Humans regularly produce new utterances that are understood by other members of the same language community 1 . Linguistic theories account for this ability through the use of syntactic rules (or generative grammars) that describe the acceptable structure of utterances 2 . The recursive, hierarchical embedding of language units (for example, words or phrases within shorter sentences) that is part of the ability to construct new utterances minimally requires a 'context-free' grammar 2, 3 that is more complex than the 'finite-state' grammars thought sufficient to specify the structure of all non-human communication signals. Recent hypotheses make the central claim that the capacity for syntactic recursion forms the computational core of a uniquely human language faculty 4,5 . Here we show that European starlings (Sturnus vulgaris) accurately recognize acoustic patterns defined by a recursive, self-embedding, context-free grammar. They are also able to classify new patterns defined by the grammar and reliably exclude agrammatical patterns. Thus, the capacity to classify sequences from recursive, centre-embedded grammars is not uniquely human. This finding opens a new range of complex syntactic processing mechanisms to physiological investigation.The computational complexity of generative grammars is formally defined 3 such that certain classes of temporally patterned strings can only be produced (or recognized) by specific classes of grammars (Fig. 1). Starlings sing long songs composed of iterated motifs (smaller acoustic units) 6 that form the basic perceptual units of individual song recognition 7-9 . Here we used eight 'rattle' and eight 'warble' motifs (see Methods) to create complete 'languages' (4,096 sequences) for two distinct grammars: a context-free grammar (CFG) of the form A 2 B 2 that entails recursive centre-embedding, and a finite-state grammar (FSG) of the form (AB) 2 that does not ( Fig. 2a, b; 'A' refers to rattles and 'B' to warbles).We trained 11 European starlings, using a go/nogo operant conditioning procedure, to classify subsets of sequences from these languages (see Methods and Supplementary Information). Nine out of eleven starlings learned to classify the FSG and CFG sequences accurately (as assessed by d', which provides an unbiased measure of sensitivity to differentiating between two classes of patterns), but this task was difficult (Fig. 2c). The rate of acquisition varied widely among the starlings that learned the task (303.44 ± 57.11 blocks to reach criterion (mean ± s.e.m.), range 94-562 blocks with 100 trials per block), and was slow by comparison to other operant song-recognition tasks 7 .To assess the possibility that starlings learned to classify correctly the motif patterns described by the CFG and FSG grammars through rote memorization of the training exemplars, we further (Fig. 3a). The mean d' over the first 100 trials with new stimuli (roughly six responses to each exemplar) was 1.08 ± 0.50, which is significantly better than chance performance (d' = 0). Over th...

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Memory consolidation resulting from sleep has been seen broadly: in verbal list learning, spatial learning, and skill acquisition in visual and motor tasks. These tasks do not generalize across spatial locations or motor sequences, or to different stimuli in the same location. Although episodic rote learning constitutes a large part of any organism's learning, generalization is a hallmark of adaptive behaviour. In speech, the same phoneme often has different acoustic patterns depending on context. Training on a small set of words improves performance on novel words using the same phonemes but with different acoustic patterns, demonstrating perceptual generalization. Here we show a role of sleep in the consolidation of a naturalistic spoken-language learning task that produces generalization of phonological categories across different acoustic patterns. Recognition performance immediately after training showed a significant improvement that subsequently degraded over the span of a day's retention interval, but completely recovered following sleep. Thus, sleep facilitates the recovery and subsequent retention of material learned opportunistically at any time throughout the day. Performance recovery indicates that representations and mappings associated with generalization are refined and stabilized during sleep.

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Children who observe gesture while learning mathematics perform better than children who do not, when tested immediately after training. How does observing gesture influence learning over time? Children (n = 184, ages = 7-10) were instructed with a videotaped lesson on mathematical equivalence and tested immediately after training and 24 hr later. The lesson either included speech and gesture or only speech. Children who saw gesture performed better overall and performance improved after 24 hr. Children who only heard speech did not improve after the delay. The gesture group also showed stronger transfer to different problem types. These findings suggest that gesture enhances learning of abstract concepts and affects how learning is consolidated over time.