Figure 1 - uploaded by Mark D'Esposito
Content may be subject to copyright.
A schematic depiction of the task is shown. there were eight positions on the screen where an X could appear; each position corresponded to a separate key on the response keyboards. An X appeared in one of the eight positions one at a time, and participants responded by pressing the appropriate key. Stimuli were presented for 900 msec and appeared in blocks of 20 followed by 10 sec of fixation.
Source publication
Previous studies of motor learning have proposed a distinction betweenfast and slow learning, but these mechanisms have rarely been examined simultaneously. We examined the influence of long-term motor expertise (slow learning) while pianists and nonpianists performed alternating epochs of sequenced and random keypresses in response to visual cues...
Contexts in source publication
Context 1
... to scanning, the participants were not told about any sequences in the stimuli, and they did not practice the task in order to maximize the detection of fast-learning-related activation during the scanning ses- sion. During scanning, the participants were instructed to respond as quickly and accurately as possible, making bimanual keypresses in response to xs presented in eight possible locations on the screen that mapped spatially onto response boxes (see Figure 1). The par- ticipants used all of their fingers except their thumbs (four fingers on each hand) to make keypresses. ...
Context 2
... participant viewed a backlit projection screen at his or her waist from within the magnet bore through a mirror mounted on the head coil. The participants responded to stimuli presented on the screen by making keypresses on two nonmagnetic bimanual response keyboards (each containing five keys, corresponding to the right and left hands) designed for use in the scanner (see Figure 1). ...
Similar publications
The goal of this study was to characterize the dynamics and functional connectivity of brain networks associated with fast (short-term) learning of handwriting using functional magnetic resonance imaging. Participants (n = 12) performed a graphomotor sequence learning task (naïve subjects learning to draw simple, 3-stroke Chinese word characters),...
Object-selective cortical regions exhibit a decreased response when an object stimulus is repeated [repetition suppression (RS)]. RS is often associated with priming: reduced response times and increased accuracy for repeated stimuli. It is unknown whether RS reflects stimulus-specific repetition, the associated changes in response time, or the com...
In our everyday motor interactions with objects, we often encounter situations where the features of an object are determinate (i.e., not perceptually ambiguous), but the mapping between those features and appropriate movement patterns is indeterminate, resulting in a lack of any clear preference for one posture over another. We call this indetermi...
Previous neuroimaging studies in the field of motor learning have shown that learning a new skill induces specific changes of neural gray
and white matter in human brain areas necessary to control the practiced task. Former longitudinal studies investigating motor skill
learning have used strict training protocols with little ecological validity ra...
Somatosensory input to the brain is known to be modulated during voluntary movement. It has been demonstrated that the response in the primary somatosensory cortex (SI) is generally gated during simple movement of the corresponding body part. This study investigated sensorimotor integration in the SI during manual movement using a motor task combin...
Citations
... 50 Accordingly, early bilingualism not only induces plastic changes within language networks, but also in those mediating executive functions (for review, see 54 ). Similar long-term effects inducing neuroplastic changes have been reported in cases of chronic practice of focused tasks, such as musical performance 55,56 and mental calculations on an abacus. 57 It was also put forward that physical environment seems to determine one's initial preference and its later development for adopting either ventral stream (system of what) or dorsal stream (system of where) processing styles when conducting visual inspection. ...
The present review attempts to discuss how some of the central concepts from the Lurian corpus of theories are relevant to the modern neuropsychology of epilepsy and epilepsy surgery. Through the lenses of the main Lurian concepts (such as the qualitative syndrome analysis), we discuss the barriers to clinical reasoning imposed by quadrant-based views of the brain, or even atheoretical, statistically-based and data-driven approaches. We further advice towards a systemic view inspired by Luria's clinical work and theorizing, given their importance towards our clinical practice, by contrasting it to the modular views when appropriate. Luria provided theory-guided methods of assessment and rehabilitation of higher cortical functions. Although his work did not specifically address epilepsy, his theory and clinical approaches actually apply to the whole neuropathology spectrum and accounting for the whole panorama of neurocognition. This holistic and systemic approach to the brain is consistent with the network approach of the neuroimaging era. As to epilepsy, the logic of cognitive functions organized into complex functional systems, contrary to modular views of the brain, heralds current knowledge of epilepsy as a network disease, as well as the concept of the functional deficit zone.
... A larger codeswitching positivity for individuals who are more experienced with codeswitching may appear counterintuitive given findings that expertise often results in more efficient processing (e.g., during motor learning, see Landau and D'Esposito 2006). However, both the interpretation that the codeswitching positivity may be a form of a P3 or P600 and that it may reflect a task-decision stage of processing, leads to a prediction that the positivity would be positively correlated with experience. ...
For multilinguals, acquiring and processing language is similar to other cognitive skills: they are grounded in mechanisms of sensory processing and motor control (Paradis, 2019). Recent clinical and experimental research on multilingualism have introduced innovative neuroimaging measures and psychological methods that have significantly shed light on what we know (and do not know) about how multiple languages are processed, represented, and controlled in the mind/brain (Schwieter, 2019).
Since the 1990s and 2000s, a plethora of behavioral and neurological research has demonstrated that for multilinguals, all languages are active to some degree in the mind, even when only using one. Furthermore, the need for the mind to manage the ongoing competition that arises from this parallel activation has been shown to affect cognition (e.g., executive functioning) (Giovannoli et al., 2020), modify the structure and functioning of the brain (e.g., changes in the areas where language control and executive control overlap) (Costa and Sebastian-Galles, 2014), and slow the onset or progression of cognitive and neural decline (Bialystok, 2017).
The goal of “Multilingualism: Consequences for brain and mind” is to bring together state-of-the art papers that examine the cognitive and neurological consequences of multilingualism through an exploration of how two or more languages are processed, represented, and/or controlled in one brain/mind. The included peer-reviewed papers are either theoretically or empirically oriented and present new findings, frameworks, and/or methodologies on how multilingualism affects the brain and mind.
... A larger codeswitching positivity for individuals who are more experienced with codeswitching may appear counterintuitive given findings that expertise often results in more efficient processing (e.g., during motor learning, see Landau and D'Esposito 2006). However, both the interpretation that the codeswitching positivity may be a form of a P3 or P600 and that it may reflect a task-decision stage of processing, leads to a prediction that the positivity would be positively correlated with experience. ...
Switching between languages, or codeswitching, is a cognitive ability that multilinguals can perform with ease. This study investigates whether codeswitching during sentence reading affects early access to meaning, as indexed by the robust brain response called the N400. We hypothesize that the brain prioritizes the meaning of the word during comprehension with codeswitching costs emerging at a different stage of processing. Event-related potentials (ERPs) were recorded while Spanish–English balanced bilinguals (n = 24) read Spanish sentences containing a target noun that could create a semantic violation, codeswitch or both. Self-reported frequency of daily codeswitching was used as a regressor to determine if the cost of reading a switch is modulated by codeswitching experience. A robust N400 to semantic violations was followed by a late positive component (LPC). Codeswitches modulated the left anterior negativity (LAN) and LPC, but not the N400, with codeswitched semantic violations resulting in a sub-additive interaction. Codeswitching experience modulated the LPC, but not the N400. The results suggest that early access to semantic memory during comprehension happens independent of the language in which the words are presented. Codeswitching affects a separate stage of comprehension with switching experience modulating the brain’s response to experiencing a language switch.
... Musical expertise such as playing piano professionally has been demonstrated to have a beneficial effect on nonmusical cognitive abilities (for reviews, see Rodrigues et al., 2010;Sittiprapaporn, 2012) including explicit (Talamini et al., 2017) and implicit memory processes (Landau & D'Esposito, 2006;Romano Bergstrom et al., 2012). The advantage in explicit memory abilities (i.e., when assessed with short-term or working memory tasks) as revealed through a meta-analysis was most pronounced in domainspecific tasks, such as when musical stimuli were involved (Talamini et al., 2017). ...
... Only a few studies have used the SRT to examine pianists or other musicians. For example, Landau and D'Esposito (2006) tested a small group of pianists and controls executing a modified version of the SRT during an fMRI scan session. Instead of the four locations typically used in this task, this version displayed eight locations arranged horizontally, so that each location corresponded to one of the four fingers of both hands (except for thumbs). ...
... Considering that pianists are usually trained to express explicit awareness to a certain degree during their piano performance (Chaffin et al., 2009), one may expect that pianists would show more explicit awareness than controls. However, this was not the case in the studies by Landau et al. (2006) and Romano Bergstrom et al. (2012). This finding may be related to the high grade of sequence masking. ...
Playing piano professionally has been shown to benefit implicit motor sequence learning. The aim of the current study was to determine whether this advantage reflects generally enhanced implicit sequence learning unrelated to pianists’ higher motor and/or visual-motor coordination abilities. We examined implicit sequence learning using the ocular serial reaction time (O-SRT) task, a manual-free eye-tracked version of the standard SRT, in 29 pianists and 31 controls. Reaction times (RT) and correct anticipations (CA) of several phases describing implicit sequence learning were analyzed. Furthermore, explicit sequence knowledge was compared between the groups, and relationships between implicit sequence learning with explicit sequence knowledge or demographic measures were evaluated. Pianists demonstrated superiority in all critical phases of implicit sequence learning (RT and CA). Moreover, pianists acquired higher explicit sequence knowledge, and only in pianists was explicit sequence knowledge related to implicit sequence learning. Our results demonstrate that pianists’ superiority in implicit sequence learning is due to a higher general implicit sequence learning ability. Hence, we can exclude that higher motor and/or visual-motor coordination abilities are related to pianists’ higher implicit sequence learning. Furthermore, the significant relationship of implicit sequence learning and explicit sequence knowledge suggests that pianists either used explicit strategies to support implicit sequence learning, had better explicit access to sequence knowledge, or both.
... However, we believe the role that previous experience plays with individual learning capability is an important one and deserves further study. Despite this limitation, these results are consistent with previous research examining differences in motor performance and learning between experts and novices which predominantly demonstrates that experts not only acquire skill faster but also appear to employ a different strategy than novices to accomplish the task goal (Landau & D'Esposito, 2006;Ranganathan & Carlton, 2007). However, other research investigating music capability demonstrates that the amount of deliberate practice on music capability is not causal but instead, genetic factors that make practice itself heritable contributes to capability, i.e. quality of deliberate practice is inherited through gene expression that then results in changes in capability (Mosing, Madison, Pedersen, Kuja-Halkola, & Ullén, 2014). ...
The existence of individual differences in motor learning capability is well known but the behaviors or strategies that contribute to this variability have been vastly understudied. What performance characteristics distinguish an expert level performer from individuals who experience little to no success, those labeled non-learners? We designed a rule-based visuomotor task which requires identification (discovery) and then exploitation of specific explicit and implicit task components that requires a specific movement pattern, the task rule, for goal achievement. When participants first attempt the task, they are informed about the goal, but are naïve to the task rule. Therefore, the purpose of this experiment is to determine how acquisition of both implicit and explicit task components, the inherent elements of the task rule, reveals differing strategies associated with performance and task success. We test the hypothesis that an examination of performance will reveal sub-groups with varying levels of success. Further, for each subgroup, we expect to find a unique relationship between visual Time-in-Target feedback (a measure of success) and subsequent updating of each task component. Out of 32 non-disabled adults, we identified three distinct sub-groups: (Low Performer/Non-Learner (LP, N = 9), Moderate Performer (MP, N = 12) and High Performer (HP, N = 11)). A quantitative analysis of behavioral patterns reveals three findings: First, the LP sub-group demonstrated significantly lower task success which was associated with difficulty identifying the explicit component of the task. Second, the HP sub-group acquired the two task components in parallel over practice. Third, when both explicit and implicit component performance is plotted across sub-groups, a task component continuum emerges that seamlessly progresses from low to moderate to high performer groups. An exploratory analysis reveals that self-reported level of prior lifetime accumulation of video game and physical activity experience is a significant predictor of individual task performance (R² = 0.50). In summary, what appears to be a key distinction between varying levels of human rule-based motor learning is the process by which feedback is used to update performance of inherent elements of the task rule. Evidence of a performance continuum and limited prior experience suggests that Low Performer/Non-Learners are generally inexperienced with these kinds of tasks, although the role of genetics and other innate learning capabilities in visuomotor learning is still largely unknown. These findings provoke new research directions toward probing the differential performance strategies associated with expertise and the development of interventions aimed to convert non-learners into learners.
... J. Bryden et al. 2011;Vaid et al. 1989), which may explain why their motor system is less lateralized. This is consistent with the reduced functional asymmetry in experienced pianists that were trained to play with both hands (Landau and D'Esposito 2006). However, it remains unclear whether exposure to bimanual tasks has an impact on lateralization of somatosensory and pain processing in left-handed individuals. ...
Some pain-related information is processed preferentially in the right cerebral hemisphere. Considering that functional lateralization can be affected by handedness, spinal and cerebral pain-related responses may be different between right- and left-handed individuals. Therefore, this study aimed at investigating the cortical and spinal mechanisms of nociceptive integration when nociceptive stimuli are applied to right -handed vs. left -handed individuals. The NFR, evoked potentials (ERP: P45, N100, P260), and event-related spectral perturbations (ERSP: theta, alpha, beta and gamma band oscillations) were compared between ten right-handed and ten left-handed participants. Pain was induced by transcutaneous electrical stimulation of the lower limbs and left upper limb. Stimulation intensity was adjusted individually in five counterbalanced conditions of 21 stimuli each: three unilateral (right lower limb, left lower limb, and left upper limb stimulation) and two bilateral conditions (right and left lower limbs, and the right lower limb and left upper limb stimulation). The amplitude of the NFR, ERP, ERSP, and pain ratings were compared between groups and conditions using a mixed ANOVA. A significant increase of responses was observed in bilateral compared with unilateral conditions for pain intensity, NFR amplitude, N100, theta oscillations, and gamma oscillations. However, these effects were not significantly different between right- and left-handed individuals. These results suggest that spinal and cerebral integration of bilateral nociceptive inputs is similar between right- and left-handed individuals. They also imply that pain-related responses measured in this study may be examined independently of handedness.
... This pattern would align with prior findings of more extensive activations in experts in other domains (e.g., Maguire et al. 1997;Schneider et al. 2002;Olesen et al. 2004;Russel et al. 2010) and would suggest that polyglotism and bilingualism manifest similarly in the brain. Alternatively, polyglots might exhibit weaker and less extensive activations, reflecting greater processing efficiency, in line with prior work on activation reduction as a function of practice with a task (e.g., Poldrack et al. 1998;Fletcher et al. 1999;Gauthier et al. 1999;Schneider et al. 2002;Maguire et al. 2003;McCandliss et al. 2003;Calvo-Merino et al. 2004;Kelly and Garavan 2005;Landau and d'Esposito 2006;Bavelier et al. 2012;Bernardi et al. 2013;Protzner et al. 2016), including linguistic tasks (e.g., Reichle et al. 2000;Xue et al. 2006;Prat et al. 2007;Xue and Poldrack 2007;Prat and Just 2010;Grogan et al. 2012;Glezer et al. 2015;Hervais-Adelman et al. 2018). Further, aptitude for nonnative language learning has been linked with functional responses in the right homologs of the language areas (e.g., Kepinska et al. 2017a;Qi et al. 2019; see Qi and Legault 2020 for a review), leading to a possible prediction of differences in the activity of the right hemisphere language areas. ...
... Compared with non-polyglot controls, polyglots recruited less extensive cortical areas within the frontotemporal language network of the left hemisphere (reflected in smaller region volumes) and activated these areas to a lesser degree (reflected in smaller response magnitudes). These differences in the properties of the language network are in line with prior reports of functional changes in the brain in response to knowledge and skill acquisition (e.g., Poldrack et al. 1998;Fletcher et al. 1999;Gauthier et al. 1999;Schneider et al. 2002;McCandliss et al. 2003;Calvo-Merino et al. 2004;Kelly and Garavan 2005;Landau and d'Esposito 2006;Bavelier et al. 2012;Bernardi et al. 2013;Protzner et al. 2016), including in the domain of language (Reichle et al. 2000;Xue et al. 2006;Prat et al. 2007;Xue and Poldrack 2007;Prat and Just 2010;Grogan et al. 2012;Glezer et al. 2015). At the same time, our finding of decreased activity within the language network in polyglots stands in sharp contrast to the pattern of increased activity reported previously in bilinguals (e.g., Park et al. 2012;Jasinska and Petitto 2013;Román et al. 2015;Coderre et al. 2016) and observed as a numerical trend here (SI Fig. 1). ...
Acquiring a foreign language is challenging for many adults. Yet certain individuals choose to acquire sometimes dozens of languages and often just for fun. Is there something special about the minds and brains of such polyglots? Using robust individual-level markers of language activity, measured with fMRI, we compared native language processing in polyglots versus matched controls. Polyglots (n = 17, including nine "hyper-polyglots" with proficiency in 10-55 languages) used fewer neural resources to process language: Their activations were smaller in both magnitude and extent. This difference was spatially and functionally selective: The groups were similar in their activation of two other brain networks-the multiple demand network and the default mode network. We hypothesize that the activation reduction in the language network is experientially driven, such that the acquisition and use of multiple languages makes language processing generally more efficient. However, genetic and longitudinal studies will be critical to distinguish this hypothesis from the one whereby polyglots' brains already differ at birth or early in development. This initial characterization of polyglots' language network opens the door to future investigations of the cognitive and neural architecture of individuals who gain mastery of multiple languages, including changes in this architecture with linguistic experiences.
... Perhaps due to historic 68 resistance against the idea of a "cognitive cerebellum" (Schmahmann, 2010), the cerebellum 69 Running head : Involvement of the cerebellum in the serial reaction time task (SRT) 4 is often neglected in cognitive neuroimaging data acquisition and analysis. As a case in point, 70 we believe that 3 studies / subject groups in the review of Janacsek et al. (2019) should be 71 excluded on these grounds (Landau & D'Esposito, 2006;Werheid et al., 2003;Zedkova et 72 al., 2006). In particular, Werheid et al. (2003) explicitly state they did not scan the cerebellum 73 in their protocol (featuring an axial depth of 8.4 cm). ...
An ALE meta-analysis focused on the serial reaction time task published in NeuroImage (Janacsek et al., 2019) demonstrated consistent activation of the basal ganglia across neuroimaging studies featuring sequence > random block contrasts and no consistent cerebellar activation. To enable valid conclusions regarding the role of the cerebellum in this context, some of the included studies should be excluded (e.g., because the cerebellum was explicitly not scanned). After omitting 6 of 16 studies / subject groups, 70% of the remaining studies did report cerebellar activation. While an ALE analysis of the remaining contrasts confirmed the original results, it may lack the power to detect cerebellar effects. We argue the conclusion that the cerebellum is not involved in sequence-specific learning should be treated with caution.
... (Krumhansl, 1990;Loui & Wessel, 2007;Tillmann & McAdams, 2004) and others) and with electrophysiological and neuroimaging indices, even among people who have received no explicit musical training (e.g. (Koelsch, Gunter, Friederici, & Schroger, 2000;Landau & D'Esposito, 2006), among others). This robust evidence for musical knowledge without explicit instruction suggests that music can be a valuable model system that provides a window into how the human mind implicitly acquires knowledge from exposure. ...
Knowledge of speech and music depends upon the ability to perceive relationships between sounds in order to form a stable mental representation of statistical structure. Although evidence exists for the learning of musical scale structure from the statistical properties of sound events, little research has been able to observe how specific acoustic features contribute to statistical learning independent of the effects of long-term exposure. Here, using a new musical system, we show that spectral content is an important cue for acquiring musical scale structure. In two experiments, participants completed probe-tone ratings before and after a half-hour period of exposure to melodies in a novel musical scale with a predefined statistical structure. In Experiment 1, participants were randomly assigned to either a no-exposure control group, or to exposure groups who heard pure tone or complex tone sequences. In Experiment 2, participants were randomly assigned to exposure groups who heard complex tones constructed with odd harmonics or even harmonics. Learning outcome was assessed by correlating pre/post-exposure ratings and the statistical structure of tones within the exposure period. Spectral information significantly affected sensitivity to statistical structure: participants were able to learn after exposure to all tested timbres, but did best at learning with timbres with odd harmonics, which were congruent with scale structure. Results show that spectral amplitude distribution is a useful cue for statistical learning, and suggest that musical scale structure might be acquired through exposure to spectral distribution in sounds.
... Sequence learning underlies a range of motor, cognitive, and social skills that are integral to our everyday life. For example, when we dance, type, use our smartphones, play video games, play a musical instrument, perform arithmetic operations, produce or understand language, or interact with others in social contexts, we seem to be relying at least in part on sequential knowledge (Evans and Ullman, 2016;Landau and D'Esposito, 2006;Lieberman, 2000;Nemeth et al., 2011;Norman and Price, 2012;Romano Bergstrom et al., 2012;Ullman, 2004Ullman, , 2016. The acquisition of this knowledge generally occurs gradually and implicitly through practice, resulting in rapid and reliable processing that is characteristic of automatized skills (Ashby et al., 2010;Berry and Dienes, 1993;Penhune and Steele, 2012;Squire, 1986;Ullman, 2004). ...
Sequence learning underlies numerous motor, cognitive, and social skills. Previous models and empirical investigations of sequence learning in humans and non-human animals have implicated cortico-basal ganglia-cerebellar circuitry as well as other structures. To systematically examine the functional neuroanatomy of sequence learning in humans, we conducted a series of neuroanatomical meta-analyses. We focused on the serial reaction time (SRT) task. This task, which is the most widely used paradigm for probing sequence learning in humans, allows for the rigorous control of visual, motor, and other factors. Controlling for these factors (in sequence-random block contrasts), sequence learning yielded consistent activation only in the basal ganglia, across the striatum (anterior/mid caudate nucleus and putamen) and the globus pallidus. In contrast, when visual, motor, and other factors were not controlled for (in a global analysis with all sequence-baseline contrasts, not just sequence-random contrasts), premotor cortical and cerebellar activation were additionally observed. The study provides solid evidence that, at least as tested with the visuo-motor SRT task, sequence learning in humans relies on the basal ganglia, whereas cerebellar and premotor regions appear to contribute to aspects of the task not related to sequence learning itself. The findings have both basic research and translational implications.
