Search results for "Sequence learning"

2019

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call "snippets". These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available and make learni…

Dissociation between priming and recognition in the expression of sequential knowledge

2002

Exposure to a repeating sequence of target stimuli in a speeded localization task can support both priming of sequence-consistent responses and recognition of sequence components. Here, a task is introduced in which measures of priming and recognition are obtained concurrently, and it is demonstrated that priming of sequence-consistent responses occurs even when test stimuli are not recognized. The results show that sequence knowledge can be expressed in the absence of conscious recognition. However, we also show that this result is consistent with a simple model in which priming and recognition depend on exactly the same underlying memory strength variable.

Implicit learning and statistical learning: one phenomenon, two approaches.

2006

The domain-general learning mechanisms elicited in incidental learning situations are of potential interest in many research fields, including language acquisition, object knowledge formation and motor learning. They have been the focus of studies on implicit learning for nearly 40 years. Stemming from a different research tradition, studies on statistical learning carried out in the past 10 years after the seminal studies by Saffran and collaborators, appear to be closely related, and the similarity between the two approaches is strengthened further by their recent evolution. However, implicit learning and statistical learning research favor different interpretations, focusing on the forma…

Methods for studying unconscious learning

2005

One has to face numerous difficulties when trying to establish a dissociation between conscious and unconscious knowledge. In this paper, we review several of these problems as well as the different methodological solutions that have been proposed to address them. We suggest that each of the different methodological solutions offered refers to a different operational definition of consciousness, and present empirical examples of sequence learning studies in which these different procedures were applied to differentiate between implicit and explicit knowledge acquisition. We also show how the use of a sensitive behavioral method, the process dissociation procedure, confers a distinctive adva…

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning

2016

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies n…

Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry.

2020

ABSTRACTIn contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our…

Implicit learning

2008

International audience; All of us have learned much about language, music, physical or social environment, and other complex domains, out of any intentional attempts to acquire information. This chapter describes first how studies investigating this form of learning in laboratory situations have shifted from a rule-based interpretation to interpretations assuming a progressive tuning to the statistical regularities of the environment. The next section examines the potential of statistical learning, and whether statistical learning stems from statistical computations or chunk formation. Then the acceptations in which this form of learning may be qualified as implicit are analysed. Finally, i…

The emergence of explicit knowledge during the early phase of learning in sequential reaction time tasks

1997

Five experiments investigated the formation of explicit knowledge of a repeating sequence in a sequential reaction time task. Reliable explicit knowledge was obtained even though various conditions prevented the selective improvement of RTs (Exps. 1–4). This knowledge emerged early during training. Participants were able to recognize segments of the sequence (Exps. 3 and 4) or correctly assess the probabilities of transition of the target between successive locations (Exp. 5) after only two blocks of training trials. These findings rule out an interpretation of sequence learning that posits that explicit knowledge emerges from implicit knowledge during the course of training. Although these…

Sequential Learning with LS-SVM for Large-Scale Data Sets

2006

We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We improve upon existing approaches (esp. the kernel recursive least squares algorithm) by proposing a new, supervised criterion for the selection of the relevant basis functions that takes into account the approximation error incurred from approximating the kernel as well as the reduction of the cost in th…

The effect of explicit knowledge on sequence learning: A graded account

2004

In this paper, we study the effect of conscious knowledge on implicit sequence learning. To do so, in three sequence learning experiments, we manipulated (1) the extent to which instructions were intentional vs. incidental - intentional participants were informed of the existence of sequential regularities, and (2) the amount of explicit knowledge given to participants about the stimulus material. Results indicated that explicit knowledge improves sequence learning, as indexed by an increase in reaction times when the training sequence is unexpectedly replaced by another one. To enable us to differentiate between implicit and explicit learning, we applied the process dissociation procedure …