Eliminating unpredictable variation through iterated learning

Smith, Kenny; Wonnacott, Elizabeth

doi:10.1016/j.cognition.2010.06.004

Cited by 176 publications

(239 citation statements)

References 35 publications

(29 reference statements)

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“…In this way, our results complement previous artificial language learning studies of phonology (23,27), lexical, and syntax acquisition (22,26,29,30) showing behavioral evidence for linguistic universals. Together, these and our studies demonstrate the power of the artificial language learning paradigm as a complement to typological work on linguistic universals (cf., ref.…”

Section: Discussionsupporting

confidence: 77%

“…However, very few participants showed full case marker generalization (Materials and Methods), suggesting that the tradeoff between successful communication and effort was indeed at work during learning. The observed bias toward efficient linguistic systems is not reducible to previously documented tendencies of learners to regularize inconsistent structures (18,19), biases to reduce the representational complexity of linguistic systems (29,30), or a native language bias because we exposed native speakers of English (a language with no case marking on nouns) to an artificial language with optional case marking.…”

Section: Discussionmentioning

confidence: 84%

“…1). This paradigm has been successfully used to investigate language acquisition in children (18,19) and adults (22,30,29,48). This work has found that adults typically match the statistics of the input miniature language very closely (18,19).…”

Section: Differential Case Marking Systemsmentioning

confidence: 99%

“…This paradigm, in which participants learn miniature languages in the laboratory, has provided evidence that certain grammatical systems are easier to learn than others, consistent with hypothesized linguistic universals (21-28). General learning biases (for example, a bias toward more regular lexical or grammatical systems) can also be demonstrated in artificial language learning (18,19,29,30).…”

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confidence: 99%

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Language learners restructure their input to facilitate efficient communication

Fedzechkina¹,

Jaeger

Newport

2012

Proc. Natl. Acad. Sci. U.S.A.

161

194

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Languages of the world display many structural similarities. We test the hypothesis that some of these structural properties may arise from biases operating during language acquisition that shape languages over time. Specifically, we investigate whether language learners are biased toward linguistic systems that strike an efficient balance between robust information transfer, on the one hand, and effort or resource demands, on the other hand, thereby increasing the communicative utility of the acquired language. In two experiments, we expose learners to miniature artificial languages designed in such a way that they do not use their formal devices (case marking) efficiently to facilitate robust information transfer. We find that learners restructure such languages in ways that facilitate efficient information transfer compared with the input language. These systematic changes introduced by the learners follow typologically frequent patterns, supporting the hypothesis that some of the structural similarities found in natural languages are shaped by biases toward communicatively efficient linguistic systems.language universals | learning biases | efficient information transmission | communicative pressures

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 84%

Section: Differential Case Marking Systemsmentioning

confidence: 99%

mentioning

confidence: 99%

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Language learners restructure their input to facilitate efficient communication

Fedzechkina¹,

Jaeger

Newport

2012

Proc. Natl. Acad. Sci. U.S.A.

161

194

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“…Learners were told that the language was similar to English-English lexical items were used (following ref. 51; Materials and Methods)-but they would notice some differences. During training, English phrases were presented orthographically, and learners heard an informant produce a translation.…”

Section: Methodsmentioning

confidence: 99%

Language learners privilege structured meaning over surface frequency

Culbertson

Adger

2014

Proc. Natl. Acad. Sci. U.S.A.

111

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Although it is widely agreed that learning the syntax of natural languages involves acquiring structure-dependent rules, recent work on acquisition has nevertheless attempted to characterize the outcome of learning primarily in terms of statistical generalizations about surface distributional information. In this paper we investigate whether surface statistical knowledge or structural knowledge of English is used to infer properties of a novel language under conditions of impoverished input. We expose learners to artificial-language patterns that are equally consistent with two possible underlying grammars-one more similar to English in terms of the linear ordering of words, the other more similar on abstract structural grounds. We show that learners' grammatical inferences overwhelmingly favor structural similarity over preservation of superficial order. Importantly, the relevant shared structure can be characterized in terms of a universal preference for isomorphism in the mapping from meanings to utterances. Whereas previous empirical support for this universal has been based entirely on data from cross-linguistic language samples, our results suggest it may reflect a deep property of the human cognitive system-a property that, together with other structure-sensitive principles, constrains the acquisition of linguistic knowledge. A central goal of linguistics is to provide a formal characterization of human knowledge of language. It has long been argued that this knowledge crucially involves rules that refer to abstract structure rather than surface word order (1-3). One classic example is the relationship between English declarative and interrogative sentences. Although in many cases a rule forming the interrogative from the declarative could simply change the surface position of the auxiliary verb, the full range of English facts can only be captured by a rule making reference to the structural position of the auxiliary. For example, to generate the correct interrogative for complex declaratives such as "The man who is a fool is amusing" and "The man is a fool who is amusing," a rule referring to the superficial surface position of the auxiliary-for instance, leftmost or rightmost-will not do. Rather, the rule must pick out the auxiliary in the main clause. Because these complex cases are relatively rare, language learners could initially entertain a surface-based rule before converging on the structure-based alternative. Interestingly, though, children acquiring English do not seem to do this, suggesting that structurebased generalizations are preferred from the very start (4).The idea that explaining the syntax of natural languages requires abstract structure-and that learners posit structure-based generalizations early in the acquisition process-has been challenged from multiple angles. For example, simple recurrent networks trained on English input can replicate correct interrogative ordering patterns, suggesting that surface-based generalizations may be sufficient to characterize linguistic knowledge (...

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The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition

Gauvrit

Zenil

Tegnér

2017

Representation and Reality in Humans, Other Living Organisms and Intelligent Machines

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We survey concepts at the frontier of research connecting artificial, animal and human cognition to computation and information processing-from the Turing test to Searle's Chinese Room argument, from Integrated Information Theory to computational and algorithmic complexity. We start by arguing that passing the Turing test is a trivial computational problem and that its pragmatic difficulty sheds light on the computational nature of the human mind more than it does on the challenge of artificial intelligence. We then review our proposed algorithmic information-theoretic measures for quantifying and characterizing cognition in various forms. These are capable of accounting for known biases in human behavior, thus vindicating a computational algorithmic view of cognition as first suggested by Turing, but this time rooted in the concept of algorithmic probability, which in turn is based on computational universality while being independent of computational model, and which has the virtue of being predictive and testable as a model theory of cognitive behavior.

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Eliminating unpredictable variation through iterated learning

Cited by 176 publications

References 35 publications

Language learners restructure their input to facilitate efficient communication

Language learners restructure their input to facilitate efficient communication

Language learners privilege structured meaning over surface frequency

The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition

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