PoN-S : a systematic approach for applying the Physics of Notation (PoN)

Visual Modeling Languages (VMLs) are important instruments of communication between modelers and stakeholders. Thus, it is important to provide guidelines for designing VMLs. The most widespread approach for analyzing and designing concrete syntaxes for VMLs is the so-called Physics of Notation (PoN). PoN has been successfully applied in the analysis of several VMLs. However, despite its popularity, the application of PoN principles for designing VMLs has been limited. This paper presents a systematic approach for applying PoN in the design of the concrete syntax of VMLs. We propose here a design process establishing activities to be performed, their connection to PoN principles, as well as criteria for grouping PoN principles that guide this process. Moreover, we present a case study in which a visual notation for representing Ontology Pattern Languages is designed

Integrating Routine, Variety Seeking and Compensatory Choice in a Utility Maximizing Framework

Given the large number of choices that consumers make each day it seems likely that they will generally adopt decision strategies that minimize cognitive effort, particularly with low price products such as most items found in a supermarket. One such strategy may be to simply choose what has been chosen in the past, i.e. to fall into a pattern of routine choices or decisions. In contrast, there may be preferences for variety in markets for low price, highly differentiated goods. We develop a conceptual and empirical model of routine choice, and the factors that result in transitions to two strategies other than routine selection, to wit, utility maximizing choice among available alternatives and a variety seeking strategy. The empirical approach we employ provides a mechanism for the examination of panel data that avoids the state dependence issues present in most applications to these types of data. We apply this framework to the choice of two food products that illustrate the heterogeneity across types of products in decision strategies and routine choice patterns.Choice modeling, routine behavior, variety‐seeking, panel data, Consumer/Household Economics, Demand and Price Analysis, Institutional and Behavioral Economics, D12, D03, C25,

Specific impairments in cognitive development: a dynamical systems approach

Neuropsychologists have frequently proposed that domain-specific deficits can be observed in developmental disorders (e.g., phonology in dyslexia, theory of mind in autism, grammar in specific language impairment, face recognition in prosopagnosia, mathematics in dyscalculia). These deficits appeal to a modular cognitive architecture. However, specific developmental deficits are at odds with theories that posit a high degree of interactivity between cognitive abilities across development. If there are early deficits, why do these not spread across the cognitive system during development? Or experience compensatory help from other initially intact components? We address these questions within a dynamical systems framework (van der Maas et al., 2006). We explore the conditions for deficit spread and compensation for a range of possible cognitive architectures, from modular to fully distributed. While preliminary, the results point to the importance of specifying precisely the normal developmental architecture of a system prior to characterizing patterns of impairment that might emerge from it

Region-Referenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach

Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to represent time-localized frequency domain characterizations of EEG data as region-referenced functional data. This representation is coupled with a hierarchical modeling approach to multivariate functional observations. Within this familiar setting, we discuss how several prior models relate to structural assumptions about multivariate covariance operators. An overarching modeling framework, based on infinite factorial decompositions, is finally proposed to balance flexibility and efficiency in estimation. The motivating application stems from a study of implicit auditory learning, in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. Using the proposed model, we examine differential band power dynamics as brain function is interrogated throughout the duration of a computer-controlled experiment. Our work offers a novel look at previous findings in psychiatry, and provides further insights into the understanding of ASD. Our approach to inference is fully Bayesian and implemented in a highly optimized Rcpp package

The role of the individual in the coming era of process-based therapy

For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy over the last few decades. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era.Accepted manuscrip

A role for the developing lexicon in phonetic category acquisition

Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning