Brain Networks of Explicit and Implicit Learning

Forrest

Neurobiology of Learning and Memory

et al. 2014

N.J. Mackintosh (2104). Associations and Propositions: The case for a dual-process account of learning in humans. Neurobiology of Learning and Memory, 108,. AbstractWe review evidence that supports the conclusion that people can and do learn in two distinct ways -one associative, the other propositional. No one disputes that we solve problems by testing hypotheses and inducing underlying rules, so the issue amounts to deciding whether there is evidence that we (and other animals) also rely on a simpler, associative system, that detects the frequency of occurrence of different events in our environment and the contingencies between them. There is neuroscientific evidence that associative learning occurs in at least some animals (e.g., Aplysia californica), so it must be the case that associative learning has evolved. Since both associative and propositional theories can in principle account for many instances of successful learning, the problem is then to show that there are at least some cases where the two classes of theory predict different outcomes. We offer a demonstration of cue competition effects in humans under incidental conditions as evidence against the argument that all such effects are based on cognitive inference. The latter supposition would imply that if the necessary information is unavailable to inference then no cue competition should occur. We then discuss the case of unblocking by reinforcer omission, where associative theory predicts an irrational solution to the problem, and consider the phenomenon of the Perruchet effect, in which conscious expectancy and conditioned response dissociate. Further discussion makes use of evidence that people will sometimes provide one solution to a problem when it is presented to them in summary form, and another when they are presented in rapid succession with trial-by trial information. We also demonstrate that people trained on a discrimination may show a peak shift (predicted by associative theory), but given the time and opportunity to detect the relationships between S+ and S-, show rule-based behaviour instead. Finally, we conclude by presenting evidence that research on individual differences suggests that variation in intelligence and explicit problem solving ability are quite unrelated to variation in implicit (associative) learning, and briefly consider the computational implications of our argument, by asking how both associative and propositional processes can be accommodated within a single framework for cognition.

Section: Figure 4 About Here Pleasementioning

confidence: 73%

Associations and propositions: The case for a dual-process account of learning in humans

Forrest

Neurobiology of Learning and Memory

et al. 2014

Journal of Neurolinguistics

“…Data-driven approaches have recently been developed to enable researchers to examine both the activation and connection at a given time (contemporaneously) and their directions of influence across different times (sequentially). In particular, Gates, Molenaar, Hillary, Ram, and Rovine (2010) developed the unified structural equation modeling (uSEM) and the extended unified structural equation modeling (euSEM; see Methods for a description of uSEM and euSEM), which have allowed us to study brain networks of cognition and language (see two recent publications that have applied euSEM; Nichols, Gates, Molenaar, & Wilson, 2014;Yang & Li, 2012). In this study we will use both uSEM and euSEM to do effective connectivity analyses on our fMRI data, in order to show the dynamic interactions between key brain regions that subserve the learning of a second language vocabulary.…”

Section: Short-term L2 Training Studies: a Brief Reviewmentioning

confidence: 99%

“…The procedure for using the uSEM and euSEM is similar to that of Yang and Li (2012), but with the addition of the recently developed Group Iterative Multiple Model Estimation (GIMME; https://www. nitrc.org/projects/gimme/), an automatic, freely distributed MATLAB-based program (Mathworks, 2010: www.mathworks.com).…”

Section: Effective Connectivitymentioning

confidence: 99%

Neural changes underlying successful second language word learning: An fMRI study

Yang

Gates

Molenaar

et al. 2015

Self Cite

125

100

a b s t r a c tA great deal of research has examined behavioral performance changes associated with second language learning. But what changes are taking place in the brain as learning progresses? How can we identify differences in brain changes that reflect successes of learning? To answer these questions, we conducted a functional magnetic resonance imaging (fMRI) study to examine the neural activities associated with second language word learning. Participants were 39 native English speakers who had no prior knowledge of Chinese or other tonal language, and were trained to learn a novel tonal vocabulary in a six-week training session. Functional MRI scans as well as behavioral performances were obtained from these learners at two different times (pre-and post-training). We performed region of interest (ROI) and connectivity analyses to identify effective connectivity changes associated with success in second language word learning. We compared a learner group with a control group, and also examined the differences between successful learners and less successful learners within the learner group across the two time points. Our results indicated that (1) after training, learners and non-learners rely on different patterns of brain networks to process tonal and lexical information of target L2 words; (2) within the learner group, successful learners compared to less successful learners showed significant differences in language-related regions; and (3) successful learners

“…Hence we predict dynamic changes in resting-state activity over a six-h daytime consolidation period, and we tested this by comparing resting state activity at three time points after serial reaction time task (SRTT) learning. We hypothesize that SRTT learning under implicit conditions will initially engage sensory motor circuits (Wilkinson et al, 2010), and learning under explicit conditions will also engage prefrontal cortex (Grafton et al, 1995;Yang and Li, 2012).…”

Section: Introductionmentioning

confidence: 99%

The Time Course of Task-Specific Memory Consolidation Effects in Resting State Networks

2014

Previous studies have reported functionally localized changes in resting-state brain activity following a short period of motor learning, but their relationship with memory consolidation and their dependence on the form of learning is unclear. We investigate these questions with implicit or explicit variants of the serial reaction time task (SRTT). fMRI resting-state functional connectivity was measured in human subjects before the tasks, and 0.1, 0.5, and 6 h after learning. There was significant improvement in procedural skill in both groups, with the group learning under explicit conditions showing stronger initial acquisition, and greater improvement at the 6 h retest. Immediately following acquisition, this group showed enhanced functional connectivity in networks including frontal and cerebellar areas and in the visual cortex.Thirtyminuteslater,enhancedconnectivitywasobservedbetweencerebellarnuclei,thalamus,andbasalganglia,whereasat6htherewas enhanced connectivity in a sensory-motor cortical network. In contrast, immediately after acquisition under implicit conditions, there was increased connectivity in a network including precentral and sensory-motor areas, whereas after 30 min a similar cerebello-thalamo-basal ganglionicnetworkwasseenasinexplicitlearning.Finally,6hafterimplicitlearning,wefoundincreasedconnectivityinmedialtemporalcortex, but reduction in precentral and sensory-motor areas. Our findings are consistent with predictions that two variants of the SRTT task engage dissociable functional networks, although there are also networks in common. We also show a converging and diverging pattern of flux between prefrontal, sensory-motor, and parietal areas, and subcortical circuits across a 6 h consolidation period.