What can blocking effects tell us about mutual
exclusivity?
Michael Ramscar & Joseph Klein
Department of Psychology, Stanford University
GANGURRU?
Walking along one day on the newly-discovered coast of Australia, Captain Cook saw an extraordinary animal leaping through the bush.
“What's that?” he asked one of the aborigines accompanying him.
“Uh – gangurru,” the aboriginal replied… Captain Cook duly noted down the name of the peculiar beast as ‘kangaroo.’ Some time later, Cook had the opportunity to compare notes with Captain King, and mentioned the kangaroo.
“No, no, Cook,” said King, “the word for that animal is ‘meenuah’ - I've checked it carefully.”
“So what does ‘kangaroo’ mean?”
“Well, I think,” said King “it probably means something like ‘I don't know’...”
But it was too late, and so ever since, the English word ‘kangaroo’ has been based on a misunderstanding, and really means ‘I don't know.’
A PUZZLE OUT OF APOCRYPHA
As this amusing – if likely apocryphal – story illustrates, learning the precise meaning of a word is not necessarily a straightforward endeavor. Indeed, one of the most interesting problems in early lexical learning research is the question of how children prove so
successful at acquiring the proper meanings of words. How is it that children learn what a word actually means? For example, a word used in the presence of a new toy could refer to the toy itself; an action that could be taken upon the toy; the event for which the toy is
relevant; or a descriptor akin to ‘fun’ or ‘shiny’ – and this is ignoring more complex
possibilities (see the classic example of the ‘gavagai,’ Quine, 1960). The dilemma a child faces in word learning has long been thought to be one of selecting from among multiple competing hypotheses, on the basis of relatively little evidence from the input (Woodward, Markman & Fitzsimmons, 1994). If there are so many possible applications of a word to a referent, how is it that children are able to consistently arrive at the same narrow set of
applications as everyone else who speaks their language? Since children are able to solve this problem so adeptly, we need some means of accounting for this surprising capacity.
SOLUTION 1: IT’S ALL INNATE
One possible way children might sort through the seemingly unlimited hypotheses about word reference is by acquiring information about what a word does not refer to, in order to reject some less helpful hypotheses. The problem is that, by and large, children do not get this type of explicit negative evidence, and may not always use it even when corrected.
This is one of the major starting points for so-called ‘poverty of the stimulus’ arguments in support of linguistic nativism (Chomsky, 1980). These arguments suggest that because children arrive at a consistent language from inconsistent, seemingly insufficient evidence, language must arise from an innate framework, such as a ‘universal grammar’ or an in-built store of concepts (Chomsky, 2000; Fodor, 1988).
SOLUTION 2: CONSTRAINTS ON WORD LEARNING
Alternatively, some researchers have proposed that children use prior knowledge about plausible word meanings as constraints on word learning (Carey, 1978; Markman, 1989; Bloom, 1994). Hypothetically, these constraints would allow learners to immediately reject many possible but unnatural hypotheses, allowing for ‘fast mapping’ of words to categories. Three of the best-known proposals are the ‘whole object’ constraint, the
‘taxonomic’ constraint, and the ‘mutual exclusivity’ constraint (Markman, 1989).
SOLUTION 3: LEARNING
Maybe learning can offer insight into how children acquire the basic building blocks of language, without recourse to constraints or innately specified grammatical or conceptual
“atoms”
Research in animals has shown that learning is information sensitive (Rescorla & Wagner, 1972; Gallistel, 2003). Learning seeks to establish the informativeness of any cues
available to a learner.
Learning is discriminative and probabilistic: it is a process that discriminates less informative from more informative cues.
Informally: we use words in ways that we hope are informative. Perhaps word-learning is a process of finding the information in the way words are used.
LEARNING THEORY
Learning is a process of learning to predict environmental regularities.
Learning occurs whenever there are discrepancies between what an animal has learned to expect and what actually happens (error-driven learning). Learning produces expectations, and any differences between the what is expected and what is observed result in prediction-error, and further learning.
“a useful shorthand is that organisms adjust their Pavlovian associations only when they are ‘surprised’” (Rescorla,1988).
The predictive value of cues are strengthened when events are under-predicted, and weakened when they are over-predicted. As a result, cues compete for relevance, and the outcome of this competition is shaped both by positive evidence about co-
occurrences between cues and predicted events, and negative evidence about non- occurrences of predicted events.
INFORMATION? (RESCORLA, 1968)
Condition groups of rats on tones mild shocks.
Association rate between tones and shocks held constant. Background rate of tones varied.
t - s . . . . t - s . . . . t - s . . . . t - s . .
or
t - s . .t .t . t - s . .t .t . t - s . .t .t . t - s .
Learning sensitive to background rate of tones.
Nb. Shows how neither explicit feedback
(no one shouted "hey - you weren't shocked" at the rats) nor an explicit event such as a reward or a punishment are necessary conditions for Pavlovian learning
WHAT IS WORD LEARNING?
From the perspective of learning and information theory…
Learning a word can be seen as the process of discriminating which cues in the environment best predict that sound symbol.
For example, learning a noun involves learning which features of objects (animate and inanimate) best predict that particular noun, and not others.
INFORMATION & WORD LEARNING?
Informally, to take Quine’s “Gavagai” example:
Given one “Gavagai” event, the best “hypothesis” for the meaning of Gavagai will depend on the background rate of the cues in the scene above
(“Rabbit” and “Undetatched Rabbit Part” are indistinguishable given this information)
BLOCKING EFFECTS
Because learning processes seek to maximize predictive information, learning is always sensitive to history. What is already known has a big influence on what gets learned.
Prior learning can affect current learning in two ways:
Previously learned cues fully predict an event, in which case any new cues are uninformative (i.e., redundant) and little or no learning occurs (Kamin, 1969)
Cues that have been learned about in relation to other events will be less informative than novel cues. Pre-exposed cues can, by definition, play less of a role in reducing
uncertainty about current events, because they are also cues to events that are NOT occurring. Learning rates are negatively affected by pre-exposure (i.e., cues with a high background rate will be learned slower than relatively novel cues).
TESTING LEARNING THEORY AGAINST MUTUAL EXCLUSIVITY
The current study is designed to test whether toddlers a simple word-learning tasks in
which they are asked to rapidly learn several novel label-object pairings are sensitive to the information in the various stimuli they are exposed to.
Our experiment is similar to previous rapid word learning studies (e.g. Woodward et al, 1994), but the training has been modified to represent a possible real-world learning experience. The real world is not presented to children in a neatly divided way (as it sometimes is in experiments); rather, it is often messy and ambiguous.
To mimic this, the children in this experiment were presented with two objects for every label they heard. Our expectation was that children would forge object-label associations using error-driven learning processes. Thus, we would expect that their choices should closely reflect the predictions of our learning model. At the same time, because our model makes predictions that run counter to the ‘mutual exclusivity’ constraint, our experimental design allows us to test which learning principle better explains our behavioral data.
EXPERIMENT
Children aged 2- to 2.5-years old watched a video in which novel words were paired with novel objects in a purposefully ambiguous manner. Each child received training on 3 different sets of objects and words, and was tested for word learning at the end of each training session and again at the end of the experiment.
PARTICIPANTS: 15 children participated in this study (M = 2 years, 4.8 months), with a near even balance between genders (6 boys and 9 girls). Participants were recruited from Stanford and the surrounding community.
DESIGN: The experimental design was modeled on classic word learning studies in young children, and consisted of: familiarization, training, short distraction, and a recall test.
Pilot testing indicated that children, when presented with physical objects, would
sometimes reach for the objects or attempt to play with one or more during the training session. To avoid biased attention towards any particular object during training, the training was conducted using a narrated video of the objects. Using video training also allowed for consistency of length and presentation.
EXPERIMENTAL STIMULI
3 sets of objects, with 3 toys per set, were created from craft materials. The objects were designed to look like possible toys, without appearing too much like any common objects.
Within each set, the objects varied in size, color, and texture, allowing for easy discrimination between each object. Pilot testing indicated that within each set, no particular object was consistently preferred to the other objects.
3 sets of novel nouns, with 3 words per set, were invented or reused from previous word-learning studies. The words within each set were designed to sound like natural
English words and were matched for the same number of syllables. The same set of words was always paired with the same set of objects. However, which word was tested for each set was counterbalanced across subjects, meaning that a general preference for one object would not bias the results.
TRAINING
At the start of each training session, the puppet announced that she would be showing the child some of her toys. First, Objects A and B would appear on screen while the narrator used Label 1; then, Objects B and C would appear while the narrator used Label 2. In both cases, the narrator would use the Labels conversationally, saying things like “Do you see the Dax? I really like the Dax.” In total, the puppet said the label nine times while the objects were visible. Additionally, the puppet asked the child to repeat the Label.
At the end of each individual training session, the puppet stated that the child was now going to play with the researcher for a little while. The researcher then stopped the video, moved the screen off of the table, and brought out all three objects. The researcher asked the child to “show me the [target label],” and repeated the question again if the child was hesitant. Once the child chose an object, the researcher recorded it and encouraged the child to play with each of the objects briefly before moving on to the next set of objects.
Training for each Label lasted approximately 2 minutes, and the test portion on average lasted about 1 minute, such that children interacted with each object set for approximately 5 minutes. This was done for 3 sets of objects, such that the child learned about 6 labels and 9 objects
TESTING
There were three test conditions: asking for Label 1, asking for Label 2, or asking for a novel label, not heard in training, Label 3. Each child participated in all three conditions, with one condition per object set. The order of the conditions was counterbalanced across subjects, and all subjects were tested on each type of label only once. To conclude the experiment, the researcher repeated the three tests again, providing a second measure of the child’s word-object pair learning. The retest at the end of the experiment followed the same order as the training sessions.
SCHEMATIC Training:
OBJECT A OBJECT B “Can you see the Dax? I love my Dax”
OBJECT B OBJECT C “Can you see the Wug? I love my Wug”
Testing:
OBJECT A OBJECT B OBJECT C
“Where is the….” Dax or Wug or Mido?
INFOMATIVITY
DAX
OBJECT A OBJECT B “Can you see the Dax? I love my Dax”
A & B equally predict Dax
OBJECT B OBJECT C “Can you see the Wug? I love my Wug”
B predicts Dax (resulting in unlearning against Dax).
P(A = Dax) > P(B = Dax)
A more informative about “Dax” than B.
INFOMATIVITY
WUG
OBJECT A OBJECT B “Can you see the Dax? I love my Dax”
OBJECT B OBJECT C “Can you see the Wug? I love my Wug”
B has been pre-exposed to Dax, and so is less informative than C, which is novel (will result in a slower learning rate for B than C).
P(C = Wug) > P(B = Wug)
C more informative about “Wug” than B.
INFOMATIVITY
MIDO
OBJECT A OBJECT B “Can you see the Dax? I love my Dax”
OBJECT B OBJECT C “Can you see the Wug? I love my Wug”
B has been pre-exposed to Dax and Wug, and so is less informative than A, which is only seen on Dax-trials and C which is only seen on Wug0trials (will result in a
slower learning rate for B than A or C, which are equally pre-exposed).
P(A v B = Mido) > P(B = Mido);) P(A = Mido) = P(C = Mido)
A & C equally informative about “Mido.” Both more informative than B.
RESULTS
COMPETING HYPOTHESES – WHICH WINS OUT?
SAME EXPERIMENT WITH UNDERGRADS
FINALLY – CHILDREN’SPERFORMANCE IN THE EXPERIMENT AS COMPARED TO THE EXPECTATIONS OF DEVELOPMENTAL PROFESSORS AND GRADUATE
STUDENTS
