Philosophy of Education in a Poor Historical Moment: A Personal Account

Under the post-metaphysical sky “old” humanistic-oriented education is possible solely at the cost of its transformation into its negative, into a power that is determined to diminish human potentials for self-exaltation. Nothing less than total metamorphosis is needed to rescue the core of humanistic genesis: the quest for edifying Life and resistance to the call for “home-returning” into the total harmony that is promised to us within nothingness.     

Conceptualizing Self-determination from a Behavioral Perspective: The Role of Choice,Self-control,and Self-management

The goal of education can be defined in many ways; but in searching the literature, we found that in most cases, people consider the goal of education to be developing a self-determined individual. Self-determination is an abstract term. Behavior analysts may find this term difficult to define. Therefore, it may be difficult to observe and measure whether “self-determined behaviors” have developed. Many other service providers use this term frequently; thus, behavior analysts working with these service providers must come to terms with this concept in order to better collaborate. We argue that self-determination can be operationally defined with the concepts of choice, self-control, and self-management. By using the measurable behaviors included in these concepts, we believe that services can be developed to teach self-determination skills. This paper explores these concepts and how they can contribute to an operational definition of self-determination, and ultimately, help behavior analysts work with other providers to effectively teach self-determination to individuals with developmental disabilities.

     

A Dyadic IRT Model

Psychometrika - We propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors,...     

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.