Scale-Invariant Transition Probabilities in Free Word Association Trajectories

Free-word association has been used as a vehicle to understand the organization of human thoughts. The original studies relied mainly on qualitative assertions, yielding the widely intuitive notion that trajectories of word associations are structured, yet considerably more random than organized linguistic text. Here we set to determine a precise characterization of this space, generating a large number of word association trajectories in a web implemented game. We embedded the trajectories in the graph of word co-occurrences from a linguistic corpus. To constrain possible transport models we measured the memory loss and the cycling probability. These two measures could not be reconciled by a bounded diffusive model since the cycling probability was very high (16% of order-2 cycles) implying a majority of short-range associations whereas the memory loss was very rapid (converging to the asymptotic value in ∼7 steps) which, in turn, forced a high fraction of long-range associations. We show that memory loss and cycling probabilities of free word association trajectories can be simultaneously accounted by a model in which transitions are determined by a scale invariant probability distribution

Critical approaches to education in the work of Lorenzo Milani and Paulo Freire

Paulo Freire and Lorenzo Milani are considered as key figures in a number of Southern European countries for providing signposts for a critical approach to education. In this paper I will view their ideas and biographical trajectories comparatively to glean some important insights for a critical pedagogy. The common theme throughout this comparative analysis is that of education for social justice based on critical literacy. The paper also deals with such themes as the relationship between education and politics, the relationship between education and life, the collective dimension of learning and the ability to read as well as write the word and the world.peer-reviewe

General Piecewise Growth Mixture Model: Word Recognition Development for Different Learners in Different Phases

The General Piecewise Growth Mixture Model (GPGMM), without losing generality to other fields of study, can answer six crucial research questions regarding children’s word recognition development. Using child word recognition data as an example, this study demonstrates the flexibility and versatility of the GPGMM in investigating growth trajectories that are potentially phasic and heterogeneous. The strengths and limitations of the GPGMM and lessons learned from this hands-on experience are discussed

Fast trajectory matching using small binary images

This paper proposes a new trajectory matching method using logic operations on binary images. By using small binary images we are able to effectively utilize the large word size offered in modern CPU architectures, resulting in a very efficient evaluation of similarities between trajectories. The efficiency is caused by the fact that all bits in the same word are processed in parallel. Representing trajectories as small binary images has other advantages, such as a low space requirement and good noise resistance. The proposed method is evaluated on a publicly available dataset, and is compared to the more sophisticated Longest Common Subsequence (LCSS) method. In addition, synthetic experiments show the good efficiency and accuracy of the proposed method, enabling real time trajectory retrieval on databases with millions of trajectories.postprin

Cracking the social code of speech prosody using reverse correlation

Human listeners excel at forming high-level social representations about each other, even from the briefest of utterances. In particular, pitch is widely recognized as the auditory dimension that conveys most of the information about a speaker's traits, emotional states, and attitudes. While past research has primarily looked at the influence of mean pitch, almost nothing is known about how intonation patterns, i.e., finely tuned pitch trajectories around the mean, may determine social judgments in speech. Here, we introduce an experimental paradigm that combines state-of-the-art voice transformation algorithms with psychophysical reverse correlation and show that two of the most important dimensions of social judgments, a speaker's perceived dominance and trustworthiness, are driven by robust and distinguishing pitch trajectories in short utterances like the word "Hello," which remained remarkably stable whether male or female listeners judged male or female speakers. These findings reveal a unique communicative adaptation that enables listeners to infer social traits regardless of speakers' physical characteristics, such as sex and mean pitch. By characterizing how any given individual's mental representations may differ from this generic code, the method introduced here opens avenues to explore dysprosody and social-cognitive deficits in disorders like autism spectrum and schizophrenia. In addition, once derived experimentally, these prototypes can be applied to novel utterances, thus providing a principled way to modulate personality impressions in arbitrary speech signals