Minimal model of associative learning for cross-situational lexicon acquisition

An explanation for the acquisition of word-object mappings is the associative learning in a cross-situational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for acquiring a one-to-one mapping between $N$ objects and $N$ words based solely on the co-occurrence between objects and words. In particular, a learning trial in our learning scenario consists of the presentation of $C + 1 < N$ objects together with a target word, which refers to one of the objects in the context. We find that the learning times are distributed exponentially and the learning rates are given by $\ln{[\frac{N(N-1)}{C + (N-1)^{2}}]}$ in the case the $N$ target words are sampled randomly and by $\frac{1}{N} \ln [\frac{N-1}{C}]$ in the case they follow a deterministic presentation sequence. This learning performance is much superior to those exhibited by humans and more realistic learning algorithms in cross-situational experiments. We show that introduction of discrimination limitations using Weber's law and forgetting reduce the performance of the associative algorithm to the human level

Extracting finite structure from infinite language

This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set

Discounting of reward sequences: a test of competing formal models of hyperbolic discounting

Humans are known to discount future rewards hyperbolically in time. Nevertheless, a formal recursive model of hyperbolic discounting has been elusive until recently, with the introduction of the hyperbolically discounted temporal difference (HDTD) model. Prior to that, models of learning (especially reinforcement learning) have relied on exponential discounting, which generally provides poorer fits to behavioral data. Recently, it has been shown that hyperbolic discounting can also be approximated by a summed distribution of exponentially discounted values, instantiated in the μAgents model. The HDTD model and the μAgents model differ in one key respect, namely how they treat sequences of rewards. The μAgents model is a particular implementation of a Parallel discounting model, which values sequences based on the summed value of the individual rewards whereas the HDTD model contains a non-linear interaction. To discriminate among these models, we observed how subjects discounted a sequence of three rewards, and then we tested how well each candidate model fit the subject data. The results show that the Parallel model generally provides a better fit to the human data

The effect of music-induced emotion on visual-spatial learning in people with Parkinson's disease: A pilot study

Introduction: Emotional states have been shown to influence cognitive processes including visual-spatial learning. Parkinson's Disease (PD), besides manifesting with the cardinal motor symptoms, presents cognitive and affective disturbances. Here we aimed at investigating whether manipulation of the emotional state by means of music was able to influence the performance of a visual-spatial learning task in a group of PD participants. Methods: Ten PD patients and 11 healthy elderly (ELD) were asked to perform a visual-spatial learning task while listening two musical pieces evoking a neutral emotion or fear. Targets were presented on a screen in a preset order over four blocks and subjects were asked to learn the sequence order by attending to the display. At the end of each block, participants were asked to verbally recall the sequence and a score was assigned (Verbal Score, VS). Results: Analysis of variance-type statistic test on the VS disclosed a significant effect of Music and sequence Blocks (p = 0.01 and p &lt; 0.001, respectively) and a significant interaction between Group and sequence Blocks. Sequence learning occurred across the training period in both groups, but PD patients were slower than ELD and at the end of the training period learning performance was worse in PD with respect to ELD. In PD patients, like in ELD, fear-inducing music has a detrimental effect on visual-spatial learning performances, which are slower and decreased. Conclusion: These findings confirm an impairment in visual-spatial learning in PD and indicates that the emotional state influences this learning ability similarly to healthy controls

Teaching Research Methodology Using A Project-Based Three Course Sequence Critical Reflections On Practice

This article presents a reflective analysis of teaching research methodology through a three course sequence using a project-based approach.  The authors reflect critically on their experiences in teaching research methods courses in an undergraduate business management program.  The introduction of a range of specific techniques including student developed research projects, student-centered learning experiences, and public presentation of research projects are reflected upon.  In 2001, the Department of Management reviewed the curriculum and established a research sequence linking major management course offerings.  Three courses: Statistics, Research Methodology and Organizational Behavior were selected to be taught in sequence and designed to build research expertise.  This article addresses the processes, procedures and practices for implementing and sustaining an undergraduate research sequence.  Techniques are included for course design, development, and facilitation that emphasize product based learning strategies and interactive elements.  Techniques for garnering student engagement and excitement for research projects will be presented

The role of alpha oscillations in premotor-cerebellar connectivity in motor sequence learning: Insights from transcranial alternating current stimulation

Alpha oscillations (8-13 Hz) have been suggested to play an important role in dynamic neural processes underlying learning and memory. The goal of this work was to scrutinize the role of alpha oscillations in communication within a cortico-cerebellar network implicated in motor sequence learning. To this end, we conducted two EEG experiments using a serial reaction time task. In the first experiment, we explored changes in alpha power and cross-channel alpha coherence as subjects learned a motor sequence. We found a gradual decrease in spectral alpha power over left premotor cortex (PMC) and sensorimotor cortex (SM1) during learning blocks. In addition, alpha coherence between left PMC/SM1 and left cerebellar crus I was specifically decreased during sequence learning, possibly reflecting a functional decoupling in the broader motor learning network. In the second experiment in a different cohort, we applied 10Hz transcranial alternating current stimulation (tACS), a method shown to entrain local oscillatory activity, to left M1 (lM1) and right cerebellum (rCB) during sequence learning. We observed a tendency for diminished learning following rCB tACS compared to sham, but not following lM1 tACS. Learning-related alpha power following rCB tACS was increased in left PMC, possibly reflecting increase in local inhibitory neural activity. Importantly, learning-specific alpha coherence between left PMC and right cerebellar lobule VIIb was enhanced following rCB tACS. These findings provide strong evidence for a causal role of alpha oscillations in controlling information transfer in a premotor-cerebellar loop during motor sequence learning. Our findings are consistent with a model in which sequence learning may be impaired by enhancing premotor cortical alpha oscillation via external modulation of cerebellar oscillations.:1 List of Abbreviations 2 Introduction 2.1 Motor Learning Stages 2.2 Motor Learning Tasks 2.3 Motor Learning Network 2.4 Theoretical Models of Motor Learning 2.5 Functional Connectivity of Motor Brain Regions 2.6 Effective Connectivity of Motor Brain Regions 2.7 Oscillations in Neuronal Communication 2.8 Alpha Oscillations 2.8.1 Role of Alpha Oscillations in Motor Sequence Learning 2.9 Transcranial Electric Stimulation 2.9.1 Transcranial Alternating Current Stimulation (tACS) 2.10 Summary of Study Rationale 3 Publication 4 Summary 5 List of References 6 Supplementary Materials 7 Contribution of Authors / Darstellung des eigenen Beitrags 8 Declaration of Authorship 9 Curriculum Vitae 10 Publication and Presentation 11 Acknowledgement / Danksagun