Neural and computational representations of decision variables

These studies explore how, where, and when representations of variables critical to decision-making are represented in the brain. In order to produce a decision, humans must first determine the relevant stimuli, actions, and possible outcomes before applying an algorithm that will select an action from those available. When choosing amongst alternative stimuli, the framework of value-based decision-making proposes that values are assigned to the stimuli and that these values are then compared in an abstract “value space” in order to produce a decision. Despite much progress, in particular regarding the pinpointing of ventromedial prefrontal cortex (vmPFC) as a region that encodes the value, many basic questions remain. In Chapter 2, I show that distributed BOLD signaling in vmPFC represents the value of stimuli under consideration in a manner that is independent of the type of stimulus it is. Thus the open question of whether value is represented in abstraction, a key tenet of value-based decision-making, is confirmed. However, I also show that stimulus-dependent value representations are also present in the brain during decision-making and suggest a potential neural pathway for stimulus-to-value transformations that integrates these two results.

More broadly speaking, there is both neural and behavioral evidence that two distinct control systems are at work during action selection. These two systems compose the “goal-directed system”, which selects actions based on an internal model of the environment, and the “habitual” system, which generates responses based on antecedent stimuli only. Computational characterizations of these two systems imply that they have different informational requirements in terms of input stimuli, actions, and possible outcomes. Associative learning theory predicts that the habitual system should utilize stimulus and action information only, while goal-directed behavior requires that outcomes as well as stimuli and actions be processed. In Chapter 3, I test whether areas of the brain hypothesized to be involved in habitual versus goal-directed control represent the corresponding theorized variables.

The question of whether one or both of these neural systems drives Pavlovian conditioning is less well-studied. Chapter 4 describes an experiment in which subjects were scanned while engaged in a Pavlovian task with a simple non-trivial structure. After comparing a variety of model-based and model-free learning algorithms (thought to underpin goal-directed and habitual decision-making, respectively), it was found that subjects’ reaction times were better explained by a model-based system. In addition, neural signaling of precision, a variable based on a representation of a world model, was found in the amygdala. These data indicate that the influence of model-based representations of the environment can extend even to the most basic learning processes.

Knowledge of the state of hidden variables in an environment is required for optimal inference regarding the abstract decision structure of a given environment and therefore can be crucial to decision-making in a wide range of situations. Inferring the state of an abstract variable requires the generation and manipulation of an internal representation of beliefs over the values of the hidden variable. In Chapter 5, I describe behavioral and neural results regarding the learning strategies employed by human subjects in a hierarchical state-estimation task. In particular, a comprehensive model fit and comparison process pointed to the use of "belief thresholding". This implies that subjects tended to eliminate low-probability hypotheses regarding the state of the environment from their internal model and ceased to update the corresponding variables. Thus, in concert with incremental Bayesian learning, humans explicitly manipulate their internal model of the generative process during hierarchical inference consistent with a serial hypothesis testing strategy.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

A propaganda negativa : estratégia e voto nas eleições brasileiras

Esta tese se insere no conjunto de pesquisas que procura entender como funcionam as eleições no Brasil. Especificamente, o objetivo é investigar a propaganda negativa durante as eleições presidenciais. Para tal foram desenvolvidos cinco capítulos. O primeiro situa o leitor no debate normativo sobre o papel da propaganda negativa para a democracia eleitoral. Nele, é debatida a importância dos ataques em uma série de circunstâncias, como mobilização política, ambiente informacional e decisão do voto. O segundo capítulo constitui ampla análise do conteúdo da propaganda negativa exibida no âmbito do Horário Gratuito de Propaganda Eleitoral durante as eleições presidenciais de 1989, 1994, 1998, 2002, 2006 e 2010, primeiro e segundo turnos. A metodologia seguiu as orientações formuladas por Figueiredo et all. (1998), mas adaptadas para as especificidades da propaganda negativa. Neste objetivo, tendências interessantes foram descobertas, a mais interessante, sem dúvida, é o baixo índice de ataques ocorrido entre os candidatos. O terceiro busca investigar o uso estratégico das inserções durante as campanhas presidenciais. Debato o caráter regulamentado do modelo brasileiro de propaganda. Ainda assim, aponto estratégias divergentes no uso estratégico das inserções negativas, sendo o horário noturno o lócus predominante dos ataques. O quarto capítulo procura criar um modelo de campanha negativa com base na teoria dos jogos. No modelo, procuro responder às seguintes questões: quem ataca quem, quando e por quê? Argumento que a propaganda negativa é o último recurso utilizado pelos candidatos na conquista por votos. Ela tem como propósito central alterar a tendência do adversário. Por essa razão, é utilizada principalmente por candidatos em situações de desvantagem nos índices de intenção de voto. O quinto e último capítulo desenvolve modelo estatístico para medir o impacto da propaganda negativa nos índices de intenção de voto.

Interactions of age-dependent mortality and selectivity functions in age-based stock assessment models

The natural mortality rate (M) of fish varies with size and age, although it is often assumed to be constant in stock assessments. Misspecification of M may bias important assessment quantities. We simulated fishery data, using an age-based population model, and then conducted stock assessments on the simulated data. Results were compared to known values. Misspecification of M had a negligible effect on the estimation of relative stock depletion; however, misspecification of M had a large effect on the estimation of parameters describing the stock recruitment relationship, age-specific selectivity, and catchability. If high M occurs in juvenile and old fish, but is misspecified in the assessment model, virgin biomass and catchability are often poorly estimated. In addition, stock recruitment relationships are often very difficult to estimate, and steepness values are commonly estimated at the upper bound (1.0) and overfishing limits tend to be biased low. Natural mortality can be estimated in assessment models if M is constant across ages or if selectivity is asymptotic. However if M is higher in old fish and selectivity is dome-shaped, M and the selectivity cannot both be adequately estimated because of strong interactions between M and selectivity.

Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

Steady-state analytical model for the trench insulated gate bipolar transistor

A steady-state, physically-based analytical model for the Trench Insulated Gate Bipolar Transistor which accounts for a combined PIN diode - PNP transistor carrier dynamics is proposed. Previous models (i.e. PIN model and PNP transistor model) cannot account properly for the carrier dynamics in Trench IGBT since neither the PNP transistor nor the PIN diode effect can be neglected. An optimized Trench IGBT with a large ratio between the accumulation layer and the cell size leads to substantially improved on-state characteristics, which makes the Trench IGBT potentially the most attractive device in the area of high voltage fast switching devices.

Optimal design of Functionally Graded Materials using a procedural model and Particle Swarm Optimization

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples. © 2011 Elsevier Ltd. All rights reserved.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

Values and actions in aversion

Model-based and model-free controllers can, in principle, learn arbitrary actions to optimize their behavior, at least those actions that can be expressed and explored. Indeed, these are often referred to as instrumental controllers because their choices are learned to be instrumental for the delivery of desired outcomes. Although this flexibility is very powerful, it comes with an attendant cost of learning. Evolution appears to have endowed everything from the simplest organisms to us with powerful, pre-specified, but inflexible alternatives. These responses are termed Pavlovian, after the famous Russian physiologist and psychologist Pavlov. The responses of the Pavlovian controller are determined by evolutionary (phylogenetic) considerations rather than (ontogenetic) aspects of the contingent development or learning of an individual. These responses directly interact with instrumental choices arising from goal-directed and habitual controllers. This interaction has been studied in a wealth of animal paradigms, and can be helpful, neutral, or harmful, according to circumstance. Although there has been less careful or analytical study of it in humans, it can be interpreted as underpinning a wealth of behavioral aberrations. © 2009 Elsevier Inc. All rights reserved.

Steering manufacturing firms towards service business model innovation

Increasingly, manufacturing firms are turning to services as a new way of creating and capturing value. Despite its potential benefits, many new product-service providers struggle to deploy service activities effectively, not least because they fail to refect the presence of service activities in their performance management systems. This article reports the results of an in-depth case study, which examines how manufacturers can steer the transition towards services. It shows that manufacturing firms need to emphasize two separate but related dimensions of the market performance of service activities: "service adoption," refecting the proportion of customers who purchase the manufacturer's services; and "service coverage," signaling the range of service elements or the comprehensiveness of the service contract that customers opt for. These two indicators, refecting service market performance, should be supplemented with a "complementarity index" designed to disclose whether the relationship between products and services is reinforcing or substitutive. When combined, these indicators allow manufacturing firms to deploy a service-based business model in an integrated and sustainable manner. © 2013 by The Regents of the University of California. All rights reserved.

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

Stratigraphic Model Predictions of Geoacoustic Properties

Geoacoustic properties of the seabed have a controlling role in the propagation and reverberation of sound in shallow-water environments. Several techniques are available to quantify the important properties but are usually unable to adequately sample the region of interest. In this paper, we explore the potential for obtaining geotechnical properties from a process-based stratigraphic model. Grain-size predictions from the stratigraphic model are combined with two acoustic models to estimate sound speed with distance across the New Jersey continental shelf and with depth below the seabed. Model predictions are compared to two independent sets of data: 1) Surficial sound speeds obtained through direct measurement using in situ compressional wave probes, and 2) sound speed as a function of depth obtained through inversion of seabed reflection measurements. In water depths less than 100 m, the model predictions produce a trend of decreasing grain-size and sound speed with increasing water depth as similarly observed in the measured surficial data. In water depths between 100 and 130 m, the model predictions exhibit an increase in sound speed that was not observed in the measured surficial data. A closer comparison indicates that the grain-sizes predicted for the surficial sediments are generally too small producing sound speeds that are too slow. The predicted sound speeds also tend to be too slow for sediments 0.5-20 m below the seabed in water depths greater than 100 m. However, in water depths less than 100 m, the sound speeds between 0.5-20-m subbottom depth are generally too fast. There are several reasons for the discrepancies including the stratigraphic model was limited to two dimensions, the model was unable to simulate biologic processes responsible for the high sound-speed shell material common in the model area, and incomplete geological records necessary to accurately predict grain-size

Synthesis and characterization of new carbazole/fluorene-based derivatives for blue-light-emitting devices

2,7-Bis(9-ethylcarbazol-3-yl)-9,9-di(2-ethylhexyl)fluorene and a segmented copolymer composed of the same chromophores alternated with hexamethylene fragments were synthesized. The obtained materials possess good solubility in common organic solvents, high thermal stability with 1% weight loss temperature of 350-370 degrees C, and suitable glass transition temperatures. Both derivatives show blue fluorescence in dilute solutions as well as in solid state, demonstrating that excimers are not formed in the thin films. The fluorescence spectra of the materials do not show any peaks in the long-wavelength region even after annealing at 200 degrees C in air. An organic LED with the configuration of ITO/copolymer/Al generates blue electroluminescence with the maximum peak at 416 nm, rather low turn-on voltage (4.0 V), and brightness of about 400 cd/m(2). The heterostructure device based on model derivative emitted stable blue light with low operation voltage (100 cd/m(2) at similar to 11 V) and demonstrated luminescence efficiency of 0.8 cd/A.

吗啡奖赏效应及其敏化与空间学习能力的关系

Rewarding experience after drug use is one of the mechanisms of substance abuse. Previous evidence indicated that rewarding experience was closely related to learning processes. Neuroscience studies have already established multiple-mode learning model. Reference memory system and habit memory are associated with hippocampus and dorsa striatum respectively, which are also involved in the rewarding effect of morphine. However, the relationship between spatial/habit learning and morphine reward property is still unclear. After drug use, with sensitization to rewarding effect, spatial learning is also changed. To study the mechanism of increment of spatial learning would provide new perspective about reward learning. Based on the individual difference between spatial learning and reward learning, the experiments studied relationship between the two leaning abilities and tested the function of dorsal hippocampus and dorsal striatum in morphine-induced CPP. The results were summarized below： 1 In a single－rule learning water maze task, subjects better in spatial learning also excelled in rewarding learning. In a multi-rule learning task, morphine administration was more rewarding to subjects of use place strategy. 2 Treatment potentiating the rewarding effect of morphine also increased place-rule learning, with no significant improvement in habit learning. 3 Intracranial injections into CA1 of hippocampus or dorsal striatum of M1 antagonist, Pirenzepine, could block the establishment of morphine CPP after three days morphine treatment. In contrast, the antagonist of D1 receptor SCH23390 had no blocking effect. Both Pirenzepine and SCH23390 blocked the locomotor-stimulating effect of morphine. In summary, spatial learning stimulated the behavioral expression of morphine’s rewarding effect, in which CA1 of hippocampus was critically involved. On the other side, a pretreatment schedule of morphine, while increased the rewarding effect, improved place-rule learning, indicating that spatial learning might be one chain of sensitization to drug rewards effects

Investigating the evolution and stability of a resource limited artificial immune system.

Timmis J and Neal M J. Investigating the evolution and stability of a resource limited artificial immune system. In Proceedings of GECCO - special workshop on artificial immune systems, pages 40-41. AAAI press, 2000.