Differences

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--- aslin_newport [2015/11/22 23:16] – silvia
+++ aslin_newport [2016/02/13 11:03] (current) – silvia
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-====== Statistical Learning: From Acquiring
+====== Statistical Learning: From Acquiring Specific Items to Forming General Rules ======
-Specific Items to Forming General Rules ======
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 listening to) the input.
 \\
+Saffran et al. (1996) suggested the term statistical learning to
+refer to the process by which learners acquire information about
+distributions of elements in the input. Thus, the probability that one syllable followed another
+within a word (the transitional probability) was 1.0, whereas
+the transitional probability of syllable pairs at word boundaries
+was 0.33.
+\\
+Thus, statistical learning is a powerful and domaingeneral
+mechanism available early in development to infants
+who are naïve (i.e., uninstructed) about how to negotiate a
+complex learning task.
+These results show that a statistical-learning mechanism
+enables learners to extract one or more statistics and use this
+information to make an implicit decision about the stimulus materials
+that were present in the input. This ability is important for
+learning which syllables form words, for estimating the number
+of peaks in a distribution of speech sounds, and for discovering
+which visual features form the parts of a scene. But this does not
+address the question of how learners form rules—abstractions
+about patterns that could be generalized to elements that have
+never been seen or heard. How do learners who are exposed to a
+subset of the possible patterns in their input go beyond this to
+infer a set of general principles or “rules of the game”.
+\\
+Several studies have documented that infants can make the
+inductive leap from observed stimuli to novel stimuli that follow
+the same rules.
+\\
+**Some researchers have claimed that statistical learning and
+rule learning are two separate mechanisms, because statistical
+learning involves learning about elements that have been presented
+during exposure, whereas rule learning can be applied
+to novel elements and novel combinations** (see Endress &
+Bonatti, 2007; Marcus, 2000). **But why do learners sometimes
+keep track of the specific elements in the input they are
+exposed to and at other times learn a rule that extends beyond
+the specifics of the input? An alternate hypothesis is that these
+two processes are in fact not distinct, but rather are different
+outcomes of the same learning mechanism.**
+\\
+\\
+//MyNote//:this is the most relevant question they ask in this study.
+\\
+\\
+For example, some stimulus dimensions are naturally more
+salient than others. **If stimuli are encoded in terms of their
+salient dimensions rather than their specific details, then learners
+will appear to generalize a rule by applying it to all stimuli
+that exhibit the same pattern on these salient dimensions.**
+\\
+\\
+//MyNote//: what triggers encoding in terms of the salient dimensions that apply to all stimuli?
+\\
+\\
+Although perceptual cues can serve as powerful constraints on
+statistical learning, perceptual salience is not how most rules
+are defined in the natural environment.
+\\
+**They acquire rules when patterns in the input indicate
+that several elements occur interchangeably in the same contexts, but acquire specific instances when the patterns
+apply only to the individual elements.**
+\\
+\\
+//MyNote//: **CRUCIAL POINT**: what features of the input indicate that elements occur interchangeably? How much evidence is needed to this end for generalization to new elements to occur?
+\\
+\\
+For example, Xu and
+Tenenbaum (2007) have shown that if children hear the word
+“glim” applied to three different dogs, they will infer that
+“glim” means dog. In contrast, if “glim” is used three times to
+refer to the same dog, children interpret it as the dog’s name.
+The same contrast between learning items and learning rules
+can occur for syllable and word sequences.
+\\
+Gerken (2006) has made this argument by reconsidering
+and modifying the design of the Marcus et al. (1999) rulelearning
+experiment (see Fig. 3). Marcus et al. presented 16
+different AAB strings in the learning phase of their experiment.
+Notice in Figure 3 that four strings ended in di, four
+ended in je, four ended in li, and four ended in we. Thus,
+infants could have learned the general AAB rule, or they could
+have learned a more specific pattern: that every string ended in
+di, je, li, or we. The more consistent or reliable cue was the
+repetition of the first two syllables—the AAB rule—because it
+applied to every string, whereas the “ends in di (or je, or li, or
+we)” rule applied to only one-fourth of the strings.
+Gerken (2006) asked whether infants presented with a subset
+of the 16 strings from the Marcus et al. (1999) study would
+favor the “repetition of the first two syllables” rule or the
+“ends in di, je, li, or we” rule. Infants who heard only four
+AAB strings that ended in the same syllable (e.g., di in the
+leftmost column of Fig. 3) were tested on two equally plausible
+rules: (1) all strings involve an AAB repetition, and (2) all
+strings end in di. These infants failed to generalize the first
+rule to a novel string that retained the AAB pattern but did not
+end in di. In contrast, infants who heard only four AAB strings
+lying along the diagonal in Figure 3 replicated the Marcus
+et al. result. Because these strings shared an AAB pattern but
+ended in four different syllables, only the AAB rule was
+reliable.
+\\
+\\
+//MyNote//: **QUESTION**:
+\\
+Consider the set of 4 strings: //leledi, wiwije, jijili, dedewe//
+\\
+The following rules are equally reliable for all strings:
+\\
+. AAB
+\\
+. starts with 2x //le, wi, ji or de//
+\\
+. ends in //di, je, li, we//
+\\
+Why do learners sometimes stick to the narrow generalizations [2,3] and sometimes make a wider generalization (category-based) [1]?
+\\
+\\
+In recent work, we (Reeder, Newport, & Aslin, 2009, 2010)
+demonstrated a similar phenomenon—and described some of
+the principles for its operation—in the learning of an artificiallanguage
+grammar. In our experiments, adult learners were
+presented with sentences made up of nonsense words that
+came from three different grammatical categories (A, X, and
+B), much like subjects, verbs, and direct objects in sentences
+such as “Bill ate lunch.” Depending on the experiment, the
+input included sentences in which **all of the words within a
+particular category occurred in the same contexts** (e.g., words
+X1, X2, and X3 all occurred after any of the A words and before
+any of the B words), or **the input included only sentences in
+which the X words occurred in a limited number of overlapping
+A-word or B-word contexts**.
+Adult learners are surprisingly sensitive to these differences.
+Our results showed that **//participants’ tendency to generalize
+depended on the precise degree of overlap among word
+contexts that they heard in the input, and also on the consistency
+with which a particular A or B word was missing from
+possible X-word contexts//**.
+\\
+\\
+**Adults generalize rules when the
+shared contexts are largely the same, with only an occasional
+absence of overlap (i.e., a “gap”). However, when the gaps are
+persistent, adults judge them to be legitimate exceptions to the
+rule and no longer generalize to these contexts.**
+\\
+\\
+//MyNote//: this is a broad description of the observed results, but no explanation as to why this is the case, and no precision in describing: "largely", "persistent" -> What is large enough? When is persistent enough? Why?
+\\
+\\
+Thus, similar
+to the results of Gerken (2006), our findings showed that it
+was the consistency of context cues that led learners to generalize
+rules to novel strings, and it was the inconsistency of
+context cues that kept learners from generalizing and led them
+to treat some strings as exceptions.
+The key point here is that in terms of the reliability of context
+cues, statistical learning and rule learning are not different
+mechanisms (see Orban, Fiser, Aslin, & Lengyel, 2008). When
+there are strong perceptual cues, such as the repetition of elements
+in an AAB sequence, a statistical-learning mechanism
+can compute the regularities of the repetitions (i.e., they are
+either present or absent) or of the elements themselves (e.g.,
+the particular syllables). And, as hypothesized by Gerken
+(2006) and Reeder et al. (2009, 2010), even when there are no
+perceptual cues, the consistency of how the context cues are
+distributed across strings of input determines whether a rule is
+formed—enabling generalization to novel strings—or whether
+specific instances are learned. According to this hypothesis,
+statistical learning is a single mechanism whose outcome
+applies either to elements that have been experienced or to
+generalization beyond experienced elements, depending on
+the manner and consistency with which elements are patterned
+in the learner’s input. Importantly, this balance of learning is
+accomplished without instruction, through mere exposure to
+structured input.
+----
+**Conclusion:**
+\\
+Perceptual salience and the patterning of context cues are not
+the only factors that can influence what learners acquire via a
+statistical-learning mechanism. An extensive literature in linguistics
+has argued that languages of the world display a small
+number of universal patterns—or a few highly common patterns,
+out of many that are possible—and has suggested that
+language learners will fail to acquire languages that do not
+exhibit these regularities (Chomsky, 1965, 1995).
+Recently, a number of studies using
+artificial grammars have indeed shown that both children and
+adults will more readily acquire languages that observe the
+universal or more typologically common patterns found in
+natural languages.
+For example, Hudson Kam and Newport (2005, 2009) and
+Austin and Newport (2011) presented adults and children with
+miniature languages containing inconsistent, probabilistically
+occurring forms (e.g., nouns were followed by the nonsense
+word ka 67% of the time and by the nonsense word po the
+remaining 33% of the time). This type of probabilistic variation
+is not characteristic of natural languages, but it does occur
+in the speech of nonnative speakers who make grammatical
+errors. Adult learners in these experiments matched the probabilistic
+variation they had heard in their input when they produced
+sentences using the miniature language, but young
+children formed a regular rule, producing ka virtually all of the
+time, thereby restoring to the language the type of regularity
+that is more characteristic of natural languages.
+\\
+\\
+It is not always clear why learners acquire certain types of
+patterns more easily than others (and why languages therefore
+more commonly exhibit these patterns). Some word orders
+place prominent words in more consistent positions across different
+types of phrases; other patterns are more internally regular
+or conform better to the left-to-right biases of auditory
+processing. A full understanding of the principles underlying
+these learning outcomes awaits further research. What is clear,
+however, is that statistical learning is not simply a veridical
+reproduction of the stimulus input. Learning is shaped by a
+number of constraints on perception and memory, at least
+some of which may apply not only to languages but also to
+nonlinguistic patterns.