Behavioural Processes 85 (2010) 246–251
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Functional relationships for determining similarities and differences in
Anthony A. Wright
Department of Neurobiology and Anatomy, Medical School, University of Texas Health Center at Houston, P.O. Box 20708, Houston, TX 77225, United States
Received 6 May 2010
Received in revised form 22 July 2010
Accepted 30 July 2010
Pigeons, capuchin monkeys and rhesus monkeys were trained in nearly identical same/different tasks
with an expanding 8-item training set and showed qualitatively similar functional relationships: increas-
ing novel-stimulus transfer (i.e., concept learning) as a function of the training-set size and the level of
transfer eventually becoming equivalent to baseline training performance. There were also some quan-
titative functional differences: pigeon transfer increases were more gradual and baseline-equivalent
transfer occurred at a larger set size (256 items) than for monkeys (128 items). Other pigeon groups
trained at 32 and 64-item initial set sizes showed improved transfer (relative to expanding the 8-item
training set), equivalent to the monkey species’ transfer at these same training set sizes. This ﬁnding
of equivalent concept learning over a portion of the functional range (8, 32, and 64 items or 64–4096
training pairs) is discussed in terms of species differences: carryover effects from smaller-set training,
evolved neural systems, cognitive and cortical modules, and general distributed learning systems for
“higher-order” cognitive abilities.
© 2010 Elsevier B.V. All rights reserved.
Recent articles have rekindled the debate about which human
cognitive abilities (e.g., tool use, teaching, social competence,
theory of mind, abstract-concept learning, analogical reasoning,
domain-general cognition) are unique and distinguish humans
from other animals (Herrmann et al., 2007; Penn et al., 2008;
Premack, 2007, 2010).
Proposals of special cognitive abilities are not limited to humans.
Comparative cognition has a long history of focusing on which
species can or cannot learn certain cognitive tasks. Hierarchies of
cognitive ability have been constructed on the basis of such learning
differences often with abstract-concept learning or analogical rea-
soning at the top (e.g., D’Amato et al., 1985; Herman et al., 1989;
Herrnstein, 1990; Premack, 1978, 1983a,b; Thomas, 1980, 1996;
Thompson, 1995; Thompson and Oden, 2000). For the most part,
the concentration is on which species has what cognitive ability
and which species does not (cf., Tomasello and Call, 1997). Many
tests are all-or-none; the species learns or not, transfers or not,
etc. Learning and (transfer) performance can fail in many more
ways than it can succeed; such failures, including partial failures,
are difﬁcult to interpret (e.g., Macphail, 1985, 1996). May be the
right experiment just was not conducted for the task to be learned
or transfer performance to be equivalent to the baseline training
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The approach taken in this article is that functional relation-
ships can better identify similarities and differences in cognitive
ability. Functional relationships are measures of cognitive behavior
at several points along a continuum of a critical variable. Functional
relationships can identify aspects that are similar and aspects that
are different. Tests at only a single value (on the continuum) are
more likely to discover differences with little hope of discovering
qualitative similarity between functional relationships because lots
of variables can affect the absolute performance level.
Similarities and differences in cognitive abilities may be divided
into two general categories: qualitative similarity and quantita-
tive similarity. From the standpoint of how some cognitive ability
works, qualitative similarity is perhaps the more important. Nev-
ertheless, I will argue that functional relationships are necessary to
show either. Take the example of visual list memory across species
as diverse as pigeons, new and old world monkeys, and humans.
All of these species showed similar changes in their serial position
functions (SPFs) as the retention delay is increased (e.g., see Wright,
1998, 2007; Wright et al., 1985). The SPFs showed strong recency
effects (last item memory) at short retention delays. The primacy
effect (good ﬁrst item memory) came in at intermediate delays and
strengthens as delay is extended. The recency effect waned as the
primacy effect strengthened. These similar SPFs showed qualitative
similarity in visual list-memory processing across these species.
Notwithstanding the qualitative similarity, there were some quan-
titative differences. For example, the time scale of these changes
varied across species with these changes taking place most rapidly
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