The Impact of Motor Learning on Motor Behavior and Cortical Dynamics in a Complex Stressful Social Environment

An economy of effort is a core characteristic of highly skilled motor performance often described as being effortless or automatic. Electroencephalographic (EEG) evaluation of cortical activity in elite performers has consistently revealed a reduction in extraneous associative cortical activity and an enhancement of task-relevant cortical processes. However, this has only been demonstrated under what are essentially practice-like conditions. Recently it has been shown that cerebral cortical activity becomes less efficient when performance occurs in a stressful, complex social environment. This dissertation examines the impact of motor skill training or practice on the EEG cortical dynamics that underlie performance in a stressful, complex social environment. Sixteen ROTC cadets participated in head-to-head pistol shooting competitions before and after completing nine sessions of skill training over three weeks. Spectral power increased in the theta frequency band and decreased in the low alpha frequency band after skill training. EEG Coherence increased in the left frontal region and decreased in the left temporal region after the practice intervention. These suggest a refinement of cerebral cortical dynamics with a reduction of task extraneous processing in the left frontal region and an enhancement of task related processing in the left temporal region consistent with the skill level reached by participants. Partitioning performance into ‘best’ and ‘worst’ based on shot score revealed that deliberate practice appears to optimize cerebral cortical activity of ‘best’ performances which are accompanied by a reduction in task-specific processes reflected by increased high-alpha power, while ‘worst’ performances are characterized by an inappropriate reduction in task-specific processing resulting in a loss of focus reflected by higher high-alpha power after training when compared to ‘best’ performances. Together, these studies demonstrate the power of experience afforded by practice, as a controllable factor, to promote resilience of cerebral cortical efficiency in complex environments.

Psychosocial predictors of the use of enhanced podcasting in student learning

The current study examined the influence of psychosocial constructs, from a theory of planned behavior (TPB) perspective, to predict university students’ (N = 159) use of a newly offered on-line learning tool, enhanced podcasts. Pre-semester, students completed questionnaires assessing the TPB predictors (attitude, subjective norm, perceived behavioral control) related to intended enhanced podcast use until the middle of semester. Mid-semester, students completed similar items relating to podcast use until the end of semester. Self-report measures of podcast use were obtained at the middle and end of semester. At both time points, students’ attitudes predicted their intentions and, at the initial time point, subjective norm also predicted intended podcast use. An examination of the beliefs underlying attitudes, the only construct to predict intentions at both time points, revealed differences between those students higher, rather than lower on intentions to use the podcasts, especially for the perceived educational benefits of podcast use later in the semester. Intentions to use enhanced podcasting only predicted self-reported use in the second half of the semester. Overall, this study identified some of the determinants which should be considered by those aiming to encourage student use of novel on-line educational tools.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup).

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Noising around : investigations in mobile learning

In this paper we present an account of children's interactions with a mobile technology prototype within a school context. The Noise Detectives trial was conducted in a school setting with the aim of better understanding the role of mobile technology as a mediator within science learning activities. Over eighty children, aged between ten and twelve, completed an outdoor data gathering activity using a mobile learning prototype that included paper and digital components. They measured and recorded noise levels at a range of locations throughout the schools. We analyzed the activity to determine how the components of the prototype were integrated into the learning activity, and to identify differences in behavior that resulted from using these components. We present design implications that resulted from observed differences in prototype use and appropriation.

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Preservice teachers’ views : Issues for learning to teach SOSE in an overcrowded curriculum

A cohort of third-year preservice teachers (n=24) was given the opportunity to observe and participate in Studies of Society and Environment (SOSE) in primary classrooms through a series of school visits during a semester-long unit. These visits were designed to give preservice teachers opportunities to connect SOSE teaching theories studied in the university setting to SOSE teaching practices within schools. This study investigates the extent of the preservice teachers’ opportunities to observe SOSE teaching in the primary school. Responses from a survey showed that the majority of preservice teachers only agreed with 6 of the 25 items associated with the six categories (personal-professional skill development, system requirements, teaching practices, student behavior, feedback to students, and reflection on practice). Written responses from the questionnaire concurred that most had not experienced SOSE teaching. Various issues are discussed around providing preservice teachers with SOSE teaching experiences. School executives, teachers and university staff need to be part of the process to ensure preservice teachers are receiving quality SOSE teaching experiences that will assist in their pedagogical development. A wider question is also raised through this paper. If preservice teachers are unable to experience quality SOSE teaching in school visits designed for such a purpose, does this signal a changing emphasis in education that leaves the social sciences and humanities off the education agenda?

Learning personalized tag ontology from user tagging information

The cross-sections of the Social Web and the Semantic Web has put folksonomy in the spot light for its potential in overcoming knowledge acquisition bottleneck and providing insight for "wisdom of the crowds". Folksonomy which comes as the results of collaborative tagging activities has provided insight into user's understanding about Web resources which might be useful for searching and organizing purposes. However, collaborative tagging vocabulary poses some challenges since tags are freely chosen by users and may exhibit synonymy and polysemy problem. In order to overcome these challenges and boost the potential of folksonomy as emergence semantics we propose to consolidate the diverse vocabulary into a consolidated entities and concepts. We propose to extract a tag ontology by ontology learning process to represent the semantics of a tagging community. This paper presents a novel approach to learn the ontology based on the widely used lexical database WordNet. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. We provide empirical evaluations by using the semantic information contained in the ontology in a tag recommendation experiment. The results show that by using the semantic relationships on the ontology the accuracy of the tag recommender has been improved.

Personalization in tag ontology learning for recommendation making

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

Discrepancy between antennal and behavioral responses for enantiomers of a-Pinene : electrophysiology and behavior of helicoverpa armigera (Lepidoptera)

The ability of adult cotton bollworm, Helicoverpa armigera (Hübner), to distinguish and respond to enantiomers of α-pinene was investigated with electrophysiological and behavioral methods. Electroantennogram recordings using mixtures of the enantiomers at saturating dose levels, and single unit electrophysiology, indicated that the two forms were detected by the same receptor neurons. The relative size of the electroantennogram response was higher for the (−) compared to the (+) form, indicating greater affinity for the (−) form at the level of the dendrites. Behavioral assays investigated the ability of moths to discriminate between, and respond to the (+) and (−) forms of α-pinene. Moths with no odor conditioning showed an innate preference for (+)-α-pinene. This preference displayed by naïve moths was not significantly different from the preferences of moths conditioned on (+)-α-pinene. However, we found a significant difference in preference between moths conditioned on the (−) enantiomer compared to naïve moths and moths conditioned on (+)-α-pinene, showing that learning plays an important role in the behavioral response. Moths are less able to distinguish between enantiomers of α-pinene than different odors (e.g., phenylacetaldehyde versus (−)-α-pinene) in learning experiments. The relevance of receptor discrimination of enantiomers and learning ability of the moths in host plant choice is discussed.

Mice selectively bred for High and Low fear behavior show differences in the number of pMAPK (p44/42 ERK) expressing neurons in lateral amygdala following Pavlovian fear conditioning

Individual variability in the acquisition, consolidation and extinction of conditioned fear potentially contributes to the development of fear pathology including posttraumatic stress disorder (PTSD). Pavlovian fear conditioning is a key tool for the study of fundamental aspects of fear learning. Here, we used a selected mouse line of High and Low Pavlovian conditioned fear created from an advanced intercrossed line (AIL) in order to begin to identify the cellular basis of phenotypic divergence in Pavlovian fear conditioning. We investigated whether phosphorylated MAPK (p44/42 ERK/MAPK), a protein kinase required in the amygdala for the acquisition and consolidation of Pavlovian fear memory, is differentially expressed following Pavlovian fear learning in the High and Low fear lines. We found that following Pavlovian auditory fear conditioning, High and Low line mice differ in the number of pMAPK-expressing neurons in the dorsal sub nucleus of the lateral amygdala (LAd). In contrast, this difference was not detected in the ventral medial (LAvm) or ventral lateral (LAvl) amygdala sub nuclei or in control animals. We propose that this apparent increase in plasticity at a known locus of fear memory acquisition and consolidation relates to intrinsic differences between the two fear phenotypes. These data provide important insights into the micronetwork mechanisms encoding phenotypic differences in fear. Understanding the circuit level cellular and molecular mechanisms that underlie individual variability in fear learning is critical for the development of effective treatment of fear-related illnesses such as PTSD.

Myosin light chain kinase regulates synaptic plasticity and fear learning in the lateral amygdala

Learning and memory depend on signaling mole- cules that affect synaptic efficacy. The cytoskeleton has been implicated in regulating synaptic transmission but its role in learning and memory is poorly understood. Fear learning depends on plasticity in the lateral nucleus of the amygdala. We therefore examined whether the cytoskeletal-regulatory protein, myosin light chain kinase, might contribute to fear learning in the rat lateral amygdala. Microinjection of ML-7, a specific inhibitor of myosin light chain kinase, into the lateral nucleus of the amygdala before fear conditioning, but not immediately afterward, enhanced both short-term memory and long-term memory, suggesting that myosin light chain kinase is involved specifically in memory acquisition rather than in posttraining consolidation of memory. Myosin light chain kinase inhibitor had no effect on memory retrieval. Furthermore, ML-7 had no effect on behavior when the train- ing stimuli were presented in a non-associative manner. An- atomical studies showed that myosin light chain kinase is present in cells throughout lateral nucleus of the amygdala and is localized to dendritic shafts and spines that are postsynaptic to the projections from the auditory thalamus to lateral nucleus of the amygdala, a pathway specifically impli- cated in fear learning. Inhibition of myosin light chain kinase enhanced long-term potentiation, a physiological model of learning, in the auditory thalamic pathway to the lateral nu- cleus of the amygdala. When ML-7 was applied without as- sociative tetanic stimulation it had no effect on synaptic responses in lateral nucleus of the amygdala. Thus, myosin light chain kinase activity in lateral nucleus of the amygdala appears to normally suppress synaptic plasticity in the cir- cuits underlying fear learning, suggesting that myosin light chain kinase may help prevent the acquisition of irrelevant fears. Impairment of this mechanism could contribute to pathological fear learning.

Objectively measured physical activity behavior in children attending a half day preschool program

Obesity rates are increasing in children of all ages, and reduced physical activity (PA) is a likely contributor to this trend. Little is known about the physical activity behavior of preschool-age children, or about the influence of preschool attendance on physical activity. Purpose The purpose of this study was to quantify the physical activity levels of children attending a center-based half-day preschool program. Methods Forty-two 3-to-5-year old children (Mean age = 4.0 ± 0.7, 54.8% Male, Mean BMI = 16.5 ± 5.5, Mean BMI %tile = 52.1 ± 33.5) from four class groups (two morning and two afternoon), wore an Actigraph 7164 accelerometer for the entire halfday program (including classroom learning experiences, snack and recess time) 2 times per week, for 10 weeks (20 activity monitoring records in total). Activity counts for each 5-sec interval were uploaded to a customized data reduction program to determine total counts, minutes of moderate PA (MPA) (3–5.9 METs), and minutes of vigorous PA (VPA) (> = 6 METs) per session. Counts were categorized as either MPA or VPA using the cutpoints developed by Sirard and colleagues (2001). Results Across the four 2.5 hour programs, the average MPA, VPA and total counts (× 103) were 12.4 ± 3.1 minutes, 18.3 ± 4.6 minutes, and 171.1 ± 29.7 counts, respectively. Thus, on average, children accumulated just over 12 minutes of moderateto-vigorous PA per hour of program attendance. The PA variables did not differ significantly by gender, weight status, or time of day. There were, however, significant age differences, with 3-year-olds exhibiting significantly less PA than their 4- and 5-year-old counterparts. Conclusions These results suggest that young children are relatively lowactive while attending preschool. Accordingly, interventions to increase movement opportunities during the preschool day are warranted.