EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.     

How to tell a happy person: Accuracy of subjective well-being perception from texts

Motivation and Emotion - Although perceptions of subjective well-being (SWB) in unacquainted others have been shown to play a major role in impression formation, little is known about how accurate...     

Does Partisan Bias Modulate Neural Processing of Political Information? An Analysis of the Neural Correlates of Corruption and Positive Messages

Daily worldwide newspapers print articles exposing government corruption. Yet these messages do not lead to a loss of votes for the corrupt parties. Sympathizers’ partisan bias, which respectively minimizes and maximizes corruption and positive messages of their own parties respectively, is widely considered the main cause of the loss of effectiveness of political communications. Despite the well-established existence of such bias when processing political information, little is known as to its psychological origin. Through the use of neuroscience (fMRI), this study explores the underlying brain mechanisms of negative (corruption) and positive political messages related to a conservative and a socialist Spanish political party, as well as the differences between their sympathizers. The findings reveal that negative (vs. positive) political messages exert the greatest neuroimaging impact on the electorate, as shown in aversive, risk, and disappointment-related brain regions. Interestingly, we show that there exists a main partisan bias against opposite parties (and not a positive bias toward one’s own party) that stems from a higher risk, ambiguity, and disbelief provoked by both positive and negative information about rival parties. Furthermore, this bias was more pronounced among conservative supporters. The current findings provide valuable insights for political parties to improve their communication campaigns.     

Truth Without Reference: The Use of Fictional Names

Topoi - Singular terms without referents are called empty or vacuous terms. But not all of them are equally empty. In particular, not all proper names that fail to name an existing object fail in...     

Does cognitive reflection mediate the math gender gap at university admission in Chile?

Social Psychology of Education - In Chile, a vast and persistent gender gap in math performance at university admission has negative consequences for women’s opportunities. International...     

Emotional adjustment in victims and perpetrators of cyberbullying and traditional bullying

Given the rise in cyberbullying among secondary education students and the importance of certain psychological adjustment variables for the comprehension of this type of violent behavior, both in bullies and in victims, the purpose of the present study was to analyse the emotional adjustment of those involved in cyber- and traditional bullying. The adjustment variables studied were self-concept, perceived stress, loneliness, depressive symptomatology, social anxiety, life satisfaction, and emotional intelligence. Using a sample of 1318 adolescents (47% boys), aged between 11 and 17 years, four groups were established to compare victims and cybervictims (uninvolved students, traditional victims, cybervictims, and traditional–cybervictims). The analysis of variance showed that students who performed the same role (bully or victim) in both contexts (at school and online). In particular, those who suffered traditional or cyberbullying or both conjointly presented lower scores in physical and social self-concept, life satisfaction, emotional clarity, and emotion regulation, as well as higher scores in perceived stress, loneliness, depression, and social anxiety. In bullies—traditional, cyberbullying or both simultaneously—higher scores were observed in perceived stress, loneliness, depressive symptomatology, fear of negative assessment, avoidance, and general social anxiety, and lower scores in the dimensions of academic and family self-concept, life satisfaction, emotional clarity, and emotion regulation. In general, the findings indicate that students who were involved in bullying situations, both victims and bullies, presented more damaged emotional profiles than those who are uninvolved, especially students who performed the same role (bully or victim) in both contexts (at school and online).

     

Effect of social organisation on interspecific differences in overmarking behaviour of foals in African equids

Animal Cognition - Overmarking remains an unstudied topic in juvenile mammals. We have previously documented a very high rate of overmarking by foals in four captive African equid species: mountain...     

Satisfiability Testing for Boolean Formulas Using Δ-trees

The tree-based data structure of -tree for propositional formulas is introduced in an improved and optimised form. The -trees allow a compact representation for negation normal forms as well as for a number of reduction strategies in order to consider only those occurrences of literals which are relevant for the satisfiability of the input formula. These reduction strategies are divided into two subsets (meaning- and satisfiability-preserving transformations) and can be used to decrease the size of a negation normal form A at (at most) quadratic cost. The reduction strategies are aimed at decreasing the number of required branchings and, therefore, these strategies allow to limit the size of the search space for the SAT problem.     

Comparative analysis of different correction methods for measuring Rod-and-Frame Test performance

A comparative analysis was conducted of five different methods for scoring the Rod-and-Frame Test to examine the convergent validities of each of the five methods with performance on the Embedded Figures Test for a sample of 408 13- to 16-yr.-olds. The scoring methods included the absolute error, the algebraic error, the absolute value of the algebraic error, the frame effect, and an assessment of relative field dependence. Correlations obtained among the five methods and the Embedded Figures Test indicated different patterns for boys and girls. In general, the method which employed the absolute value of the algebraic error yielded the strongest convergent validity coefficients with scores on the Embedded Figures Test and Group Embedded Figures Test for boys (.62 and -.54, respectively), although, for girls, the absolute algebraic error and absolute error both yielded coefficients of .50 with Embedded Figures Test scores and -.46 with Group Embedded Figures Test scores.