Network-based stochastic competitive learning approach to disambiguation in collaborative networks

Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

High-order feature-based mixture models of classification learning predict individual learning curves and enable personalized teaching

Pattern classification learning tasks are commonly used to explore learning strategies in human subjects. The universal and individual traits of learning such tasks reflect our cognitive abilities and have been of interest both psychophysically and clinically. From a computational perspective, these tasks are hard, because the number of patterns and rules one could consider even in simple cases is exponentially large. Thus, when we learn to classify we must use simplifying assumptions and generalize. Studies of human behavior in probabilistic learning tasks have focused on rules in which pattern cues are independent, and also described individual behavior in terms of simple, single-cue, feature-based models. Here, we conducted psychophysical experiments in which people learned to classify binary sequences according to deterministic rules of different complexity, including high-order, multicue-dependent rules. We show that human performance on such tasks is very diverse, but that a class of reinforcement learning-like models that use a mixture of features captures individual learning behavior surprisingly well. These models reflect the important role of subjects' priors, and their reliance on high-order features even when learning a low-order rule. Further, we show that these models predict future individual answers to a high degree of accuracy. We then use these models to build personally optimized teaching sessions and boost learning.

Unifying pedagogical reasoning and epistemic trust

Researchers have argued that other people provide not only great opportunities for facilitating children's learning but also great risks. Research on pedagogical reasoning has argued children come prepared to identify and capitalize on others' helpfulness to teach, and this pedagogical reasoning allows children to learn rapidly and robustly. In contrast, research on epistemic trust has focused on how the testimony of others is not constrained to be veridical, and therefore, children must be prepared to identify which informants to trust for information. Although these problems are clearly related, these two literatures have, thus far, existed relatively independently of each other. We present a formal analysis of learning from informants that unifies and fills gaps in each of these literatures. Our analysis explains why teaching--learning from a knowledgeable and helpful informant--supports more robust inferences. We show that our account predicts specific inferences supported in pedagogical situations better than a standard account of learning from teaching. Our analysis also suggests that epistemic trust should depend on inferences about others' knowledge and helpfulness. We show that our knowledge and helpfulness account explains children's behavior in epistemic trust tasks better than the standard knowledge-only account. We conclude by discussing implications for development and outline important questions raised by viewing learning from testimony as joint inference over others' knowledge and helpfulness.

The effects of the previous outcome on probabilistic choice in rats

This study examined the effects of previous outcomes on subsequent choices in a probabilistic-choice task. Twenty-four rats were trained to choose between a certain outcome (1 or 3 pellets) versus an uncertain outcome (3 or 9 pellets), delivered with a probability of .1, .33, .67, and .9 in different phases. Uncertain outcome choices increased with the probability of uncertain food. Additionally, uncertain choices increased with the probability of uncertain food following both certain-choice outcomes and unrewarded uncertain choices. However, following uncertain-choice food outcomes, there was a tendency to choose the uncertain outcome in all cases, indicating that the rats continued to "gamble" after successful uncertain choices, regardless of the overall probability or magnitude of food. A subsequent manipulation, in which the probability of uncertain food varied within each session as a function of the previous uncertain outcome, examined how the previous outcome and probability of uncertain food affected choice in a dynamic environment. Uncertain-choice behavior increased with the probability of uncertain food. The rats exhibited increased sensitivity to probability changes and a greater degree of win-stay/lose-shift behavior than in the static phase. Simulations of two sequential choice models were performed to explore the possible mechanisms of reward value computations. The simulation results supported an exponentially decaying value function that updated as a function of trial (rather than time). These results emphasize the importance of analyzing global and local factors in choice behavior and suggest avenues for the future development of sequential-choice models.

Sequential task predictability in task switching

Many studies of task switching have found that a prolonged preparation time reduces switch costs. An alternative manipulation of task preparation is based on sequential task predictability, rather than preparation time. In Experiments 1 and 2 of the present study, participants performed explicitly instructed task sequences (i.e., AABB) and were then transferred to a random sequence. The observed benefit of predictability-based task preparation was not switch specific. In Experiment 3, the participants changed from random to predictable tasks. The observed predictability benefit again was not switch specific. The data thus suggest that task switching does not necessarily require a switch-specific reconfiguration process. Rather, task-specific control processes may be needed in both task switches and repetitions.

[When generative and preventive mechanisms meet with compatible and incompatible information in casual reasoning probability]

Several recent models of probabilistic causal reasoning in adults propose the existence of multiple interactions between ascending and descending factors. The aim of the present study is to evaluate the potential interactions between knowledge about generative and preventive mechanisms, the delta p of the data, and the relative importance given to the type of data provided. Two experiments involving 54 participants each are conducted, in which participants are invited to quantify the nature of a potential link (causal or associative) between adding a chemical substance to the asphalt of the roads and the formation of a slippery road in the winter, after being given information suggestive of (1) a generative mechanism, (2) a preventive mechanism, or (3) nothing special. Results show an influence of the suggested mechanisms on the reading of data that were provided, especially those with a delta p that is compatible with the a priori mechanism. These results are interpreted and discussed in line with the importance of considering multiple factors in probabilistic causal reasoning. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Context-gated statistical learning and its role in visual-saccadic decisions

Adaptive behavior in a nonstationary world requires humans to learn and track the statistics of the environment. We examined the mechanisms of adaptation in a nonstationary environment in the context of visual-saccadic inhibition of return (IOR). IOR is adapted to the likelihood that return locations will be refixated in the near future. We examined 2 potential learning mechanisms underlying adaptation: (a) a local tracking or priming mechanism that facilitates behavior that is consistent with recent experience and (b) a mechanism that supports retrieval of knowledge of the environmental statistics based on the contextual features of the environment. Participants generated sequences of 2 saccadic eye movements in conditions where the probability that the 2nd saccade was directed back to the previously fixated location varied from low (.17) to high (.50). In some conditions, the contingency was signaled by a contextual cue (the shape of the movement cue). Adaptation occurred in the absence of contextual signals but was more pronounced in the presence of contextual cues. Adaptation even occurred when different contingencies were randomly intermixed, showing the parallel formation of multiple associations between context and statistics. These findings are accounted for by an evidence accumulation framework in which the resting baseline of decision alternatives is adjusted on a trial-by-trial basis. This baseline tracks the subjective prior beliefs about the behavioral relevance of the different alternatives and is updated on the basis of the history of recent events and the contextual features of the current environment.

Unifying pedagogical reasoning and epistemic trust

Researchers have argued that other people provide not only great opportunities for facilitating children's learning but also great risks. Research on pedagogical reasoning has argued children come prepared to identify and capitalize on others' helpfulness to teach, and this pedagogical reasoning allows children to learn rapidly and robustly. In contrast, research on epistemic trust has focused on how the testimony of others is not constrained to be veridical, and therefore, children must be prepared to identify which informants to trust for information. Although these problems are clearly related, these two literatures have, thus far, existed relatively independently of each other. We present a formal analysis of learning from informants that unifies and fills gaps in each of these literatures. Our analysis explains why teaching--learning from a knowledgeable and helpful informant--supports more robust inferences. We show that our account predicts specific inferences supported in pedagogical situations better than a standard account of learning from teaching. Our analysis also suggests that epistemic trust should depend on inferences about others' knowledge and helpfulness. We show that our knowledge and helpfulness account explains children's behavior in epistemic trust tasks better than the standard knowledge-only account. We conclude by discussing implications for development and outline important questions raised by viewing learning from testimony as joint inference over others' knowledge and helpfulness.

Developing a concept of choice

Our adult concept of choice is not a simple idea, but rather a complex set of beliefs about the causes of actions. These beliefs are situation-, individual- and culture-dependent, and are thus likely constructed through social learning. This chapter takes a rational constructivist approach to examining the development of a concept of choice in young children. Initially, infants' combine assumptions of rational agency with their capacity for statistical inference to reason about alternative possibilities for, and constraints on, action. Preschoolers' build on this basic understanding by integrating domain-specific causal knowledge of physical, biological, and psychological possibility into their appraisal of their own and others' ability to choose. However, preschoolers continue to view both psychological and social motivations as constraints on choice--for example, stating that one cannot choose to harm another, or to act against personal desires. It is not until later that children share the adult belief that choice mediates between conflicting motivations for action. The chapter concludes by suggesting avenues for future research--to better characterize conceptual changes in beliefs about choice, and to understand how such beliefs arise from children's everyday experiences.

Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology, and machine learning to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection.

The influence of social information on children's statistical and causal inferences

Constructivist accounts of learning posit that causal inference is a child-driven process. Recent interpretations of such accounts also suggest that the process children use for causal learning is rational: Children interpret and learn from new evidence in light of their existing beliefs. We argue that such mechanisms are also driven by informative social cues and suggest ways in which such information influences both preschoolers' and infants' inferences. In doing so, we argue that a rational constructivist account should not only focus on describing the child's internal cognitive mechanisms for learning but also on how social information affects the process of learning.

The Motivated Strategies for Learning Questionnaire: score validity among medicine residents

CONTEXT

The Motivated Strategies for Learning Questionnaire (MSLQ) purports to measure motivation using the expectancy-value model. Although it is widely used in other fields, this instrument has received little study in health professions education.

OBJECTIVES

The purpose of this study was to evaluate the validity of MSLQ scores.

METHODS

We conducted a validity study evaluating the relationships of MSLQ scores to other variables and their internal structure (reliability and factor analysis). Participants included 210 internal medicine and family medicine residents participating in a web-based course on ambulatory medicine at an academic medical centre. Measurements included pre-course MSLQ scores, pre- and post-module motivation surveys, post-module knowledge test and post-module Instructional Materials Motivation Survey (IMMS) scores.

RESULTS

Internal consistency was universally high for all MSLQ items together (Cronbach's α = 0.93) and for each domain (α ≥ 0.67). Total MSLQ scores showed statistically significant positive associations with post-test knowledge scores. For example, a 1-point rise in total MSLQ score was associated with a 4.4% increase in post-test scores (β = 4.4; p < 0.0001). Total MSLQ scores showed moderately strong, statistically significant associations with several other measures of effort, motivation and satisfaction. Scores on MSLQ domains demonstrated associations that generally aligned with our hypotheses. Self-efficacy and control of learning belief scores demonstrated the strongest domain-specific relationships with knowledge scores (β = 2.9 for both). Confirmatory factor analysis showed a borderline model fit. Follow-up exploratory factor analysis revealed the scores of five factors (self-efficacy, intrinsic interest, test anxiety, extrinsic goals, attribution) demonstrated psychometric and predictive properties similar to those of the original scales.

CONCLUSIONS

Scores on the MSLQ are reliable and predict meaningful outcomes. However, the factor structure suggests a simplified model might better fit the empiric data. Future research might consider how assessing and responding to motivation could enhance learning.

MULTI-LAYERED GREEDY NETWORK-GROWING ALGORITHM: EXTENSION OF GREEDY NETWORK-GROWING ALGORITHM TO MULTI-LAYERED NETWORKS

In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.

Bayes and blickets: effects of knowledge on causal induction in children and adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults' judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children's judgments (Experiments 3 and 5) agreed qualitatively with this account.

Detecting disease genes based on semi-supervised learning and protein-protein interaction networks

OBJECTIVE

Predicting or prioritizing the human genes that cause disease, or "disease genes", is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is carried out upon the key assumption of "the network-neighbour of a disease gene is likely to cause the same or a similar disease", and mostly employs data regarding well-known disease genes, using supervised learning methods. This work aims to find an effective method to exploit the disease gene neighbourhood and the integration of several useful omics data sources, which potentially enhance disease gene predictions.

METHODS

We have presented a novel method to effectively predict disease genes by exploiting, in the semi-supervised learning (SSL) scheme, data regarding both disease genes and disease gene neighbours via protein-protein interaction network. Multiple proteomic and genomic data were integrated from six biological databases, including Universal Protein Resource, Interologous Interaction Database, Reactome, Gene Ontology, Pfam, and InterDom, and a gene expression dataset.

RESULTS

By employing a 10 times stratified 10-fold cross validation, the SSL method performs better than the k-nearest neighbour method and the support vector machines method in terms of sensitivity of 85%, specificity of 79%, precision of 81%, accuracy of 82%, and a balanced F-function of 83%. The other comparative experimental evaluations demonstrate advantages of the proposed method given a small amount of labeled data with accuracy of 78%. We have applied the proposed method to detect 572 putative disease genes, which are biologically validated by some indirect ways.

CONCLUSION

Semi-supervised learning improved ability to study disease genes, especially a specific disease when the known disease genes (as labeled data) are very often limited. In addition to the computational improvement, the analysis of predicted disease proteins indicates that the findings are beneficial in deciphering the pathogenic mechanisms.

Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning

Theories of selective attention in associative learning posit that the salience of a cue will be high if the cue is the best available predictor of reinforcement (high predictiveness). In contrast, a different class of attentional theory stipulates that the salience of a cue will be high if the cue is an inaccurate predictor of reinforcement (high uncertainty). Evidence in support of these seemingly contradictory propositions has led to: (i) the development of hybrid attentional models that assume the coexistence of separate, predictiveness-driven and uncertainty-driven mechanisms of changes in cue salience; and (ii) a surge of interest in identifying the neural circuits underpinning these mechanisms. Here, we put forward a formal attentional model of learning that reconciles the roles of predictiveness and uncertainty in salience modification. The issues discussed are relevant to psychologists, behavioural neuroscientists and neuroeconomists investigating the roles of predictiveness and uncertainty in behaviour.

The wisdom of the crowd playing The Price Is Right

In The Price Is Right game show, players compete to win a prize, by placing bids on its price. We ask whether it is possible to achieve a "wisdom of the crowd" effect, by combining the bids to produce an aggregate price estimate that is superior to the estimates of individual players. Using data from the game show, we show that a wisdom of the crowd effect is possible, especially by using models of the decision-making processes involved in bidding. The key insight is that, because of the competitive nature of the game, what people bid is not necessarily the same as what they know. This means better estimates are formed by aggregating latent knowledge than by aggregating observed bids. We use our results to highlight the usefulness of models of cognition and decision-making in studying the wisdom of the crowd, which are often approached only from non-psychological statistical perspectives.

Neurobiology of decision making in depressed adolescents: a functional magnetic resonance imaging study

OBJECTIVE

Despite evidence that impaired reward- and risk-related behavior during adolescence can have potentially serious short- and long-term consequences, few studies have investigated the impact of depression on reward-related selection in adolescents. This study examined the relationship between reward-related behavior and prefrontal activations in depressed and healthy adolescents during a decision-making task.

METHOD

A total of 22 adolescents with no personal or family history of psychiatric illness and 22 adolescents with major depressive disorder were administered a monetary, two-option decision-making task, the Wheel of Fortune, using a functional magnetic resonance imaging protocol. The analysis was focused on the selection phase, i.e., the first phase of the decision-making process, which typically includes two more phases, the anticipation of outcome and the feedback.

RESULTS

Similar prefrontal regions were activated in healthy and depressed adolescents during reward-related selection. However, in a contrast involving the selection of high-risk (low-probability/high-magnitude reward) versus equal-risk (50% chance of reward) options, healthy adolescents showed greater activation than patients in the right lateral orbitofrontal cortex (OFC), whereas participants with depression showed greater activation than healthy subjects in the left dorsal OFC and right caudal anterior cingulate cortex. In addition, healthy adolescents, but not participants with depression, showed a negative correlation between high-risk behavior and neuronal activation in prespecified prefrontal regions.

CONCLUSIONS

These results suggest subtle changes in the neural responses to reward selection in depressed adolescents. These findings should be replicated in larger samples, and the association of these neuronal changes with treatment response and prognosis should be examined.

Altered probabilistic learning and response biases in schizophrenia: behavioral evidence and neurocomputational modeling

OBJECTIVE

Patients with schizophrenia (SZ) show reinforcement learning impairments related to both the gradual/procedural acquisition of reward contingencies, and the ability to use trial-to-trial feedback to make rapid behavioral adjustments.

METHOD

We used neurocomputational modeling to develop plausible mechanistic hypotheses explaining reinforcement learning impairments in individuals with SZ. We tested the model with a novel Go/NoGo learning task in which subjects had to learn to respond or withhold responses when presented with different stimuli associated with different probabilities of gains or losses in points. We analyzed data from 34 patients and 23 matched controls, characterizing positive- and negative-feedback-driven learning in both a training phase and a test phase.

RESULTS

Consistent with simulations from a computational model of aberrant dopamine input to the basal ganglia patients, patients with SZ showed an overall increased rate of responding in the training phase, together with reduced response-time acceleration to frequently rewarded stimuli across training blocks, and a reduced relative preference for frequently rewarded training stimuli in the test phase. Patients did not differ from controls on measures of procedural negative-feedback-driven learning, although patients with SZ exhibited deficits in trial-to-trial adjustments to negative feedback, with these measures correlating with negative symptom severity.

CONCLUSIONS

These findings support the hypothesis that patients with SZ have a deficit in procedural "Go" learning, linked to abnormalities in DA transmission at D1-type receptors, despite a "Go bias" (increased response rate), potentially related to excessive tonic dopamine. Deficits in trial-to-trial reinforcement learning were limited to a subset of patients with SZ with severe negative symptoms, putatively stemming from prefrontal cortical dysfunction.

Probabilistic reinforcement learning in adults with autism spectrum disorders

BACKGROUND

Autism spectrum disorders (ASDs) can be conceptualized as disorders of learning, however there have been few experimental studies taking this perspective.

METHODS

We examined the probabilistic reinforcement learning performance of 28 adults with ASDs and 30 typically developing adults on a task requiring learning relationships between three stimulus pairs consisting of Japanese characters with feedback that was valid with different probabilities (80%, 70%, and 60%). Both univariate and Bayesian state-space data analytic methods were employed. Hypotheses were based on the extant literature as well as on neurobiological and computational models of reinforcement learning.

RESULTS

Both groups learned the task after training. However, there were group differences in early learning in the first task block where individuals with ASDs acquired the most frequently accurately reinforced stimulus pair (80%) comparably to typically developing individuals; exhibited poorer acquisition of the less frequently reinforced 70% pair as assessed by state-space learning curves; and outperformed typically developing individuals on the near chance (60%) pair. Individuals with ASDs also demonstrated deficits in using positive feedback to exploit rewarded choices.

CONCLUSIONS

Results support the contention that individuals with ASDs are slower learners. Based on neurobiology and on the results of computational modeling, one interpretation of this pattern of findings is that impairments are related to deficits in flexible updating of reinforcement history as mediated by the orbito-frontal cortex, with spared functioning of the basal ganglia. This hypothesis about the pathophysiology of learning in ASDs can be tested using functional magnetic resonance imaging.

Optimizing vs. matching: response strategy in a probabilistic learning task is associated with negative symptoms of schizophrenia

Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance.