Perceptual Learning via Modification of Cortical Top-Down Signals

The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1. However, any rewiring of V1 would deteriorate other perceptual abilities different from the trained one. We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact. Consistent with biological data, the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1. Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning.

Learning software-maintenance tasks in offshoring projects: a cognitive-load perspective

Individual learning is central to the success of the transition phase in software mainte-nance offshoring projects. However, little is known on how learning activities, such as on-the-job training and formal presentations, are effectively combined during the tran-sition phase. In this study, we present and test propositions derived from cognitive load theory. The results of a multiple-case study suggest that learning effectiveness was highest when learning tasks such as authentic maintenance requests were used. Con-sistent with cognitive load theory, learning tasks were most effective when they imposed moderate cognitive load. Our data indicate that cognitive load was influenced by the expertise of the onsite coordinator, by intrinsic task complexity, by the degree of specifi-cation of tasks, and by supportive information. Cultural and semantic distances may in-fluence learning by inhibiting supportive information, specification, and the assignment of learning tasks.

Learning Software-Maintenance Tasks in the Transition Phase of Offshore Outsourcing Projects: Two Learning-Theoretical Perspectives

The increasing practice of offshore outsourcing software maintenance has posed the challenge of effectively transferring knowledge to individual software engineers of the vendor. In this theoretical paper, we discuss the implications of two learning theories, the model of work-based learning (MWBL) and cognitive load theory (CLT), for knowledge transfer during the transition phase. Taken together, the theories suggest that learning mechanisms need to be aligned with the type of knowledge (tacit versus explicit), task characteristics (complexity and recurrence), and the recipients’ expertise. The MWBL proposes that learning mechanisms need to include conceptual and practical activities based on the relative importance of explicit and tacit knowledge. CLT explains how effective portfolios of learning mechanisms change over time. While jobshadowing, completion tasks, and supportive information may prevail at the outset of transition, they may be replaced by the work on conventional tasks towards the end of transition.

SUSYFLAVOR v2: A Computational tool for FCNC and CP-violating processes in the MSSM

We present SUSY_FLAVOR version 2 — a Fortran 77 program that calculates low-energy flavor observables in the general R-parity conserving MSSM. For a set of MSSM parameters as input, the code gives predictions for: 1. Electric dipole moments of the leptons and the neutron. 2. Anomalous magnetic moments (i.e. g − 2) of the leptons. 3. Radiative lepton decays (μ → eγ and τ → μγ , eγ ). 4. Rare Kaon decays (K0 L → π0 ¯νν and K+ → π+ ¯νν). 5. Leptonic B decays (Bs,d → l+l−, B → τ ν and B → Dτ ν). 6. Radiative B decays (B → ¯ Xsγ ). 7. ΔF = 2 processes ( ¯ K0–K0, ¯D–D, ¯Bd–Bd and ¯Bs–Bs mixing). Comparing to SUSY_FLAVOR v1, where the matching conditions were calculated strictly at one-loop level, SUSY_FLAVOR v2 performs the resummation of all chirally enhanced corrections, i.e. takes into account the enhanced effects from tan β and/or large trilinear soft mixing terms to all orders in perturbation theory. Also, in SUSY_FLAVOR v2 new routines calculation of B → (D)τ ν, g − 2, radiative lepton decays and Br(l → l′γ ) were added. All calculations are done using exact diagonalization of the sfermion mass matrices. The program can be obtained from http://www.fuw.edu.pl/susy_flavor.

Early adolescents' enjoyment experienced in learning situations and its relation to student achievement

While many studies confirm that positive emotions, including enjoyment, lead to better student achievement, less empirical evidence exists about possible mediator variables that link achievement to enjoyment. It is proposed that achievement and enjoyment form a circular dependency; enjoyment in learning leads to higher achievement but a degree of achievement is required to enjoy learning. This study provides insight into the reverse of the much studied enjoyment to achievement link and provides practical recommendations on how to use these findings. Founded in Control-value theory, which suggests that control and value cognitions are important variables that mediate the connection between enjoyment and achievement, this study explores the reciprocal achievement-cognition-enjoyment link. The reciprocal link was investigated by applying a one year longitudinal design to students of grade 6 and 7 (N = 356). This age group was chosen because early adolescence represents a critical period during which a strong decrease in positive learning emotions is observed. Part of the work involved identifying factors that might be responsible for this negative development. Results of cross-lagged path analysis identified reciprocal effects between student achievement and enjoyment with control and value cognitions functioning as partial mediators. High achievement goes with high control and value cognitions, which in turn positively affect enjoyment. However, cross-lagged correlations could only be partly confirmed. The results are discussed in terms of theoretical and practical implications.

Learning enjoyment in early adolescence

There is empirical evidence showing that positive emotional and motivational factors in formal learning contexts decrease at the stage of young adolescence. According to Stage-Environment-Fit Theory and Self-Determination Theory, this change should be explained by a non-fulfilment of students' needs. By combining two different methods (questionnaires and day-to-day diaries) and applying a longitudinal design, this study aimed to explore the change in and the determinants of habitual and actual learning enjoyment. The sample consisted of 356 students. Quantitative results indicated that learning enjoyment and classroom practices decreased between Grades 6 and 7. Path analyses revealed that classroom practices are the source of students' learning enjoyment, while self-efficacy functions as a partial mediator. Data from students' diaries showed that a teacher's neglect of students' needs for competence and relatedness were significant sources of impeded learning enjoyment. Practical implications suggest the relevance of adjusting learning conditions to the needs of young adolescents in order to provide a facilitating basis for learning enjoyment.

A Normative Theory of Forgetting: Lessons from the Fruit Fly

Recent experiments revealed that the fruit fly Drosophila melanogaster has a dedicated mechanism for forgetting: blocking the G-protein Rac leads to slower and activating Rac to faster forgetting. This active form of forgetting lacks a satisfactory functional explanation. We investigated optimal decision making for an agent adapting to a stochastic environment where a stimulus may switch between being indicative of reward or punishment. Like Drosophila, an optimal agent shows forgetting with a rate that is linked to the time scale of changes in the environment. Moreover, to reduce the odds of missing future reward, an optimal agent may trade the risk of immediate pain for information gain and thus forget faster after aversive conditioning. A simple neuronal network reproduces these features. Our theory shows that forgetting in Drosophila appears as an optimal adaptive behavior in a changing environment. This is in line with the view that forgetting is adaptive rather than a consequence of limitations of the memory system.

Early adolescents’ enjoyment experienced in learning situations at school and its relation to student achievement

While many studies confirm that positive emotions, including enjoyment, lead to better student achievement, less empirical evidence exists about possible mediator variables that link achievement to enjoyment. It is proposed that achievement and enjoyment form a circular dependency; enjoyment in learning leads to higher achievement but a degree of achievement is required to enjoy learning. This study provides insight into the reverse of the much studied enjoyment to achievement link and provides practical recommendations on how to use these findings. Founded in Control-value theory, which suggests that control and value cognitions are important variables that mediate the connection between enjoyment and achievement, this study explores the reciprocal achievement-cognition-enjoyment link. The reciprocal link was investigated by applying a one year longitudinal design to students of grade 6 and 7 (N = 356). This age group was chosen because early adolescence represents a critical period during which a strong decrease in positive learning emotions is observed. Part of the work involved identifying factors that might be responsible for this negative development. Results of cross-lagged path analysis identified reciprocal effects between student achievement and enjoyment with control and value cognitions functioning as partial mediators. High achievement goes with high control and value cognitions, which in turn positively affect enjoyment. However, cross-lagged correlations could only be partly confirmed. The results are discussed in terms of theoretical and practical implications

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.