Effects of a Token Economy on a Student with Autism Exhibiting Disruptive Behavior in a General Education Classroom

The Centers for Disease Control and Prevention defines autism spectrum disorder (ASD) as a developmental disability that can cause significant social, communication, and behavioral challenges; and estimates that one in 68 children in the United States are affected by it (CDC, 2014). This prevalence rate is much higher than that of prior decades and has led to trends and factors related to educational programs that include an increase in the inclusion of students with ASD in general education classrooms. For this reason, it is crucial that teachers have access to efficient, teacher-friendly, and research-based interventions for students with ASD in the general education environment. One strategy that has been implemented in many different settings to influence behavior is a token economy system. The purpose of the current study was to examine the effectiveness of a token economy in decreasing disruptive behavior displayed by a student with ASD in a fourth-grade general education classroom. Using an ABAB single subject design, results showed that the student’s disruptive behavior was at an increased level during the initial baseline condition; decreased as the intervention was introduced; returned to an increased level during the second baseline condition; and decreased again once the intervention was reinstated. Furthermore, the student’s behavior continued to stay at decreased levels during a maintenance phase

Systematic Visual Reasoning through Object-Centric Relational Abstraction

Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent work in computer vision has introduced models with the capacity to extract object-centric representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other recent models have employed inductive biases for relational abstraction to achieve systematic generalization of learned abstract rules, but have generally assumed the presence of object-focused inputs. Here, we combine these two approaches, introducing Object-Centric Relational Abstraction (OCRA), a model that extracts explicit representations of both objects and abstract relations, and achieves strong systematic generalization in tasks (including a novel dataset, CLEVR-ART, with greater visual complexity) involving complex visual displays

Zero-shot visual reasoning through probabilistic analogical mapping

Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a model of visual reasoning that synthesizes these two approaches. VisiPAM employs learned representations derived directly from naturalistic visual inputs, coupled with a similarity-based mapping operation derived from cognitive theories of human reasoning. We show that without any direct training, visiPAM outperforms a state-of-the-art deep learning model on an analogical mapping task. In addition, visiPAM closely matches the pattern of human performance on a novel task involving mapping of 3D objects across disparate categories

Liver transplantation for late-onset acute liver failure in Wilson’s disease: the UK experience over two decades

BACKGROUNDS AND AIMS: Acute liver failure as the initial presentation of Wilson’s disease is usually associated with onset in childhood, adolescence or early adulthood. Outcomes after transplantation for late-onset presentations, at or after 40 years, are seldom reported in the literature METHODS: We report a case, review the literature and provide unpublished data from the UK Transplant Registry on late-onset acute liver failure in Wilson’s disease. RESULTS: We describe a 62-year-old man presenting with acute liver failure who was successfully treated with urgent liver transplantation. We identified seven cases presenting at age 40 years or over in the literature where individual outcomes were reported; three were treated with transplantation and two survived. We identified a further eight cases listed for transplantation in the UK between 1995 and 2014; seven were treated with transplantation and six survived. One patient was de-listed for unknown reasons. CONCLUSIONS: We discuss the need to consider Wilson’s disease in older adults presenting with acute liver failure and importance of making the diagnosis prior to transplantation. We suggest that urgent liver transplantation has good outcomes for late-onset presentations and recommend that urgent transplantation should always be considered in Wilson’s disease presenting with acute liver failure

Learning Representations that Support Extrapolation

Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, temporal context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.Comment: ICML 202

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This effort has often been framed in terms of a dichotomy between empiricist and nativist approaches, most recently embodied by debates concerning deep neural networks and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain

Biomineralisation in the Palaeozoic oceans: evidence for simultaneous crystallisation of high and low magnesium calcite by phacopine trilobites

The chemical composition and microstructure of the calcite cuticles of eleven species of phacopine trilobites have been investigated by electron beam imaging, diffraction, and microanalysis, and results reveal that the lenses of their schizochroal eyes differed significantly in chemical composition from the rest of the cuticle in vivo. Apart from the eye lenses, most cuticles are inferred to have escaped extensive recrystallisation because their constituent crystals are sub-micrometre in size and have a preferred orientation that is consistent between species. Their current compositions of ~1.4 to 2.4 mol% MgCO3 are likely to be close to original values, although as they commonly luminesce and contain detectable manganese and iron, some diagenetic alteration has taken place. The associated lenses have a microstructure that is suitable for focusing light, yet are optically turbid owing to the presence within calcite of micropores and crystals of microdolomite, apatite, celestite and pyrite. The microdolomite indicates that lenses recrystallised from an original high-Mg calcite composition and this is supported by the presence of nanometre-scale modulated microstructures in both the calcite and dolomite. These lenses currently contain ~1 to 6 mol% MgCO3, and by comparison with the proportion of magnesium lost from echinoderm stereom in the same thin sections, may have contained ~7.5 mol% MgCO3 in vivo. In some samples, more extensive diagenetic alteration is evidenced by recrystallisation of the cuticle including lenses to coarse equant calcite or enrichment of the cuticle, but not necessarily the lenses, in magnesium accompanying replacement by a Mg–Fe phyllosilicate. The phacopine trilobites had to modify partition coefficients for magnesium considerably in order to grow lenses with contrasting compositions to the rest of their cuticles, and such a strong vital effect on biomineralisation suggests that incorporation of magnesium was essential for functioning of their calcite optical s

The Space Density and Colors of Massive Galaxies at 2<z<3: the Predominance of Distant Red Galaxies

Using the deep multi-wavelength MUSYC, GOODS, and FIRES surveys we construct a stellar mass-limited sample of galaxies at 2<z<3. The sample comprises 294 galaxies with M>10^11 Solar masses distributed over four independent fields with a total area of almost 400 sq arcmin. The mean number density of massive galaxies in this redshift range is (2.2+-0.6) x 10^-4 Mpc^-3. We present median values and 25th and 75th percentiles for the distributions of observed R mags, observed J-K colors, and rest-frame UV continuum slopes, M/L(V) ratios, and U-V colors. The galaxies show a large range in all these properties. The ``median galaxy'' is faint in the observer's optical (R=25.9), red in the observed near-IR (J-K=2.48), has a rest-frame UV spectrum which is relatively flat (beta=-0.4), and rest-frame optical colors resembling those of nearby spiral galaxies (U-V=0.62). We determine which galaxies would be selected as Lyman break galaxies (LBGs) or Distant Red Galaxies (DRGs, having J-K>2.3) in this mass-limited sample. By number DRGs make up 69% of the sample and LBGs 20%, with a small amount of overlap. By mass DRGs make up 77% and LBGs 17%. Neither technique provides a representative sample of massive galaxies at 2<z<3 as they only sample the extremes of the population. As we show here, multi-wavelength surveys with high quality photometry are essential for an unbiased census of massive galaxies in the early Universe. The main uncertainty in this analysis is our reliance on photometric redshifts; confirmation of the results presented here requires extensive near-infrared spectroscopy of optically-faint samples.Comment: Accepted for publication in ApJ Letter