Adaptive behavior

Here we describe the development of serial expertise in 4 experimentally naive rhesus monkeys that learned, by trial and error, the correct order in which to respond to 3-, 4-, and 7-item lists of arbitrarily selected photographs. The probabilities of guessing the correct sequence on 3-, 4-, and 7-item lists were, respectively, 1/6, 1/24, and 1/5,040. Each monkey became progressively more efficient at determining the correct order in which to respond on new lists. During subsequent testing, the subjects were presented with all possible pairs of the 28 items used to construct the four 7-item lists (excluding pairs of items that occupied the same ordinal position in different lists). Subjects responded to pairs from different lists in the correct order 91% of the time on the first trials on which these pairs were presented. These features of subjects' performance, which cannot be attributed to procedural memory, satisfy two criteria of declarative memory: rapid acquisition of new ...

Few problems have created the combined interest of so many unrelated areas as the evolution of cooperation. As a result, several mechanisms have been identified to work as catalyzers of cooperative behavior. Yet, these studies, mostly grounded on evolutionary dynamics and game theory, have neglected the important role played by intention recognition in behavioral evolution. Here we address explicitly this issue, characterizing the dynamics emerging from a population of intention recognizers. We derive a Bayesian network model for intention recognition in the context of repeated social dilemmas and evolutionary game theory, by assessing the internal dynamics of trust between intention recognizers and their opponents. Intention recognizers are then able to predict the next move of their opponents based on past direct interactions, which, in turn, enables them to prevail over the most famous strategies of repeated dilemmas of cooperation, even in presence of noise. Overall, our framewo...

The aim of this study was to investigate the efficacy of mindfulness based cognitive insight in pregnant women. In this study, a quasi-experimental pre-test - post-test for both test and control groups were used. The study population consisted of all pregnant women in urban health centers of Urmia and the sample included 30 pregnant women referred to health centers of the available sample of health centers of Urmia were selected. All women before initiation of treatment and then filled out questionnaires, cognitive insight. Mindfulness training as a group in 8 sessions (a two-hour session a week) was conducted on the experimental group. MANCOVA for examining the research hypotheses, analysis of covariance was used. Analysis of the results showed that the level of significance (p < 0/05) is. So it can be concluded that mindfulness training can be found on cognitive insight and its components (self-thinkers and self-confidence), will have a significant positive effect.

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Objective This study determines the validity and reliability of the Persian version of the adaptive behavior assessment system (ABAS) scale in children aged 1 to 42 months in Tehran City, Iran. Materials & Methods The translation and adaptation were carried out based on the international quality of life instrument project protocol. After a pilot study, 253 parents of 1-42 month-aged children in Tehran City, Iran, completed the Persian version of the ABAS scale to check the construct validity by exploratory factor analysis and internal consistency. To determine reliability, 12 parents completed this scale after 2 weeks again. Results The translation desirability was obtained by suggestions of the expert group and cognitive interviews by mothers; subsequently, face validity was obtained. The content validity ratio was calculated at 0.59 according to the participation of 11 experts. The content validity ratio for item 25 (communication), items 18, 20 and 22 (community use), items 11, 14, 15, 17, 18 and 21 (functional pre-academic) and item 18 (leisure) was below the acceptable value (0.59); however, the remaining substances demonstrated an acceptable content validity ratio, leading to no items being removed. The content validity index regarding the relevance for item 25 (communication), items 18 and 22 (community use), items 11, 18, 23 and 22 (functional pre-academic) and the clarity for item 9 (functional pre-academic) scored between 0.7 and 0.79, indicating required revision. The other items achieved an acceptable content validity index value. The Cronbach α coefficient tested the reliability and internal consistency. This coefficient was 0.957 in communication, 0.957 in community use, 0.943 in the functional pre-academic, 0.961 in home life, 0.960 in health and safety, 0.952 in leisure, 0.948 in self-care, 0.947 in self-direction, 0.954 in social and 0.962 in motor subscales, and 0.991 in total scale. To determine the structural validity, according to the eigenvalue and the percentage explained by the first factor in each subscale, it was determined that a single-factor model is the most favorable condition for implementing factor analysis in the items of each subscale. In addition, according to the scree plot, the contribution of the first factor in each subscale in explaining the variance of the total items is significant and different from the contribution of the other factors. By calculating the intraclass correlation coefficient (ICC), a good correlation was obtained in nine subscales and a moderate correlation was obtained in one subscale. Conclusion The adaptation validity and reliability of the ABAS scale have a desirable and acceptable quality. Also, the Persian version of this scale has content and construct validity, reliability in test-retest, and internal consistency. Therefore, the Persian version of the ABAS scale can be used in research as a tool to evaluate the adaptive behavior of 1 to 42-month Persian language children.

This paper aims to determine whether an adaptive agent population performs better than a static population. A static population is evolved on historical equity market data from the DAX-30, split into training and testing segments. An adaptive population is retrained continuously over the most recent available data that becomes available with each passing day. For comparison their performance over the out-of-sample test data is measured. Results obtained indicate a clear superiority of the adaptive over the static approach.

Advanced Cognitive Technologies can use cognitive architectures as a basis for higher level reasoning in Artificial Intelligence (AI). Adaptive Control of Thought – Rational (ACT-R) is one such cognitive architecture that attempts to replicate aspects of human thought and reasoning. The research reported in this paper has developed an enhancement to ACT-R that will allow greater understanding of the environment the AI is situated in. Former research has shown that humans perform simple mental simulations to predict the outcomes of events when faced with complex physical problems. Inspired by this, the research reported here has developed Predictive ACT-R (PACT-R), based upon integrating a three dimensional (3D) simulation of the AI’s environment to allow it to predict, reason about, and then act on, what is happening, or about to happen, in its environment. Here, it is demonstrated by application in an autonomous squash player that the predictive version of ACT-R achieves significan...

The problem of action selection has two components: What is selected? How is it selected? To understand what is selected, it is necessary to recognize that animals do not choose among behaviours per se; rather, behaviour reflects observed interactions among brains, bodies, and environments (embeddedness). To understand what guides selection, it is useful to take a normative, functional perspective that evaluates behaviour in terms of a fitness metric. This perspective can be especially useful for understanding apparently irrational action selection. Bringing together these issues therefore requires integrating function and mechanism in models of action selection. This chapter describes ‘optimal agent-based modelling’, a methodology integrates functional and mechanistic perspectives in the context of embedded agent-environment interactions. Using this methodology, I demonstrate that successful action selection can arise from the joint activity of parallel, loosely coupled sensorimoto...

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality', for characterizing causal interactions generated within intact neural mechanisms. This method, called causal connectivity analysis, is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.

One.of.the.unique.features.of.the.organization.in.honey.bee.societies.is.the.ability. to.adapt.to.environmental.circumstances.in.a.highly.decentralized.way..This.adaptation. takes. the. form. of. changes. in. the. allocation. of. bees. to. roles,. whereby. bees. individually.decide.to.take.up.certain.roles.that.require.attention..Within.the.domain. of.computational.modeling.of.multiagent.systems,.a.trend.has.developed.to.model. such.systems.from.an.organizational.perspective..Usually.this.is.done.by.describing.multiagent.systems.as.organizations.using.structural.elements.such.as.roles.and. groups. and. behavioral. properties. of. these. elements,. for. example,. expressions. for. 27 28 In Silico Bees role.behavior,.specifying.how.agents.should.behave.once.they.fulfill.a.certain.role.. In. dynamic. environments,. changes. in. environmental. circumstances. may. require. changes.in.such.organizations.as.well..In.this.chapter,.the.change.process.as.seen. in.honey.bee.colonies.has.been.modeled.by.means.of.techniques.from.the.domain. of. multiagent. organizations.. This. results. in. an. adaptive. multiagent. organizational. model.that.is.able.to.cope.with.changing.environmental.circumstances.

Monothematic delusions and beliefs in conspiracy theories share some important features: they both typically have bizarre contents and are resistant to counterevidence. Yet conspiracy beliefs are generally taken to be a normal range phenomenon, whilst monothematic delusions are considered to involve doxastic pathology. In this paper, we argue that this difference in conceptualization is not warranted, and that, if we're right, the correct response is to de-pathologize monothematic delusions. We identify three reasons which could justify taking monothematic delusions to be pathological beliefs whilst not treating conspiracy beliefs in such terms. First, we consider what have been identified as initial provoking conditions in monothematic delusions (anomalous experience) and conspiracy beliefs (epistemic mistrust). Second, we consider the role of cognitive biases or deficits in these phenomena. Third, we consider the different roles played by testimony and one's social environment in the formation and maintenance of monothematic delusions and conspiracy beliefs. We argue that there are no grounds from any of these quarters for the different approaches taken with respect to the question of doxastic pathology. Our primary conclusion then is that monothematic delusions and conspiracy beliefs should be treated the same in this respect. Our secondary conclusion is that the correct way to do so is to de-pathologize monothematic delusions.

Parent and teacher ratings of adaptive behavior were compared using a new instrument designed to measure adaptive functioning for limited-English-proficient children: the Universal Brief Adaptive Behavior Index (UBABI). The UBABI was designed to briefly assess constructs of adaptive behavior for all children, and particularly for at-risk children who have limited use of the English language. The instrument measures adaptive functioning in 10 areas in accordance with the 1992 AAMR definition of adaptive behavior. Participants were 135 teachers and 115 parents of students in elementary, middle, and high schools from six states. Students were selected randomly from regular education classrooms. UBABIs were first completed by the parents and, upon return of the completed parent form, then completed by the classroom teacher. Cronbach alpha correlation coefficients provided evidence of the UBABI's reliability and ranged from .86 to .90 for the 10 domains. The reliability for the compo...

A major goal for researchers in neuroevolution is to evolve artificial neural networks (ANNs) that can learn during their lifetime. Such networks can adapt to changes in their environment that evolution on its own cannot anticipate. However, a profound problem with evolving adaptive systems is that if the impact of learning on the fitness of the agent is only marginal, then evolution is likely to produce individuals that do not exhibit the desired adaptive behavior. Instead, because it is easier at first to improve fitness without evolving the ability to learn, they are likely to exploit domaindependent static (i.e. non-adaptive) heuristics. This paper proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm, which opens up a new avenue in the evolution of adaptive systems because it can exploit the behavioral difference between learning and nonlearning individuals. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely and has shown prior promising results in other domains. This paper shows that novelty search significantly outperforms fitness-based search in a tunably deceptive T-Maze navigation domain because it fosters the emergence of adaptive behavior.

Biological brains can adapt and learn from past experience. Yet neuroevolution, that is, automatically creating artificial neural networks (ANNs) through evolutionary algorithms, has sometimes focused on static ANNs that cannot change their weights during their lifetime. A profound problem with evolving adaptive systems is that learning to learn is highly deceptive. Because it is easier at first to improve fitness without evolving the ability to learn, evolution is likely to exploit domain-dependent static (i.e., nonadaptive) heuristics. This article analyzes this inherent deceptiveness in a variety of different dynamic, reward-based learning tasks, and proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely. A series of experiments and an in-depth analysis show how behaviors that could ...

We propose that moral behavior of artificial agents could (and should) be intrinsically grounded in their own sensorymotor experiences. Such an ability depends critically on seven types of competences. First, intrinsic morality should be grounded in the internal values of the robot arising from its physiology and embodiment. Second, the moral principles of robots should develop through their interactions with the environment and with other agents. Third, we claim that the dynamics of moral (or social) emotions closely follows that of other non-social emotions used in valuation and decision making. Fourth, we explain how moral emotions can be learned from the observation of others. Fifth, we argue that to assess social interaction, a robot should be able to learn about and understand responsibility and causation. Sixth, we explain how mechanisms that can learn the consequences of actions are necessary for a robot to make moral decisions. Seventh, we describe how the moral evaluation mechanisms outlined can be extended to situations where a robot should understand the goals of others. Finally, we argue that these competences lay the foundation for robots that can feel guilt, shame and pride, that have compassion, and that know how to assign responsibility and blame.

We present a robot architecture and experiments to investigate some of the roles that pleasure plays in the decision making (action selection) process of an autonomous robot that must survive in its environment. We have conducted three sets of experiments to assess the effect of different types of pleasure-related versus unrelated to the satisfaction of physiological needs-under different environmental circumstances. Our results indicate that pleasure, including pleasure unrelated to need satisfaction, has value for homeostatic management in terms of improved viability and increased flexibility in adaptive behavior.

In the context of our work in developmental robotics regarding robot-human caregiver interactions, in this paper we investigate how a "baby" robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a "caregiver" to the preferences shown by the caregiver in terms of varying responsiveness. We build on two strands of previous work that assessed independently (a) the differences between two "idealized" robot profiles-a "needy" and an "independent" robot-in terms of their use of a caregiver as a means to regulate the "stress" (arousal) produced by the exploration and learning of a novel environment, and (b) the effects on the robot behaviors of two caregiving profiles varying in their…

Reinforcement learning (RL) in the context of artificial agents is typically used to produce behavioral responses as a function of the reward obtained by interaction with the environment. When the problem consists of learning the shortest path to a goal, it is common to use reward functions yielding a fixed value after each decision, for example a positive value if the target location has been attained and a negative value at each intermediate step. However, this fixed strategy may be overly simplistic for agents to adapt to dynamic environments, in which resources may vary from time to time. By contrast, there is significant evidence that most living beings internally modulate reward value as a function of their context to expand their range of adaptivity. Inspired by the potential of this operation, we present a review of its underlying processes and we introduce a simplified formalization for artificial agents. The performance of this formalism is tested by monitoring the adaptat...

This article introduces a formalization of the dynamics between sensorimotor interaction and homeostasis, integrated in a single architecture to learn object affordances of consummatory behaviors. We also describe the principles necessary to learn grounded knowledge in the context of an agent and its surrounding environment, which we use to investigate the constraints imposed by the agent’s internal dynamics and the environment. This is tested with an embodied, situated robot, in a simulated environment, yielding results that support this formalization. Furthermore, we show that this methodology allows learned affordances to be dynamically redefined, depending on object similarity, resource availability, and the rhythms of the agent’s internal physiology. For example, if a resource becomes increasingly scarce, the value assigned by the agent to its related effect increases accordingly, encouraging a more active behavioral strategy to maintain physiological stability. Experimental re...

This chapter explores the possibility of integrating the enactive and the Free Energy Principle's (FEP) approaches to life and mind. Both frameworks have been linked to the lifemind continuity thesis, but recent debates challenge their potential integration. Critics argue that the enactive approach, rooted in autopoiesis theory, has an internalist view of life and a contentful view of cognition, making it challenging to account for adaptive behaviour and minimal cognition. Similarly, some find the FEP's stationary view of life biologically implausible. Here, I address recent challenges in integrating the FEP and enactivism, focusing on the lifemind continuity thesis. I suggest that the FEP, without explicitly defining life and mind, can be used to model the autopoietic dynamics of organisms. Additionally, I argue that the enactive conception of cognition as sense-making overcomes issues associated with contentful views of cognition. Furthermore, I refute the misinterpretation of the FEP's assertion of stationary organisms, allowing for the modeling of enactive adaptive behaviour through free energy minimisation. Ultimately, I offer a constructive and interactionist approach to life and mind, transcending internalist and externalist perspectives.

This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, non-communicating segments. The comparison of the results shows that maximizing the predictive information per wheel leads to a higher coordinated behavior of the physically connected robots compared to a maximization per robot. Another focus of this paper is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those of shorter length and more complex controllers. The reason is found and discussed in the information-geometric interpretation of the learning process.

This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, noncommunicating segments. The comparison of the results shows that maximizing the predictive information per wheel leads to a higher coordinated behavior of the physically connected robots compared with a maximization per robot. Another focus of this article is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those of shorter length and more complex controllers. The reason is found and discussed in the information-geometric interpretation of the learning process.

Background: Children with autism should interact and get along with their surrounding environment. Therefore, the researchers must try to explain the reasons of challenges in their adaptive behaviors. This study aimed to evaluate levels of challenges in sensory processing and the motor domain of adaptive behavior among children with autism in Egypt, and to explore the effect of sensory processing on subdomains of gross and fine motor skills. Methods: This is a cross-sectional study. Study questionnaires were distributed to 180 caregivers of children with autism in Cairo, Egypt, between July 2017 and February 2018. The caregivers were selected by convenience sampling method. The questionnaires used were the sensory checklist and adaptive behavior scales. Descriptive statistics and comparing means were used to determine the levels of sensory processing and motor domain of adaptive behavior. Linear regression analyses was used to test the effects of sensory domains on motor areas. SPSS-PC V. 25 was used to analyze the obtained data. Results: The results demonstrated various problems in children's sensory processing and the motor domain of adaptive behavior. Caregivers reported that 96.67% of the children had frequent sensory challenges in at least one component of sensory domains, mostly in visual system (27.2%). Subdomain of fine motor skills (Mean=1.4951) showed higher challenges than gross motor skills (Mean=1.9455). This study proved significant inverse effect for movement processing on gross motor subdomain at α=0.05. Conclusion: Parents of children with autism, occupational therapists, and other healthcare practitioners should gain in-depth knowledge regarding their children's behaviors and their possible reasons.

Background: Children with autism should interact and get along with their surrounding environment. Therefore, the researchers must try to explain the reasons of challenges in their adaptive behaviors. This study aimed to evaluate levels of challenges in sensory processing and the motor domain of adaptive behavior among children with autism in Egypt, and to explore the effect of sensory processing on subdomains of gross and fine motor skills. Methods: This is a cross-sectional study. Study questionnaires were distributed to 180 caregivers of children with autism in Cairo, Egypt, between July 2017 and February 2018. The caregivers were selected by convenience sampling method. The questionnaires used were the sensory checklist and adaptive behavior scales. Descriptive statistics and comparing means were used to determine the levels of sensory processing and motor domain of adaptive behavior. Linear regression analyses was used to test the effects of sensory domains on motor areas. SPSS-PC V. 25 was used to analyze the obtained data. Results: The results demonstrated various problems in children's sensory processing and the motor domain of adaptive behavior. Caregivers reported that 96.67% of the children had frequent sensory challenges in at least one component of sensory domains, mostly in visual system (27.2%). Subdomain of fine motor skills (Mean=1.4951) showed higher challenges than gross motor skills (Mean=1.9455). This study proved significant inverse effect for movement processing on gross motor subdomain at α=0.05. Conclusion: Parents of children with autism, occupational therapists, and other healthcare practitioners should gain in-depth knowledge regarding their children's behaviors and their possible reasons.

We present a novel model, SCP1, of monkey sequence learning that takes the processes of stimulus recognition and motor planning seriously in addressing a robust dataset on list learning obtained through the Simultaneous Chaining Paradigm (SCP). Strikingly, SCP violates stimulus-response (S-R) mappings in that after several different lists are learned, monkeys are able to conserve this learning on a new list which is ''conserved'' in the sense that the jth element is specified as the jth element of any one of the previously learned lists. We demonstrate list acquisition as a result of multiple concurrent learning processes that together contribute to competent performance. In addition to reproducing behavioral results, we offer observations linking the work to macaque neurophysiology.

Knowledge of color has strong individual, environmental, and cultural differences that may systematically influence performance in cognitive tasks. For example, color knowledge has been shown to influence recall of color (Persaud & Hemmer, 2014). This manifests as a systematic regression to the mean effect, where memory is biased towards the mean hue of each universal color category. What remains unclear is whether differences, such as culture and environment, might differentially influence memory. We tested recognition memory for color in the Tsimane’ of Bolivia; an indigenous population with little or no modern schooling, whose environment is very different from industrialized societies. We found that recognition regressed towards the mean of some universal color categories, but for others was systematically biased toward neighboring categories. A cluster analysis suggested that the Tsimane’ use five underlying color categories—not the standard universals. This might be shaped by ...

A wealth of parent-report research shows adaptive functioning difficulties in autistic children, with parent-report influenced by a number of child factors. Adaptive functioning in autistic children is known to vary across settings; however, no research has yet explored factors influencing education professional-report. This study investigated the rate and profile of impairment, and child factors influencing education professional-reported adaptive skills in 248 autistic children. Twelve children were < 3 years (min age for available normative data on the adaptive function measure), so were removed from the analyses. Results replicated parent-literature; adaptive skills were negatively associated with age and informant-reported autism severity, and positively associated with nonverbal ability and expressive language. Adaptive functioning is important for real-world outcomes, e.g. educational attainment, independence, and support needs. Improving our understanding of adaptive func...

Introduction Past and recent research provided evidence that children growing up in 'nontraditional' families (i.e., single-parent, divorce and step-families) are more likely to experience behavioral, social, emotional and academic problems than children growing up in families where the two-biological parents are present (e.g., Acock et al. & Demo, 1994; Bronstein et al., 1993). Earlier findings have also associated living in such nontraditional family arrangements with children's delinquency, alcohol and substance abuse, lower self-esteem, psychiatric problems, and earlier initiation of sexual intercourse (e.g., Allison et al. & Furstenberg, 1989; Brook et al., Whiteman, & Gordon, 1985; Dornbusch et al., 1985; Harper et al. & Ryder, 1986; Newcomer et al.& Udry, 1987). These findings forewarn parents, educators, family practitioners and researchers about the detrimental impact of divorce and remarriage for children's adjustment and well-being.

Cognitive radio (CR) is a cutting-edge technology in fifth generation (5G) applications. The performance of the CR
network (CRN) can be enhanced by effective implementation of spectrum management, which is a decisive function in the network.
Deep learning framework (DLF) is a transformative technology for effective spectrum management in CRN.
The present work proposes a DLF for effective spectrum management using convolutional neural network (CNN), which
consists of a dataset, a predefined architecture, and fully connected layers. The dataset with 300 constellation diagrams of various
digital modulation schemes at a signal to noise ratio (SNR) of 10 dB is generated using the open source Python programming
language. Initially, the dataset is preprocessed, and feature extraction is conducted using a predefined architecture, AlexNet. Then,
classification of constellation diagrams is executed using fully connected layers. The hyperparameters such as learning rate, number
of epochs, and batch size are optimized to enhance the performance of the proposed DL framework.
The model was created using 60% of the training and 20% of the validation dataset. The created model was evaluated on 20%
of the dataset. The results outperformed with 95% accuracy, 96% precision, 95% recall, and 95% F1 score. The DLF that exhibits
substantial waveform classification performance can be recommended for spectrum management in CRN. The work was carried
out using open-source Python software and the licensed Colab Pro platform.

Our previous study showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) can learn a behavior as they avoid stimulation from outside. In a sense, the embodied neural network can autonomously change their activity to avoid external stimuli. In this paper, as a result of our experiments using cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: if the network cannot learn to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes as if to ignore the input neurons. We also show such a behavior is reproduced by spiking neural networks with asymmetric-STDP. We consider that these properties can be regarded as autonomous regulation of self and non-self for the network.

Adaptation addresses the complexity and overload caused by an increasing amount of available resources by the identification and presentation of relevant resources. Drawbacks of existing approaches to this end include the limited degree of customization, difficulties in the acquisition of model information and the lack of control and transparency of the system's adaptive behavior. This paper proposes an approach which addresses these drawbacks to perform meaningful and effective adaptation by the application of semantic web technologies. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. The focus of this paper lies in the presentation of adaptation rules.

OBJECTIVES. Periventricular hemorrhagic infarction is a serious complication of germinal matrix-intraventricular hemorrhage in premature infants. Our objective was to determine the neurodevelopmental and adaptive outcomes of periventricular hemorrhagic infarction survivors and identify early cranial ultrasound predictors of adverse outcome.METHODS. We retrospectively evaluated all cranial ultrasounds of 30 premature infants with periventricular hemorrhagic infarction and assigned a cranial ultrasound–based periventricular hemorrhagic infarction severity score (range: 0–3) on the basis of whether periventricular hemorrhagic infarction (1) involved ≥2 territories, (2) was bilateral, or (3) caused midline shift. We then performed neuromotor, visual function, and developmental evaluations (Mullen Scales of Early Learning, Vineland Adaptive Behavior Scale). Developmental scores below 2 SD from the mean were defined as abnormal.RESULTS. Median adjusted age at evaluation was 30 months (ran...

OBJECTIVE. The survival of very low birth weight infants has increased markedly in recent years. Unfortunately, the prevalence of significant and lifelong motor, cognitive, and behavioral dysfunction has remained a major problem confronting these children. The objective of this study was to perform screening tests for early autistic features in children with a history of very low birth weight and to identify risk factors associated with a positive screening result. METHODS. We studied 91 ex-preterm infants ≤ 1500 g at birth. Infants underwent conventional MRI studies at preterm and/or term-adjusted age. We collected pertinent demographic, prenatal, intrapartum, acute postnatal, and short-term outcome data for all infants. Follow-up assessments were performed at a mean age of 21.9 ± 4.7 months, using the Modified Checklist for Autism in Toddlers, the Vineland Adaptive Behavior Scale, and the Child Behavior Checklist. RESULTS. Twenty-six percent of ex-preterm infants had a positive re...

Embodied conversational agents have been proven to be powerful tools for engaging users in interactions and thus are suitable for training scenarios that rely on a role-playing metaphor. In the CUBE-G project, the authors propose an approach for culture-adaptive behavior generation of such agents, which can be employed in edutainment applications for increasing cultural awareness and for learning some of the appropriate behavior routines. The authors present the methodological approach of a standardized ...

Self-amplification processes are at the origin of several collective decision phenomena in insect societies. Understanding these processes requires linking individual behavioral rules of insects to a choice dynamics at the colony level. In a homogeneous environment, the German cockroach Blattella germanica displays self-amplified aggregation behavior. In a heterogeneous environment where several shelters are present, groups of cockroaches collectively select one of them. In this article, we demonstrate that the restriction of the self-amplified aggregation behavior to distinct zones in the environment can explain the emergence of a collective decision at the level of the group. This hypothesis is tested with robotics experiments and dedicated computer simulations. We show that the collective decision is influenced by the available spaces to explore and to aggregate in, by the size of the population involved in the aggregation process and by the probability of encounter zones while t...

Although play occurs in a wide variety of animals, models of the origins of play behavior are lacking. We propose a novel computational model exploring the evolution of non-social frivolous play. Asexually reproducing semelparous animals can either rest or forage. Foraging occurs when an organism is below an energy threshold. Success is determined by the combination of skill and availability of resources, which declines over time but replenishes for each generation. Play was introduced as a mutant strategy: a frivolous activity that uses energy and increases the probability of dying over resting with no direct fitness benefit. Simulations show that play behavior becomes fixed in the population and the time spent playing is maintained at a low rate in spite of its costly nature. When play behavior is functional by increasing foraging ability, it evolves quickly and the time individuals spend playing increases, but eventually the population of players collapses and play disappears. We...

Decision-making is an important function that can be quantified using a two-choice prediction task. Individuals with Autistic Disorder (AD) often show highly restricted and repetitive behavior that may interfere with adaptive decision-making. We assessed whether AD adults showed repetitive behavior on the choice task that was unaffected by changing task demands, by examining the influence of experimenterdetermined error rates on decision-making. Sixteen AD adults and 14 typically developed subjects were administered a two-choice task using three error rate conditions. Although AD subjects showed occurrences of stereotyped responding, their decision-making behavior was strongly affected by changes in task demands, especially when they experienced frequent success. Thus, behavioral paradigms that provide frequent reinforcement may be helpful in modifying decision-making abilities in AD.

Adaptation is increasingly used in the development of safety-critical embedded systems, in particular to reduce hardware needs and to increase availability. However, composing a system from many reconfigurable components can lead to a huge number of possible system configurations, inducing a complexity that cannot be handled during system design. To overcome this problem, we propose a new component-based modeling and verification method for adaptive embedded systems. The component-based modeling approach facilitates abstracting a composition of components to a hierarchical component. In the hierarchical component, the number of possible configurations of the composition is reduced to a small number of hierarchical configurations. Only these hierarchical configurations have to be considered when the hierarchical component is used in further compositions such that design complexity is reduced at each hierarchical level. In order to ensure well-definedness of components, we provide a m...

Organizations increasingly rely on teams to respond to crises. While research on team effectiveness during nonroutine events is growing, naturalistic studies examining team behaviors during crises are relatively scarce. Furthermore, the relevant literature offers competing theoretical rationales concerning effective team response to crises. In this article, the authors investigate whether high-versus average-performing teams can be distinguished on the basis of the number and complexity of their interaction patterns. Using behavioral observation methodology, the authors coded the discrete verbal and nonverbal behaviors of 14 nuclear power plant control room crews as they responded to a simulated crisis. Pattern detection software revealed systematic differences among crews in their patterns of interaction. Mean comparisons and discriminant function analysis indicated that that higher performing crews exhibited fewer, shorter, and less complex interactions patterns. These results illustrate the limitations of standardized response patterns and highlight the importance of team adaptability. Implications for future research and for team training are included.

Contingent relations between sensory events render the environment predictable and thus facilitate adaptive behavior. The human capacity to detect such relations has been comprehensively demonstrated in paradigms in which contingency rules were task-relevant or in which they applied to motor behavior. The extent to which contingencies can also be extracted from events that are unrelated to the current goals of the organism has remained largely unclear. The present study addressed the emergence of contingency-related effects for behaviorally irrelevant auditory stimuli and the cortical areas involved in the processing of such contingency rules. Contingent relations between different features of temporally separate events were embedded in a new dynamic protocol. Participants were presented with the auditory stimulus sequences while their attention was captured by a video. The mismatch negativity (MMN) component of the event-related brain potential (ERP) was employed as an electrophysiological correlate of contingency detection. MMN generators were localized by means of scalp current density (SCD) and primary current density (PCD) analyses with variable resolution electromagnetic tomography (VARETA). Results show that task-irrelevant contingencies can be extracted from about fifteen to twenty successive events conforming to the contingent relation. Topographic and tomographic analyses reveal the involvement of the auditory cortex in the processing of contingency violations. The present data provide evidence for the rapid encoding of complex extrapolative relations in sensory areas. This capacity is of fundamental importance for the organism in its attempt to model the sensory environment outside the focus of attention.

Many biped robot control schemes adopt a zero moment point (ZMP) criterion, where the motion is initially planned as the positional trajectories such that ZMP stays within the support polygon, while the feedback control of each joint is later applied to follow the planned reference motion. Although this method is powerful, the ZMP is not always controlled in a feedback manner. Namely, when the environment such as the gradient of the ground varies, the planned motion may cause the tumble and so replanning or modification is sometimes required in order to avoid it. With respect to the environmental variations, the ZMP trajectory is invariant in the lateral plane of the biped robot, in which the ZMP moves from the one side to the other and vice versa. From this point of view, we propose a biped control method for the frontal plane motion based on the ZMP position feedback. It does not required the reference motion of the upper body and the motion replanning or modification of the reference motion are free against environmental variation. This method is applied in the in-place stepping motion and the stability of this method is examined analytically as well as by computer simulations. Finally, the effectiveness of this method is demonstrated by the robot experiment with some improvement points.

A robot capable of understanding emotion expressions can increase its own capability of solving problems by using emotion expressions as part of its own decision-making, in a similar way to humans. Evidence shows that the perception of human interaction starts with an innate perception mechanism, where the interaction between different entities is perceived and categorized into two very clear directions: positive or negative. While the person is developing during childhood, the perception evolves and is shaped based on the observation of human interaction, creating the capability to learn different categories of expressions. In the context of human–robot interaction, we propose a model that simulates the innate perception of audio–visual emotion expressions with deep neural networks, that learns new expressions by categorizing them into emotional clusters with a self-organizing layer. The proposed model is evaluated with three different corpora: The Surrey Audio–Visual Expressed Emo...

We have previously shown that acute stress-induced release of norepinephrine (NE) facilitates anxiety-like behavioral responses to stress, such as reduction in open-arm exploration on the elevated-plus maze and in social behavior on the social interaction test. Since these responses represent inhibition of ongoing behavior, it is important to also address whether NE facilitates a response that represents an activation of behavior. Correspondingly, it is unknown how a chronic elevation in tonic steady-state noradrenergic (NA) neurotransmission induced by NE reuptake blockade might alter this acute modulatory function, a regulatory process that may be pertinent to the anxiolytic effects of NE reuptake blockers such as desipramine (DMI). Therefore, in this study, we investigated noradrenergic modulation of the shock-probe defensive burying response in the lateral septum (LS). In experiment 1, shock-probe exposure induced an acute 3-fold increase in NE levels measured in LS of male Sprague-Dawley rats by microdialysis. Shock-probe exposure also induced a modest rise in plasma ACTH, taken as an indicator of perceived stress, that returned to baseline more rapidly in rats that were allowed to bury the probe compared to rats prevented from burying by providing them with minimal bedding, indicating that the active defensive burying behavior is an effective coping strategy that reduces the impact of acute shock probe-induced stress. In experiment 2, blockade of either α 1-or β-adrenergic receptors in LS by local antagonist microinjection immediately before testing reduced defensive burying and increased immobility. In the next experiment, chronic DMI treatment increased basal extracellular NE levels in LS, and attenuated the acute shock probe-induced increase in NE release in LS relative to baseline. Chronic DMI treatment decreased shock-probe defensive burying behavior in a time-dependent manner, apparent only after 2 weeks or more of drug treatment. Moreover, rats treated chronically with DMI showed no significant rise of plasma ACTH in response to shock-probe exposure. Thus, acute stress-induced release of NE in LS facilitated defensive burying, an active, adaptive behavioral coping response. Chronic treatment with the NE reuptake blocker and antidepressant drug DMI attenuated acute noradrenergic facilitation of the active burying response, and also attenuated the level of perceived stress driving that response. These results suggest that long-term regulation of the acute modulatory function of NE by chronic treatment with reuptake blockers may contribute to the mechanisms by which such drugs exert their anxiolytic effects in the treatment of stress-related psychiatric conditions, including depression and anxiety.

Studies examining the importance of employability skill development from students' perspectives are rather limited. For this reason, the purpose of this study is to investigate the basic transferable academic skills, personal attributes and any other crucial employability skills that help future employees be easily integrated in a workplace. As a sample, twenty applied English language students from the Higher Institute of Languages in Gabes, Tunisia constituted the basic informant group. Upon the completion of their internship period, they provided data necessary for this research paper by filling in a questionnaire. The qualitative analysis of the research findings revealed that the trainees valued skill development as they were equipped with considerable knowledge in dealing with ICT usage, in displaying good communication skills and in possessing a rich repertoire of financial and legal terminologies useful for any business field. The findings have also revealed that the trainees held a wide range of intelligences, displayed active problem solving, demonstrated critical thinking skills, showed creative reasoning, and maintained developed self regulatory and metacognitive strategies. Other pertinent factors that help them be integrated within the workplace include the willingness to be cooperative, motivated and flexible.