Dataset Information

Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds.

ABSTRACT: Previous work has demonstrated that adults are capable of learning patterned relationships among adjacent syllables or tones in continuous sequences but not among nonadjacent syllables. However, adults are capable of learning patterned relationships among nonadjacent elements (segments or tones) if those elements are perceptually similar. The present study significantly broadens the scope of this previous work by demonstrating that adults are capable of encoding the same types of structure among unfamiliar nonlinguistic and nonmusical elements but only after much more extensive exposure. We presented participants with continuous streams of nonlinguistic noises and tested their ability to recognize patterned relationships. Participants learned the patterns among noises within adjacent groups but not within nonadjacent groups unless a perceptual similarity cue was added. This result provides evidence both that statistical learning mechanisms empower adults to extract structure from nonlinguistic and nonmusical elements and that perceptual similarity eases constraints on nonadjacent pattern learning. Supplemental materials for this article can be downloaded from pbr.psychonomic-journals.org/content/supplemental.

SUBMITTER: Gebhart AL

PROVIDER: S-EPMC2799498 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds.

Gebhart Andrea L AL Newport Elissa L EL Aslin Richard N RN

Psychonomic bulletin & review 20090601 3

Previous work has demonstrated that adults are capable of learning patterned relationships among adjacent syllables or tones in continuous sequences but not among nonadjacent syllables. However, adults are capable of learning patterned relationships among nonadjacent elements (segments or tones) if those elements are perceptually similar. The present study significantly broadens the scope of this previous work by demonstrating that adults are capable of encoding the same types of structure among ...[more]

PMID: 19451373

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Project description:The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., "A-B") versus nonadjacent (e.g., "A-X-B") dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M?=?22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together.

Project description:The regular behavior of sound sources helps us to make sense of the auditory environment. Regular patterns may, for instance, convey information on the identity of a sound source (such as the acoustic signature of a train moving on the rails). Yet typically, this signature overlaps in time with signals emitted from other sound sources. It is generally assumed that auditory regularity extraction cannot operate upon this mixture of signals because it only finds regularities between adjacent sounds. In this view, the auditory environment would be grouped into separate entities by means of readily available acoustic cues such as separation in frequency and location. Regularity extraction processes would then operate upon the resulting groups. Our new experimental evidence challenges this view. We presented two interleaved sound sequences which overlapped in frequency range and shared all acoustic parameters. The sequences only differed in their underlying regular patterns. We inserted deviants into one of the sequences to probe whether the regularity was extracted. In the first experiment, we found that these deviants elicited the mismatch negativity (MMN) component. Thus the auditory system was able to find the regularity between the non-adjacent sounds. Regularity extraction was not influenced by sequence cohesiveness as manipulated by the relative duration of tones and silent inter-tone-intervals. In the second experiment, we showed that a regularity connecting non-adjacent sounds was discovered only when the intervening sequence also contained a regular pattern, but not when the intervening sounds were randomly varying. This suggests that separate regular patterns are available to the auditory system as a cue for identifying signals coming from distinct sound sources. Thus auditory regularity extraction is not necessarily confined to a processing stage after initial sound grouping, but may precede grouping when other acoustic cues are unavailable.

Project description:UnlabelledBackgroundMuch progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multiple-residue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families.ResultsThe k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function.ConclusionsOur results demonstrate that the method we present here using a k-modes site clustering algorithm based on interdependency evaluation among sites obtained from a sequence alignment of homologous proteins can provide significant insights into the complex, hierarchical inter-residue structural relationships within the 3D structure of a protein family.

Dataset Information

Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds.

Publications

Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds.

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