Additivity Pretraining and cue competition effects: Developmental evidence for a reasoning account of causal learning

The effect of additivity pretraining on blocking has been taken as evidence for a reasoning account of human and animal causal learning. If inferential reasoning underpins this effect, then developmental differences in the magnitude of this effect in children would be expected. Experiment 1 examined cue competition effects in children's (4- to 5-year-olds and 6- to 7-year-olds) causal learning using a new paradigm analogous to the food allergy task used in studies of human adult causal learning. Blocking was stronger in the older than the younger children, and additivity pretraining only affected blocking in the older group. Unovershadowing was not affected by age or by pretraining. In experiment 2, levels of blocking were found to be correlated with the ability to answer questions that required children to reason about additivity. Our results support an inferential reasoning explanation of cue competition effects. (c) 2012 APA, all rights reserved.

Dual-processes in learning and judgment:Evidence from the multiple cue probability learning paradigm

Multiple cue probability learning (MCPL) involves learning to predict a criterion based on a set of novel cues when feedback is provided in response to each judgment made. But to what extent does MCPL require controlled attention and explicit hypothesis testing? The results of two experiments show that this depends on cue polarity. Learning about cues that predict positively is aided by automatic cognitive processes, whereas learning about cues that predict negatively is especially demanding on controlled attention and hypothesis testing processes. In the studies reported here, negative, but not positive cue learning related to individual differences in working memory capacity both on measures of overall judgment performance and modelling of the implicit learning process. However, the introduction of a novel method to monitor participants' explicit beliefs about a set of cues on a trial-by-trial basis revealed that participants were engaged in explicit hypothesis testing about positive and negative cues, and explicit beliefs about both types of cues were linked to working memory capacity. Taken together, our results indicate that while people are engaged in explicit hypothesis testing during cue learning, explicit beliefs are applied to judgment only when cues are negative. © 2012 Elsevier Inc.

Behavior, pain perception, and the extremely low-birth weight survivor

This article explores the literature concerning responses to pain of both premature and term-born newborn infants, the evidence for short-term and long-term effects of pain, and behavioral sequelae in individuals who have experienced repeated early pain in neonatal life as they mature. There is no doubt that pain causes stress in babies and this in turn may adversely affect long-term neurodevelopmental outcome. Although there are methods for assessing dimensions of acute reactivity to pain in an experimental setting, there are no very good measures available at the present time that can be used clinically. In the clinical setting repeated or chronic pain is more likely the norm rather than infrequent discrete noxious stimuli of the sort that can be readily studied. The wind-up phenomenon suggests that, exposed to a cascade of procedures as happens with clustering of care in the clinical setting in an attempt to provide periods of rest for stressed babies, an infant may in fact perceive procedures that are not normally viewed as noxious, as pain. Pain exposure during lifesaving intensive medical care of ELBW neonates may also affect subsequent reactivity to pain in the neonatal period, but behavioral differences are probably not likely to be clinically significant in the long term. Prolonged and repeated untreated pain in the newborn period, however, may produce a relatively permanent shift in basal autonomic arousal related to prior NICU pain experience, which may have long-term sequelae. In the long run, the most significant clinical effects of early pain exposure may be on neurodevelopment, contributing to later attention, learning, and behavior problems in these vulnerable children. Although there is considerable evidence to support a variety of adverse effects of early pain, there is less information about the long-term effects of opiates and benzodiazepines on the developing central nervous system. Current evidence reviewed suggests that judicious use of morphine for adjustment to mechanical ventilation may ameliorate the altered autonomic response. It may be very important, however, to distinguish stress from pain. Animal evidence suggests that the neonatal brain is affected differently when exposed to morphine administered in the absence of pain than in the presence of pain. Pain control may be important for many reasons but overuse of morphine or benzodiazepines may have undesirable long-term effects. This is a rapidly evolving area of knowledge of clear relevance to clinical management likely to affect long-term outcomes of high-risk children.

Effects of thyroxine treatment on histology and behavior using the methimazole model of congenital hypothyroidism in the rat

The timing of thyroxine (T4) replacement treatment in congenital hypothyroidism (CH) has been suggested to be important for optimizing cognitive recovery in humans; however this has not been fully established using modern animal models of CH. Consequently, the current studies investigated the ameliorating effects of postnatal T4 treatment on neuropathology and behavior in CH rats. Rat dams were administered methimazole to produce CH offspring, then brain tissue from male CH pups was analyzed to determine the effects of postnatal (P3, P7, P14 and P21) T4 treatment on hippocampal dendritic branching and the expression of nerve growth factor (NGF). Two operant behavioral procedures were employed to confirm and extend previous findings obtained using this model, and to investigate timelines for instigating T4 treatment on improved behavioral outcomes. T4 treatment initiated at P14 was protective of a reduction in dendritic branching in the hippocampus, and initiated at P7 was protective of a reduction of NGF expression in the fimbria of the hippocampus. Induction of CH did not affect the acquisition of simple operant response rules but had a significant effect on the acquisition of complex operant rules subsequently imposed. Furthermore, T4 treatment initiated at P3 protected learning deficits seen following the imposition of complex operant response rules. These findings indicate T4 treatment initiated at P7 is sufficient for the protection of hippocampal NGF expression and dendritic branching but for the protection of complex behavioral abilities T4 treatment is necessary prior to or approximating P3.

Learning, fatigue and preference formation in discrete choice experiments

While the repeated nature of Discrete Choice Experiments is advantageous from a sampling efficiency perspective, patterns of choice may differ across the tasks, due, in part, to learning and fatigue. Using probabilistic decision process models, we find in a field study that learning and fatigue behavior may only be exhibited by a small subset of respondents. Most respondents in our sample show preference and variance stability consistent with rational pre-existent and
well formed preferences. Nearly all of the remainder exhibit both learning and fatigue effects. An important aspect of our approach is that it enables learning and fatigue effects to be explored, even though they were not envisaged during survey design or data collection.

Perceptual learning of acoustic noise generates memory-evoked potentials

Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Seller, buyer and VPP agents behavior on a simulated electricity market

Competitive electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is an electricity market simulator able to model market players and simulate their operation in the market. As market players are complex entities, having their characteristics and objectives, making their decisions and interacting with other players, a multi-agent architecture is used and proved to be adequate. MASCEM players have learning capabilities and different risk preferences. They are able to refine their strategies according to their past experience (both real and simulated) and considering other agents’ behavior. Agents’ behavior is also subject to its risk preferences.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Reverse problem-based learning - a case study with a Braille machine

Engineering Education includes not only teaching theoretical fundamental concepts but also its verification during practical lessons in laboratories. The usual strategies to carry out this action are frequently based on Problem Based Learning, starting from a given state and proceeding forward to a target state. The possibility or the effectiveness of this procedure depends on previous states and if the present state was caused or resulted from earlier ones. This often happens in engineering education when the achieved results do not match the desired ones, e.g. when programming code is being developed or when the cause of the wrong behavior of an electronic circuit is being identified. It is thus important to also prepare students to proceed in the reverse way, i.e. given a start state generate the explanation or even the principles that underlie it. Later on, this sort of skills will be important. For instance, to a doctor making a patient?s story or to an engineer discovering the source of a malfunction. This learning methodology presents pedagogical advantages besides the enhanced preparation of students to their future work. The work presented on his document describes an automation project developed by a group of students in an engineering polytechnic school laboratory. The main objective was to improve the performance of a Braille machine. However, in a scenario of Reverse Problem-Based learning, students had first to discover and characterize the entire machine's function before being allowed (and being able) to propose a solution for the existing problem.

Comportamento do consumidor digital: o caso do e-learning nas licenciaturas do ensino superior português

Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação do professor Doutor Manuel Silva