned manystudies on social cognition and cognitive developmentin great apes. Given how richly primate lives are sociallyembedded, their merits are obvious. Among these mod-els may be included models of understanding others’mental states, or theory of mind, which some propose tounderpin much cognitive progress in early human child-hood (e.g., Carruthers & Smith 1996). Two such modelshave been applied to great apes, both proposing thatgeneral-purpose cognitive processes in the rudimen-tary symbolic range underwrite this progress (second-order representation – Leslie 1987; secondary represen-tation – Perner 1991). While both offer useful tools forassessing rudimentary symbolic processes, their focuson one ability series in the social domain risks under-representing the breadth of great apes’ achievements.

Tab

le6.

1.P

roce

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ndstr

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osite

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tary

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licle

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deve

lopm

enta

lper

spec

tive

Age

Piag

etC

ase1

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ger2

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lie3

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er4

(yr)

(gen

eral

)(c

ausa

lity)

(logi

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ath)

(The

ory

ofM

ind)

(The

ory

ofM

ind)

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nsor

imot

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age

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mat

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nsor

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age

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pres

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tion

1.5

2

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ratio

nalc

onso

lidat

ion

(Int

er-)

rela

tiona

lcog

nitio

nun

ifoca

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atio

nalo

pera

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2nd-

orde

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der

repr

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nm

etar

epre

sent

atio

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cond

ary

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pres

enta

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age

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mbo

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iona

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geSy

mbo

licsu

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iod

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lope

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ns(1

st-o

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)5

Intu

itive

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labo

rate

dre

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(2nd

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lic)

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defin

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ajor

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7).F

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sint

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)(st

rong

met

arep

rese

ntat

ion)

.

Great ape cognitive systems 79

More broadly, reservations are that Vygotsky-basedmodels tend to emphasize socio-cultural factors to theneglect of individual and biological ones, while Piaget-based models suffer the opposite bias. Together, thesemodels offer rich portrayals of cognitive developmentand have spawned comparative models situating primatecognition in developmental and evolutionary perspec-tive (e.g., P&M).

I favor development frameworks because they allowassessment of symbolic processes, their constitution,and continuities as well as discontinuities betweenhuman and nonhuman primates. I adopt them here asthe basis for interpreting evidence.

EVIDENCE

For evidence, I relied on recent integrative reviews(RWB, P&M, T&C) more than edited volumes, to priv-ilege syntheses over the breadth of current views, plusfindings appearing since their publication (post 1998).I concentrated on achievements linked with symbolismas the critical cognitive threshold and feral great apes1 asmost relevant to evolutionary questions. Table 6.2 sum-marizes this evidence, arranged by the cognitive struc-tures inferred in terms of cognitive domain (broad areasof knowledge, typically physical, logico-mathematical,social, linguistic), problem-specific structures (abilityseries), and complexity (level). This arrangementderives from models of human cognitive developmentnear the rudimentary symbolic range (Table 6.1).

My coverage of the evidence is inevitably incom-plete but sufficient to establish broad patterns. Evidencefor complex achievements is substantial, for instance,and the relevance of complex skills to feral life is clear inall cognitive domains even though little evidence derivesfrom feral subjects. Equally clear and needing explana-tion are the repertoire’s impressive breadth and “open-ness” (i.e., including apparently “atypical” language andmathematical abilities). Disputes in any case lie less withwhat great apes achieve than with cognitive inferences,so more important cautions are that inferences are con-troversial, numerous factors complicate interpretation,and I inevitably glossed over subtleties and debates inworking towards an overall picture.

A long-standing concern is variability in achieve-ments across problem types, individuals, species,and contexts. Some report great apes outperforming5- to 6-year-old humans (e.g., Call & Rochat 1996),

others report them failing at simple levels of under-standing (e.g., Povinelli 2000). While this variabil-ity may be meaningful (e.g., cognitive differencesbetween species, significant features of cognitive devel-opment, module-like cognitive architecture), it alsoreflects confounding factors extensive enough to under-mine interpretation.2 Because quantitative breakdownsremain un-interpretable, I have not provided them.Most experts in any case consider that all great apes shareroughly equivalent cognitive capacities (RWB, P&M,T&C) and it is these similarities that are of primaryinterest here.

COGNITIVE LEVELS: THEHIGH-MINDED

An important consideration in analyzing the cognitiongoverning great apes’ complex achievements is that itmay involve higher levels of cognitive abstraction, notjust very rapid processing, extended working memory,or new types of abilities (Roberts & Mazmanian 1988).Humans, great apes, and some monkeys can master mak-ing and using tools, for instance, so all share the means–end type of cognition; great apes and humans differ inachieving higher levels of means–end cognition that sup-port more complex tool use (e.g., Visalberghi & Limon-gelli 1996). What levels great apes attain is a major focusof current debate. Three levels recognized in humandevelopment beyond pre-symbolic, sensorimotor cogni-tion (with its schemata, i.e., first-order or primary rep-resentations) are probably important to resolving thisdebate (see Table 6.2). These are:

(1) Emergence of rudimentary symbols.Around 1.5 years of age, humans begin creating andusing simple symbols, like mental images, to stand forreferents instead of having to use direct sensorimotorinformation. A classic example is inferring where anitem is hidden after watching it be displaced “invisibly,”along a trajectory that passes behind barriers; thisshows that the actor can mentally reconstruct eventsit did not directly perceive (de Blois, Novak & Bond1998). Early symbols have been attributed to strongrepresentation (Piaget 1952, 1954; P&M), understand-ing relational categories between entities external tothe actor (Herrnstein 1990; Rumbaugh & Pate 1984;Spearman 1927; Thompson & Oden 2000; T&C), orrepresenting single object–object relations (Case 1985).This level is usefully viewed as a transition, i.e., a phase

Tab

le6.

2.G

reat

apes

’cog

nitiv

eac

hiev

emen

tsan

dco

gniti

veab

ilitie

s

Phys

ical

Dom

ain

Cog

nitio

nSe

ries

/lev

elA

chie

vem

ent

Com

men

ts–

exam

ples

Sour

cesa

Obj

ectc

once

pt:d

evel

opin

gth

eco

ncep

tof“

obje

ct”

inth

een

viro

nmen

t(ex

tend

sonl

yto

sens

orim

otor

stage

6T

rans

ition

al(1

.5–2

yrs)

Tra

ckin

visi

ble

disp

lace

men

ts1

(2)3

416

Cau

sali

ty:d

ynam

icre

latio

nsbe

twee

nob

ject

swhe

nex

tern

alfo

rces

affe

ctth

emT

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ition

alIn

cons

iste

ntbu

tins

ight

ful

(Inc

onsi

sten

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cess

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23

12(1

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yrs)

mak

e&

use

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tool

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leob

ject

–obj

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elat

ions

Ter

tiary

rela

tions

betw

een

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12

3R

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1017

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2.

Tab

le6.

2.(c

ont.)

Log

ical

-Mat

hem

atic

alD

omai

n

Cog

nitio

nSe

ries

/lev

elA

chie

vem

ent

Com

men

tsan

dex

ampl

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sa

Cla

ssif

icat

ion:

orga

nize

obje

ctsb

yfe

atur

esan

dca

tego

ries

Tra

nsiti

onal

Dou

ble

sets

+ex

chan

geC

once

ptfo

rmat

ion

Sim

ple

rela

tiona

lcat

egor

y

2nd-

orde

rcl

assi

ficat

ion

Pred

ator

s,fo

ods,

othe

rsp

ecie

sT

PR,e

.g.,

iden

tity,

odd,

sam

e-di

ffer

ent

16b

24b

24b

8R

udim

enta

rysy

mbo

lic2n

d-or

der

clas

sify

,ope

ratio

nsA

nalo

gica

lrea

soni

ngU

seab

stra

ctco

des

Mul

tiplic

ativ

ecl

assi

ficat

ion

To

leve

lslik

ehu

man

s24

–30

mo

Abs

trac

trel

atio

nsbe

twee

nre

latio

ns

Sim

ulta

neou

sm

ulti-

feat

ure

sort

1(2

)56b

7b15

1(2

)4b

7b

14b

1(2

)C

lass

ifyby

func

tion

Min

imal

3rd-

orde

rcl

assi

fyH

iera

rchi

calp

art–

who

lere

latio

nsR

outin

est

ruct

ure

“Too

l”cl

ass,

sort

bott

les

with

caps

Prer

equi

site

for

hier

arch

ical

clas

sific

atio

n

For

obje

ct–o

bjec

trel

atio

ns;h

iera

rchi

cal

(2)4

b10

56b

7b

16b

16b

Ser

iati

on:o

rgan

ize

obje

ctse

tsw

ithre

spec

tto

ordi

nalit

yan

dtr

ansit

ivity

Tra

nsiti

onal

Seri

ate

nest

ing

cups

(“po

t”)

By

“pot

”st

rate

gy(o

necu

pin

toan

othe

r)(1

1)ru

dim

enta

rysy

mbo

licSp

onta

neou

sse

riat

ion

Seri

ate

nest

ing

cups

(nes

ting)

b

Seri

ate:

tran

sitiv

ityba

sed

Tra

nsiti

vity

Ord

erst

icks

,ord

erto

ols

into

olse

tB

ysu

bass

embl

y(s

ohi

erar

chic

al)

Ope

ratio

nall

ogic

,2-w

ayre

latio

nsIn

soci

alra

nk;i

na

seri

alle

arni

ngta

sk

1(2

)12

1(2

)(11

)1

(2)4

b11

191

(2)3

21

Tab

le6.

2.(c

ont.)

Log

ical

-Mat

hem

atic

alD

omai

n

Cog

nitio

nSe

ries

/lev

elA

chie

vem

ent

Com

men

tsan

dex

ampl

esSo

urce

sa

Num

ber/

Qua

ntit

y:as

sess

obje

ctse

tsw

ithre

spec

tto

num

bero

rqua

ntity

Tra

nsiti

onal

Sequ

entia

llyta

gse

vera

lite

ms

Sequ

entia

llyta

g+

labe

lnum

ber

12

12

Rud

imen

tary

sym

bolic

bC

ount

Com

pare

prop

ortio

nsC

onse

rve

num

ber

(1:1

)Su

mm

atio

nQ

uant

ified

(soc

ial)

reci

proc

ityPl

anne

dnu

mer

ical

orde

ring

Rev

erse

cont

inge

ncy

task

Sym

bolic

quan

tity

judg

men

t

Exa

ctnu

mbe

rof

item

sin

arra

ysF

ract

ion,

quan

tity-

base

dan

alog

yU

nder

stan

d1:

1co

rres

pond

ence

Add

ing

item

sin

crea

ses

quan

tity

Mea

tsha

reru

les,

exch

ange

groo

m/f

avor

sSe

quen

ceal

lite

ms

befo

reac

ting

Cho

ose

smal

ler

of2

arra

ysto

getm

ore

Sele

ctar

ray

for

quan

tity

usin

gsy

mbo

ls

1(2

)14

1(2

)1

(2)

(2)9

1314

1(2

)39

2022

15 1718

17

Con

serv

atio

n:co

nser

vepr

oper

tieso

fobj

ects

that

unde

rgo

tran

sform

atio

nsR

udim

enta

rysy

mbo

licb

Con

serv

equ

antit

y(c

once

ptua

l)Ph

ysic

ally

tran

sfor

med

(sol

id&

liqui

d)1

(2)

Not

es:

aco

gniti

veat

trib

utio

nsba

sed

oncr

iteri

adi

scus

sed

inte

xt;s

ourc

ebr

acke

ted

whe

nm

yat

trib

utio

ndi

ffer

sfr

omth

atof

the

auth

ors

cite

d.b

sym

bol-

trai

ned

grea

tape

ste

sted

.So

urce

s:1,

Park

er&

McK

inne

y19

99;2

,Tom

asel

lo&

Cal

l199

7;3,

Byr

ne19

95;4

,Tho

mps

on&

Ode

n20

00;5

,Lan

ger

2000

;6,P

otıe

tal.

1999

;7,S

pino

zzie

tal.

1999

;8,T

anak

a20

01;9

,Sou

sa&

Mat

suza

wa

2001

;10,

Rus

son

1999

a;11

,Joh

nson

-Pyn

net

al.1

999;

John

son-

Pynn

&F

raga

szy

2001

;12,

Ber

mej

o&

Ille

ra19

99;

13,C

all2

000;

14,B

eran

2001

;15,

Bir

o&

Mat

suza

wa

1999

;16,

Spin

ozzi

&L

ange

r199

9;17

,Boy

sen

&B

ernt

son

1995

;Boy

sen

etal

.199

6;18

,Shu

mak

eret

al.2

001;

19,K

awai

&M

atsu

zaw

a20

00;2

0,B

oesc

h&

Boe

sch-

Ach

erm

ann

2000

;21,

Tom

onag

a&

Mat

suza

wa

2000

;22,

Mita

ni&

Wat

ts20

01;M

itani

,Wat

ts&

Mul

ler

2002

.(N

ote:

rela

ted

stud

ies

are

grou

ped.

)

Tab

le6.

2.(c

ont.)

Soci

aldo

mai

n

Cog

nitio

nSe

ries

/lev

elA

chie

vem

ent

Com

men

tsan

dex

ampl

esSo

urce

sa

Soc

iall

earn

ing

&im

itat

ion:

soci

ally

influ

ence

dle

arni

ng;

imita

tion

isle

arni

ngto

done

wac

tsby

seei

ngth

emdo

neT

rans

ition

alD

efer

red

imita

tion

Act

ion-

leve

lim

itatio

n•g

estu

res

•act

ions

onob

ject

sIm

itate

actio

nse

quen

ce

Del

ayed

imita

tion

ofno

vela

ctio

ns“I

mpe

rson

atio

n,”

toso

me

Spon

tane

ous

gest

ures

,ges

ture

sign

sSi

mpl

eto

olus

e,ob

ject

man

ipul

atio

n2-

actio

nse

quen

ceor

long

er

124

b

13

12

315

221

23

414

1824

b29

3437

23

624

b

Rud

imen

tary

Sym

bolic

Prog

ram

-lev

elim

itatio

nM

ime

inte

nt,r

eque

st,t

each

Rou

tine

stru

ctur

e,re

latio

ns-b

etw

een-

rela

tions

Act

out(

for

othe

r),e

xpre

ssin

tent

13

45

1423

24b

1(2

)37

33b

Pre

tens

e:re

-ena

ctac

tions

outsi

deth

eiru

sual

cont

exta

ndw

ithou

tthe

irus

ualo

bjec

tives

Tra

nsiti

onal

Re-

enac

teve

nts

(scr

ipts

)B

asic

sym

bolic

play

“Fee

d”do

ll,“t

ake”

phot

ow

ithca

mer

a1

27

81

Rud

imen

tary

Sym

bolic

Sym

bolic

obje

ctus

eA

dvan

ced

sym

bolic

play

Rol

epl

ayD

emon

stra

tion

teac

h

With

subs

titut

eob

ject

(e.g

.,lo

gba

by)

Play

mot

her’

sor

anot

her’

sro

le

1(2

)1

(2)3

1 13

Soc

ialk

now

ledg

e&

theo

ryof

min

d:un

ders

tand

ing

othe

rs:

beha

vior

s,ro

les,

and

men

tals

tate

sT

rans

ition

alM

irro

rse

lf-re

cogn

ition

Gai

not

her’

sat

tent

ion

Inte

rpre

tvis

ualp

ersp

ectiv

eT

hird

-par

tyre

latio

ns(T

&C

)Pr

e-se

lect

allie

sC

onve

rsat

iona

lcon

tinge

ncy

Impu

tein

tent

ions

Wai

tfor

,voc

aliz

e/ge

stur

eto

gain

atte

ntio

nT

rack

othe

r’s

gaze

(e.g

.,to

getf

ood)

Cur

ryfa

vor

with

pote

ntia

lhel

pers

Con

text

-app

ropr

iate

resp

onse

sU

nfini

shed

,del

iber

ate

(vs.

acci

dent

al)a

cts

12

31

23

3032

33b

391

23

1127

2835

362 2 31

b

1(2)

311

(17)

3439

Tab

le6.

2.(c

ont.)

Soci

aldo

mai

n

Cog

nitio

nSe

ries

/lev

elA

chie

vem

ent

Com

men

tsan

dex

ampl

esSo

urce

sa

Rud

imen

tary

Sym

bolic

Impu

tekn

owle

dge,

com

pete

nce

Em

path

yT

ake

com

plem

enta

ryro

leR

ole

reve

rsal

Coo

pera

tion

(ena

ct,p

lan)

Qua

ntifi

edre

cipr

ocity

2nd-

orde

rin

tent

iona

lity

Tra

nsiti

vity

inso

cial

rank

Com

plex

coal

ition

s/al

lianc

es

Kno

wle

dge-

sens

itive

soci

alac

tivity

Con

sole

(nb.

post

deat

h),m

edia

tere

conc

iliat

ion

Coo

pera

tive

hunt

,rol

e-ba

sed

team

wor

k

Bal

ance

dre

veng

e,sh

are,

lose

rhe

lp2n

d-or

der

tact

ical

dece

ptio

n(w

ithho

ldite

m,

mis

lead

,cou

nter

-dec

eive

),te

ach

Soci

alse

riat

ion

1(2

)311

1921

2829

391

1011

1(2

)31

(2)3

281

(2)3

111

(2)3

1238

1(2

)328

1(2

)33

9

Sen

seof

self

:se

lfaw

aren

essa

ndse

lfun

ders

tand

ing

(cog

nitio

nsab

outt

hese

lf)T

rans

ition

alM

irro

rse

lfre

cogn

ition

Self

labe

lSe

lfco

nsci

ous

beha

vior

Self

conc

ept

Und

erst

and

see-

know

inse

lf

Pers

onal

pron

ouns

As

aca

usal

agen

tK

now

ifyo

ukn

ow,b

ased

onw

haty

ousa

w

12

31

31 1

23

26R

udim

enta

rySy

mbo

licIn

dire

ctse

lfre

cogn

ition

Sens

eof

poss

essi

onSe

lfev

alua

tive

emot

ions

Pict

ure,

shad

ow

Sham

e,gu

ilt,p

ride

112

13 1

Not

es:

aco

gniti

veat

trib

utio

nsba

sed

oncr

iteri

adi

scus

sed

inte

xt;s

ourc

ebr

acke

ted

whe

nm

yat

trib

utio

ndi

ffer

sfr

omth

atof

the

auth

ors

cite

d.b

sym

bol-

trai

ned

grea

tape

ste

sted

.So

urce

s:1,

Park

er&

McK

inne

y19

99;2

,Tom

asel

lo&

Cal

l199

7;3,

Byr

ne19

95;4

,Myo

wa-

Yam

akos

hi&

Mat

suza

wa

1999

;5,B

yrne

&R

usso

n19

98;6

,Whi

ten

1998

b;7,

Rus

son

2002

b;8,

Sudd

endo

rf&

Whi

ten

2001

;9,P

arke

r,C

hapt

er4,

this

volu

me;

10,d

eW

aal&

Aur

eli1

996;

11,B

oesc

h&

Boe

sch-

Ach

erm

ann

2000

;12,

Patt

erso

n&

Lin

den

1981

;13,

Noe

,de

Waa

l&va

nH

ooff

1980

;14,

Rus

son

1999

a;15

,Tan

ner

&B

yrne

unpu

blis

hed;

16,H

are

etal

.200

0;17

,Cal

l&T

omas

ello

1998

;18,

Stoi

nski

etal

.200

1;19

,Whi

ten

2000

;20,

Whi

ten

1998

a;21

,Boy

sen

1998

;22,

Cal

l200

1b;2

3,St

okes

&B

yrne

2001

;24,

Ber

ing,

Bjo

rkla

nd&

Rag

an20

00;

Bjo

rkla

ndet

al.

2002

;25

,C

all,

Agn

etta

&T

omas

ello

2000

;26

,C

all

&C

arpe

nter

2001

;27

,C

all,

Har

e&

Tom

asel

lo19

98,

2000

;H

are

etal

.20

00,

2001

;T

omas

ello

,Cal

l&H

are

1998

;Tom

asel

lo,H

are

&A

gnet

ta19

99;T

omas

ello

,Har

e&

Fogl

eman

2001

;28,

Hir

ata

&M

atsu

zaw

a20

01;2

9,H

irat

a&

Mat

suza

wa

2000

;30

,Hos

tett

er,C

ante

ra&

Hop

kins

2001

;31,

Jens

vold

&G

ardn

er20

00;3

2,L

eave

ns&

Hop

kins

1999

;33,

Men

zel1

999;

34,M

yow

a-Ya

mak

oshi

&M

atsu

zaw

a20

00;

35,I

taku

ra&

Tan

aka

1998

;36,

Peig

not&

And

erso

n19

99;3

7,C

usta

nce

etal

.200

1;38

,Mita

ni&

Wat

ts20

01;M

itani

,Wat

ts&

Mul

ler

2002

;39,

Bla

ke,C

hapt

er5,

this

volu

me.

(Not

e:re

late

dst

udie

sar

egr

oupe

d.)

Tab

le6.

2.(c

ont.)

Lin

guis

tic(S

ymbo

licC

omm

unic

atio

n)D

omai

n

Cog

nitio

nSe

ries

/lev

elA

chie

vem

ent

Com

men

tsan

dex

ampl

esSo

urce

sa

Lex

ical

Tra

nsiti

onal

Wor

dpr

oduc

tion

Cre

ate

new

sign

Cre

ate

“Dav

em

issi

ngfin

ger”

nam

e1

36

Sem

anti

cT

rans

ition

alSi

mpl

ere

fere

ntia

lsym

bols

Cre

ate

new

mea

ning

Tre

edr

um,l

eafg

room

,lin

guis

ticsy

mbo

l1

23

92

8R

udim

enta

rySy

mbo

licT

each

sign

sM

ould

orm

ime

requ

est

Sym

bol-

base

dso

lutio

nSo

lve

reve

rse

cont

inge

ncy

task

with

sym

bols

13

(2)5

8(2

)7

Gra

mm

arT

rans

ition

alN

ovel

2-w

ord

com

bina

tions

Wild

sym

bolc

ombi

natio

nsR

elia

ble

use

ofw

ord

orde

rN

este

dco

mbi

natio

ns

drum

s,dr

um2

tree

sin

sequ

ence

asa

cont

rast

ive

sym

bolic

devi

ce

12

38

24

82 1

3R

udim

enta

rySy

mbo

licT

hree

-wor

dut

tera

nces

Gra

mm

atic

alm

orph

emes

Fun

ctio

nals

ign

clas

ses

(Plu

sco

mpr

ehen

sion

beyo

nd)

Cal

latt

entio

n,re

ques

t,de

clar

e

1(2

)38

18

(2)3

Not

e:a

cogn

itive

attr

ibut

ions

base

don

crite

ria

disc

usse

din

text

;sou

rce

brac

kete

dw

hen

my

attr

ibut

ion

diff

ers

from

that

ofth

eci

ted

auth

ors.

Sour

ces:

1,Pa

rker

&M

cKin

ney

1999

;2,T

omas

ello

&C

all1

997;

3,B

yrne

1995

;4,B

oesc

h&

Boe

sch-

Ach

erm

ann

2000

;5,R

usso

n20

02b;

6,M

iles,

Mitc

hell

&H

arpe

r19

96;7

,Boy

sen

&B

ernt

son

1995

;Boy

sen

etal

.199

6;8,

Bla

ke,C

hapt

er5,

this

volu

me;

9,M

enze

l,Sa

vage

-Rum

baug

h&

Men

zel2

002.

86 A. E. RUSSON

of reorganizing or transforming lower-level structuresinto new, higher-level ones (Case 1985, 1992). Eatingwith a spoon, for instance, can be achieved either usinga complex action strategy governed by a combinationof sensorimotor-level motor action schemata or using asimple higher-level strategy that consolidates this com-bination of schemata into one operation on a relation-ship. Importantly, behavior in transitional periods mayowe to cognitive structures at either the lower or higherlevel – here, sensorimotor schemata or simple symbols.

(2) Rudimentary (first-order) symbolic-levelstructures. From about 1.5–2 to 3.5–4 years of age,human children create cognitive structures that repre-sent simple events and relationships among them (Case1985; P&M). Behavioral examples are simple word com-binations, using two tools in interrelated fashion, andsymbolic pretend play.

Several models portray cognitive development inthis phase in terms of creating higher-level cognitivestructures derived from sensorimotor ones, i.e., theyrepresent, in the sense of recoding or redescribing, exist-ing representations. Case (1985) construes this as oper-ating on relations-between-relations, where one rela-tionship is subordinated to another or used as a wayto effect change in another. Included are coordinatingtwo different relationships into one “inter-relational”cognitive structure (e.g., hammer-hit-nut with nut-on-anvil) and coordinating two inter-relational structures.Other models are second-order cognition (Langer 2000)and second-order representations (Leslie 1987). Second-order cognition is exemplified by creating two sets con-currently, so that items are similar within each setand different between sets (e.g., red balls, blue balls);this involves simultaneously managing the relationshipwithin each set (same item class) and a higher-order rela-tionship between two sets (different classes). Second-order representations are derivatives of realistic (first-order) representations, for example using a banana as atelephone. To avoid confusion, Leslie argued, “banana-as-telephone,” must remain linked to its first-order rep-resentation, “banana-as-banana,” yet decoupled from it(i.e., marked as an imagined copy). Making a decoupledcopy requires re-representing an existing representa-tion, so second-order representations are higher-levelstructures.

A competing model of cognition in this rangeis secondary representations (Perner 1991), where re-presentations are subsequent presentations of something

previously present in the mind. Examples are entertain-ing past or future representations of a situation or bring-ing schemata to mind without their normal sensorimo-tor cues. Secondary representation may be what allowscoordinating multiple models of a situation, which mayenable tracking where an object went after it moved alongan invisible trajectory, pretending that an empty cup isfull, or interpreting external representations of a situ-ation. Children in the secondary representation phasecan represent how things might be as well as how theyactually are; previously, they could only represent thelatter (Whiten 1996). Secondary representations, likesecond-order ones, are representations of a situationentertained concurrently with the situation’s realisticor current representation and they represent somethingabout the relations among multiple representations of asituation; differently, secondary representations are nothigher-level structures. They remain pre-symbolic inPerner’s view; strong metarepresentation, which followsthem, is the simplest symbolic process.

(3) Strong (second-order) symbolic-levelstructures. Strong symbolism emerges around 4 yearsof age. Understanding that people can hold false beliefsabout the world is the accepted benchmark (Whiten1996). To Perner, this requires appreciating that othersmay have different thoughts about reality than oneself,i.e., understanding re-presentations as re-presentations(interpretations) or strong metarepresentation. Analternative model is third-order cognition, where third-order structures are structures that encompass multiplesecond-order ones in superordinate–subordinatefashion (Langer 1998, 2000). An example is composingthree matching sets of items, which creates hierarchicalcorrespondences between the sets, i.e., a superordinatecategory subsuming two subordinate, second-orderones. Three sets is the minimum needed for hierarchicalclassification, which enables truly hierarchical cognition(Langer 1998).

Levels in great ape cognition

I attributed cognitive levels to great apes’ complexachievements, per Table 6.2, using recognized indi-cators of early symbolic processes in humans. Indica-tors of rudimentary symbolic-level cognition includedweak hierarchization (e.g., routines that subsume sub-routines), tasks first solved by children between 2 and3.5 years of age, tests of abilities accepted as higher-order

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ones (e.g., analogies), and manipulating relations-between-relations. I interpreted achievements emergingin 1.5- to 2-year-old humans and taken to mark thethreshold of weak symbolism as transitional. I consid-ered levels that original authors attributed but privilegedthe indicators noted above. T&C did not analyze greatape achievements individually, for example, so some oftheir cognitive attributions lack substance. On this basis,I consider the four positions currently entertained on thecognitive levels great apes attain.

(1) Great ape cognition operates with same low-level associative processes attributed to all nonhumanspecies (e.g., Balda, Pepperberg & Kamil 1998). Allthree reviews reject this position because of substan-tial evidence for higher-level cognitive processes in greatapes. Recent informed opinion concurs (e.g., Matsuzawa2001b; Suddendorf & Whiten 2001; Thompson & Oden2000; Table 6.2). Low-level associative processes liketrial-and-error learning and sequential chaining are nec-essary but not sufficient to account for great apes’achievements.

(2) Great apes share with all anthropoid primates acognitive level beyond other mammals, understandingthird-party relations (TPRs) (T&C). T&C define TPRsas interactions among third parties in which the actordoes not participate, for example separating interven-tions and mediating reconciliations. Tomasello’s groupadvocates this position but most other experts disagree(Matsuzawa 2001b, P&M, Russon 1999b, RWB, Sud-dendorf & Whiten 2001, Thompson & Oden 2000).T&C consistently interpret great apes’ achievementswith undue skepticism and monkeys’ with undue gen-erosity; for instance, no evidence supports their claimthat monkeys can perceive, let alone judge, relations-between-relations (Parker 1998, Chapter 4, this volume;Rumbaugh 2000; Russon 1999b; Thompson & Oden2000).

T&C’s relational cognition model is itself prob-lematic (Russon 1999b), although many agree withthem that understanding relational categories andrelations-between-relations is among great apes’ crown-ing achievements. T&C characterize great apes’ rela-tional achievements as understanding TPRs, construedas a generalized ability governed by advanced sen-sorimotor cognition (stages 5 and 6). This cognitionreaches into a transitional range where either sensorimo-tor or symbolic structures can generate achievements.Stage 6 also supports understanding single relational

categories but not relations-between-relations; the lat-ter requires rudimentary symbolic cognition becauseit concerns relations between abstract entities (Case1985). T&C rely exclusively on sensorimotor measures,so they fail to assess whether early symbolic or sen-sorimotor processes generate achievements and theyunderrate achievements involving relations-between-relations, such as great apes’ meta-tool and tool set use.Their TPR model also conflates transitional with rudi-mentary symbolic achievements, confounding two levelsof probable significance in distinguishing great ape frommonkey cognition.

(3) Great apes surpass other nonhuman primates inattaining secondary representation, which may charac-terize the 1.5- to 3.5-year phase in human cognitivedevelopment, but fall short of strong symbolic lev-els (e.g., Suddendorf & Whiten 2001; Whiten 1996).Suddendorf and Whiten’s (2001) review of great apes’achievements on invisible displacements, means–endreasoning, pretense, mirror self-recognition, mentalstate attribution, and understanding imitation supportstheir conclusion that great apes achieve secondary rep-resentation up to the level of 2-year-old humans. This isconsistent with the common characterization that greatapes acquire language abilities up to the level of human2 year olds (e.g., Blake, Chapter 5, this volume).

This review neglects to consider great apes’ highestlevel achievements in pretense and means–ends reason-ing, however, or any of their achievements in logico-mathematical or spatial reasoning (e.g., Langer 1996;Mitchell 2002; Table 6.2) so it does not provide athorough test of position 3. It also emphasizes humanachievements in the second year and underplays thethird, situating it closer to position 2 than position 3.Scale model use and minimal third-order classifying,which humans master in their third year, have beenshown in great apes (Kuhlmeier, Boysen & Mukobi1999; Poti et al. 1999; Spinozzi et al. 1999). Scale modeluse in particular may involve using models as repre-sentations, putting great apes on the brink of strongmetarepresentation.

Secondary representation also fails to account forthe higher level structures that can enrich cognitionbeyond sensorimotor levels, especially those concern-ing relations-between-relations. Great apes’ complexfeeding techniques and their logical and quantitativeachievements offer prime evidence of such higher-levelcognitive structures (Byrne & Byrne 1991; Byrne et al.

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2001; Langer 2000; Matsuzawa 1996, 2001b; P&M;Russon 1998; Spinozzi & Langer 1999). The secondaryrepresentation concept fails to address the structure thatindividuals may add to a representation by re-presentingit or precisely how multiple representations of a situa-tion are related; models proposing higher-order struc-tures fill this gap (Case 1985; Leslie 1994; Whiten 2000).Actors may not only recall alternative realistic repre-sentations of a situation (e.g., past, future), for instance,they may also re-represent the situation differently fromany reality they have experienced (e.g., a banana as atelephone) and/or at a higher level (one relationshipvs. multiple schemata). While the secondary represen-tation concept is valuable in suggesting where higher-level cognitive structures are not used to entertain mul-tiple representations of a situation, it fails to considercircumstances in which they are.

(4) Great apes surpass other nonhuman primatesin attaining rudimentary symbolic-level cognition (e.g.,RWB; Langer 1996; Matsuzawa, 2001b; P&M; Russon1998, 1999a). P&M, RWB, and many recent studies(Table 6.2) support this position for all great ape species,in all cognitive domains, based on recognized indicesof weak symbolism (weak hierarchization, abilities rec-ognized to involve higher-order processes, relations-between-relations). Comparable achievements claimedfor monkeys have been shown to involve performancesbased on response rules generated by simpler processes,probably associative ones (Parker, Chapter 4, this vol-ume; Thompson & Oden 2000).

Many current disagreements stem from whatassessment tools are used and what meanings of sym-bolism, metarepresentation, and hierarchization areapplied (Whiten 1996). With the meanings and assess-ments used here, the best interpretation of current evi-dence is that great apes attain rudimentary symbolic-level cognition and in this, they surpass other nonhumanprimates.

The levels that great apes achieve within therudimentary symbolic range are relatively uncharted.Assessment remains an impediment because many cur-rent tests for symbolism use threshold criteria (e.g.,metarepresentation, hierarchization). Indices of earlysymbolic levels have been used in a few cases, e.g., num-ber of relational operators, complexity of classification,depth of hierarchies, or human age norms (Byrne &Russon 1998; Kuhlmeier et al. 1999; Matsuzawa 2001b;P&M; Poti’ et al. 1999; Russon & Galdikas unpublished;

Spinozzi et al. 1999; Thompson & Oden 2000). Thesesuggest great apes’ cognitive ceiling at a hierarchicaldepth of about three levels (e.g., use a hammer stone tohit (a nut placed on (an anvil stone placed on a wedge,to level it)) – Matsuzawa 2001b; and see Yamakoshi,Chapter 9, this volume), coordinating three object–object relations in one inter-relational structure (e.g.,coordinate anvil-on-wedge, nut-on-anvil, and hammer-hit-nut – P&M; Russon & Galdikas unpublished), orminimal third-order classification (e.g., create three con-temporaneous sets with similar items within sets anddifferences between sets – Langer 2000; Poti’ et al.1999; Spinozzi et al. 1999). All remain consistent withPremack’s (1988) rule of thumb, that great apes reachlevels like 3.5-year-old children but not beyond.

COGNITIVE INTERCONNECTION:THE ORCHESTRALLY MINDED

Cognitive facilitation refers to achievements madethrough interplay among different types of cognition.It is an important source of an actor’s cognitive power:tasks that require interconnecting several abilities can besolved, and individual abilities can advance by exploitingother abilities (Langer 1996). Cracking a nut, forexample, might require using a stone hammer–anviltool set (means–end reasoning), identifying a substitutewhen the best hammer is unavailable (logical reason-ing), and obtaining the substitute from a companion(social cognition), or classification abilities might beextended by categorizing according to causal utility.Cognitive facilitation almost certainly occurs in greatapes. Chimpanzees skilled in symbol use solved anal-ogy problems better than chimpanzees without symbolskills, for example (Premack 1983; Thompson, Oden &Boysen 1997).

Facilitation has received little attention in greatapes despite its implications for cognitive architecture.If it occurs, especially across domains, then qualitativelydifferent cognitive structures can operate and inter-act beyond the bounds of the problem types for whichthey were designed: that is, the cognitive system cannotsimply comprise a collection of independent, special-purpose modules. Facilitation is also important com-paratively because it has been claimed to be uniquelyor at least characteristically human, for whom it hasbeen likened to fluidity of thought, multiple intelligencesfunctioning seamlessly together, a passion for the

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analogical, and mapping across knowledge systems (e.g.,Gardner 1983; Mithen 1996). What enables facilitationis unresolved. Hypotheses include analogical reasoning,which transfers knowledge from one problem type toanother (Thompson & Oden 2000), or synchronizingdevelopmental progress across distinct types of cogni-tion so that their structures build upon common expe-riences, which promote interplay by serving as bridgesbetween cognitive structures (Langer 1996, 2000). Pos-sibilities typically require hierarchization; analogicalreasoning, for instance, involves judging if one relation-ship is the same as another, i.e., logical equivalences atabstract levels, which is founded on the ability to judgerelations-between-relations.

In part, systematic evidence on facilitation in greatapes is meager because studies of nonhuman cognitionhave tended to control against using multiple abilitiesin aiming for clean tests of single abilities. Among thefew sources of systematic evidence are studies of logic,which show that analogical reasoning is within the nor-mal reach of great apes but not other nonhuman primates(Oden, Thompson & Premack 1990; Thompson et al.1997; Thompson & Oden 2000). For feral great apes,P&M is the only review to have systematically consid-ered achievements that may involve facilitation. I con-sider evidence for facilitation across physical, logical,and social domains as the most important in compara-tive perspective.

Logical–PhysicalGreat apes interconnect logical with physical cognitionwhen they classify items by function or functional rela-tions, for example sort items into sets of toys and toolsor sort bottles with caps (Savage-Rumbaugh et al. 1980;Tanaka 1995; Thompson & Oden 2000), use substi-tute tools (Figure 6.1), or classify foods on the basis ofthe technique for obtaining them (Russon 1996, 1999a,2002a). A rehabilitant orangutan stored termite nestfragments on specific parts of his body, in the order inwhich he planned to open them, to streamline his termiteforaging (Russon 2002a) and a rehabilitant chimpanzeemade and used a seriated set of stick tools (ordered fromsmallest to largest) to extract honey from a bee’s nest(Brewer & McGrew 1990).

Social–physicalGreat apes use socially mediated learning in acquiringfood processing skills (Boesch 1991; Byrne & Byrne

Figure 6.1. Princess, an adult female rehabilitant orangutan, blowson the burning tip of a mosquito coil. A paper marked with two dotsis at her feet. She had drawn the dots by touching the coil’s burningtip to the paper, i.e., substituting the coil for a pen. She oftenscribbled in notebooks with pens, so she used a functional similaritybetween pens and the coil, that both have tips that can mark paper.She did not simply confuse the two tools. She drew differentlywith the coil (touch vs. scribble) and she fixed it differently (if apen did not mark when she scribbled, she fixed it by biting at itstip or by clicking the pen’s switch to advance the tip; to fix her coil,she blew on its tip).

1993; Inoue-Nakamura & Matsuzawa 1997; Matsuzawa& Yamakoshi 1996; Russon 1999a, 2003a,b). When theyuse imitation or demonstration to advance complexfood processing skills, social cognition contributes tophysical cognition at rudimentary symbolic levels. Themost complex cases known concern stone nut-crackingin west African chimpanzees: mothers demonstrate totheir offspring how to use stone hammers, and offspringreplicate the techniques they were shown (Boesch 1991,1993). Mithen (1996) argued that food sharing, used as

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a medium for social interaction with formalized sharingrules, uses “natural history” cognition to enhance socialproblem solving. If so, chimpanzees show this capability:they share meat in rule-governed fashion to serve socialfunctions and social relationships are important in dis-tribution (Boesch & Boesch-Achermann 2000; Goodall,1986; Mitani & Watts 2001; Mitani, Watts & Muller2002).

Logical–Social–SymbolicBoysen et al. (1996) used a reverse contingency taskto test if chimpanzees could select the smaller of twoarrays to gain greater rewards against a social competi-tor. Boysen showed two dishes of candies to a dyad ofsymbol-trained chimpanzees, had one choose a dish bypointing, and then gave the chosen dish to the otherchimpanzee and the leftover dish to the chooser. Shownreal candies, choosers consistently picked the dish withmore – to their disadvantage. When number symbolsreplaced candies, choosers consistently picked the dishwith fewer – to their advantage. Symbols improved thesechimpanzees’ ability to solve a quantification (logical)problem. Orangutans also solve this task, without sym-bol skills and using real candies (Shumaker et al. 2001).If subjects interpreted this as a competitive social task, asintended, their quantification (logical) abilities assistedtheir social problem solving.

Complex facilitationsSome expertise taps all three domains interactively. Themost complex is chimpanzee cooperative hunting inthe Taı forest (Boesch & Boesch-Achermann 2000).Once a hunting group detects a red colobus group,the ideal hunt has four phases involving four roles(driver, chaser, ambusher, captor). Participants must beable to alter their actions flexibly and rapidly to trackcolobus’ attempts to escape; they also take differentroles and accommodate their actions to chimpanzeesin other roles. If successful, they share the meat for-mally according to each participant’s role in the hunt,age, and dominance. Successful cooperative hunting inthe forest, a three-dimensional space with low visibility,requires hunters to “perceive other hunters as indepen-dent agents with their own intentions and competence,attribute abilities to the prey that differ from those ofconspecifics, and understand the causality of the exter-nal relation between prey and other hunters” (Boesch& Boesch-Achermann 2000: 242). It requires cognitiveabilities in the physical domain (space – arboreal loco-

motion and routes; causality – predicting how chasing,blocking, or driving will affect colobus’ flight path andthe canopy), the social domain (self-manipulating thepresentation of oneself to the colobus; figuring one’sweight into arboreal travel; enacting complementaryroles), and the logical domain (quantifying how to dis-tribute meat sharing). Hunters can change roles repeat-edly over the course of a hunt, so some must have allor most of these cognitive capabilities and use them ininterconnected fashion.

Evidence for cognitive facilitation jibes with thecomplex, varying, and multifaceted challenges facinggreat apes in their natural habitat (Boesch & Boesch-Achermann 2000; Russon in 2003b). Their foragingoffers a prime example: it calls for a wide spectrumof abilities to organize biological knowledge, constructforaging techniques, acquire alternative strategies, andnegotiate cooperative and competitive social foragingsituations (Russon 2002a; Stokes 1999). The multi-faceted nature of complex foraging tasks calls for com-bining high-level abilities, and interactions among taskcomponents call for interconnecting them. Evidence forcognitive facilitation also jibes with evidence that greatapes spontaneously transfer expertise from one domainto another (Thompson & Oden 2000), with Parker’s(1996) apprenticeship model of interconnected physicaland social abilities, and with arguments that intercon-necting mechanisms of some sort are essential to cogni-tive systems that handle different types of information inparallel using distinct modules (Mithen 1996). It clearlyrefutes strictly modular models of cognition in greatapes.

GENERATING GREAT APECOGNITION

The variability and flexibility of great apes’ cognitiveabilities, including the capacity to generate unusual abili-ties as needed and the roughly consistent cognitive ceil-ing across abilities, domains, and species, suggest thattheir cognitive systems may be better characterized bythe processes that generate them than by specific abilitiessuch as tool use or self-concept. Generative processes areconsidered below.

Development and culture

Developmental models of human cognition have prob-ably been fruitful in studying great apes because their

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cognitive structures develop in similar fashion (Langer1996, 2000; P&M). Like humans, great apes experi-ence extensive and lengthy sensory, motor, socio-sexual,brain, and cognitive development that is affected byage and experience and is concentrated in immaturity(Boesch & Boesch-Achermann 2000; Inoue-Nakamura& Matsuzawa 1997; Langer 1996; Matsuzawa 2001b;P&M; Poti et al. 1999; Russon in 2003b; Spinozzi et al.1999). Their complex structures develop on the basisof simpler ones and emerge late in immaturity (Langer1996, 2000; Matsuzawa 2001b; P&M). Their complexforaging techniques, for example, develop piecemealover many years with youngsters first acquiring basicelements, next assembling them into a basic strat-egy, then gradually elaborating it (Fox, Sitompul &van Schaik 1999; Inoue-Nakamura & Matsuzawa 1997;Russon 2002a; 2003a).

In life history perspective, developmental mod-els are also consistent with evidence that: (1) cogni-tive capacity peaks in juveniles and levels off after ado-lescence; (2) parents contribute to acquiring advancedjuvenile as well as basic infant skills; (3) rudimentarysymbolic level abilities emerge post-infancy, around themove to semi-independent life; (4) most adult-levelexpertise is mastered by adolescence, around the moveto fully independent life; and (5) post-adolescent learn-ing seems less flexible (Boesch 1991; Boesch & Boesch-Achermann 2000; Ingmanson 1996; Inoue-Nakamura& Matsuzawa 1997; King 1994; Parker 1996; P&M). Allcorrelate with the slower pace and disproportionatelyprolonged immaturity that distinguish great ape devel-opment from that of other nonhuman primates (P&M;Kelley, Chapter 15, Ross, Chapter 8, this volume). Com-pared with humans, great apes’ cognitive developmentis faster in the first year of life but subsequently slower(P&M; Poti et al. 1999; Spinozzi et al. 1999), whichexplains why some of the distinctive abilities they sharewith humans develop later and persist longer.

Social–cultural influences, interwoven with indi-vidual experience, also contribute to cognitively gov-erned achievements in great apes, as they do in humans(e.g., P&M; Tomasello 1999; T&C; van Schaik et al.2003; Whiten et al. 1999). The distribution of “atypical”abilities and some complex skills in the wild, for instance,shows that great apes may not realize some com-plex achievements without appropriate socio-culturalsupport despite appropriate individual opportunities(van Schaik et al. 2003; Whiten et al. 1999). If theirachievements are products of combining socio-cultural

with individual experience during development, thenenculturation should be primarily responsible. In greatapes enculturation probably resembles apprenticeship(guided participation in shared activities of a routinenature; Rogoff 1992) and supports and perhaps extendstheir natural behavioral repertoires (Boesch & Boesch-Achermann 2000; Matsuzawa et al. 2001; Parker 1996;P&M; Russon 1999b, 2003b; Suddendorf & Whiten2001). It has been assigned responsibility for achieve-ment variability across wild, captive-reared, and human-enculturated great apes (e.g., Donald 2000; T&C).

Great apes’ cultural and cognitive processes aremore tightly interwoven than this scenario suggests.Cultural processes depend on what information canbe shared and how, which depend on information pro-cessing capabilities, i.e., cognition. Great apes’ culturalprocesses may be exceptionally powerful among non-human primates because they access high-level cogni-tive capabilities unique to great apes and humans (e.g.,imitation, self-awareness, demonstration; Parker 1996).Conversely, great apes’ cognitive achievements are prob-ably boosted by cultural processes. Chimpanzee cul-tures show ratcheting, for instance, the accumulationof cultural variants over time, in the form of cumulativemodifications to complex techniques (McGrew 1998;Yamakoshi & Sugiyama 1995). This probably allowslearners to acquire more complex techniques than theywould have constructed independently. If enculturationhas a special role to play in cognitive development, itmay primarily affect high levels, as it typically does inhumans (P&M; Tomasello 1999). No convincing evi-dence exists, however, for claims that human encul-turation induces higher-level cognitive structures ingreat apes than species-normal enculturation (Boesch &Boesch-Achermann 2000; Langer 2000; P&M; Russon1999b; Spinozzi et al. 1999; Suddendorf & Whiten2001).

Generating cognitive structures

A final issue is what mental processes generate great apecognitive development and how, especially their distinc-tive cognitive structures. Great apes consistently attainthe same cognitive level across cognitive domains, rudi-mentary symbolism, which suggests that centralizedgenerative processes that operate across the whole cog-nitive system govern their cognitive development, ratherthan processes specific to a single cognitive domain orproblem type. That the level achieved supports simple

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symbols suggests hierarchization as a good candidatefor that centralized generative process: It is consideredessential to the cognitive abilities and achievements thatdistinguish the great apes among nonhuman primates(e.g., simple language, abstract level problem solving,complex tool and manual foraging techniques) (Case1985; Piaget 1954) and, among nonhuman primates,only great apes show evidence of hierarchization (e.g.,Byrne 1997; Langer 1996; Matsuzawa 2001b; Russonet al. 1998).

Great apes’ flexible range of high-level cognitiveabilities could be generated by hierarchization usedin conjunction with combinatorial mechanisms, in theform of hierarchical mental construction (e.g., Byrne1997; Gibson 1990, 1993; Langer 1994, 1996). Com-binatorial mechanisms are centralized generative pro-cesses that combine, decompose, and recombine mul-tiple mental units at a time, as in combining actions orobjects in sequence; they probably generate cognitivestructures in all primates (Langer 2000). The patternin which great apes acquire food processing techniquesis consistent with a hierarchical mental constructionmodel of cognitive development (e.g., Inoue-Nakamura& Matsuzawa 1997; Russon 2002a; Stokes & Byrne2001). Infant chimpanzees acquiring stone nut-crackingskills, for instance, first learn the individual basic actionsneeded to crack nuts and apply single actions to singleobjects (only stone, only nut); next, they apply mul-tiple actions to multiple objects (some stones, some nuts,stones and nuts) combined in sequence (some are inef-fective, e.g., put a nut on a stone but hit the nut witha hand then pick up a piece of kernel from a brokenshell on the ground and eat that); finally, they integrateappropriate combinations into more complex, hier-archically organized techniques showing understandingof action–object relationships (Inoue-Nakamura &Matsuzawa 1997). To date, other nonhuman primateshave not shown hierarchically organized techniques(Harrison 1996). Great apes reach only rudimentarysymbolic levels, however, and their achievements arerougher-grained than humans’, i.e., focused primarilyon general problem features and less able to incorpo-rate fine ones (Langer 1996; P&M; Spinozzi & Langer1999). Their low symbolic ceiling may reflect limitedhierarchization relative to humans, described as shal-low (Byrne 1997; Matsuzawa 2001b) or protohierarchi-cal (Langer 2000). The rougher grain may reflect lowerlimits on the number of units they can combine at once.

Cognitive facilitation may take great ape cognitionbeyond modularity, and it may hinge on hierarchization(RWB; Case 1985; Karmiloff-Smith 1992; Thompson& Oden 2000; see Langer 1996, 2000 for alternatives).3

This link is supported by evidence that cognitive facili-tation in great apes is limited, because this is consistentwith shallow hierarchization. Shallow hierarchizationgenerates only rudimentary hierarchical cognitive struc-tures, which remain more isolated than the higher-levelcognitive structures that human hierarchization gener-ates (Case 1985).

This sort of model, which characterizes greatape cognition in terms of central generative processes,may help explain several features that have puzzledscholars. The “atypical” abilities that emerge in greatapes under highly nurturing human rearing conditions(e.g., linguistic and mathematical abilities: Gardner,Gardner & van Cantfort 1989; Tomasello 1999) maysimply be customized abilities of the sort expected fromgenerative cognitive systems that build structures to suitthe specific challenges encountered during development(Boesch & Boesch-Achermann 2000; P&M; Rumbaugh2000; Swartz, Sarauw & Evans 1999). Marked individ-ual differences in achievements may similarly be normalfeatures of generative cognitive systems. “Atypical” abil-ities may also have feral counterparts, making them lessunusual than suggested. Feral communication sugges-tive of symbolism has been reported, for example treedrumming, leaf clipping, knuckle-knocking, demonstra-tion teaching (Boesch 1991, 1993, 1996), symbolic eat-ing (Schaller 1963, Russon 2002b), miming requests(Russon 2002b), and placing leaves to indicate traveldirection (Savage-Rumbaugh et al. 1996) (see also Blake,Chapter 5, this volume), as have complex quantita-tive abilities such as seriation (arranging items in agraded series: Brewer & McGrew 1990) and body-part counting (using body parts to order items: Russon2002a).

Generative models also suggest how modularity–generality may play out in great apes. In humans,module-like structures may be products of genera-tive processes operating in the context of problem-specific constraints and innately founded structures(e.g., Elman et al. 1996; Greenfield 1991; Karmiloff-Smith 1992; Langer 2000; P&M). Human cognitivestructures change with development: they have beencharacterized as relatively undifferentiated at their earl-iest (i.e., applicable generally, across problem types),

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subsequently differentiated into domain- and problem-specific structures with module-like features (applicableto specific problem types), and finally interconnected(applicable across problem types when used in combi-nation) (e.g., Case 1996; Greenfield 1991; Langer 1996,2000; P&M). Generalized capabilities may then existin undifferentiated and interconnected forms, module-like structures may be developmental products, anddevelopment may affect the qualities of modularity andgenerality and the balance between them. Great apes’cognitive development shows similar patterns althoughcomparatively, differentiation and interconnection pro-ceed more slowly after the first year and are ultimatelyless powerful (e.g., P&M). Great ape cognition theninvolves both modularity and generality, and charac-terizations in terms of generality–modularity are likelyflawed if they fail to consider developmental change or todistinguish undifferentiated from interconnected formsof generality (e.g., Mithen 1996).

DISCUSSION

Evidence consistently supports conclusions that greatapes differ cognitively from other nonhuman primates.Virtually all experts agree, there is no longer any justi-fication for reducing great ape cognition to associativeprocesses or lumping great apes with other nonhumanprimates. What sets great ape cognitive achievementsapart is not specific problem-specific abilities such as tooluse, imitation, or self-concept. It is rather the broaderand more open repertoire of abilities, rudimentary sym-bolic levels achieved across domains, and limited inter-connectedness among them. What underpins this suiteof cognitive structures may be centralized generativeprocesses that operate ontogenetically, limited hier-archization and perhaps facilitation being the bestcurrent candidates. This characterization is not new.Revisiting it, however, helps articulate what needs evolu-tionary explanation: more powerful generative processesthat produce rudimentary symbolism and limited fluid-ity of thought.

This characterization helps explain why it has beendifficult to get a handle on great ape mentality. First,if variable achievement is intrinsic to great ape cogni-tion, then studies that have tested great apes as imma-tures or reared in non-stimulating environments havefailed to tap their full potential. Second, achievementsduring the transition from sensorimotor to rudimentary

symbolic cognition may be governed by either advancedsensorimotor or primitive symbolic-level structures(Case 1985). It is possible to distinguish the two behav-iorally, and studies that failed to do probably under-estimated subjects’ level of cognitive functioning (e.g.,Byrne & Russon 1998; Russon 1998). Third, if enter-taining multiple representations of a situation under-pins rudimentary symbolic-level cognition then greatapes, like 2- to 3.5-year-old children, should be able toentertain symbolic and perceptual representations con-currently. In such children, when the two representa-tions conflict, perceptual representations tend to over-ride symbolic ones for control of behavior; they havebeen described as perception bound because they areeasily swayed by perceptual cues (Case 1996; P&M).Chimpanzees have shown similar tendencies. Theysolved a reverse contingency task (what you pick goesto your partner) when it was presented with symbolsbut failed when it was presented with real candies,so they can function symbolically but not when per-ceptual cues are salient (Boysen et al. 1996, 1999).This suggests that their symbol use is unstable andthey, like young children, may fail symbolic tasks notbecause they lack the capability but because percep-tual cues activate this bias. Orangutans without sym-bol skills solved the reverse contingency task with realcandies (Shumaker et al. 2001), so even great apes cansometimes privilege symbolic over perceptual solutions.These difficulties do not render it futile to study rudi-mentary symbolic cognition in great apes: many diffi-culties are assessment related and have been resolvedfor humans. What is needed is greater attention to thequalities of rudimentary symbolic cognition and fac-tors that contribute to variability in its development andapplication.

The characteristics of great ape cognition thatrequire evolutionary explanation are among those cur-rently treated as evolutionary achievements of thehuman lineage. That these qualities appeared earlier inprimate evolutionary history does not alter their signif-icance but it does change their role, from innovationsof the human lineage to foundations for its elaborations.This affects evolutionary reconstructions of human cog-nition that use great apes to represent the ancestral cog-nition from which human cognition evolved becausethey typically assume great apes to be incapable of sym-bolic cognition (see Russon, Chapter 1, this volume). Wenow know that this assumption is incorrect, in at least

94 A. E. RUSSON

one form. Accurate models of great ape cognition arethen important next steps towards better understand-ing of great ape and human cognitive evolution.

ACKNOWLEDGMENTS

My thanks to David Begun and Sue T. Parker for valu-able comments on earlier drafts of this paper. Theresearch on which this chapter was based was sup-ported by funding from the Natural Sciences and Engi-neering Council of Canada, Glendon College, and YorkUniversity.

1 Feral, here, includes wild and reintroduced individuals liv-

ing free in natural habitat. I grouped reintroduced with wild

great apes because both face species-typical rather than human-

devised problems. Their achievements may differ in their speci-

fic nature (e.g., reintroduced orangutans often show complex

tool use but wild ones rarely do) but not in cognitive complexity,

which is the major concern here (Russon 1999b).

2 (1) This body of evidence is expected to be small because complex

achievements should be rare relative to average ones. (2) If evi-

dence on great ape cognition is notoriously patchy, evidence on

complex cognition should be even more so. (3) On tasks tapping

an actor’s highest-level capabilities, high performance variability

is expected (Spinozzi et al. 1999; Swartz et al. 1999). (4) Method-

ological confounds can cause performance variation, especially

misleading cues that undermine performance and scaffolding

that boosts it. The number of items that must be held concur-

rently in working memory to solve a task affects success for

example, and how a task is presented can increase or decrease

that number (Pascual-Leone & Johnson 1999). If threshold tests

are used, such confounds can affect assessments of cognitive

levels. (5) Few studies have verified that their tests for great

apes are commensurate with human benchmarks; close scrutiny

often shows they are not (e.g., P&M). (6) In children at rudi-

mentary symbolic levels, perceptual processes readily dominate

symbolic ones and unstable achievement is common. If great

apes function at this level, comparable instabilities are prob-

able (e.g., Boysen et al. 1996; Case 1985; Boysen, Mukobi &

Berntson 1999).

3 Similar suggestions use terms like representational redescription

(Karmiloff-Smith 1992), abstract level generalization (RWB),

higher levels of abstraction (Case 1985), and analogical reason-

ing (Thompson & Oden 2000). Langer’s (1996, 2000) alternative

is that facilitation may owe to developmental synchronization,

i.e., yoking developmental progress across distinct types of

cognition so they develop together rather than independently;

this offers the best timing pattern possible for interconnecting

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