Baby Wordsmith From Associationist to Social Sophisticate Roberta Michnick Golinkoff1 and Kathy Hirsh-Pasek2

1 University of Delaware and

2 Temple University

ABSTRACT—How do infants acquire their first words? Word

reference, or how words map onto objects and events, lies

at the core of this question. The emergentist coalition model

(ECM) represents a new wave of hybrid developmental

theories suggesting that the process of vocabulary devel-

opment changes from one based in perceptual salience and

association to one embedded in social understanding. Be-

ginning at 10 months, babies learn words associatively,

ignoring the speaker’s social cues and using perceptual

salience to guide them. By 12 months, babies attend to so-

cial cues, but fail to recruit them for word learning. By 18

and 24 months, babies recruit speakers’ social cues to learn

the names of particular objects speakers label, regardless

of those objects’ perceptual attraction. Controversies about

how to account for the changing character of word ac-

quisition, along with the roots of children’s increasing re-

liance on speakers’ social intent, are discussed.

KEYWORDS—word learning; language development

There is power in language. It can start wars or ruin marriages.

Readers of these words barely remember a time when they did

not have language. But every word you know had to be learned.

Imagine bending over your car engine with your mechanic and

being told, ‘‘Your zorch is shot.’’ You follow your mechanic’s eyes

and body orientation to the part he is examining. That rusty metal

protrusion must be the zorch. How do we learn the mapping

between words and the objects and events they represent?

THE WORD-LEARNING PROBLEM

Establishing a Word’s Referent: Perceptual, Social, and

Linguistic Cues

Infants are motivated to learn names for the same reason that

adults are: Knowing what to call something allows one to share

the contents of one’s mind with another person (Bloom & Tinker,

2001), even when the object is not present. Indeed, a great

deal is known about the course of word learning. At 10 months,

babies have an average comprehension vocabulary of 50 words,

saying virtually nothing. By 30 months, average production

vocabulary soars to 550 words (see Table 1; Fenson, Dale,

Reznick, Bates, Thal, & Pethick, 1994), and children speak in

full sentences.

Describing vocabulary growth, however, is only a first step

toward unpacking the mechanisms behind word learning. How

do words get ‘‘hooked’’ to objects and events? How do we (or

children) learn that zorch refers to that whole rusty protrusion

rather than to the object’s color or size? Any object presents an

array of possible referents, a problem Quine (1960) called the

indeterminacy of reference. A number of diverse theories have

arisen to explain how children solve this problem.

One theory is that children approach the word-learning

problem with a set of constraints or principles biasing them to

entertain certain hypotheses about word reference over others.

For example, children seem to attach names to whole objects

rather than to parts (Markman, 1989; Golinkoff, Mervis, &

Hirsh-Pasek, 1994).

A second theory dismisses Quine’s conundrum, claiming that

children map words onto the most salient objects or actions in the

environment. Early word learning is but word–object associa-

tions (learned links) between noticeable (moving, brightly-

colored) objects and concurrent sound sequences (words).

Finally, a third solution suggested by the family of social-

pragmatic theories proposes that infants are attuned to the social

cues speakers offer when labeling objects. Eighteen-month-

olds, for example, only learn novel words when it is clear that

objects are being labeled for their benefit (Baldwin, Markman,

Bill, Desjardins, Irwin, & Tidball, 1996). If a speaker is on the

telephone, toddlers resist learning a novel name for an object in

front of them, even if the name is uttered with great excitement.

This ‘‘failure’’ is adaptive: Fully 50% of parents’ talk is not about

the child’s focus of attention (Baldwin et al., 1996). To learn

words, children must note more than just the temporal contiguity

between a verbal label and an object they are attending to.

Although the description of these theories is a bit of a cari-

cature, each family of theories emphasizes only a part of the

Address correspondence to Roberta M. Golinkoff, School of Educa- tion, University of Delaware, Newark, DE 19716; e-mail: roberta@ udel.edu.

C U R R E N T D I R E C T I O N S I N P S Y C H O L O G I C A L S C I E N C E

30 Volume 15—Number 1Copyright r 2006 Association for Psychological Science

word-learning process and appeals to one causal mechanism

as paramount. There have been a number of calls for hybrid

theories that recognize the complexity of word acquisition and

integrate diverse inputs (e.g., Waxman & Lidz, 2006). The

emergentist coalition model (ECM; Hollich et al., 2000) offers

one example that has yielded fruit.

Tracing the Changing Process of Word Learning

The ECM recasts the issue of word learning by asking which

components of which theories govern word learning at different

phases of development; rather than providing an overall snap-

shot, it tracks changing strategies over time. Progress has been

made in testing this more complex account (e.g., Hollich et al.,

2000); in fact, the ECM is currently the only hybrid model that

has been empirically evaluated. Here we present evidence il-

lustrating how complex models of word learning can be put to the

test. By examining infants’ shifting use of associative and social

strategies across time, we offer a glimpse of evidence for a piece

of the ECM.

Three Fundamental Tenets of the ECM

The model has three basic tenets. First, children are sensitive to

multiple cues in word learning: perceptual, social, and linguistic

(see Fig. 1). Second, word-learning cues change their relative

importance over time. Although a range of cues in the coalition is

always available, not all cues are equally utilized in the service

of word learning. Children beginning to learn words rely on a

perceptual subset of the available cues in the coalition. Only

later do they recruit social cues like other people’s eye gaze and

handling of objects to learn words.

Third, the principles of word learning are emergent, changing

over time. Infants may start with an immature principle of ref-

erence, such that a word will be mapped to the most salient

object from the infant’s point of view. Later, children sensitive to

speaker intent map a word onto an object from the speaker’s

point of view.

CHANGING PROCESSES IN WORD LEARNING:

THE EVIDENCE

To investigate transformations in the word-learning process, a

method was needed that could measure comprehension and that

could succeed with children between 10 and 24 months. Com-

prehension provides a sensitive index of word-learning compe-

tence not restricted by factors that may limit language

production, such as articulatory control or motivation to talk.

Further, the method must permit the putative cues involved in

word learning to be placed in competition so that infants’ relative

reliance on these cues can be gauged. Hollich et al. (2000)

created just such a method using infant visual fixation on target

objects as the dependent variable. Babies saw two objects, one

interesting (more salient) and one boring (colorless and mo-

tionless). Standing between the objects, which were placed on a

table out of the infant’s reach, a speaker verbally labeled either

the interesting or boring object and used social cues like eye

gaze and sometimes handling to indicate which object was being

labeled. The method ensured that children were learning words

and not just examining interesting objects. After word training

and a test to see if children learned the name of the target object,

another new, deliberately ambiguous label was introduced that

the child had not previously heard. This ‘‘new label’’ trial tested

whether children would continue to stare at the named, target

object even in the presence of another name or would look away

or look at the alternative object upon hearing a new name. In a

final ‘‘recovery trial,’’ infants were asked to look again at the

original object.

These different kinds of test trials in combination constitute

a powerful test of word learning. If children are operating at

the associative level, failing to use social cues, they should

simply attach the label to the object they find most interesting.

TABLE 1

Median Number of Words (and Ranges) in the Comprehension

and Production Vocabularies of Children at Different Ages,

According to Parental Report From the MacArthur

Communicative Development Inventory (CDI)

Age (months)

Comprehensionn Production

Median Range Median Range

10 42 11–154 2 0–10

12 74 31–205 6 2–30

18 – – 75 14–220

24 – – 308 56–520

30 – – 555 360–630

Note. From ages 18 to 30 months the CDI does not include comprehension vocabulary. This table is adapted from figures in Fenson, Dale, Reznick, Bates, Thal, & Pethick (1994).

Fig. 1. The coalition of cues available for establishing word reference and utilized differently across developmental time. Children shift from Phase I, a reliance on attentional cues such as how compelling an object is (perceptual salience) and the coincident appearance of an object and a label (temporal contiguity), to Phase II, a greater dependency on social and linguistic cues like eye gaze and grammar. By 12 months, dependence on Phase I cues has begun to wane and shift to the social cues in Phase II.

Volume 15—Number 1 31

Roberta Michnick Golinkoff and Kathy Hirsh-Pasek

Alternatively, children sensitive to social cues should learn the

name for the object that the speaker labels, even if it is boring.

Hollich et al. (2000) found that, by 24 months, children

convincingly used social information, learning the names for

both the interesting and boring objects. Nineteen-month-olds

were still attracted to perceptual cues even though they could

use social information to learn the label for the boring object.

Twelve-month-olds showed an entirely different pattern. So-

cial information was necessary, but not sufficient, to ensure word

learning. They only learned the novel word when social and

perceptual cues were ‘‘in alignment,’’ or when children heard the

speaker label the interesting object. They failed to learn a word

when the speaker labeled the boring object. Had 12-month-olds

been pure associationists, they should have mismapped the

word, thinking that the novel word labeled the interesting object

regardless of speaker cues. The fact that they did not do this

suggests they detected the speaker’s social cues.

Is there ever a time in word acquisition when children mismap

labels, relying totally on the use of perceptual salience? Pruden,

Hirsh-Pasek, Golinkoff, and Hennon (in press) found that, un-

like their older peers, 10-month-olds were pure associationists,

mapping a novel word onto the object that they found the most

interesting, regardless of which object the speaker labeled. Ten-

month-olds acted as if social cues to reference did not exist.

These data suggest that the processes infants use for word

learning change over time. Beginning as associationists, chil-

dren move to attending to social cues, and then to recruiting the

speaker’s social cues to decide which object is being named.

WHAT CAUSES DEVELOPMENTAL CHANGE?

The discovery that word-learning processes change across the

first 2 years of life raises additional questions. How does the

perceptually driven 10-month-old become the socially aware

19-month-old? One interesting possibility is that around the end

of the first year, infants come to recognize people as intentional

beings who have goals, act autonomously, and act rationally (e.g.,

Gergely & Csibra, 2003). Once infants understand other beings

as having minds and intentions distinct from their own, they can

recognize the relevance of those intentions for word learning.

Noting speaker intent allows infants to tap into the lexicons of

accomplished word learners so that they might add to their store

of vocabulary items.

There is, however, another account of the shift to the use of

social cues. As Perner and Ruffman (2005) argued, what appears

to be sensitivity to social intent may be the ability to form an

association between speaker gaze and speaker talk. With word-

learning experience, children may note that people generally

look at things they talk about. This may lead children to begin to

use social cues like eye gaze for word mapping even before they

understand the social intent behind the use of those cues. Even

this more restrictive social sensitivity would confer advantages

to learners. Once children restrict word-to-world mappings to

those objects that adults look at, they have narrowed the range of

word referents. Of course, this restricted range still leaves many

alternative referents for a novel label (e.g., the shape, color, or size

of the object). Here, principles or constraints such as ‘‘pay atten-

tion to shape’’ (Smith, 2000) or ‘‘label the whole object’’ (Mark-

man, 1989) may also help children narrow their referent choices.

A less restrictive social account that assumes children have

access to speaker intent allows for more rapid word learning.

Once children can make inferences about what the speaker in-

tends to label, they can learn words incidentally, from conver-

sation. Thus, the use of social information under either account

enhances word learning. Whether the use of social information is

seen as accessing a speaker’s intent or as a more restricted as-

sociation of words with social cues, one can begin to explain why

early word learning (at least production) is so slow (1 to 2 words

per week) relative to the fast-paced learning that occurs around

19 months of age, when children use social information.

Is it ever possible to distinguish between the use of associative

cues versus social intent? Finding definitive evidence to dis-

confirm reductionist views and affirm accounts that impute more

sophisticated capabilities to children is not a simple problem.

One preliminary way is to pit associative and social cues against

each other, as is done in the ECM framework (Hollich et al.,

2000). Another is to examine corollary findings suggesting

children’s sensitivity to social intent. That research is abundant.

By 18 months, for example, children complete a task on which an

adult has feigned failure. Successful completion depends on

inferring adult intention (Meltzoff, 1995). Finally, studies

involving autistic children allow us to separate the effect of

attention to social cues from that of interpreting the intention

behind them. Since autistic children appear not to have the

ability to detect social intent, they learn words associatively

(Hennon, 2002; Priessler & Carey, 2005). Perhaps autistic

children’s vocabularies fail to grow at a rapid rate for this reason.

Tests of hybrid models of word learning speak to much larger

issues within developmental psychology. Word learning is de-

ceptively simple, calling upon a range of processes that seem to

take on different values for the learner over time. The ECM

provides a window onto cognitive complexity and onto the var-

ious routes by which word learning can occur. In this way, the

ECM parallels current trends in the exploration of social de-

velopment that take multiple levels of influence into account. In

the study of cognitive development, this is rare. Understanding

the various pathways to word learning has implications for

crafting interventions that are targeted to the processes children

actually use at different ages to learn words.

Considering word learning from this vantage point might also

inform the controversy about how and when children develop an

understanding of other minds (e.g., Perner & Ruffman, 2005).

That is, children must come to appreciate that others have

thoughts, feelings, and perceptions different from their own. In

communication, this translates into the very motivation for

learning language: Wanting to share what one knows with

32 Volume 15—Number 1

Baby Wordsmith

someone who does not yet know it (Bloom & Tinker, 2001).

Concurrently, children should pay special attention to social

cues used in the context of language, for these cues provide a

conduit to what is on the mind of another person. In this sense,

the study of word learning offers yet another inroad into the study

of how children conceptualize other minds.

Since Plato’s time, scholars have discussed how words map

onto the world. We have witnessed unparalleled progress in

understanding both the course of word learning and the mech-

anisms fueling that development. The birth of words is a psy-

chological watershed in language acquisition. The research

reviewed here suggests that competing theories are best united

under a hybrid view that incorporates changing mechanisms of

development. Our next task is to understand how children who

begin as associationists become social sophisticates.

Recommended Reading Golinkoff, R.M., Hirsh-Pasek, K., Bloom, L., Smith, L., Woodward, A.,

Akhtar, N. Tomasello, M., & Hollich, G. (Eds.) (2000). Becoming a word learner: A debate on lexical acquisition. New York: Oxford University Press.

Hollich, G.J., Hirsh-Pasek, K., Golinkoff, R.M., Brand, R.J., Brown, E.,

Chung, H.L., Hennon, E., & Rocroi, C. (2000). (See References)

Waxman, S.R. & Lidz, J. (2006). (See References)

Acknowledgments—This research was supported by Grants

SBR9601306 and SBR9615391 to both authors from the Na-

tional Science Foundation.

REFERENCES

Baldwin, D.A., Markman, E.M., Bill, B., Desjardins, N., Irwin, J.M., &

Tidball, G. (1996). Infants’ reliance on a social criterion for

establishing word–object relations. Child Development, 67, 3135–3153.

Bloom, L., & Tinker, E. (2001). The intentionality model and language

acquisition: Engagement, effort, and the essential tension. Mon-

ographs of the Society for Research in Child Development, 66(Serial No. 267).

Fenson, L., Dale, P., Reznick, S., Bates, E., Thal, D., & Pethick, S.

(1994). Variability in early communicative development. Mono- graphs of the Society for Research in Child Development, 59(Serial No. 242).

Gergely, G., & Csibra, G. (2003). Teleological reasoning in infancy: The

naı̈ve theory of rational action. Trends in Cognitive Sciences, 7, 287–292.

Golinkoff, R.M., Mervis, C.V., & Hirsh-Pasek, K. (1994). Early object

labels: The case for a developmental lexical principles framework.

Journal of Child Language, 21, 125–155.

Hennon, E.A. (2002). How children with autistic disorder use attentional and intentional social information for word learning. Unpublished doctoral dissertation, Temple University.

Hollich, G.J., Hirsh-Pasek, K., Golinkoff, R.M., Brand, R.J., Brown, E.,

Chung, H.L., Hennon, E., & Rocroi, C. (2000). Breaking the lan-

guage barrier: An emergentist coalition model for the origins of

word learning. Monographs of the Society for Research in Child Development, 65(3, Serial No. 262).

Markman, E.M. (1989). Categorization and naming in children: Prob- lems of induction. Cambridge, MA: The MIT Press.

Meltzoff, A.N. (1995). Understanding the intentions of others: Re-

enactment of intended acts by 18-month-old children. Develop- mental Psychology, 31, 838–850.

Perner, J., & Ruffman, T. (2005). Infants’ insight into the mind: How

deep? Science, 308, 214–216.

Preissler, M.A., & Carey, S. (2005). The role of inferences about ref-

erential intent in word learning: Evidence from autism. Cognition, 97, 813–823.

Pruden, S.M., Hirsh-Pasek, K., Golinkoff, R.M., & Hennon, E.A. (in

press). The birth of words: Ten-month-olds learn words through

perceptual salience. Child Development.

Quine, W.V.Q. (1960). Word and object. Cambridge, England: Cam- bridge University Press.

Smith, L. (2000). Learning how to learn words: An associative crane. In.

R.M. Golinkoff, K. Hirsh-Pasek, L. Bloom, L.B. Smith, A.L.

Woodard, N. Akhtar, M. Tomasello, & G. Hollich (Eds.), Becoming a word learner: A debate on lexical acquisition (pp. 51–80). New York: Oxford University Press.

Waxman, S.R. & Lidz, J. (2006). Early word learning. In D. Kuhn &

R. Siegler (Eds.), Handbook of child psychology (6th ed.,Vol. 2). New York: Wiley.

Volume 15—Number 1 33

Roberta Michnick Golinkoff and Kathy Hirsh-Pasek

Research Article

When Development and Learning Decrease Memory Evidence Against Category-Based Induction in Children Vladimir M. Sloutsky and Anna V. Fisher

The Ohio State University

ABSTRACT—Inductive inference is crucial for learning: If one

learns that a cat has a particular biological property, one could

expand this knowledge to other cats. We argue that young

children perform induction on the basis of similarity of com-

pared entities, whereas adults may induce on the basis of cat-

egory information. If different processes underlie induction at

different points in development, young children and adults

would form different memory traces during induction, and

would subsequently have different memory accuracy. Experi-

ment 1 demonstrates that after performing an induction task,

5-year-olds exhibit more accurate memory than adults. Experi-

ment 2 indicates that after 5-year-olds are trained to perform

induction in an adultlike manner, their memory accuracy drops

to the level of adults. These results, indicating that sometimes 5-

year-olds exhibit better memory than adults, support the claim

that, unlike adults, young children perform similarity-based

rather than category-based induction.

The ability to make inductive generalizations is crucial for learning: If

one learns that a cat has a particular unobserved biological property,

one could extend this knowledge to other cats, and possibly to other

mammals. Furthermore, by some accounts, ‘‘inductive inference is the

only process . . . by which new knowledge comes into the world’’

(Fisher, 1935/1951, p. 7).

There is much evidence that even infants and young children can

perform simple inductions (Baldwin, Markman, & Melartin, 1993;

Gelman & Markman, 1986; Sloutsky, Lo, & Fisher, 2001; Welder &

Graham, 2001). However, the representations and processes under-

lying this ability remain unclear.

According to one view, people, including young children, hold

several conceptual assumptions that drive their induction (see Keil,

Smith, Simons, & Levin, 1998, and Murphy, 2002, for reviews of these

assumptions). In particular, people hold a category assumption—they

assume that each individual entity is a member of a class or category,

that count nouns refer to categories, and that members of the same

category share many unobserved properties. Conceptual assumptions

are a priori—they are not learned, but are rather a precondition of

learning, and are present early in development (Gelman & Hirschfeld,

1999; Keil et al., 1998). In the course of induction, people first

identify presented entities as members of categories and then perform

inductive inferences on the basis of categorization (Gelman, 1988;

Gelman & Markman, 1986). Therefore, when presented with a rabbit

and told that it has hollow bones inside its body, a child is more likely

to generalize this property to another rabbit than to a dog because the

child (presumably) understands that both rabbits belong to the same

category, and members of the same category share many properties. It

has been argued that this tendency to perform induction on the basis

of categorization, or category-based induction, is especially pro-

nounced when entities are members of familiar categories (Davidson

& Gelman, 1990). In short, according to this view, induction is a

function of categorization, whereas categorization is a function of a

priori conceptual assumptions.

According to another view, young children perform induction (as

well as categorization) by detecting multiple correspondences, or

similarities, among presented entities (e.g., see Jones & Smith, 2002;

McClelland & Rogers, 2003; Sloutsky, 2003; Sloutsky et al., 2001).

Because members of a category often happen to be more similar to

each other than they are to nonmembers, young children are more

likely to induce unobserved properties to members of the category than

to nonmembers. According to this view, induction and categorization in

young children are variants of the same process, which is driven by the

detection of multiple correspondences rather than by a priori con-

ceptual assumptions. Furthermore, conceptual knowledge often found

in adults (e.g., knowledge that entities are members of categories) is

not a priori, but is a product of learning and cognitive development.

Learning accounts of conceptual knowledge support this position,

while weakening the claims that conceptual knowledge is a priori. For

example, it has been claimed that young children’s tendency to use

similar shape as a reliable categorization cue is a product of a priori

conceptual knowledge (Diesendruck & Bloom, 2003; Soja, Carey, &

Spelke, 1991), whereas a convincing learning account of this shape

bias (Smith, Jones, Landau, Gershkoff-Stowe, & Samuelson, 2002) has

weakened the a priori claims by rendering them unnecessary.

Address correspondence to Vladimir M. Sloutsky, Center for Cogni- tive Science, 208C Ohio Stadium East, 1961 Tuttle Park Place, The Ohio State University, Columbus, OH 43210; e-mail: sloutsky.1@ osu.edu.

PSYCHOLOGICAL SCIENCE

Volume 15—Number 8 553Copyright r 2004 American Psychological Society

Overall, the two positions have several fundamental differences.

According to the former position, when entities are members of fa-

miliar categories, induction is a function of categorization, and cate-

gorization is a function of conceptual knowledge. Therefore, induction

is a function of conceptual knowledge. In addition, conceptual

knowledge is a priori rather than learned. According to the latter

position, early in development induction and categorization are a

function of perceptual similarity among entities, whereas conceptual

knowledge is a product of learning and development. Thus, the two

positions assume different kinds of processing underlying induction

and different developmental courses of induction and categorization.

One way of contrasting these theoretical positions is to compare pre-

dictions derived from them. For example, there is evidence in memory

research that spontaneous categorization of items may lead to memory

distortions, such as false recognition of critical lures, or nonpresented

items that belong to the same category as previously presented items

(Koutstaal & Schacter, 1997). These distortions may occur because

participants form category-level or gist representations, whereas de-

tails of each individual item are not encoded or are encoded poorly

(Brainerd, Reyna, & Forrest, 2002; Koutstaal & Schacter, 1997). When

participants are required to focus on perceptual properties of items,

they amply encode individual items, thus exhibiting accurate memory

(Marks, 1991; McDaniel, Friedman, & Bourne, 1978).

Thus, similarity-based induction and category-based induction may

result in differential remembering of information presented during an

induction task: Whereas similarity-based induction may lead to accu-

rate memories for perceptually distinct individual items, category-based

induction may result in memory distortions, such as poor discrimination

of presented items and critical lures. Therefore, if an induction task

precedes a memory test, the memory test would reveal processing un-

derlying induction. If people perform category-based induction and

form category-level memory traces, their ability to discriminate items

seen during the induction task from critical lures should be poor

(compared with their performance on a baseline no-induction task).

However, if they perform similarity-based induction, they should amply

encode perceptual information, forming item-specific memory traces,

and their discrimination should be as high as the baseline level.

It has been argued that when entities are members of familiar

categories, adults may perform induction in a category-based manner

(Osherson, Smith, Wilkie, Lopez, & Shafir, 1990), in which case an

induction task should attenuate their recognition memory compared

with the baseline. In contrast, if young children perform induction in a

similarity-based manner, they should exhibit high accuracy in both

baseline and induction conditions. Thus, following an induction task,

young children may exhibit greater memory accuracy than adults. The

prediction is nontrivial because typically adults’ memory is markedly

more accurate than that of young children (see Schneider & Bjork-

lund, 1998, for a review).

If adults’ induction with familiar categories is indeed category

based, whereas young children’s induction is similarity based, how

does this category-based induction develop? The category-based po-

sition argues that conceptual knowledge (e.g., the category assump-

tion) is a priori rather than learned (Gelman & Hirschfeld, 1999; Keil

et al., 1998). However, providing a learning account of category-based

induction weakens this position by rendering the a priori nature of

conceptual knowledge unnecessary.

To test the target prediction, we conducted Experiment 1, in which

we compared the effects of an induction task on recognition memory of

5-year-olds and adults. In Experiment 2, we trained 5-year-olds to

perform category-based induction and examined the effects of training

on their recognition memory.

EXPERIMENT 1

Method

Participants

Participants were 77 young children (M age 5 5.43 years, SD 5 0.28

years) and 71 introductory psychology students (M age 5 19.3 years,

SD 5 1.33).

Materials, Design, and Procedure

Materials were 44 color photographs of animals presented against a

white background (see Fig. 1 for examples of the stimuli). During the

study phase, participants were presented with 30 pictures, 1 picture at

a time, from three categories (10 cats, 10 bears, and 10 birds). During

the recognition phase, they were presented with 28 pictures, 1 picture

at a time, and were asked whether they had seen each exact picture

during the study phase. Half of the recognition pictures had been

presented during the study phase, and the other half were new pic-

tures. These recognition pictures also represented three categories:

cats (7 old and 7 new), bears (all 7 old), and squirrels (all 7 new). To

ascertain that all of these animals were well familiar to children, we

pretested them with 5-year-olds in an earlier naming study. Only those

pictures that were consistently named by a basic-level name (i.e.,

‘‘cat,’’ ‘‘bear,’’ ‘‘bird,’’ or ‘‘squirrel’’) by more than 85% of the children

were selected for the present study.

The experiment included three between-subjects conditions:

baseline, induction, and blocked categorization, with each condition

consisting of a study phase and a recognition phase. The recognition

phase was identical in all three conditions, whereas the study phase

differed across the conditions. Participants were randomly assigned to

Fig. 1. Examples of stimuli used in the study and recognition phases (copied from the Corel Draw database).

554 Volume 15—Number 8

Induction and Memory

one of the three conditions. There were 27 children and 29 adults in the

induction condition, 24 children and 23 adults in the blocked-categori-

zation condition, and 26 children and 19 adults in the baseline condition.

In the study phase of the baseline condition, participants were

presented with 30 pictures of animals, and their task was to remember

these pictures for a subsequent recognition test.

In the study phase of the induction condition, participants were first

presented with a picture of a cat and informed that it had ‘‘beta cells

inside its body.’’ Participants were then presented with 30 pictures of

animals (identical to those presented in the baseline condition) and

were asked whether each of the presented animals also had beta cells.

After responding, the participants were provided with yes/no feedback

indicating that only cats, but not bears or birds, had beta cells. The

fact that 5-year-olds do not know what beta cells are was of no concern

because young children easily induce unfamiliar properties or blank

predicates, and as we show in the Results and Discussion section, they

had no difficulty inducing this property. The recognition test was not

mentioned in the study phase of this condition.

In the study phase of the blocked-categorization condition, par-

ticipants were first presented with a picture of a cat and informed that

it was young. Participants then were presented with 30 pictures of

animals (identical to those presented in the baseline and induction

conditions) and asked whether each of the presented animals was

young or mature. Participants were provided with random yes/no

feedback. The purpose of this random feedback was to block infer-

ences based on the animal-kind information and to force participants

to focus on perceptual features of individual items. As in the induction

condition, the recognition test was not mentioned in the study phase.

The recognition phase was presented immediately after the study

phase. At recognition, participants were presented with 28 pictures

and were asked to determine whether each was ‘‘old’’ (i.e., exactly the

one presented during the study phase) or ‘‘new.’’ No feedback was

provided during the recognition phase. The young children were

tested individually in their day-care centers by female experimenters

blind to the hypotheses. The undergraduate students were tested in-

dividually in a laboratory on campus. For all participants, stimuli were

presented on a computer screen in a self-paced manner, and stimulus

presentation was controlled by SuperLab Pro 2 (1999) software.

Results and Discussion

Recall that young children were expected to perform induction by

comparing each animal with the target animal and thus to remember

study-phase animals well, accurately accepting old animals and re-

jecting new ones. At the same time, it was expected that adults would

spontaneously categorize animals as cats, bears, and birds when

performing induction, and thus they would form gist or category-level

memory traces. As a result, it was expected that they would remember

category information, but not item-specific information, and thus fail

to discriminate between old items and critical lures (i.e., new mem-

bers of a studied category). Also, recall that the blocked-categoriza-

tion condition was identical to the induction condition, except that the

categorization of animals as cats, bears, and birds was blocked. Be-

cause categorization was blocked, it was expected that memory ac-

curacy for both children and adults in this condition would be

comparable to accuracy in the baseline condition.

After several trials, the majority of 5-year-olds and adults realized

that the property of having beta cells should be induced to cats, but

not to bears or birds, and they accurately performed this induction:

The average rate of correct induction was over 75% of trials for both

children and adults. Also, in the recognition phase, both children and

adults exhibited above 92% accuracy across conditions in rejecting

distractors from an unstudied category (i.e., squirrels). Therefore,

participants took the task seriously and paid attention to stimuli

during the study phase.

Recall that we were interested in participants’ discrimination of old

items and critical lures across conditions. Percentages of hits (i.e.,

correct recognitions) and false alarms on critical lures by age group and

condition are presented in Table 1. Data in the table indicate that

whereas children exhibited equivalent accuracy (i.e., hits � false alarms) across the baseline, induction, and blocked-categorization con-

ditions, F(2, 76) < 1, adults’ accuracy was dramatically lower in the

induction condition than in the other two conditions, F(2, 68) 5 11.5,

p < .0001, Zp 2 5 .252, both ps < .01 in post hoc Tukey tests.

To further examine the ability to discriminate old items from critical

lures, we computed memory sensitivity A0 scores. A0 is a nonpara-

metric analogue of the signal detection statistic d0 (Snodgrass &

Corwin, 1988; Wickens, 2002). If participants do not discriminate old

items from critical lures, A0 is at or below .5. The greater the dis-

crimination accuracy, the closer A0 is to 1. A0 scores are presented in

Figure 2. As predicted, 5-year-olds discriminated old items from

critical lures well across the three conditions (in all conditions,

A0s > .5, one-sample ts > 3, ps < .005). Adults were accurate in the

baseline and blocked-categorization conditions (both A0s > .5, one

sample ts > 7, ps < .001), whereas they were not accurate in the

induction condition: Unlike the A0s of the young children, adults’ A0s

in this condition were not different from .5, t < 1, indicating the

adults did not discriminate between old items and critical lures.

A0 values were submitted to a two-way (Age � Experimental Con- dition) analysis of variance. The analysis confirmed a significant age-

by-condition interaction, F(2, 142) 5 4.64, p 5 .001, Zp 2 5 .06.

Whereas 5-year-olds exhibited no differences in accuracy across the

conditions, all ps > .87, A0s in adults were markedly lower in the

TABLE 1

Mean Proportions of Hits and False Alarms (FA) and Mean Accuracy in Experiment 1

Condition

Children Adults

Hits FA Accuracy

(hits � FA) Hits FA Accuracy

(hits � FA)

Baseline .77 (.19) .50 (.32) .27 .89 (.10) .47 (.31) .42

Induction .72 (.24) .41 (.34) .31 .83 (.20) .76 (.25) .07

Blocked categorization .78 (.13) .51 (.20) .27 .80 (.18) .50 (.24) .30

Note. Standard deviations are in parentheses.

Volume 15—Number 8 555

Vladimir M. Sloutsky and Anna V. Fisher

induction condition than in the other two conditions, F(2, 68) 5 13.5,

p < .001, Zp 2 5 .29, post hoc Tukey test ps < .001. Furthermore, as

predicted, in the induction condition, 5-year-olds exhibited greater

accuracy than adults, one-tailed t(54) 5 2.36, p 5 .011.

In short, the induction task markedly attenuated adults’ recognition

accuracy, whereas young children remained accurate. These results

suggest that whereas adults performed category-based induction, young

children performed similarity-based induction.

It could be argued, however, that the children were accurate be-

cause of extraneous factors. For example, the children could have

been more interested in the pictures than the adults, or the children

could have forgotten gist information faster than item-specific infor-

mation, whereas the adults could have forgotten item-specific infor-

mation faster than gist information. The goal of Experiment 2 was to

eliminate these explanations by training children to perform category-

based induction. If our hypothesis is correct, such training should

differentially affect young children’s memory across conditions:

Although their accuracy should drop in the induction condition

(analogous to the drop for adults in Experiment 1), it should not drop

in the baseline condition. In addition to providing controls, Experi-

ment 2 (if successful) would provide a learning account of category-

based induction found in adults, thus weakening the claim that cat-

egory-based induction is based on a priori conceptual knowledge.

EXPERIMENT 2

Method

Participants

Participants were 42 young children (M age 5 5.25 years, SD 5 0.21

years), with 26 participating in the baseline condition and 16 par-

ticipating in the induction condition.

Materials, Design, and Procedure

Materials in both conditions were identical to those in Experiment 1.

There were two between-subjects conditions, induction and baseline,

and participants were randomly assigned to one of the two conditions.

The procedure of Experiment 2 differed from that of Experiment 1

in that prior to the recognition phase, the 5-year-olds were presented

with training in which they were taught to perform category-based

induction. They were first taught that animals that have the same name

belong to the same category—‘‘they are the same kind of animal.’’

They were then given three boxes, with each box identified by a black

outline of a lion, a rabbit, or a dog, and were presented with pictures of

lions, rabbits, and dogs (none of these categories was presented during

the main experiment). They were told that animals that have the same

name are the same kind of animal and could be placed in the same

box. The children were asked to place the pictures in the boxes face

down. All presented pictures had been pretested in a prior naming

study that revealed that each of the depicted animals could be reliably

named by 5-year-olds. The children were presented with six catego-

rization trials, and yes/no feedback was given after each trial. Both

types of feedback were accompanied by an explanation that animals

that have the same name belong to the same kind and should be

placed in the same box.

The categorization training was followed by induction training.

Participants were first reminded that animals that have the same name

are the same kind of animal. They were then told that animals of the

same kind have ‘‘the same stuff inside.’’ Then participants were given

six induction trials, each accompanied with yes/no feedback. On each

trial, they were shown a picture and told that the animal had a par-

ticular biological property (e.g., ‘‘this dog has thick blood inside its

body’’), and asked to place the picture in an appropriate box. Feed-

back was followed by an explanation that animals of the same kind

have the same name and same stuff inside. All children completed

training successfully, giving either five correct answers out of six or

four correct answers in a row in the induction training task. At the

conclusion of the training session, they were reminded that ‘‘animals

that have the same name are the same kind of animal, and these

animals have the same stuff inside.’’ They were then presented with

the main experiment, which was identical to Experiment 1.

Results and Discussion

Hits, false alarms, and recognition accuracy (hits � false alarms) across the conditions are presented in Table 2. Data in the table point

to a marked difference between 5-year-olds’ high accuracy in the

baseline condition and low accuracy in the induction condition, F(1,

40) 5 12.18, p < .005, Zp 2 5 .24. Although accuracy in the baseline

condition remained as high as in Experiment 1, accuracy in the in-

duction condition dropped dramatically to the level of adults in the

induction condition of Experiment 1.

Children’s A0 scores across the two experiments are presented in

Figure 3. As shown in the figure, children’s accuracy in the induction

condition of Experiment 2 dropped compared with their accuracy in

the baseline condition of Experiment 2, one-tailed independent-

samples t(40) 5 3.4, p < .005, as well as compared with their accu-

racy in the induction condition of Experiment 1, one-tailed inde-

pendent-samples t(41) 5 1.7, p < .05. Furthermore, after training,

their accuracy in the induction condition did not differ significantly

from .5, or from that of adults in Experiment 1, both ps > .28, thus

Fig. 2. Memory sensitivity scores (A0) of children and adults across the three experimental conditions in Experiment 1. The dashed line repre- sents the point of no sensitivity. Error bars represent the standard errors of the mean.

556 Volume 15—Number 8

Induction and Memory

indicating that like adults in Experiment 1, 5-year-olds who had re-

ceived training in category-based induction failed to discriminate

between old items and critical lures. At the same time, their accuracy

remained high in the baseline condition (A0 > .5), one-sample

t(25) 5 7.7, p < .0001.

In short, training to perform category-based induction attenuated

memory accuracy of 5-year-olds in the induction, but not the baseline,

condition. These findings suggest that the high recognition accuracy

exhibited by 5-year-olds in Experiment 1 did not stem from extra-

neous factors, but rather stemmed from similarity-based induction

leading to accurate representation of item-specific information. These

findings also provide a learning account of category-based induction.

GENERAL DISCUSSION

In the present study, (a) the induction task decreased recognition

accuracy of adults, whereas young children exhibited high recognition

accuracy, and (b) training to perform category-based induction de-

creased recognition accuracy of young children in the induction, but

not in the baseline, condition.

Recall that similarity-based induction is expected to result in item-

specific representations, whereas category-based induction is ex-

pected to result in category-level representations. Therefore, category-

based, but not similarity-based, induction may lead to errors in rec-

ognition memory. Hence, finding that young children (unlike adults)

exhibit high recognition accuracy after an induction task suggests that

young children do not spontaneously perform category-based induc-

tion, but rather perform similarity-based induction. This finding is new

evidence challenging the idea that when categories are familiar, in-

duction in young children is driven by the category assumption.

There is mounting evidence challenging the claim that young

children hold a priori conceptual assumptions; however, much of this

evidence challenges the centrality assumption—the idea that young

children assume rather than learn differential importance of different

properties. It was shown that participation in a learning task in which

allegedly central features (i.e., matching labels) were poor predictors

of biological properties, whereas allegedly peripheral features (i.e.,

similar appearances) were good predictors, resulted in young chil-

dren’s ignoring labels in favor of appearances in an induction task

presented 3 months later in a different context by a different exper-

imenter (Sloutsky & Spino, in press). In addition, children’s reliance

on linguistic labels (which are allegedly more central than perceptual

similarities) in an induction task is more pronounced for line-drawing

pictures than for real three-dimensional objects (Deak & Bauer,

1996). Furthermore, perceptual similarity could be more important for

induction than are matching labels: Young children are more likely to

rely on similarity of motion than on a matching linguistic label (Mak &

Vera, 1999). This evidence seriously undermines the idea of an a

priori centrality assumption because properties of ‘‘peripheral’’ in-

formation should not affect the centrality of ‘‘essential’’ information.

Unlike previous research, the current research challenges the cate-

gory assumption: There is little evidence that young children spon-

taneously perform induction in a category-based manner.

Finding that training to perform induction in a category-based

manner reduced memory accuracy of young children to the level of

adults supports a learning account of category-based induction, sug-

gesting that it is unnecessary to posit that conceptual knowledge is a

priori. Recall that in Experiment 2, participants were taught that (a)

animals that have the same name belong to the same kind, (b) animals

that belong to the same kind have the same stuff inside, and (c)

animals that have the same name have the same stuff inside. It is

possible that the first two points are taught in school, whereas the third

is a direct consequence of the first two. Therefore, the results of

Experiment 2 may explain the transition from the similarity-based

induction exhibited by children to the category-based induction ex-

hibited by adults, suggesting that category-based induction and req-

uisite conceptual knowledge could be a product of feedback-based

learning. These findings are consistent with previous research indi-

cating that smart behaviors do not have to be a priori—they can de-

velop from simpler representations and processes (e.g., Jones & Smith,

2002; Smith, Jones, & Landau, 1996; Smith et al., 2002).

Could the reported results stem from extraneous factors, such as

differential forgetting of gist and item-specific information or differ-

ential interest in pictures in children and adults? Although these

factors could account for the results of Experiment 1, it is unclear how

they can account for the results of both experiments. Recall that

training to perform category-based induction attenuated recognition

memory of 5-year-olds in the induction but not the baseline condition.

TABLE 2

Mean Proportions of Hits and False Alarms (FA) and Mean

Accuracy in Experiment 2

Condition Hits FA Accuracy

(hits � FA)

Baseline .74 (.16) .42 (.28) .32

Induction .85 (.21) .77 (.29) .08

Note. Standard deviations are in parentheses.

Fig. 3. Young children’s memory sensitivity scores (A0) in the induction and baseline conditions in Experiments 1 and 2. The dashed line rep- resents the point of no sensitivity. Error bars represent the standard errors of the mean.

Volume 15—Number 8 557

Vladimir M. Sloutsky and Anna V. Fisher

It seems very difficult to come up with a plausible differential-for-

getting or differential-interest account that would explain why learn-

ing to perform category-based induction would differentially affect

recognition in the two conditions.

The reported results reflect effects of induction on encoding of

information: Whereas categorization-based induction results in cate-

gory-level representations leading to memory distortions, similarity-

based induction results in item-specific representations leading to

accurate recognition. These results suggest that young children per-

form induction in a similarity-based manner, thus challenging the

position that young children’s induction is category based.

Acknowledgments—This research was supported by a grant from the

National Science Foundation (BCS 0078945) to V.M.S. We thank

Patricia Bauer, James Cutting, James Hampton, Valerie Kuhlmeier,

Ed Wasserman, Aaron Yarlas, and an anonymous reviewer for their

helpful comments.

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