4,768 research outputs found

    Implicit learning of recursive context-free grammars

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    Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

    Experiment-friendly kinetic analysis of single molecule data in and out of equilibrium

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    We present a simple and robust technique to extract kinetic rate models and thermodynamic quantities from single molecule time traces. SMACKS (Single Molecule Analysis of Complex Kinetic Sequences) is a maximum likelihood approach that works equally well for long trajectories as for a set of short ones. It resolves all statistically relevant rates and also their uncertainties. This is achieved by optimizing one global kinetic model based on the complete dataset, while allowing for experimental variations between individual trajectories. In particular, neither a priori models nor equilibrium have to be assumed. The power of SMACKS is demonstrated on the kinetics of the multi-domain protein Hsp90 measured by smFRET (single molecule F\"orster resonance energy transfer). Experiments in and out of equilibrium are analyzed and compared to simulations, shedding new light on the role of Hsp90's ATPase function. SMACKS pushes the boundaries of single molecule kinetics far beyond current methods.Comment: 11 pages, 8 figure

    Recursive music elucidates neural mechanisms supporting the generation and detection of melodic hierarchies

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    The ability to generate complex hierarchical structures is a crucial component of human cognition which can be expressed in the musical domain in the form of hierarchical melodic relations. The neural underpinnings of this ability have been investigated by comparing the perception of well-formed melodies with unexpected sequences of tones. However, these contrasts do not target specifically the representation of rules generating hierarchical structure. Here, we present a novel paradigm in which identical melodic sequences are generated in four steps, according to three different rules: The Recursive rule, generating new hierarchical levels at each step; The Iterative rule, adding tones within a fixed hierarchical level without generating new levels; and a control rule that simply repeats the third step. Using fMRI, we compared brain activity across these rules when participants are imagining the fourth step after listening to the third (generation phase), and when participants listened to a fourth step (test sound phase), either well-formed or a violation. We found that, in comparison with Repetition and Iteration, imagining the fourth step using the Recursive rule activated the superior temporal gyrus (STG). During the test sound phase, we found fronto-temporo-parietal activity and hippocampal de-activation when processing violations, but no differences between rules. STG activation during the generation phase suggests that generating new hierarchical levels from previous steps might rely on retrieving appropriate melodic hierarchy schemas. Previous findings highlighting the role of hippocampus and inferior frontal gyrus may reflect processing of unexpected melodic sequences, rather than hierarchy generation per se

    The cognitive architecture of recursion: Behavioral and fMRI evidence from the visual, musical and motor domains

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    In this manuscript, we summarize the results of our research program aiming at describing the cognitive architecture underlying the representation of recursive hierarchical embedding. After conducting a series of behavioral and fMRI experiments in the visual, musical and motor domains, we found that, behaviorally, the acquisition of recursive rules seems supported by cognitive resources that are general across domains. However, when we test well-trained participants in the fMRI, their representation of recursion seems supported by activating schemas stored in (visual, musical and motor) domain-specific repositories. This suggests that the resources necessary to acquire recursive rules are different from those necessary to utilize these rules after extensive training
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