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Corvids Outperform Pigeons and Primates in Learning a Basic Concept



Corvids (birds of the family Corvidae) display intelligent behavior previously ascribed only to primates, but such feats are not directly comparable across species. To make direct species comparisons, we used a same/different task in the laboratory to assess abstract-concept learning in black-billed magpies ( Pica hudsonia). Concept learning was tested with novel pictures after training. Concept learning improved with training-set size, and test accuracy eventually matched training accuracy-full concept learning-with a 128-picture set; this magpie performance was equivalent to that of Clark's nutcrackers (a species of corvid) and monkeys (rhesus, capuchin) and better than that of pigeons. Even with an initial 8-item picture set, both corvid species showed partial concept learning, outperforming both monkeys and pigeons. Similar corvid performance refutes the hypothesis that nutcrackers' prolific cache-location memory accounts for their superior concept learning, because magpies rely less on caching. That corvids with "primitive" neural architectures evolved to equal primates in full concept learning and even to outperform them on the initial 8-item picture test is a testament to the shared (convergent) survival importance of abstract-concept learning.
Psychological Science
2017, Vol. 28(4) 437 –444
© The Author(s) 2017
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DOI: 10.1177/0956797616685871
Research Article
Birds are much maligned when it comes to intelligence,
as shown by such phrases as “bird brain” or “for the
birds.” Birds’ intelligence may get a bad rap from their
primitive appearance (e.g., beaks), apparent hard-wired
behaviors (e.g., songs), and small primitive brains (no
neocortex) thought to be capable of only lower-order
cognition. Because birds and mammals evolved along
different lineages beginning about 145 million years ago
(late Jurassic), they would be expected to look different.
But looks can be deceiving when it comes to intelligence.
And, as we argue here, neither their small brain size, nor
their hard-wired behaviors, nor their lack of neocortex
has prevented birds from evolving highly intelligent
Among the many intelligent bird behaviors docu-
mented in the past 20 or so years include remarkable
feats, particularly by members of the corvid family: tool
making and tool use by New Caledonia crows (Corvus
moneduloides) in the wild and the laboratory; identifying
“self” in a mirror (Pica pica and Nucifraga columbiana);
recaching to prevent pilfering by an observing raven
(Corvus corax), but only if the caching raven had itself
been a pilferer; and nutcrackers storing thousands of pine
seeds in hundreds of caches that are recovered months
later in the alpine forest (e.g., Bugnyar & Heinrich, 2006;
Clary & Kelly, 2016; Hunt, 1996; Prior, Schwarz, &
Güntürkün, 1998; Vander Wall, 1982; Weir, Chappell, &
Kacelnik, 2002). Because tool making and tool use were
critical in developing modern civilization, it has been
assumed that such inventive behavior was evidence of supe-
rior human intelligence (compared with other animals).
685871PSSXXX10.1177/0956797616685871Wright et al.Abstract-Concept Learning and Brain Evolution
Corresponding Author:
Anthony A. Wright, McGovern Medical School, University of Texas
Health Science Center at Houston, 6431 Fannin St., Houston, TX 77030
Corvids Outperform Pigeons and
Primates in Learning a Basic Concept
Anthony A. Wright1, John F. Magnotti2, Jeffrey S. Katz3,
Kevin Leonard4, Alizée Vernouillet4, and Debbie M. Kelly4
1Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas
Health Science Center at Houston; 2Department of Neurosurgery, Baylor College of Medicine;
3Department of Psychology, Auburn University; and 4Department of Psychology, University of Manitoba
Corvids (birds of the family Corvidae) display intelligent behavior previously ascribed only to primates, but such
feats are not directly comparable across species. To make direct species comparisons, we used a same/different task
in the laboratory to assess abstract-concept learning in black-billed magpies (Pica hudsonia). Concept learning was
tested with novel pictures after training. Concept learning improved with training-set size, and test accuracy eventually
matched training accuracy—full concept learning—with a 128-picture set; this magpie performance was equivalent to
that of Clark’s nutcrackers (a species of corvid) and monkeys (rhesus, capuchin) and better than that of pigeons. Even
with an initial 8-item picture set, both corvid species showed partial concept learning, outperforming both monkeys
and pigeons. Similar corvid performance refutes the hypothesis that nutcrackers’ prolific cache-location memory
accounts for their superior concept learning, because magpies rely less on caching. That corvids with “primitive” neural
architectures evolved to equal primates in full concept learning and even to outperform them on the initial 8-item
picture test is a testament to the shared (convergent) survival importance of abstract-concept learning.
comparative intelligence, abstract-concept learning, same/different learning, evolution, magpies, corvids, nutcrackers,
Received 9/8/16; Revision accepted 12/3/16
438 Wright et al.
The same can be said for birds identifying their own
reflection as “self,” attributing intentions to a conspecific
on the basis of their own pilfering behavior (theory of
mind), and superior long-term memory for hundreds of
caches, months later when covered by snow that obscures
local landmarks.
Even from these few examples, it should be clear that
at least some birds have considerable intelligence, as well
as higher-order cognition. But as more examples accu-
mulate, the comparison of intelligent behavior across
bird species and with other animals (e.g., nonhuman pri-
mates) becomes more pressing. Many of these remark-
able feats of intelligence are niche-specific and likely
evolved over many thousands of years before becoming
what we observe today, making comparisons of intelli-
gence across ecological niches difficult, if not impossible.
For example, how would the feat of fashioning a twig
into a hooked tool to fish grubs out of tree holes be com-
pared with retrieving caches of pine seeds made months
before that are now covered by snow?
Such individual feats of intelligence are not, for the
most part, directly comparable, nor can they be assigned a
level of cognitive processing for meaningful comparisons.
Instead, what is needed is a task that all species to be com-
pared can readily perform and that requires higher-order
abstraction that can be manipulated in a functional man-
ner to test for an optimal solution. The most basic of such
abstract tasks is the same/different task, proposed in 1890
by William James as “the very keel and backbone of our
thinking” and which has been shown to be functionally
important in human development for equivalence opera-
tions involved in novel sentence construction and mathe-
matical operations (James, 1890/1950, p. 459; see also
Christie, Gentner, Call, & Haun, 2016; Gentner, 1988;
Siegler, 1996; Smith, Langston, & Nisbett, 1992).
We developed a same/different task that different bird
species including pigeons (Columba livia) and Clark’s
nutcrackers (Nucifraga columbiana), a corvid species, as
well as different nonhuman primate species including
rhesus monkeys (Macaca mulatta) and capuchin mon-
keys (Cebus apella) could all learn readily and perform to
similar levels of high accuracy (Katz & Wright, 2006; Katz,
Wright, & Bachevalier, 2002; Magnotti, Katz, Wright, &
Kelly, 2015; Wright & Katz, 2006; Wright, Magnotti, Katz,
Leonard, & Kelly, 2016; Wright, Rivera, Katz, & Bachevalier,
2003). Critically, we used the same pairs of pictures (scenes,
objects, animals, buildings, people, etc.) in the same order
of testing, the same sequences, and the same relative
picture size (visual angle) for training and abstract-
concept testing across all these species. When trained
with a small set of 8 pictures, all of these species learned
to accurately (> 80% correct) identify pairs as the same or
different. After learning, we assessed abstract-concept
learning by testing for transfer on occasional trials con-
taining novel pictures; these trials were intermixed with
training trials containing familiar pictures. The training
set was then progressively doubled, and learning was fol-
lowed by transfer testing. The progressive increase in
training-set size was the critical manipulation that
increased abstract-concept learning. Doubling the train-
ing set accelerated expansion of examples of the con-
cept, which in turn promoted learning of the abstract
concept. All species and all individuals tested attained
full abstract-concept learning as shown by equivalence
of transfer performance (with pairs of novel pictures) to
their respective baseline performance (with pairs of pic-
tures used in training)—that is, they achieved the optimal
solution referred to earlier.
Other evidence directly related to our same/different
experiments contributed to understanding the nature of
our subjects’ relational processing and substantiated that
this task assessed bona fide same/different abstract-
concept learning: First, our task used only two stimuli per
trial to avoid low-level perceptual entropy cues common
with tasks using multiple-stimulus displays (e.g., Young,
Wasserman, & Garner, 1997). Second, our modeling of
faster learning with increasing set size ruled out stimulus
generalization as a contributor to abstract-concept learn-
ing (Wright & Katz, 2007). Third, our subjects’ rapid tran-
sition to delays (1–2 s) ruled out possible effects of
low-level translational-symmetry cues on same trials, tri-
als with two identical pictures, and entropy cues on dif-
ferent trials, trials with two different pictures (Katz, Sturz,
& Wright, 2010; Wright et al., 2003). Fourth, our pro-
active-interference tests using the delayed (1 s, 10 s, or 20
s) same/different task verified that our subjects were
making bona fide relational stimulus comparisons across
as many as eight trials and 5 min before testing (Devkar
& Wright, 2016; Wright, Katz, & Ma, 2012).1
In addition to the finding of full concept learning com-
mon to all species, there were, however, some fundamen-
tal species’ differences in same/different abstract learning:
Corvids, including nutcrackers, and in a more recent study
black-billed magpies (Pica hudsonia; Magnotti, Wright,
Leonard, Katz, & Kelly, 2016), revealed better transfer
(66–67% correct) with the initial eight-picture set than did
pigeons, rhesus monkeys, or capuchin monkeys, all of
which performed at chance level (50% correct). In addi-
tion, nutcrackers required fewer exemplars to attain full-
concept learning (128-picture training set) than pigeons
(256-picture training set), and their performance was
equivalent to that of rhesus monkeys and capuchin mon-
keys in this regard.
We think it is remarkable that nutcrackers (or any bird
species) could attain full concept learning with the same
number of training exemplars as two monkey species in
Abstract-Concept Learning and Brain Evolution 439
a basic relational same/different task. This advanced pro-
cessing may be facilitated by the nutcracker’s predisposi-
tion for relational processing and memory of the many
cache locations over several months of retention time.
Cache location memory depends on relational process-
ing of the so-called what (pine seeds) and where (loca-
tion in forest) for literally thousands of cache locations
that change from year to year. Changing cache locations
yearly means that nutcrackers must not confuse locations
made in prior years with those of the current year, which
therefore adds a “when” component to the relational pro-
cessing. The what, where, and when are closely related
to episodic memory (cf. Gould, Ort, & Kamil, 2012), and
primates have been thought to be best equipped in terms
of brain development for having episodic memory (e.g.,
Beran et al., 2016; Clark & Squire, 2013; Davachi, 2006;
Ranganath, 2010; Squire, Wixted, & Clark, 2007). More-
over, nutcrackers do not have the luxury of gradually
learning these relationships to retrieve cache locations;
they have to rapidly acquire this skill to a high accuracy
level because they are totally dependent on cached pine
nuts during long alpine winters (Tomback, 1998).
To test whether nutcrackers are unique among corvids
in rapid full concept learning because of their evolved
dependence on extensive caching and retrieval, we tested
another corvid species, black-billed magpies, using the
same expanding training sets in the same task previously
used with nutcrackers (and monkeys and pigeons). Mag-
pies rely much less on cached foods than nutcrackers do
(e.g., Trost, 1999). They are more omnivorous and have
better access to their preferred foods (e.g., insects, car-
rion, eggs, berries, seeds, nuts) throughout the year. If the
nutcrackers’ extensive caching and retrieval was instru-
mental in their rapid full concept learning, then the mag-
pies’ full concept learning might turn out to be similar
to that of pigeons. Alternatively, some other relational-
processing skill (e.g., a social skill) highly developed in
magpies might be instrumental in magpies outperform-
ing nutcrackers (and monkeys). Finally, if skill in rela-
tional processing was built into the evolved neural
architecture of these two corvid species, then the mag-
pies’ full concept learning should be equivalent to that
of nutcrackers (and monkeys).
The subjects were 10 wild-caught black-billed magpies (4
females) that were hand-raised from the prefledgling
stage and maintained at approximately 85% to 90% of
their ad libitum weight by supplemental feeding with a
mixture of Pedigree wet dog food, Kirkland Signature dry
dog food, mixed fruits and vegetables, and a vitamin sup-
plement on completion of daily sessions. They were
housed individually in cages (86.4 cm high × 101 cm
wide × 76.5 cm deep) in a colony room maintained at a
constant temperature of 22 °C. The room was on a 12-hr
light-dark cycle with light onset at 7:00 a.m. The magpies
were tested in wooden chambers (61 cm wide × 31 cm
deep × 56 cm high), similar to those used to test other
avian species in previous research (Katz & Wright, 2006;
Magnotti, Katz, Wright, & Kelly, 2015; Wright & Katz,
2006; Wright, Magnotti, Katz, Leonard, & Kelly, 2016).
Stimuli were displayed on an LCD monitor and were vis-
ible through cutouts in a clear acrylic template that was
33 cm wide by 26 cm high. A house light (24-V, 0.04-A
bulb; Eiko, Shawnee, KS) was centered in the subject’s
portion of the chamber ceiling.
Stimuli were distinctive color “travel-slide” pictures,
the same training and transfer pictures that have been
used to test monkeys, pigeons, and Clark’s nutcrackers
(for full color displays of these training and testing stimuli
for set sizes 8, 16, 32, 64, 128, and 256, see Wright & Katz,
2006). The stimuli were sized so that the total display
(sample and comparison pictures and white rectangle)
was matched in visual angle to those used in our previ-
ous work with pigeons and nonhuman primates, approx-
imately 69° vertically and 73° horizontally as viewed from
a perch (14.5 cm from the screen, 15.9 cm from floor);
the center of the display set at the mean height of the
magpies’ eyes, 30.5 cm from the chamber floor.
All procedures were approved by the University of
Manitoba’s Animal Care Committee in accordance with
the Canadian Council on Animal Care.
Training and testing sessions were conducted 5 to 7
days a week during the light phase of the light-dark
cycle. Trials began with the presentation of a sample pic-
ture. After 20 responses to the sample picture (trained by
systematically increasing the number of responses from 1
to 20), the comparison picture was presented beneath
the sample picture, along with a white rectangle to the
right of the comparison picture (see Fig. 1a). A peck to
the comparison picture was correct when it matched the
sample picture. A peck to the white rectangle to the right
of the comparison picture was correct when the compari-
son picture did not match the sample picture. Correct
choice responses were reinforced with mealworms deliv-
ered below the monitor via a rotating wheel. Trials were
separated by a 15-s intertrial interval with the house light
The magpies were trained in 100-trial sessions (50
same trials and 50 different trials) until they achieved
a performance criterion of 85% (or greater) accuracy,
with a minimum of three training sessions. Immediate-
ly following acquisition, abstract-concept learning was
as-sessed in six consecutive transfer sessions, each con-
taining 90 baseline (training) trials composed of famil-
iar training stimuli and intermixed with 10 transfer
trials with novel stimuli (5 same trials, 5 different trials).
440 Wright et al.
Same Different
83264 128 256 512 1,024
83264128 256512 1,024
83264128 256512 1,024
Trial Types
Magpie Set-Size Function
Avian Comparison
Percentage Correct
Percentage Correct
Percentage Correct
Magpie-Primate Comparison
Training-Set Size
Training-Set SizeTraining-Set Size
Magpie Transfer
Magpie Baseline
Nutcracker Transfer
Nutcracker Baseline
Pigeon Transfer
Pigeon Baseline
Magpie Transfer
Magpie Baseline
Rhesus Transfer
Rhesus Baseline
Capuchin Transfer
Capuchin Baseline
Fig. 1. Examples of the stimuli used in the present study and comparison of results with those of previous studies discussed in the text. In
both same trials and different trials (a), two images were presented on a black background next to a white rectangle. The birds were trained
to peck the lower picture on same trials and the white rectangle on different trials. The graph in (b) shows the mean percentage of correct
responses from 10 magpies as a function of training-set size, separately for training (baseline) trials and novel (transfer) trials. The graphs in (c)
and (d) show the results for the magpies along with the results for other bird species and primates, respectively, for purposes of comparison.
Dashed colored lines indicate mean baseline, and solid colored lines indicate transfer. Dashed gray lines indicate chance-level performance.
Error bars represent ±1 SEM.
Abstract-Concept Learning and Brain Evolution 441
Performance on the 60 transfer trials (10 per session)
was the measure of abstract-concept learning. These
transfer trials appeared within the context of ongoing
baseline trials, which were included to maintain accu-
rate performance in the task while transfer and concept
learning were being assessed. Transfer stimuli were
never repeated within or across transfer sessions to
ensure novelty. Choice responses were reinforced iden-
tically on transfer and baseline trials to prevent nonrein-
forcement (i.e., extinction) from becoming associated
with the appearance of novel stimuli.
The first set-size expansion consisted of adding 8 new
training stimuli to the original 8-item picture training set
and training the magpies until a performance accuracy of
at least 85% correct was obtained (at least two sessions of
this first set-size expansion had a correction procedure;
for details, see Wright et al., 2016). The progressive cycle
of expanding (doubling) the set size, training for three
(or more) sessions, obtaining 85% correct or better, and
novel-stimulus transfer testing was repeated for six addi-
tional doublings of the training set (32, 64, 128, 256, 512,
and 1,024 set sizes).
Figure 1b shows the mean results for the 10 magpies for
the same task used to test nutcrackers, pigeons, and
monkeys. Baseline performance on training trials was
maintained at a high level ( 85% correct) throughout
set-size expansion. Abstract-concept learning, as mea-
sured by transfer of learning to novel picture pairs (67%
correct), was above chance performance (50% correct)
after initial learning with the initial 8-item training set and
increased regularly and monotonically with the training-
set-size expansions until transfer performance was indis-
tinguishable from baseline. To assess group-level transfer
performance, we performed a two-way repeated mea-
sures analysis of variance (ANOVA) on accuracy across
trial type (baseline or transfer) and set size (8, 32, 64, 128,
256, 512, or 1,024), which yielded a significant Trial Type ×
Set Size interaction, F(6, 54) = 19.4, p = 10−11, generalized
η2 = .30, as well as main effects of trial type, F(1, 9) =
42.9, p = .0001, generalized η2 = .15, and set size, F(6, 54) =
19.5, p = 10−11, generalized η2 = .38. The interaction was
caused by the difference between baseline and transfer
performance at early training-set sizes, but not at later
training-set sizes. Comparing baseline and transfer per-
formance within each set size using paired t tests showed
significant differences for set sizes of 8, 32, and 64 items,
all t(9)s > 2.9, ps < .02, but not at set sizes of 128, 256,
512, and 1,024 items, all t(9)s < 1.21, ps > .25.
Figure 1c shows performance of the magpies com-
pared with pigeons and nutcrackers previously tested in
this same task and discussed previously. The magpies
and nutcrackers were identical in their substantial trans-
fer after training with the initial 8-item picture set. Both of
these corvid species clearly outperformed pigeons. Com-
paring transfer accuracy of just the corvids, a two-way
repeated measures ANOVA with factors of set size and
species (nutcracker or magpie) yielded a significant effect
of set size, F(6, 90) = 37.3, p = 10−22, generalized η2 =
.58, but no main effect of species, F(1, 15) = 2.49, p = .14,
generalized η2 = .07, and no significant interaction, F(6,
90) = 0.6, p = .75, generalized η2 = .02. A similar ANOVA
comparing the performance of magpies and pigeons,
however, yielded significant main effects of set size, F(6,
72) = 46.0, p = 10−22, generalized η2 = .67, and species,
F(1, 12) = 22.5, p = .0005, generalized η2 = .47, and a
significant Set Size × Species interaction, F(6, 72) = 3.1,
p = .01, generalized η2 = .12. The interaction was caused
by an initial difference in transfer accuracy between mag-
pies and pigeons that gradually diminished as training-
set size increased.
Figure 1d shows performance of the magpies com-
pared with the primates’ performance discussed previ-
ously. The magpies outperformed the monkeys in their
transfer (partial concept learning) immediately following
training on the initial 8-item picture set. Because of the
small sample sizes for both monkey species, we com-
bined data from the rhesus and capuchin monkeys to
compare with the data from magpies. A two-way repeated
measures ANOVA on transfer accuracy with factors of set
size (8, 32, 64, or 128) and species (monkey or magpie)
yielded a significant Set Size × Species interaction, F(3,
42) = 3.8, p = .017, generalized η2 = .11, and a main effect
of set size, F(3, 42) = 71.0, p = 10−15, generalized η2 = .69,
but no main effect of species, F(1, 14) = 3.0, p = .11, gen-
eralized η2 = .11.
For direct comparisons of corvids and monkeys, we
combined the magpies and nutcrackers into one group
(n = 17) and compared their performance with the per-
formance of the two monkey species combined. A two-
way repeated measures ANOVA on transfer accuracy with
group (corvid or monkey) and set size (8, 32, 64, or 128)
as factors yielded a significant interaction between group
and set size, F(3, 63) = 5.0, p = .004, generalized η2 =
.09, and a significant main effect of set size, F(3, 63) =
85.2, p = 10−21, generalized η2 = .62, but no main effect of
group, F(1, 21) = 1.5, p = .24, generalized η2 = .04.
Other comparisons across these species included
learning rates, to determine whether they might be cor-
related with (and influence) transfer and concept learn-
ing. It is noteworthy that there were no substantial
differences in learning rate on the initial acquisition:
Magpies, on average, needed 3,500 trials (thirty-five 100-
trial sessions) to reach the performance criterion with the
initial set of 8 items; this learning rate was similar to the
average for the other species (nutcrackers: 3,300; rhesus
442 Wright et al.
monkeys: 4,000; capuchin monkeys: 3,500; pigeons:
3,000). As with the other four species tested, the magpies’
learning rates declined with increasing set sizes (the min-
imum number of trials was 300): 8 items: 3,500 trials; 16
items: 570 trials; 32 items: 480 trials; 64 items: 340 trials;
128 items: 320 trials; 256 items: 330 trials; 512 items: 340
trials; and 1,024 items: 300 trials (for the set size of 256
items, the mean rate was calculated for 9 birds because
of a computer-hardware glitch with 1 bird).
Taken together, these comparisons substantiate that
(a) mean training-set size required for the magpies to
attain full concept learning was equivalent to that
required by monkeys (and nutcrackers) but was smaller
than that required by pigeons, (b) the magpies (and nut-
crackers) outperformed the monkeys and pigeons on ini-
tial transfer (partial concept learning) with the small
8-item picture training set, and (c) the lack of learning-
rate differences suggest that learning rates were not a
factor in determining the abstract-concept learning differ-
ences noted earlier in the paragraph.
It appears that the magpies’ neural apparatus and predis-
position for relational and abstract-concept learning were
not hampered by their lesser predilection for caching
food compared with nutcrackers. Moreover, the results
from these two corvid species point to the possibility that
corvids generally might be able to fully learn a higher-
order abstract concept following exposure to a similar
number of concept exemplars (128-picture-set training)
as either Old World (rhesus monkeys) or New World
(capuchin monkeys) nonhuman primates. Such a conclu-
sion has startling implications. The modern lineages of
birds and mammals evolved from survivors of a cata-
strophic asteroid event (Cretaceous-Paleogene extinction
event) that wiped out all of the world’s big land animals
(e.g., big land-living dinosaurs) some 66 million years
ago (e.g., Alvarez, Alvarez, Asaro, & Michel, 1980; Schulte,
2010). Some small burrowing land animals survived, such
as small feathered dinosaurs and small furry mammals
(e.g., monotremes, marsupials, and placentals). Body
architectures, including brains, were and are very differ-
ent for birds and mammals. Mammals, particularly pri-
mates, evolved large brains (compared with body weight)
with folded neocortex, including the prefrontal cortex,
and elaborate temporal-lobe structures (e.g., hippocam-
pus plus adjoining parahippocampal cortex), key struc-
tures for primate relational processing, abstract-concept
learning, and episodic memory.
The “bird brain,” by contrast, has until recently been
thought to be primitive. Because most birds fly, light
weight is required (e.g., they have hollow and trussed
bones and no bladder). Nevertheless, many bird brains
(particularly corvid brains) are substantial in size and
weight compared with the birds’ body weight. Birds do
have a well-developed hippocampus (e.g., Gould et al.,
2013), but they have no six-layer neocortex, as do pri-
mates; birds have nodal or nuclear structures that may
have some advantages in shorter connectivity and speed
(e.g., Clayton & Emery, 2015). Many functions of the
mammalian prefrontal cortex have been found in birds’
brain structure called the caudolateral nidopallium (e.g.,
Emery, 2006; Güntürkün, 2005; Kirsch, Güntürkün, &
Rose, 2008), a brain structure with tightly packed, high-
density neurons (Olkowicz et al., 2016).
So, how did the apparently primitive bird brain that
evolved from dinosaurs become competitive with, and
even initially outperform, the abilities of what has been
considered a more elaborate primate brain to perform
abstract-concept learning, which involves thoughts and
processes considered to be of the highest cognitive order?
The answer most certainly lies in evolution itself, a multi-
million-year process. Environmental pressures (social and
otherwise) undoubtedly selected for and shaped these dif-
ferent neural architectures to successfully accomplish
many of the same essential and intelligent behaviors for
survival, an example of convergent evolution in which
organisms not closely related (i.e., not monophyletic)
independently evolved similar traits or functions as a result
of having to adapt to similar environments or ecological
niches. But the example of convergent evolution presented
in the current study is comparatively novel and unique
because its identification required special tests of the cog-
nitive ability (trait) for the cognitive function of fully learn-
ing a same/different abstract concept to be revealed. Other
examples of convergent evolution have been based on
some obvious physical trait, such as wings, which typically
can be identified from fossil records and have an obvious
function of flying (some insects, birds, and bats).
Action Editor
Kathleen McDermott served as action editor for this article.
Author Contributions
A. A. Wright, J. F. Magnotti, J. S. Katz, and D. M. Kelly contrib-
uted to the study concept and design. D. M. Kelly, A. Vernouillet,
and K. Leonard collected the data. J. F. Magnotti analyzed the
data and prepared the figures in collaboration with A. A. Wright.
A. A. Wright drafted the manuscript. All the authors have read
the manuscript, provided revisions of the manuscript, and
approved the final version for submission. All the authors are
responsible for the integrity of the research.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Abstract-Concept Learning and Brain Evolution 443
This research was supported by Natural Sciences and Engineer-
ing Research Council of Canada Grant RGPIN/312379-2009 (to
D. M. Kelly).
1. Although imprinted same/different behavior in newly hatched
ducklings is intriguing (Martinho & Kacelnik, 2016), it is unclear
to us how it might relate to the same/different abstract-concept
learning demonstrated and discussed here and supported by
converging evidence that our subjects were learning a bona
fide abstract concept.
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... Pigeons (Columba livia) were the next species to be tested with the task and reached full acquisition but at an even larger set size of 256 images, indicating a slower learning of the concept in comparison to the monkey species . Next, corvids (i.e., Clark's nutcrackers, Nucifraga columbiana and black-billed magpies, Pica hudsonia; Wright et al., 2016Wright et al., , 2017 were tested, as corvids have been shown to possess impressive cognitive abilities that are on par with those of primates (e.g., tool use, Bluff et al., 2007;mirror self-recognition, Clary & Kelly, 2016;Prior et al., 2008;future planning, Raby et al., 2007). No difference in same/ different concept learning was found between Clark's nutcrackers and black-billed magpies. ...
... The jays were tested in custom-built chambers (61 3 31 3 56 cm, width 3 depth 3 height, respectively), which were previously used to test magpies and nutcrackers (Magnotti et al., 2015;Wright et al., 2016Wright et al., , 2017. Stimuli were displayed on an LCD monitor and were visible through cut-outs in a clear acrylic template (33 3 26 cm, width 3 height). ...
... Pinyon jays and scrub jays add to the corvid species tested with this task, which includes Clark's nutcrackers (Magnotti et al., 2015;Wright et al., 2016) and black-billed magpies (Magnotti et al., 2015;Wright et al., 2017). Previous studies showed that, at the species-level, both nutcrackers and magpies showed partial transfer during the first set-size expansion (Magnotti et al., 2015). ...
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Abstract concepts require individuals to identify relationships between novel stimuli. Previous studies have reported that the ability to learn abstract concepts is found in a wide range of species. In regard to a same/different concept, Clark’s nutcrackers (Nucifraga columbiana) and black-billed magpies (Pica hudsonia), two corvid species, were shown to outperform other avian and primate species (Wright et al., 2017). Two additional corvid species, pinyon jays (Gymnorhinus cyanocephalus) and California scrub jays (Aphelocoma californica) chosen as they belong to a different clade than nutcrackers and magpies, were examined using the same set-size expansion procedure of the same/different task (the task used with nutcrackers and magpies) to evaluate whether this trait is common across the Corvidae lineage. During this task, concept learning is assessed with novel images after training. Results from the current study showed that when presented with novel stimuli after training with an 8-image set, discrimination accuracy did not differ significantly from chance for pinyon jays and California scrub jays, unlike the magpies and nutcrackers from previous studies that showed partial transfer at that stage. However, concept learning improved with each set-size expansion, and the jays reached full concept learning with a 128-image set. This performance is similar to the other corvids and monkeys tested, all of which outperform pigeons. Results from the current study show a qualitative similarity in full abstract-concept learning in all species tested with a quantitative difference in the set-size functions, highlighting the shared survival importance of mechanisms supporting abstract-concept learning for corvids and primates.
... This does not imply that no bird species has taken these steps. For this reason, further avian research-especially with corvids-is clearly indicated (see, for example, Pepperberg, 1990;Wright et al., 2017). Broschard, et al. (2019) showed equivalent RB and II performance by rats, suggesting the same theoretical conclusion. ...
Researchers have studied non-human primate cognition along different paths, including social cognition, planning and causal knowledge, spatial cognition and memory, and gestural communication, as well as comparative studies with humans. This volume describes how primate cognition is studied in labs, zoos, sanctuaries, and in the field, bringing together researchers examining similar issues in all of these settings and showing how each benefits from the others. Readers will discover how lab-based concepts play out in the real world of free primates. This book tackles pressing issues such as replicability, research ethics, and open science. With contributors from a broad range of comparative, cognitive, neuroscience, developmental, ecological, and ethological perspectives, the volume provides a state-of-the-art review pointing to new avenues for integrative research.
... Probably, the nests at which the birds frequently visited (Nest No. 1, 2, and 7) might have been built within their territories. Additionally, species belonging to the genus Garrulus in the family Corvidae generally have high learning capacities (Shaw et al. 2013;Shaw and Clayton 2014;Wright et al. 2017). As G. glandarius can remember the location of ant nests for a long time, they repeatedly visit the same ant nests. ...
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Anting behavior, the application of ants or ant substitutes to plumage has been reported in more than 200 bird species worldwide. The peculiar behavior is highly stereotyped into two forms: active and passive. However, there is a paucity of detailed descriptions and explanations of this adaptative behavior in the past studies. This is mainly due to the low number of the observations in most of the species that practice anting. In the forests of central Japan, we observed the behavior of birds visited seven nests of ant species, Lasius spathepus and L. nipponensis, belonging to the subgenus Dendrolasius. As a result of observation from 2018 to 2020, a total of 305 visits by 20 bird species were recorded, and 102 cases of anting by eight bird species were confirmed. Of the 102 cases, 96 (94.1%) were carried out by three species: Turdus cardis, Garrulus glandarius, and Emberiza cioides. Anting by the three species was observed at the two study sites, which are approximately 90 km apart, suggesting that they constantly interact with the two ant species. Although any one anting session could not be clearly classified as either completely passive or active, each bird species exhibited behavioral patterns dependent on body size. New behavioral patterns were also confirmed: stamping and plunging. Additionally, in months when temperature and humidity were higher, anting occurred more frequently. This is the first case in which the characteristics of the anting are analyzed based on quantitative data. The observational data support the anti-parasite hypothesis as an adaptive explanation of anting: birds ridding themselves of ectoparasites and bacteria infection.
... In the last decades, the cognitive abilities, including behavioural flexibility and motor inhibitory control of corvids, have been heavily studied [28,29]. For example, black-billed magpies, Pica hudsonia, performed similarly compared with different monkey species in a basic concept learning task [30] and jungle crows, Corvus macrorhynchos, learned to discriminate shapes and form concepts [31]. In a reversal learning task, New Caledonian crows, a tool-using corvid species and carrion crows, a closely related non-tool-using species, showed similar performance, suggesting that tool use is not causing enhanced learning performance in crows [32]. ...
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Cognitive abilities allow animals to navigate through complex, fluctuating environments. In the present study, we tested the performance of a captive group of eight crows, Corvus corone and 10 domestic chickens, Gallus gallus domesticus , in the cylinder task, as a test of motor inhibitory control and reversal learning as a measure of learning ability and behavioural flexibility. Four crows and nine chickens completed the cylinder task, eight crows and six chickens completed the reversal learning experiment. Crows performed better in the cylinder task compared with chickens. In the reversal learning experiment, species did not significantly differ in the number of trials until the learning criterion was reached. The performance in the reversal learning experiment did not correlate with performance in the cylinder task in chickens. Our results suggest crows to possess better motor inhibitory control compared with chickens. By contrast, learning performance in a reversal learning task did not differ between the species, indicating similar levels of behavioural flexibility. Interestingly, we describe notable individual differences in performance. We stress the importance not only to compare cognitive performance between species but also between individuals of the same species when investigating the evolution of cognitive skills.
... Examining these three species may help to resolve whether the mechanisms supporting same/different abstract-concept learning are similar across bird species, or are related to the relative reliance on food stores. Nine wild-caught Clark's nutcrackers (Nucifraga columbiana) and ten wild-caught black-billed magpies (Pica hudsonia) were tested for their same/different abstract-concept learning with procedures very similar to those used to test pigeons including the same: stimuli, stimulus pairs, sequences of stimulus pairs used in training and transfer testing, display size, 15-s inter-trial intervals, and required 20 pecks to the sample stimuli (Magnotti, Katz, Wright, & Kelly, 2015;Wright, Magnotti, Katz, Leonard, & Kelly, 2016;Wright, Magnotti, Katz, Leonard, Vernouillet, & Kelly, 2017). Nutcrackers and magpies made their pecks from a perch in front of the stimulus display. ...
Same/different abstract-concept learning experiments were conducted with two primate species and three avian species by progressively increasing the size of the training stimulus set of distinctly different pictures from eight to 1,024 pictures. These same/different learning experiments were trained with two pictures presented simultaneously. Transfer tests of same and different learning employed interspersed trials of novel pictures to assess the level of correct performance on the very first time of subjects had seen those pictures. All of the species eventually performed these tests with high accuracy, contradicting the long-accepted notion that nonhuman animals are unable to learn the concept of same/different. Capuchin and rhesus monkeys learned the concept more readily than did pigeons. Clark’s nutcrackers and black-billed magpies learned as readily as monkeys, and even showed a slight advantage with the smallest training stimulus sets. Those tests of same/different learning were followed by delay procedures, such that a delay was introduced after the subjects responded to the sample picture and before the test picture. In the sequential same/different task, accuracy was shown to diminish when the stimulus on a previous trial matched the test picture previously shown on a different trial. This effect is known as proactive interference. The pigeons’ proactive interference was greater at 10-s delays than 1-s delays, revealing time-based interference. By contrast, time delays had little or no effect on rhesus monkeys’ proactive interference, suggesting that rhesus monkeys have better explicit memory of where and when they saw the potential interfering picture, revealing better event-based memory.
Comparative analysis of higher cognitive abilities in animals provides for assessment of the evolutionary underpinnings of the formation of human thought and language. This review will address the main approaches to studies of thought in animals and analyze the data obtained using these approaches. The results of a diversity of tests indicate that animals with high levels of brain development have a wide spectrum of cognitive abilities. As expected, the widest spectrum is found in the great apes. A quite similar spectrum is found in higher members of the class Aves (corvids and psittacines) despite their different brain structure. Convergent similarity in the level of development of cognitive abilities in higher mammals and birds reflects the operation of common factors determining their evolution. Comparison of several corvid and psittacine species indicates that the high levels of development of their cognitive abilities are due to the high levels of organization of the brains of these species rather than ecological characteristics.
Corvids possess cognitive skills, matching those of non-human primates. However, how these species with their small brains achieve such feats remains elusive. Recent studies suggest that cognitive capabilities could be based on the total numbers of telencephalic neurons. Here we extend this hypothesis further and posit that especially high neuron counts in associative pallial areas drive flexible, complex cognition. If true, avian species like corvids should specifically accumulate neurons in the avian associative areas meso- and nidopallium. To test the hypothesis, we analyzed the neuronal composition of telencephalic areas in corvids and non-corvids (chicken, pigeons, and ostriches - the species with the largest bird brain). The overall number of pallial neurons in corvids was much higher than in chicken and pigeons and comparable to those of ostriches. However, neuron numbers in the associative mesopallium and nidopallium were twice as high in corvids and, in correlation with these associative areas, the corvid subpallium also contained high neuron numbers. These findings support our hypothesis that large absolute numbers of associative pallial neurons contribute to cognitive flexibility and complexity and are key to explain why crows are smart. Since meso/nidopallial and subpallial areas scale jointly, it is conceivable that associative pallio-striatal loops play a similar role in executive decision-making as described in primates. This article is protected by copyright. All rights reserved
Cognition is likely to have fundamental effects on the behaviour and fitness of animals, but measuring cognitive abilities can be a challenge because performance on cognitive tests can be influenced by ‘noncognitive’ factors. Parasitic infections, for example, could lead to temporary changes in the ability or motivation of an individual to participate in cognitive tasks, thereby altering cognitive performance. Here, we examine the relationship between problem-solving performance of American crows, Corvus brachyrhynchos, in a string-pulling task and their infection status with Campylobacter jejuni, a common bacterial pathogen that is taxonomically and geographically widespread in birds. Sixty-five per cent of the 34 test subjects solved the string-pulling task and temporal repeatability of individual cognitive performance across trials was high. Birds infected with Campylobacter (constituting 62% of the 21 birds tested for the pathogen) were significantly less likely to solve the string-pulling task. This difference appeared to be due to both a willingness to attempt the task and an ability to solve it: all uninfected birds attempted and succeeded in the task, whereas only seven of the 13 infected birds attempted the task, three of which failed. Overall, less active birds were less likely to solve cognitive tests, but infected birds were not generally less active than uninfected birds, suggesting that lethargy did not mediate the relationship between infection and cognitive performance. These data indicate that transient changes in health, mediated by parasites like Campylobacter, could contribute to the failure of some individuals to participate in or solve cognitive tests, although it is unclear whether impaired problem-solving performance is due to an overall lack of interest in food or an unwillingness or inability to engage in cognitively challenging tasks when infected. If the latter, impaired cognitive performance could constitute a subtle, indirect cost of infection that exacerbates the direct physiological consequences of parasites like Campylobacter.
The abstract concept of sameness forms the basis of higher-order cognitive operations, including mathematics and language. Historically believed to be unique to humans, evidence of abstract-concept learning in recent decades has been demonstrated in a range of phylogenetically diverse species, indicating that the ability to judge sameness relations is a general process resulting from convergent evolution. However, to date, no research has demonstrated evidence of such learning in any canid species. We trained domestic dogs (n = 6) on a two-choice olfactory matching-to-sample task using a training set of 48 odors in trial-unique sessions. Upon meeting an acquisition criterion (two consecutive sessions ≥ 83% correct), we assessed abstract-concept learning by testing for transfer to novel odors. Dogs matched novel odors with above-chance accuracy and exceeded baseline levels, satisfying previously proposed criteria for full abstract-concept learning. Our findings provide the first evidence of MTS concept learning in dogs, illustrating qualitative similarities with other species. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Can nonhuman animals use an abstract sense of sameness to perceive relations-between-relations? This question has been studied over the last 40 years; yet, the extent to which nonhuman species can do so is still debated. Here, we review evidence suggesting that crows and parrots can acquire an abstract sameness rule after mastering a series of highly varied identity matching-to-sample (IMTS) tasks and later spontaneously apply this rule to perform relational matching-to-sample (RMTS) tasks. Such spontaneously successful performance on RMTS tasks may critically depend on the nature of an organism’s prior IMTS learning experience.
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The traditional 'mark test' has shown some large-brained species are capable of mirror self-recognition. During this test a mark is inconspicuously placed on an animal's body where it can only be seen with the aid of a mirror. If the animal increases the number of actions directed to the mark region when presented with a mirror, the animal is presumed to have recognized the mirror image as its reflection. However, the pass/fail nature of the mark test presupposes self-recognition exists in entirety or not at all. We developed a novel mirror-recognition task, to supplement the mark test, which revealed gradation in the self-recognition of Clark's nutcrackers, a large-brained corvid. To do so, nutcrackers cached food alone, observed by another nutcracker, or with a regular or blurry mirror. The nutcrackers suppressed caching with a regular mirror, a behavioural response to prevent cache theft by conspecifics, but did not suppress caching with a blurry mirror. Likewise, during the mark test, most nutcrackers made more self-directed actions to the mark with a blurry mirror than a regular mirror. Both results suggest self-recognition was more readily achieved with the blurry mirror and that self-recognition may be more broadly present among animals than currently thought.
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The ability to identify and retain logical relations between stimuli and apply them to novel stimuli is known as relational concept learning. This has been demonstrated in a few animal species after extensive reinforcement training, and it reveals the brain’s ability to deal with abstract properties. Here we describe relational concept learning in newborn ducklings without reinforced training. Newly hatched domesticated mallards that were briefly exposed to a pair of objects that were either the same or different in shape or color later preferred to follow pairs of new objects exhibiting the imprinted relation. Thus, even in a seemingly rigid and very rapid form of learning such as filial imprinting, the brain operates with abstract conceptual reasoning, a faculty often assumed to be reserved to highly intelligent organisms.
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Significance Birds are remarkably intelligent, although their brains are small. Corvids and some parrots are capable of cognitive feats comparable to those of great apes. How do birds achieve impressive cognitive prowess with walnut-sized brains? We investigated the cellular composition of the brains of 28 avian species, uncovering a straightforward solution to the puzzle: brains of songbirds and parrots contain very large numbers of neurons, at neuronal densities considerably exceeding those found in mammals. Because these “extra” neurons are predominantly located in the forebrain, large parrots and corvids have the same or greater forebrain neuron counts as monkeys with much larger brains. Avian brains thus have the potential to provide much higher “cognitive power” per unit mass than do mammalian brains.
How do children acquire the vast array of concepts, strategies, and skills that distinguish the thinking of infants and toddlers from that of preschoolers, older children, and adolescents? In this new book, Robert Siegler addresses these and other fundamental questions about children's thinking. Previous theories have tended to depict cognitive development much like a staircase. At an early age, children think in one way; as they get older, they step up to increasingly higher ways of thinking. Siegler proposes that viewing the development within an evolutionary framework is more useful than a staircase model. The evolution of species depends on mechanisms for generating variability, for choosing adaptively among the variants, and for preserving the lessons of past experience so that successful variants become increasingly prevalent. The development of children's thinking appears to depend on mechanisms to fulfill these same functions. Siegler's theory is consistent with a great deal of evidence. It unifies phenomena from such areas as problem solving, reasoning, and memory, and reveals commonalities in the thinking of people of all ages. Most important, it leads to valuable insights regarding a basic question about children's thinking asked by cognitive, developmental, and educational psychologists: How does change occur?
Abstract relational concepts depend upon relationships between stimuli (e.g., same vs. different) and transcend features of the training stimuli. Recent evidence shows that learning abstract concepts is shared across a variety species including birds. Our recent work with a highly-skilled food-storing bird, Clark’s nutcracker, revealed superior same/different abstract-concept learning compared to rhesus monkeys, capuchin monkeys, and pigeons. Here we test a more social, but less reliant on food-storing, corvid species, the Black-billed magpie (Pica hudsonia). We used the same procedures and training exemplars (eight pairs of the same rule, and 56 pairs of the different rule) as were used to test the other species. Magpies (n = 10) showed a level of abstract-concept learning that was equivalent to nutcrackers and greater than the primates and pigeons tested with these same exemplars. These findings suggest that superior initial abstract-concept learning abilities may be shared across corvids generally, rather than confined to those strongly reliant on spatial memory.
Primate Cognition is the study of cognitive processes, which represent internal mental processes involved in discriminations, decisions, and behaviors of humans and other primate species. Cognitive control involves executive and regulatory processes that allocate attention, manipulate and evaluate available information (and, when necessary, seek additional information), remember past experiences to plan future behaviors, and deal with distraction and impulsivity when they are threats to goal achievement. Areas of research that relate to cognitive control as it is assessed across species include executive attention, episodic memory, prospective memory, metacognition, and self-control. Executive attention refers to the ability to control what sensory stimuli one attends to and how one regulates responses to those stimuli, especially in cases of conflict. Episodic memory refers to memory for personally experienced, autobiographical events. Prospective memory refers to the formation and implementation of future-intended actions, such as remembering what needs to be done later. Metacognition consists of control and monitoring processes that allow individuals to assess what information they have and what information they still need, and then if necessary to seek information. Self-control is a regulatory process whereby individuals forego more immediate or easier to obtain rewards for more delayed or harder to obtain rewards that are objectively more valuable. The behavioral complexity shown by nonhuman primates when given tests to assess these capacities indicates psychological continuities with human cognitive control capacities. However, more research is needed to clarify the proper interpretation of these behaviors with regard to possible cognitive constructs that may underlie such behaviors. For further resources related to this article, please visit the WIREs website.
Three rhesus monkeys (Macaca mulatta) were tested in a same/different memory task for proactive interference (PI) from prior trials. PI occurs when a previous sample stimulus appears as a test stimulus on a later trial, does not match the current sample stimulus, and the wrong response "same" is made. Trial-unique pictures (scenes, objects, animals, etc.) were used on most trials, except on trials where the test stimulus matched potentially interfering sample stimulus from a prior trial (1, 2, 4, 8, or 16 trials prior). Greater interference occurred when fewer trials separated interference and test. PI functions showed a continuum of interference. Delays between sample and test stimuli and intertrial intervals were manipulated to test how PI might vary as a function of elapsed time. Contrary to a similar study with pigeons, these time manipulations had no discernable effect on the monkey's PI, as shown by compete overlap of PI functions with no statistical differences or interactions. These results suggested that interference was strictly based upon the number of intervening events (trials with other pictures) without regard to elapsed time. The monkeys' apparent event-based interference was further supported by retesting with a novel set of 1,024 pictures. PI from novel pictures 1 or 2 trials prior was greater than from familiar pictures, a familiar set of 1,024 pictures. Moreover, when potentially interfering novel stimuli were 16 trials prior, performance accuracy was actually greater than accuracy on baseline trials (no interference), suggesting that remembering stimuli from 16 trials prior was a cue that this stimulus was not the sample stimulus on the current trial-a somewhat surprising conclusion particularly given monkeys.
Relational reasoning is a hallmark of sophisticated cognition in humans [1, 2]. Does it exist in other primates? Despite some affirmative answers [3-11], there appears to be a wide gap in relational ability between humans and other primates-even other apes [1, 2]. Here, we test one possible explanation for this gap, motivated by developmental research showing that young humans often fail at relational reasoning tasks because they focus on objects instead of relations [12-14]. When asked, "duck:duckling is like tiger:?," preschool children choose another duckling (object match) rather than a cub. If other apes share this focus on concrete objects, it could undermine their relational reasoning in similar ways. To test this, we compared great apes and 3-year-old humans' relational reasoning on the same spatial mapping task, with and without competing object matches. Without competing object matches, both children and Pan species (chimpanzees and bonobos) spontaneously used relational similarity, albeit children more so. But when object matches were present, only children responded strongly to them. We conclude that the relational gap is not due to great apes' preference for concrete objects. In fact, young humans show greater object focus than nonhuman apes.
Same/Different abstract-concept learning by Clark's nutcrackers (Nucifraga columbiana) was tested with novel stimuli following learning of training set expansion (8, 16, 32, 64, 128, 256, 512, and 1024 picture items). The resulting set-size function was compared to those from rhesus monkeys (Macaca mulatta), capuchin monkeys (Cebus apella), and pigeons (Columba livia). Nutcrackers showed partial concept learning following initial eight-item set learning, unlike the other species (Magnotti, Katz, Wright, & Kelly, 2015). The mean function for the nutcrackers' novel-stimulus transfer increased linearly as a function of the logarithm of training set size, which intersected its baseline function at the 128-item set size. Thus, nutcrackers on average achieved full concept learning (i.e., transfer statistically equivalent to baseline performance) somewhere between set sizes of 64 to 128 items, similar to full concept learning by monkeys. Pigeons required a somewhat larger training set (256 items) for full concept learning, but results from other experiments (initial training and transfer with 32- and 64-item set sizes) suggested carryover effects with smaller set sizes may have artificially prolonged the pigeon's full concept learning. We find it remarkable that these diverse species with very different neural architectures can fully learn this same/different abstract concept, and (at least under some conditions) do so with roughly similar sets sizes (64-128 items) and numbers of training exemplars, despite initial concept learning advantages (nutcrackers), learning disadvantages (pigeons), or increasing baselines (monkeys).