Fast and exact search for the partition with minimal information loss

In analysis of multi-component complex systems, such as neural systems, identifying groups of units that share similar functionality will aid understanding of the underlying structures of the system. To find such a grouping, it is useful to evaluate to what extent the units of the system are separable. Separability or inseparability can be evaluated by quantifying how much information would be lost if the system were partitioned into subsystems, and the interactions between the subsystems were hypothetically removed. A system of two independent subsystems are completely separable without any loss of information while a system of strongly interacted subsystems cannot be separated without a large loss of information. Among all the possible partitions of a system, the partition that minimizes the loss of information, called the Minimum Information Partition (MIP), can be considered as the optimal partition for characterizing the underlying structures of the system. Although the MIP would reveal novel characteristics of the neural system, an exhaustive search for the MIP is numerically intractable due to the combinatorial explosion of possible partitions. Here, we propose a computationally efficient search to precisely identify the MIP among all possible partitions by exploiting the submodularity of the measure of information loss. Mutual information is one such submodular information loss functions, and is a natural choice for measuring the degree of statistical dependence between paired sets of random variables. By using mutual information as a loss function, we show that the search for MIP can be performed in a practical order of computational time for a reasonably large system. We also demonstrate that MIP search allows for the detection of underlying global structures in a network of nonlinear oscillators

General Type Token Distribution

We consider the problem of estimating the number of types in a corpus using the number of types observed in a sample of tokens from that corpus. We derive exact and asymptotic distributions for the number of observed types, conditioned upon the number of tokens and the latent type distribution. We use the asymptotic distributions to derive an estimator of the latent number of types and we validate this estimator numerically.Comment: This paper is accepted in Biometrika. 5 pages and no figure in the main paper. 3 pages and 1 figure in the supplementary materia

Gaze behavior in a review-a-definition task

A requirement definition document (RDD) in software development should define the necessary and sufficient conditionfor the software to satisfy. It is preferable to review and guarantee the quality of the RDD. It is, however, not easyto evaluate the goodness of the reviewer, due to various review styles and the logical complexity of such a document.Therefore, we developed a test set for the review task of the RDD and investigated the reviewers gaze behavior. The testset includes the four logical relationships between the definition and instances, and our analysis revealed that validation ofthe necessary condition is relatively easier than validation of the sufficient condition. Moreover, reviewers gaze patternswere concentrated more on a certain part of sentences when the review was successful. It may suggest that the reviewsuccess can be predicted by the reviewing eye gaze fixations on sentences with the relatively higher information gain

A connectionist account of ontological boundary shifting

Abstract. Previous research on children's categorizations has suggested that children use perceptual and conceptual knowledge to generalize object names. Especially, the relation between ontological categories and linguistic categories appears to be a critical cue to learning object categories. However, its underlying mechanism remains unclear. In this paper, we propose a connectionist model that can acquire ontological knowledge by learning linguistic categories of entities. The results suggest that linguistic cues help children attend to specific perceptual properties

Sound Symbolism Facilitates Word Learning in 14-Month-Olds

Sound symbolism, or the nonarbitrary link between linguistic sound and meaning, has often been discussed in connection with language evolution, where the oral imitation of external events links phonetic forms with their referents (e.g., Ramachandran & Hubbard, 2001). In this research, we explore whether sound symbolism may also facilitate synchronic language learning in human infants. Sound symbolism may be a useful cue particularly at the earliest developmental stages of word learning, because it potentially provides a way of bootstrapping word meaning from perceptual information. Using an associative word learning paradigm, we demonstrated that 14-month-old infants could detect Köhler-type (1947) shape-sound symbolism, and could use this sensitivity in their effort to establish a wordreferent association