A measure of agreement for use with the Holland classification system

A measure of agreement for partially ordered data is suggested that makes use of the k categories with the highest ranks, the ranks of the remaining categories being disregarded. The proposed measure is applied to the Self-Directed Search in order to evaluate its agreement with occupational preference. Other forms of agreement in the Holland classification system can also be assessed by the proposed measure. Examples are congruence and husband-wife and counselor-client agreement.     

Modeling the Influence of Language Input Statistics on Children's Speech Production

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.     

Cultural differences in process and person focus: Congratulations on your hard work versus celebrating your exceptional brain

Cultural differences in process-focused versus person-focused themes were examined using both cultural artifacts (Study 1) and self-reports (Study 2). In Study 1, the contents of Chinese and American graduation and encouragement cards were analyzed for their relative emphasis on person- versus process-focused themes. Person-focused themes center on recipients' stable traits and abilities, and their emotional well-being. In contrast, process-focused themes dwell on the recipients' hard work and effort, and emphasize the importance of continued self-improvement and growth. Messages on Chinese cards were significantly more process than person focused. The reverse was true of American cards. Chinese cards also contained more process-focused (e.g., winding roads) than person-focused images (e.g., student standing on a pedestal). American cards contained more person-focused than process-focused images, although this difference was significant only among encouragement cards. In Study 2, we presented Chinese and American participants with graduation card messages differing in focus. Chinese parents indicated that they would be more likely to select and Chinese students indicated that they would be more likely to receive process- than person-themed graduation card messages. American parents and students showed no effects of message focus. The findings illustrate how cultural beliefs are reflected in cultural artifacts and personal preferences.     

Distributional learning has immediate and long-lasting effects

Evidence of distributional learning, a statistical learning mechanism centered on relative frequency of exposure to different tokens, has mainly come from short-term learning and therefore does not ostensibly address the development of important learning processes. The present longitudinal study examines both short- and long-term effects of distributional learning of phonetic categories on non-native sound discrimination over a 12-month period. Two groups of listeners were exposed to a two-minute distribution of auditory stimuli in which the most frequently presented tokens either approximated or exaggerated the natural production of the speech sounds, whereas a control group listened to a piece of classical music for the same length of time. Discrimination by listeners in the two distribution groups improved immediately after the short exposure, replicating previous results. Crucially, this improvement was maintained after six and 12 months, demonstrating that distributional learning has long-lasting effects.     

Quantitative Standards for Absolute Linguistic Universals

Absolute linguistic universals are often justified by cross‐linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross‐linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in both it is possible to find strict linguistic universals, but that the numbers of independent languages necessary to do so is generally unachievable. This suggests that methods other than typological statistics are necessary to establish absolute properties of human language, and thus that many of the purported universals in linguistics have not received sufficient empirical justification.     

Problematic,absent and stigmatizing diagnoses in current mental disorders classifications: Results from the WHO-WPA and WHO-IUPsyS Global Surveys

This study examined English- and Spanish-speaking psychologists’ and psychiatrists’ opinions regarding problematic, absent and stigmatizing diagnoses in current mental disorders classifications (ICD-10 and DSM-IV), and their perceived need for a national classification of mental disorders. Answers to open-ended questions included in WHO-WPA and WHO-IUPsyS surveys were examined using an inductive content-analysis method. A total of 3,222 participants from 35 countries were included. The most problematic diagnostic group was personality disorders, especially among psychiatrists, because of poor validity and lack of specificity. Complex posttraumatic stress disorder was the most frequent diagnosis suggested for inclusion, mainly by psychologists, to better account for the distinct processes and consequences of complex trauma. Schizophrenia was the diagnosis most frequently identified as stigmatizing, particularly by psychiatrists, due to lack of public understanding or knowledge about the diagnosis. Of the 14.4% of participants who perceived a need for a national classification system, two-thirds were from Africa or Latin America. The rationales provided were that mental disorders classifications should consider cultural and socio-historical diversity in the expression of psychopathology, differences in the perception of what is and is not pathological in different nations, and the existence of culture-bound syndromes. Implications for ICD-11 development and dissemination are discussed.     

Object representations are biased toward each other through statistical learning

The visual system is remarkably efficient at extracting regularities from the environment through statistical learning. While such extraction has extensive consequences on cognition, it is unclear how statistical learning shapes the representations of the individual objects that comprise the regularities. Here we examine how statistical learning alters object representations. In three experiments, participants were exposed to either random arrays containing objects in a random order, or structured arrays containing object pairs where two objects appeared next to each other in fixed spatial or temporal configurations. After exposure, one object in each pair was briefly presented and participants judged the location or the orientation of the object without seeing the other object in the pair. We found that when an object reliably appeared next to another object in space, it was judged as being closer to the other object in space even though the other object was never presented (Experiments 1 and 2). Likewise, when an object reliably preceded another object in time, its orientation was biased toward the orientation of the other object even though the other object was never presented (Experiment 3). These results demonstrated that statistical learning fundamentally shapes how individual objects are represented in visual memory, by biasing the representation of one object toward its co-occurring partner. Importantly, participants in all experiments were not explicitly aware of the regularities. Thus, the bias in object representations was implicit. The current study reveals a novel impact of statistical learning on object representation: spatially co-occurring objects are represented as being closer in space, and temporally co-occurring objects are represented as having more similar features.     

Learning General Phonological Rules From Distributional Information: A Computational Model

Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles.     

The neural correlates of statistical learning in a word segmentation task: An fMRI study

Functional magnetic resonance imaging (fMRI) was used to assess neural activation as participants learned to segment continuous streams of speech containing syllable sequences varying in their transitional probabilities. Speech streams were presented in four runs, each followed by a behavioral test to measure the extent of learning over time. Behavioral performance indicated that participants could discriminate statistically coherent sequences (words) from less coherent sequences (partwords). Individual rates of learning, defined as the difference in ratings for words and partwords, were used as predictors of neural activation to ask which brain areas showed activity associated with these measures. Results showed significant activity in the pars opercularis and pars triangularis regions of the left inferior frontal gyrus (LIFG). The relationship between these findings and prior work on the neural basis of statistical learning is discussed, and parallels to the frontal/subcortical network involved in other forms of implicit sequence learning are considered.     

Competitive Processes in Cross‐Situational Word Learning

Cross‐situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross‐situational word learning provides information at multiple scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross‐situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process‐level understanding of cross‐situational learning.