A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning

Loonis, Roman; Brincat, Scott L.; Antzoulatos, Evan G.; Miller, Earl K.

doi:10.1016/j.neuron.2017.09.032

Cited by 38 publications

(30 citation statements)

References 76 publications

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“…In our study, we found significant PC following saccades, regardless of stimulus conditions and tasks. This effect occurred within frequency bands previously associated with explicit learning and relevant stimuli processing (i.e., delta/theta and alpha/beta; Brincat & Miller, 2015;Loonis et al, 2017) and despite minimal changes in visual stimulation.…”

Section: Correlation Between Lfps and Saccade Parametersmentioning

confidence: 69%

“…Interestingly, during contextual exploration hippocampal phase preceded PFC phase, while this relationship was reversed during target sampling. Similarly, Brincat and Miller (2015), as well as Loonis et al (2017), measured a strong hippocampus led phase coherence between 9 and 16 Hz following correct trial outcome feedback in macaque monkeys. Although some results must be interpreted cautiously due to the lack of homology between rodent and primate PFC (Carlén, 2017;Kaas, 2013), they reveal a degree of generalization across mammalian brains.…”

Section: Frequency-specific Pc By Visual Transient and Saccadesmentioning

confidence: 80%

“…Previous studies have proposed that hippocampal activity is modulated by task and contextually relevant information (Ekstrom & Ranganath, 2017;Loonis, Brincat, Antzoulatos, & Miller, 2017;McKenzie et al, 2016;Wirth et al, 2017). Some studies have also reported sensing or stimulus-related PC to be absent during tasks that are less likely to engage the hippocampus (Berg, Whitmer, & Kleinfeld, 2006;Givens, 1996;Hoffman et al, 2013;Jutras et al, 2013).…”

Section: Correlation Between Lfps and Saccade Parametersmentioning

confidence: 99%

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Modulation of local field potentials and neuronal activity in primate hippocampus during saccades

et al. 2019

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Primates use saccades to gather information about objects and their relative spatial arrangement, a process essential for visual perception and memory. It has been proposed that signals linked to saccades reset the phase of local field potential (LFP) oscillations in the hippocampus, providing a temporal window for visual signals to activate neurons in this region and influence memory formation. We investigated this issue by measuring hippocampal LFPs and spikes in two macaques performing different tasks with unconstrained eye movements. We found that LFP phase clustering (PC) in the alpha/beta (8–16 Hz) frequencies followed foveation onsets, while PC in frequencies lower than 8 Hz followed spontaneous saccades, even on a homogeneous background. Saccades to a solid grey background were not followed by increases in local neuronal firing, whereas saccades toward appearing visual stimuli were. Finally, saccade parameters correlated with LFPs phase and amplitude: saccade direction correlated with delta (≤4 Hz) phase, and saccade amplitude with theta (4–8 Hz) power. Our results suggest that signals linked to saccades reach the hippocampus, producing synchronization of delta/theta LFPs without a general activation of local neurons. Moreover, some visual inputs co‐occurring with saccades produce LFP synchronization in the alpha/beta bands and elevated neuronal firing. Our findings support the hypothesis that saccade‐related signals enact sensory input‐dependent plasticity and therefore memory formation in the primate hippocampus.

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Section: Correlation Between Lfps and Saccade Parametersmentioning

confidence: 69%

Section: Frequency-specific Pc By Visual Transient and Saccadesmentioning

confidence: 80%

Section: Correlation Between Lfps and Saccade Parametersmentioning

confidence: 99%

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Modulation of local field potentials and neuronal activity in primate hippocampus during saccades

et al. 2019

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“…A linear relationship between association strength and human fMRI was further observed in the MTL, frontal, temporal and parietal regions (Law et al, 2005), while beta oscillations in the monkey entorhinal cortex were shown to increase in a similar linear fashion as memories became stronger (Hargreaves et al, 2012). Outside the MTL, prefrontal oscillations in beta (Brincat & Miller, 2016) and theta frequencies (Loonis, Brincat, Antzoulatos, & Miller, 2017;Paz, Bauer, & Paré, 2008) also increase as memories are established. Together, the extant evidence suggests that a distributed network of regions tracks conditional associative learning, and recordings from nonhuman primates further indicates that theta and beta oscillations might provide a key signature of memory formation.…”

Section: Introductionmentioning

confidence: 74%

Neural oscillations during conditional associative learning

Clarke

Roberts

Ranganath

2017

Preprint

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Associative learning requires mapping between complex stimuli and behavioural responses. When multiple stimuli are involved, conditional associative learning is a gradual process with learning based on trial and error. It is established that a distributed network of regions track associative learning, however the role of neural oscillations in human learning remains less clear. Here we used scalp EEG to test how neural oscillations change during learning of arbitrary visuo-motor associations. Participants learned to associative 48 different abstract shapes to one of four button responses through trial and error over repetitions of the shapes. To quantify how well the associations were learned for each trial, we used a state-space computational model of learning that provided a probability of each trial being correct given past performance for that stimulus, that we take as a measure of the strength of the association. We used linear modelling to relate single-trial neural oscillations to single-trial measures of association strength. We found frontal midline theta oscillations during the delay period tracked learning, where theta activity was strongest during the early stages of learning and declined as the associations were formed. Further, posterior alpha and low-beta oscillations in the cue period showed strong desynchronised activity early in learning, while stronger alpha activity during the delay period were seen as associations became well learned. Moreover, the magnitude of these effects during early learning, before the associations were learned, related to improvements in memory seen on the next presentation of the stimulus. The current study provides clear evidence that frontal theta and posterior alpha/beta oscillations play a key role during associative memory formation.

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“…How can these two views be reconciled? In reward-based motor learning tasks, it is generally agreed that participants begin to reflect upon task structure and develop strategies upon encountering negative outcomes (Leow et al, 2016; Loonis et al, 2017; Manley et al, 2014; Maxwell et al, 2001), which occurs nearly immediately in the Preserve task after the introduction of binary feedback, due to a lack of generalisation of cerebellar memory (Codol et al, 2018). In contrast, in the Acquire task, participants experience an early learning phase with mainly rewarding outcomes, possibly suppressing development of explicit control and allowing for this early window of implicit reward-based learning.…”

Section: Discussionmentioning

confidence: 99%

Domain-specific working memory, but not dopamine-related genetic variability, shapes reward-based motor learning

Codol

Holland

Oxley

et al. 2019

Preprint

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The addition of rewarding feedback to motor learning tasks has been shown to increase the retention of learning, spurring interest in the possible utility for rehabilitation. However, laboratory-based motor tasks employing rewarding feedback have repeatedly been shown to lead to great inter-individual variability in performance. Understanding the causes of such variability is vital for maximising the potential benefits of reward-based motor learning. Thus, using a large cohort (n=241) we examined whether spatial (SWM), verbal (VWM) and mental rotation (RWM) working memory capacity and dopamine-related genetic profiles were associated with performance in two reward-based motor tasks. The first task assessed participant’s ability to follow a hidden and slowly shifting reward region based on hit/miss (binary) feedback. The second task investigated participant’s capacity to preserve performance with binary feedback after adapting to the rotation with full visual feedback. Our results demonstrate that higher SWM is associated with greater success and a greater capacity to reproduce a successful motor action, measured as change in reach angle following reward. Whereas higher RWM was predictive of an increased propensity to express an explicit strategy when required to make large adjustments in reach angle. Therefore, both SWM and RWM were reliable predictors of success during reward-based motor learning. Change in reach direction following failure was also a strong predictor of success rate, although we observed no consistent relationship with any type of working memory. Surprisingly, no dopamine-related genotypes predicted performance. Therefore, working memory capacity plays a pivotal role in determining individual ability in reward-based motor learning.Significance statementReward-based motor learning tasks have repeatedly been shown to lead to idiosyncratic behaviours that cause varying degrees of task success. Yet, the factors determining an individual’s capacity to use reward-based feedback are unclear. Here, we assessed a wide range of possible candidate predictors, and demonstrate that domain-specific working memory plays an essential role in determining individual capacity to use reward-based feedback. Surprisingly, genetic variations in dopamine availability were not found to play a role. This is in stark contrast with seminal work in the reinforcement and decision-making literature, which show strong and replicated effects of the same dopaminergic genes in decision-making. Therefore, our results provide novel insights into reward-based motor learning, highlighting a key role for domain-specific working memory capacity.

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A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning

Cited by 38 publications

References 76 publications

Modulation of local field potentials and neuronal activity in primate hippocampus during saccades

Modulation of local field potentials and neuronal activity in primate hippocampus during saccades

Neural oscillations during conditional associative learning

Domain-specific working memory, but not dopamine-related genetic variability, shapes reward-based motor learning

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