Attentional Strategies and the Transition From Subitizing to Estimation in Numerosity Perception.

The common view of the transition between subitizing and numerosity estimation regimes is that there is a hard bound on the subitizing range, and beyond this range, people estimate. However, this view does not adequately address the behavioral signatures of enumeration under conditions of attentional load or in the immediate post-subitizing range. The possibility that there might exist a numerosity range where both processes of subitizing and estimation operate in conjunction has so far been ignored. Here, we investigate this new proposal, that people strategically combine the processes of subitizing and estimation to maximize accuracy and precision, given time or attentional constraints. We present a process-level account of how subitizing and estimation can be combined through strategic deployment of attention to maximize the precision of perceived numerosity given time constraints. We then describe a computational model of this account and apply it in two experimental simulations to demonstrate how it can explain key findings in prior enumeration research. While recent modeling work has argued that the behavioral signatures of enumeration can best be explained through a single numerosity system with a single form of representation, we argue that our model demonstrates how the traditional two-systems view of numerical representation accounts for behavioral data through coordination with a unified attentional mechanism, rather than a unified representation.

Argument-based inductive logics, with coverage of compromised perception.

Formal deductive logic, used to express and reason over declarative, axiomatizable content, captures, we now know, essentially all of what is known in mathematics and physics, and captures as well the details of the proofs by which such knowledge has been secured. This is certainly impressive, but deductive logic alone cannot enable rational adjudication of arguments that are at variance (however much additional information is added). After affirming a fundamental directive, according to which argumentation should be the basis for human-centric AI, we introduce and employ both a deductive and-crucially-an inductive cognitive calculus. The former cognitive calculus, DCEC, is the deductive one and is used with our automated deductive reasoner ShadowProver; the latter, IDCEC, is inductive, is used with the automated inductive reasoner ShadowAdjudicator, and is based on human-used concepts of likelihood (and in some dialects of IDCEC, probability). We explain that ShadowAdjudicator centers around the concept of competing and nuanced arguments adjudicated non-monotonically through time. We make things clearer and more concrete by way of three case studies, in which our two automated reasoners are employed. Case Study 1 involves the famous Monty Hall Problem. Case Study 2 makes vivid the efficacy of our calculi and automated reasoners in simulations that involve a cognitive robot (PERI.2). In Case Study 3, as we explain, the simulation employs the cognitive architecture ARCADIA, which is designed to computationally model human-level cognition in ways that take perception and attention seriously. We also discuss a type of argument rarely analyzed in logic-based AI; arguments intended to persuade by leveraging human deficiencies. We end by sharing thoughts about the future of research and associated engineering of the type that we have displayed.

Confidence and gradation in causal judgment.

When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role.

Norms Affect Prospective Causal Judgments.

People more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments-that is, judgments about the cause of some future outcome. Four experiments show that people more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment.

The role of attention in the enumeration of canonical patterns.

We report novel findings from experiments on the enumeration of canonical patterns under attentional load. While previous studies have shown that the process of enumerating randomized arrangements can be disrupted by attentional load, the effect of attentional load on canonical patterns has been unexplored. To investigate this case, we adapted a spatial dual-task paradigm previously used to study attentional disruption during the enumeration of randomized arrangements. We begin by replicating previous findings for randomized arrangements, with enumeration error increasing with cluster numerosity and attentional load. For dice patterns, enumeration error also increased under attentional load. However, contrary to findings from studies on single-task enumeration of dice patterns, we observed conflation of patterns with similar outlines. In subsequent experiments, we manipulated the spatial location of the enumeration task, placing the dot cluster in the center. With centrally located, canonical patterns that remained in the same location across trials, enumeration accuracy was more consistent with results from single-task studies. We hypothesize that participants may be using shape cues to inform guessing during enumeration tasks when unable to both localize and fully attend to target patterns.

Best compromise nutritional menus for childhood obesity.

Childhood obesity is an undeniable reality that has rapidly increased in many countries. Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that is defined by a mathematical model. Existing mathematical models only partially address the objective and constraints of childhood obesity; therefore, the solutions provided are insufficient for health specialists to prepare nutritional menus for individual patients. This manuscript proposes a multiobjective mathematical programming model to aid in healthy nutritional menu planning that may prevent childhood obesity. This model provides a plan for combinations and amounts of food across different schedules and daily meals. This approach minimizes the major risk factors of childhood obesity (i.e., glycemic load and cholesterol intake). In addition, this approach considers the minimization of nutritional mismatch and total cost. The model is solved using a deterministic method and two metaheuristic methods. Test instances associated with children aged 4-18 years were created with the support of health professionals to complete this numerical study. The quality of the solutions generated using the three methods was similar, but the metaheuristic methods provided solutions in a shorter computational time. These results are submitted to statistical hypothesis tests to be validated. The numerical results indicate proper guidelines for personalized plans for individual children.

Much Ado About Nothing: The Mental Representation of Omissive Relations.

When the absence of an event causes some outcome, it is an instance of omissive causation. For instance, not eating lunch may cause you to be hungry. Recent psychological proposals concur that the mind represents causal relations, including omissive causal relations, through mental simulation, but they disagree on the form of that simulation. One theory states that people represent omissive causes as force vectors; another states that omissions are representations of contrasting counterfactual simulations; a third argues that people think about omissions by representing sets of iconic possibilities - mental models - in a piecemeal fashion. In this paper, we tease apart the empirical predictions of the three theories and describe experiments that run counter to two of them. Experiments 1 and 2 show that reasoners can infer temporal relations from omissive causes - a pattern that contravenes the force theory. Experiment 3 asked participants to list the possibilities consistent with an omissive cause - it found that they tended to list particular privileged possibilities first, most often, and faster than alternative possibilities. The pattern is consistent with the model theory, but inconsistent with the contrast hypothesis. We marshal the evidence and explain why it helps to solve a long-standing debate about how the mind represents omissions.

Selection enables enhancement: An integrated model of object tracking.

The diversity of research on visual attention and multiple-object tracking presents challenges for anyone hoping to develop a unified account. One key challenge is identifying the attentional limitations that give rise to competition among targets during tracking. To address this challenge, we present a computational model of object tracking that relies on two attentional mechanisms: serial selection and parallel enhancement. Selection picks out an object for further processing, whereas enhancement increases sensitivity to stimuli in regions where objects have been selected previously. In this model, multiple target locations can be tracked in parallel via enhancement, whereas a single target can be selected so that additional information beyond its location can be processed. In simulations of two psychological experiments, we demonstrate that spatial competition during enhancement and temporal competition for selection can explain a range of findings on multiple-object tracking, and we argue that the interaction between selection and enhancement captured in the model is critical to understanding attention more broadly.

Mental models and omissive causation.

Some causal relations refer to causation by commission (e.g., "A gunshot causes death"), and others refer to causation by omission (e.g., "Not breathing causes death"). We describe a theory of the representation of omissive causation based on the assumption that people mentally simulate sets of possibilities-mental models-that represent causes, enabling conditions, and preventions (Goldvarg & Johnson-Laird, 2001). The theory holds that omissive causes, enabling conditions, and preventions each refer to distinct sets of possibilities. For any such causal relation, reasoners typically simulate one initial possibility, but they are able to consider alternative possibilities through deliberation. These alternative possibilities allow them to deliberate over finer-grained distinctions when reasoning about causes and effects. Hence, reasoners should be able to distinguish between omissive causes and omissive enabling conditions. Four experiments corroborated the predictions of the theory. We describe them and contrast the results with the predictions of alternative accounts of causal representation and inference.

Automated large-scale culture and medium-throughput chemical screen for modulators of proliferation and viability of human induced pluripotent stem cell-derived neuroepithelial-like stem cells.

The aim of this study was to demonstrate proof-of-concept feasibility for the use of human neural stem cells (NSCs) for high-throughput screening (HTS) applications. For this study, an adherent human induced pluripotent stem (iPS) cell-derived long-term, self-renewing, neuroepithelial-like stem (lt-NES) cell line was selected as a representative NSC. Here, we describe the automated large-scale serum-free culture ("scale-up") of human lt-NES cells on the CompacT SelecT cell culture robotic platform, followed by their subsequent automated "scale-out" into a microwell plate format. We also report a medium-throughput screen of 1000 compounds to identify modulators of neural stem cell proliferation and/or survival. The screen was performed on two independent occasions using a cell viability assay with end-point reading resulting in the identification of 24 potential hit compounds, 5 of which were found to increase the proliferation and/or survival of human lt-NES on both occasions. Follow-up studies confirmed a dose-dependent effect of one of the hit compounds, which was a Cdk-2 modulator. This approach could be further developed as part of a strategy to screen compounds to either improve the procedures for the in vitro expansion of neural stem cells or to potentially modulate endogenous neural stem cell behavior in the diseased nervous system.

An architecture for adaptive algorithmic hybrids.

We describe a cognitive architecture for creating more robust intelligent systems. Our approach is to enable hybrids of algorithms based on different computational formalisms to be executed. The architecture is motivated by some features of human cognitive architecture and the following beliefs: 1) Most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and 2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. The key to this approach is the following two principles: 1) Algorithms based on very different computational frameworks (e.g., logical reasoning, probabilistic inference, and case-based reasoning) can be implemented using the same set of five common functions and 2) each of these common functions can be executed using multiple data structures and algorithms. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme in planning, spatial reasoning, robotics, and information retrieval illustrate that this approach to hybridizing algorithms enables qualitative and measurable quantitative advances in the abilities of intelligent systems.

Unrestricted somatic stem cells from human umbilical cord blood grow in serum-free medium as spheres.

BACKGROUND: Human umbilical cord blood-derived unrestricted somatic stem cells (USSCs), which are capable of multilineage differentiation, are currently under investigation for a number of therapeutic applications. A major obstacle to their clinical use is the fact that in vitro expansion is still dependent upon fetal calf serum, which could be a source of pathogens. In this study, we investigate the capacity of three different stem cell culture media to support USSCs in serum-free conditions; HEScGRO, PSM and USSC growth medium ACF. Our findings demonstrate that USSCs do not grow in HEScGRO or PSM, but we were able to isolate, proliferate and maintain multipotency of three USSC lines in USSC growth medium ACF. RESULTS: For the first one to three passages, cells grown in USSC growth medium ACF proliferate and maintain their morphology, but with continued passaging the cells form spherical cell aggregates. Upon dissociation of spheres, cells continue to grow in suspension and form new spheres. Dissociated cells can also revert to monolayer growth when cultured on extracellular matrix support (fibronectin or gelatin), or in medium containing fetal calf serum. Analysis of markers associated with pluripotency (Oct4 and Sox2) and differentiation (FoxA2, Brachyury, Goosecoid, Nestin, Pax6, Gata6 and Cytokeratin 8) confirms that cells in the spheres maintain their gene expression profile. The cells in the spheres also retain the ability to differentiate in vitro to form cells representative of the three germline layers after five passages. CONCLUSIONS: These data suggest that USSC growth medium ACF maintains USSCs in an undifferentiated state and supports growth in suspension. This is the first demonstration that USSCs can grow in a serum- and animal component-free medium and that USSCs can form spheres.

Ability, breadth, and parsimony in computational models of higher-order cognition.

Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern-day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher-order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model.

Characterization of human embryonic stem cell lines by the International Stem Cell Initiative.

The International Stem Cell Initiative characterized 59 human embryonic stem cell lines from 17 laboratories worldwide. Despite diverse genotypes and different techniques used for derivation and maintenance, all lines exhibited similar expression patterns for several markers of human embryonic stem cells. They expressed the glycolipid antigens SSEA3 and SSEA4, the keratan sulfate antigens TRA-1-60, TRA-1-81, GCTM2 and GCT343, and the protein antigens CD9, Thy1 (also known as CD90), tissue-nonspecific alkaline phosphatase and class 1 HLA, as well as the strongly developmentally regulated genes NANOG, POU5F1 (formerly known as OCT4), TDGF1, DNMT3B, GABRB3 and GDF3. Nevertheless, the lines were not identical: differences in expression of several lineage markers were evident, and several imprinted genes showed generally similar allele-specific expression patterns, but some gene-dependent variation was observed. Also, some female lines expressed readily detectable levels of XIST whereas others did not. No significant contamination of the lines with mycoplasma, bacteria or cytopathic viruses was detected.

Biochemical characterization and crystal structure of a Dim1 family associated protein: Dim2.

The U4/U6*U5 tri-snRNP complex is the catalytic core of the pre-mRNA splicing machinery. The thioredoxin-like protein hDim1 (U5-15 kDa) constitutes an essential component of the U5 particle, and its functions have been reported to be highly conserved throughout evolution. Recently, the Dim1-like protein (DLP) family has been extended to other proteins harboring similar sequence motifs. Here we report the biochemical characterization and crystallographic structure of a 149 amino acid protein, hDim2, which shares 38% sequence identity with hDim1. The crystallographic structure of hDim2 solved at 2.5 A reveals a classical thioredoxin-fold structure. However, despite the similarity in the thioredoxin fold, hDim2 differs from hDim1 in many significant features. The structure of hDim2 contains an extra alpha helix (alpha3) and a beta strand (beta5), which stabilize the protein, suggesting that they may be involved in interactions with hDim2-specific partners. The stability and thermodynamic parameters of hDim2 were evaluated by combining circular dichroism and fluorescence spectroscopy together with chromatographic and cross-linking approaches. We have demonstrated that, in contrast to hDim1, hDim2 forms stable homodimers. The dimer interface is essentially stabilized by electrostatic interactions and involves tyrosine residues located in the alpha3 helix. Structural analysis reveals that hDim2 lacks some of the essential structural motifs and residues that are required for the biological activity and interactive properties of hDim1. Therefore, on the basis of structural investigations we suggest that, in higher eukaryotes, although both hDim1 and hDim2 are involved in pre-mRNA splicing, the two proteins are likely to participate in different multisubunit complexes and biological processes.

Design of a novel class of peptide inhibitors of cyclin-dependent kinase/cyclin activation.

Cyclin dependent kinases (CDKs) are key regulators of the cell cycle progression and therefore constitute excellent targets for the design of anticancer agents. Most of the inhibitors identified to date inhibit kinase activity by interfering with the ATP-binding site of CDKs. We recently proposed that the protein/protein interface and conformational changes required in the molecular mechanism of CDK2-cyclin A activation were potential targets for the design of specific inhibitors of cell cycle progression. To this aim, we have designed and characterized a small peptide, termed C4, derived from amino acids 285-306 in the alpha5 helix of cyclin A. We demonstrate that this peptide does not interfere with complex formation but forms stable complexes with CDK2-cyclin A. The C4 peptide significantly inhibits kinase activity of complexes harboring CDK2 in a competitive fashion with respect to substrates but does not behave as an ATP antagonist. Moreover, when coupled with the protein transduction domain of Tat, the C4 peptide blocks the proliferation of tumor cell lines, thereby constituting a potent lead for the development of specific CDK-cyclin inhibitors.