( Daneman and Carpenter, 1980; Engle et al., 1999; Wiley and Jarosz, 2012 ). There have been several attempts to understand the organization of human WM. The arguably most influential model is the multiple-component model proposed by Baddeley and Hitch (1974) . The authors hypothesized the existence of a “central executive” component, which controls the incoming information and passes the information to two subsystems: the “phonological loop” and the “visuospatial sketchpad.” Within the phonological loop, due to the interplay of its two components— the phonological store and the articulatory loop—the verbal material representation can be kept in an active state. Verbal information is processed in perceptual systems before it enters the phonological loop in which it is temporarily stored in the phonological store and maintained through the articulatory loop using subvocal rehearsal of the information. In addition to subvocal rehearsal, the articulatory loop is also thought to be involved whenever verbal information is presented visually: whereas auditory verbal information (e.g., spoken words) can directly enter the phonological store, visually presented verbal information (e.g., written words) must first be recoded into phonological information. In other words, subvocalization is necessary in order to reroute visually derived verbal material into the phonological store ( Buchsbaum and D’Esposito, 2008 ). The visuospatial sketchpad is responsible for integrating visual and spatial information. Later, the “episodic buffer” was added ( Baddeley, 2000 ). It binds the information from the different subsystems into integrated episodes. Alternative models proposed that WM holds any type of information in a state of heightened availability ( Oberauer, 2010; Cowan et al., 2012 ) whereas others models have emphasized on the role of attentional control in WM (e.g., Kane and Engle, 2003; Unsworth and Engle, 2007 ). These different theoretical conceptualizations of WM are not necessarily mutually exclusive ( Cowan et al., 2012 ), with common features including a variety of processes such as encoding, maintaining and retrieving information of various domains (e.g., letters, geometric forms, or words), and some attentional control mechanism that supports dealing with interference from irrelevant or distracting information. Thus, the neural correlates of WM may vary depending on the processes, the type of information, and the modality of stimulation (auditory or visual). Given the variety across studies with regard to WM domain and the lack of process differentiation in most studies, the present meta-analysis focused exclusively on visually presented verbal workingmemory (vWM) across all processes involved in WM.
Sampling of Participants
In February 2012, we received the approval from the Federal Ministry for Education in Rhine- land-Palatine to conduct the study with first graders in the city of Mainz. The authority responsible for elementary schools in Mainz (ADD) contacted schools and provided us with a list of elementary schools in May 2012. We selected 12 schools for participation in the study based on two criteria: being located in the city of Mainz and the possibility of including at least two school classes per school in the study. The participating schools agreed that (i) one school lesson per day would be replaced by a workingmemory (WM) training lesson for 25 school days and (ii) the children would participate in all four planned data collection waves. In turn, schools received the IT infrastructure necessary to run the intervention, namely a notebook for each participating child (both for children assigned to the treatment as well as those assigned to the control group), rolling cases for transportation, charging and storage of the notebooks, as well as accessories like computer mice, headphones, and wifi routers. The schools retained this IT infrastructure for their permanent use.
It has proven particularly fruitful to study the deployment of attention to internal representations in relation to the already much better understood deployment of attention to external events. Experimentally, comparability between these two domains of attentional orienting (memory and perception) can be established in a straightforward fashion, namely by presenting cues either after the appearance of items to be memorized (retrocues) or before (precues), as in a classical spatial cueing paradigm (Posner, 1980). Mnemonic (internal) attention has been shown to be remarkably similar to perceptual (external) attention in terms of behavioural benefits and costs associated with valid and invalid cueing, and also with respect to the neural network of frontal, parietal and occipital areas that is involved (e.g., Dell’Acqua, Sessa, Toffanin, Luria, & Jolicoeur, 2010; Griffin & Nobre, 2003; Lepsien et al., 2005; Lepsien & Nobre, 2007; Nee & Jonides, 2009; Nobre et al., 2004; Poch, Campo, & Barnes, 2014). In spite of these commonalities, there are also notable differences, indicating that internal attention exhibits its own distinct characteristics. For instance, unlike external attention, shifts of internal attention appear not to be induced by peripheral cues (Berryhill, Richmond, Shay, & Olson, 2012) or to be influenced by the physical distance between objects at encoding (Tanoue & Berryhill, 2012). At the neural level, brain imaging and stimulation studies have revealed stronger activations in parietal regions and the selective engagement of certain frontal regions when attention is oriented within visual workingmemory (Nobre et al., 2004; Tanoue, Jones, Peterson, & Berryhill, 2013). It is thus unlikely that there is a single attentional mechanism underlying selection in perception and in workingmemory (see also Chun, Golomb, & Turk-Browne, 2011).
The current study focuses on the neurophysiological correlates of the domain interactions between manual action re-planning and workingmemory (WM). Particularly, based on a previous dual-task study showing the neurophysiological domain interactions when a WM task and manual task required manual responses, the current study investigates the role of the WM response modality (vocal response instead of manual response) in manual action re-planning- WM interactions. In a 2x2 within-subject design with the factors WM task (verbal, visuospatial) and movement planning (planned, re-planned), thirty-six participants performed verbal and visuospatial WM tasks concurrently with a manual task which required performing a grasp-and- place movement by either executing the initially prepared movement plan (planned movement condition) or changing the plan to reverse movement direction (re-planned movement condition). ERPs were extracted for planned and re-planned movements during maintenance and retrieval processes in verbal and visuospatial tasks. ERP analyses showed that in both WM tasks, during the maintenance, but not during the retrieval, re-planned movements compared to planned movements generated a larger positive slow wave with a centroparietal maximum between 200-700 ms, which indicated a P300. We interpret this P300 re-planning effect as suggesting that movement re-planning interferes with the maintenance process of verbal and visuospatial domains, resulting in domain-general, but process-specific movement re-planning costs, which seem to be similar to the re-planning costs obtained with the written report. Accordingly, we argue that the neurophysiological domain interactions of movement re- planning with WM are independent of WM response modality, rather they determined by the central cognitive resources.
In experiments 1 and 2, articulatory suppression was the distraction condition that impaired EC most dramatically. One might wonder whether these impairments of EC are actually due to reduced phonological workingmemory capacity or whether it is the dual-task situation created by the articulatory suppression trials that is responsible for the effect: In both previous experiments, subjects were required (a) to learn the contingencies and (b) to articulate numbers during the respective tri- als. To decide whether it is phonological disruption or the cognitive load produced by the dual-task situation, a third experiment was conducted without including the articulatory suppression condition. Instead, only passive phonological working mem- ory disruption was produced. Different types of speech and non-speech background sounds were played back in experiment 3 not requiring the participants to actively engage in a secondary task. Thus, the participants’ cognitive load during condition- ing is expected to be stressed less than in the previous experiments since (a) mere passive auditory disruption is supposed to be less demanding than an active artic- ulation task, and (b) subjects do not have to continuously monitor the instructing text messages any longer. Furthermore, in order to enhance contingency memory, we decided to make the pair-associate learning task easier by using fewer stimulus pairings (only 2 pairings per US valence × disruption condition). For that reason, more robust effects of EC than in experiment 2 are expected due to the availability of more workingmemory resources.
bp task 0.049 0.010 0.073
(0.969) (0.981) (0.969)
The results are based on our main specifications reported in Supplementary Tables S1– S3. The coefficients are the point estimates showing how workingmemory training changes the outcome score indicated at the left-hand side of the table (as a fraction of a standard deviation) relative to the control group. We report p-values corrected for multiple hypothesis testing and small number of clusters in parentheses below each point estimate (* p<0.10, ** p<0.05, *** p<0.01). The p-values are adjusted by controlling the family-wise error rate within each family of outcomes (corresponding to each panel in the table) using the step-down procedure by Romano & Wolf (2005, 2016), and by applying the conservative “biased reduced linearization (BRL) method” of Bell and McCaffrey (2002) to calculate clustered standard errors. The methods applied here are described in detail in Section 1.5 in the Online Appendix. Wave 2, Wave 3, and Wave 4 refer to the evaluation waves shortly after, 6 months after, and 12–13 months after the workingmemory training period. All treatment effects remain significant at the 5-percent level, except for the effect on verbal simple span in W4 (MHT-BRL corrected p-value = .173) and Reading in W4 (MHT-BRL corrected p-value = .302).
There is a considerable body of literature on reward-anticipation and the dopaminergic and limbic system as the neural bases of incentive processing. However, what we are specifically interested in, is the question in how far the anticipation of reward affects a more efficient employment of available cognitive resources and thereby leads to an improved visual WM functioning. Research on this question is rare and results are mixed. Szatkowska, Bogorodzki, Wolak, Marchewka and Szeszkowski (2008) found no behavioral incentive- improvements on a 2-back verbal WM task. Furthermore, Shiels et al. (2008) tested children with ADHD and found only incentive-related improvements in a backward-span task which demanded storage and manipulation and not in a forward-span task which demanded storage only. However, having a closer look on their performance data, even the reported effect seems not to be an actual incentive-effect. When the incentive condition followed the baseline condition, there were no differences in performance between the two conditions. The observed “incentive” effect rather seems to be driven by a heavy decline in performance when the baseline condition follows the incentive condition. Apparently, in that case the amount of effort the children invested in the task collapsed in the baseline condition as compared to the preceding incentive condition. Positive effects of incentives on a workingmemory task are reported in a study using behavioral as well as pupillometric data as indicators of effort. Pupil sizes increase with increasing effort. Subjects performed a reading span task and effort was manipulated through incentives. Both, performance accuracy as well as pupil sizes increased with incentives (Heitz, Schrock, Payne & Engle, 2008). In a visual WM task with distractors reaction times increased and activity in visual association cortices as well as frontal areas was modulated under incentives. (Krawczyk, Gazzaley & D’Esposito, 2007). Participants in the study of Small et al. (2005) performed a variant of the Posner-task, a spatial attention task, under conditions with or without incentives. Activation in posterior regions which are associated with spatial attention was enhanced under incentives.
Similar to sleeping after learning, a brief period of wakeful resting after encoding new information supports memory retention in contrast to task‑related cognition. Recent evidence suggests that workingmemory capacity (WMC) is related to sleep‑dependent declarative memory consolidation. We tested whether WMC moderates the effect of a brief period of wakeful resting compared to performing a distractor task subsequent to encoding a word list. Participants encoded and immediately recalled a word list followed by either an 8 min wakeful resting period (eyes closed, relaxed) or by performing an adapted version of the d2 test of attention for 8 min. At the end of the experimental session (after 12–24 min) and again, after 7 days, participants were required to complete a surprise free recall test of both word lists. Our results show that interindividual differences in WMC are a central moderating factor for the effect of post‑learning activity on memory retention. The difference in word retention between a brief period of wakeful resting versus performing a selective attention task subsequent to encoding increased in higher WMC individuals over a retention interval of 12–24 min, as well as over 7 days. This effect was reversed in lower WMC individuals. Our results extend findings showing that WMC seems not only to moderate sleep‑related but also wakeful resting‑related memory consolidation.
Workingmemory (WM) performance varies substantially among individuals but the precise contribution of different WM component processes to these functional limits remains unclear. By analyzing different types of responses in a spatial WM task, we recently demonstrated a functional dissociation between confident and not-confident errors reflecting failures of WM encoding and maintenance, respectively. Here, we use event-related brain potentials to further explore this dissociation. Healthy participants performed a delayed orientation-discrimination task and rated their response confidence for each trial. The encoding-related N2pc component was significantly reduced for confident errors compared to confident correct responses, which is indicative of an encoding failure. In contrast, the maintenance-related contra-lateral delay activity was similar for these response types indicating that in confident error trials, WM representations – potentially the wrong ones – were maintained accurately and with stability throughout the delay interval. However, contra-lateral delay activity measured during the early part of the delay period was decreased for not-confident errors, potentially reflecting compromised maintenance processes. These electrophysiological findings contribute to a refined understanding of the encoding and maintenance processes that contribute to limitations in WM performance and capacity.
Considering the amygdala as part of a distributed face processing network that responds to socially relevant or otherwise distinctive faces, one possible explanation for the observed increased amygdala activity and shorter RTs in Pro homozygotes might be an enhanced representation of the emotional faces that could facilitate task performance. Compatibly with this notion, Pro homozygotes showed increased activation not only in the amygdala, but also in the FFA. Efficient representation of emotional stimuli in a workingmemory task (indexed by initially high brain response followed by strong repetition suppression) has been linked to superior processing of emotional relative to neutral faces . Notably, in that study, the processing advantage for emotional faces was not only observed in the amygdala, but also in the FFA. Given the increased activation of both of these structures in Pro homozygotes, we suggest that processing the stimulus (including its emotional salience) might beneficially affect perfor- mance in Pro homozygotes, whereas the weaker emotion-related amygdala response observed in Leu carriers might be detrimental. This notion is supported by the observation that in workingmemory tasks using faces as stimuli, FFA activity is a function of workingmemory load .
Together, the current ﬁndings indicate that the relative align- ment of posterior gamma to the FM-theta peak or trough represents a highly efﬁcient gating mechanism, which controls access to/distribution of frontal cognitive resources through the dynamic synchronisation or desynchronisation of fronto- posterior networks. Similarly, we demonstrated that this active mechanism of decoupling can also be found in the default-mode network while healthy young participants are engaged in a demanding workingmemory task 31 . This theory is further sup- ported by studies indicating that local neural activity is modulated by slow oscillations 10 , 32 , 33 . Speciﬁcally, Haegens et al. 34 reported the trough of slow oscillatory activity being associated with higher neuronal spiking than the more inhibitory peak of slow brain waves, a principle also suggested to hold true for theta activity 10 , 33 . Hanslmayr and colleagues 35 were able to show in humans that visual perceptual performance was enhanced when stimuli were presented around the trough of a theta wave. Moreover, this was associated with stronger neural activity in posterior brain areas and increased functional coupling between occipital and parietal cortex. A recent study by Voytek et al. 36 even found frontal theta phase modulating local gamma activity in intracranial recordings from epileptic patients. Recently, Alekseichuk et al. 37 delivered cross-frequency transcranial alter- nating current stimulation over the prefrontal cortex; they found that high-frequency gamma stimulation in combination with the peak of a theta stimulation (inducing gamma activity locked to the frontal theta trough on cortical level) increased workingmemory performance and long-range connectivity in the brain. Based on this evidence across species and across methods, we theorize that PFC exerts greater neuronal activity during the theta trough than the theta peak. Moreover, oscillatory brain activity in the gamma range has been proposed as a marker of increased neural ﬁring 26 . At the same time it is well accepted that right temporo-parietal brain areas are associated with the processing and storage of visuospatial information 38 , 39 . Consequently, when —like in the easiest experimental condition in the current study (retention load 1)—posterior gamma activity is coupled to the
In study 2 (Cognitive strategies and transfer effects between material- and operation- specific tasks within the workingmemory training framework), we provided the first experimental evidence of the role of cognitive strategy in workingmemory transfer effects, given the rapidly expanding and evolving field of workingmemory training. Current state of knowledge suggests that transfer effects depend mostly on task-specific contents. However, the question of why workingmemory training leads to transfer on particular materials related to the trained or similar tasks, without transferring broadly to other workingmemory tasks is unclear. To unravel this question, a methodologically sound study was conducted. The current work focused on the facet model of workingmemory (Oberauer et al., 2003), which defines two workingmemory operations: storage and processing, and relational integration. Recently, Hilbert et al. (2017) found transfer between verbal and numerical materials within the same workingmemory operation. On the basis of this finding, we assumed that transfer occurs if a similar cognitive strategy is applied to solve trained and transfer tasks. Therefore, in the present study, we developed a figural (symbol) task, which is thought to be compatible with verbal or numerical tasks in terms of applying an identical strategy. We examined whether training with verbal and numerical materials could show transfer to figural (symbol) material, and vice versa. Additionally, the preferable cognitive strategies - visual and verbal might account for the occurrence of training-related transfer effects. For this purpose, 105 young adults were randomly assigned to one of ten groups: eight experimental groups, a passive, and an active control group. Four experimental groups were trained on storage and processing, and four groups on relational integration
Executive cognitive functions like workingmemory determine the success or failure of a wide variety of different cognitive tasks, such as problem solving, navigation, or planning. Estimation of constructs like workingmemory load or memory capacity from neurophysiological or psychophysiological signals would enable adaptive systems to respond to cognitive states experienced by an operator and trigger responses designed to support task performance (e.g., by simplifying the exercises of a tutor system when the subject is overloaded Gerjets et al., 2014 , or by shutting down distractions from the mobile phone). The determination of cognitive states like workingmemory load is also useful for automated testing/assessment or for usability evaluation. While there exists a large body of research work on neural and physiological correlates of cognitive functions like workingmemory activity, fewer publications deal with the application of this research with respect to single-trial detection and real-time estimation of cognitive functions in complex, realistic scenarios. Single-trial classifiers based on brain activity measurements such as electroencephalography (EEG, Kothe and Makeig, 2011; Lotte et al., 2018 ), functional near-infrared spectroscopy (fNIRS, Putze et al., 2014; Herff et al., 2015 ), physiological signals ( Fairclough et al., 2005; Fairclough, 2008 ), or eye tracking ( Putze et al., 2013 ) have the potential to classify affective ( Koelstra et al., 2010; Heger et al., 2014; Mühl et al., 2014 ) or cognitive states based upon short segments of data. For this purpose, signal processing and machine learning techniques need to be developed and transferred to real-world user interfaces.
In contrast to English, where reading and spelling skills are highly correlated with each other (r = .68–.86; see Ehri, 2000), the respective correlations are only moderate in trans- parent orthographies. For German orthography, for instance, this correlation is only around r = .56 (e.g., Moll & Landerl, 2009), thereby indicating that the developmental trajectories of these two literacy skills are more independent than in opaque orthographies. Indeed, isolated learning disabilities in either reading or spelling among German school children are at least as prevalent as a combined reading and spelling disability (Fischbach et al., 2013; Landerl & Moll, 2010). Thus, in transparent orthographies poor readers are not nec- essarily also poor spellers and vice versa. This phenomenon arises because orthographic regularity is only true for graph- eme-to-phoneme correspondence (relevant in reading), but not for phoneme-to-grapheme correspondence (relevant in spelling). For example, reading the grapheme ee leads to the distinct German sound /e:/. In contrast, when transcribing the phoneme /e:/ various graphemes, and thus different spellings can be realized (e.g., ee like in See [lake], eh like in Zeh [toe], e like in Eber [boar]). As a consequence, from a phonological processing perspective, learning to spell German is more demanding than learning to read German. The WorkingMemory Model by Baddeley
FIGURE 6 | Brain-behavior correlations (spatial workingmemory). Slope of changes in brain and behavior results showing positive (red) and negative (green) correlations. (A) Spatial workingmemory; (B) spatial workingmemory control; and (C) spatial workingmemory score. Whole brain results are overlaid onto the MNI standard template; p < 0.0005, k = 10. Right side correlation plots include contrast values extracted from the peak coordinate inside an example cluster (indicated with red or green arrows) graphed against the slope of changes in behavior results. Abbreviations: L = Left; R = Right; SWM = Spatial workingmemory.
high sugar intake can impact cognitive performance with strongest associations with certain domains of memory functioning and in dehydration, while based on inconclusive and mixed evidence, there are also hints of impacts on cognitive performance in the domain of workingmemory functions. An intervention that increases water intake and replaces intake of sugar- sweetened beverages (SSBs) with less-sugared hydration sources may therefore have an impact on workingmemory performance in participants. Several interventions targeting water intake and/or sugar-sweetened beverage consumption have been proposed and conducted ranging from policy-level interventions to community-based interventions. Multiple studies identified the worksite as a necessary environment for nutrition interventions 12-14 . The worksite environment
In today’s society, receiving and using information from the World Wide Web (WWW) has become integral part of many private, academic, and occupational activities (Leu, Kinzer, Coiro & Cammack, 2004). As a result, measures of reading web-based information have been included in international comparative studies like the Programme for International Student Assessment (PISA), which aims to evaluate the skills and knowledge of students at the end of compulsory education (OECD, 2011). Web-based information is frequently structured in the form of non-linearly organized text pieces (“nodes”) that are associated with one another and accessible through hyperlinks. Hypertexts offer readers numerous ways of collecting and combining pieces of information for specific reading purposes. However, processing information that is not presented contiguously can seriously affect comprehension of a text (Coiro, 2011; Rouet, 2006), since individuals’ cognitive resources are limited (Feldman Barrett, Tugade & Engle, 2004) and decision-making and navigation requirements add to the load on readers’ workingmemory (WM; DeStefano & LeFevre, 2007; Foltz, 1996; Scheiter, Gerjets, Vollmann & Catrambone, 2009).
memory in learning L2 vocabulary. In the control group, mental lexicon access and workingmemory (backward) predicted the knowledge of 1000-frequency vocabulary, while mental lexicon access alone accounted for 38% of variance. This result confirms the important role of memory in L2 vocabulary acqui- sition: phonological short-term memory (Baddeley, Gathercole, & Papagno, 1998; Cheung, 1996; Gathercole, Service, Hitch, Adams, & Martin, 1999; Ma- soura & Gathercole, 2005) and long-term memory (Cheung, 1996; Masoura & Gathercole, 2005). Only verbal workingmemory (backward) predicted 2000-frequency vocabulary, accounting for 28% of variance. Research has shown that digits forward and digits backward are different constructs (Rosen- thal, Riccio, Gsanger, & Jarratt, 2006). Masoura and Gathercole (2005) claim that the participation of phonological short-term and long-term memory in L2 acquisition changes with the expansion of mental lexicon. Beginners rely mostly on temporary memory, while in advanced L2 learners word representa- tions in mental lexicon mediate the learning of new words. Even though our students had studied English as L2 for a long time, their vocabulary knowl- edge could be assessed as that of a beginner, as they had only mastered the easiest set of words. Although they were able to employ their mental lexicon for familiar L2 1000-frequency words, in order to complete a more difficult L2 word task, which clearly included unfamiliar words, the students applied ver- bal short-term memory. Moreover, the lack of mental lexicon access predictive function in the 2000-frequency task may suggest that in order to perform this more challenging task the students relied on educated guesses more than on real word entries in their long-term memory.
Secondly, this work has also revealed that although the visual workingmemory is formed in the R3 ring neurons, additional ring-neuron types such as, the R2, R5, and R6 ring, along with the non-canonical ring neuron subfamilies, i.e. L-Ei and L-Em neurons, are also involved in the functioning of this memory. Additionally, we uncovered the importance of the neurons that govern heading representation in Drosophila, i.e. the compass and shift neurons, for the functioning of visual workingmemory. Subsequently, we attempted to understand the neuroanatomics between the compass neurons, and the R3 neurons in the context of visual workingmemory. We could prove that the R3 neurons and E-PG neurons are synaptic partners of each other in a bidirectional manner, and that the nature of this synaptic interaction is stronger in one direction (E-PG→R3). When the visual cues are still present, the idiothetic memory is formed, and constantly updated in the R3 (d,m&p) neurons with the aid of the orientation information provided by the E-PG neurons, and the visual input from the R2 neurons. We therefore predict that once all landmarks disappear in the detour arena, this information regarding the idiothetic memory is presumably communicated to the “executing neurons”, which facilitate its execution either independently, or via excitatory synaptic connections with other areas of the CC.