Limited Working Memory Capacity Research Articles

Behavioral signatures of the rapid recruitment of long-term memory to overcome working memory capacity limits.

Working- and long-term memory are often studied in isolation. To better understand the specific limitations of working memory, effort is made to reduce the potential influence of long-term memory on performance in working memory tasks (e.g., asking participants to remember artificial, abstract items rather than familiar real-world objects). However, in everyday life we use working- and long-term memory in tandem. Here, our goal was to characterize how long-term memory can be recruited to circumvent capacity limits in a typical visual working memory task (i.e., remembering colored squares). Prior work has shown that incidental repetitions of working memory arrays often do not improve visual working memory performance - even after dozens of incidental repetitions, working memory performance often shows no improvement for repeated arrays. Here, we used a whole-report working memory task with explicit rather than incidental repetitions of arrays. In contrast to prior work with incidental repetitions, in two behavioral experiments we found that explicit repetitions of arrays yielded robust improvement to working memory performance, even after a single repetition. Participants performed above chance at recognizing repeated arrays in a later long-term memory test, consistent with the idea that long-term memory was used to rapidly improve performance across array repetitions. Finally, we analyzed inter-item response times and we found a response time signature of chunk formation that only emerged after the array was repeated (inter-response time slowing after two to three items); thus, inter-item response times may be useful for examining the coordinated interaction of visual working and long-term memory in future work.

Working Memory Capacity of ChatGPT: An Empirical Study

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory.

The role of perceptual similarities in determining the asymmetric mixed-category advantage.

Considering working memory capacity limitations, representing all relevant data simultaneously is unlikely. What remains unclear is why some items are better remembered than others when all data are equally relevant. While trying to answer this question, the literature has identified a pattern named the mixed-category benefit in which performance is enhanced when presenting stimuli from different categories as compared to presenting a similar number of items that all belong to just one category. Moreover, previous studies revealed an asymmetry in performance while mixing certain categories, suggesting that not all categories benefit equally from being mixed. In a series of three change-detection experiments, the present study investigated the role of low-level perceptual similarities between categories in determining the mixed-category asymmetric advantages. Our primary conclusion is that items' similarity at the perceptual level has a significant role in the asymmetric performance in the mixed-category phenomenon. We measured sensitivity (d') to detect a change between sample and test displays and found that the mixed-category advantage dropped when the mixed categories shared basic features. Furthermore, we found that sensitivity to novel items was impaired when presented with another category sharing its basic features. Finally, increasing the encoding interval improved performance for the novel items, but novel items' performance was still impaired when these items were mixed with another category that shared their basic features. Our findings highlight the significant role low-level similarities play in the asymmetric mixed-category performances, for both novel and familiar categories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Advanced Chinese EFL Learners’ Performance on Request

This study aims to investigate the performance of advanced Chinese EFL learners in making requests in different situational contexts. Request, as a sub type of directives, refers to attempts made by a speaker to persuade or dissuade the hearer from performing some kind of action for the benefit of the speaker (Ellis, 2008). According to Brown and Levinson’s (1987) politeness theory, making a request is considered a face-threatening act since it involves imposing one’s will on the hearer. Consequently, indirect request strategies are generally preferred by speakers as they help mitigate any potential imposition on the hearer’s face. However, this is not always the case. Brown and Levinson (ibid.) argue that the specific semantic formula used by speakers depends on contextual factors such as power dynamics between speaker and hearer, social distance between them, and size of imposition involved. In other words, making requests requires language learners to consider various aspects including linguistic form, communicative function, and situational context; thus placing significant cognitive burden on them due to their limited working memory capacity. As a result, breakdowns in communication often occur when EFL learners attempt to make requests.

The role of visual working memory in capacity-limited cross-modal ensemble coding

Ensemble coding refers to the brain's ability to rapidly extract summary statistics, such as average size and average cost, from a large set of visual stimuli. Although ensemble coding is thought to circumvent a capacity limit of visual working memory, we recently observed a VWM-like capacity limit in an ensemble task where observers extracted the average sweetness of groups of food pictures (i.e., they could only integrate information from four out of six available items), thus suggesting the involvement of VWM in this novel form of cross-modal ensemble coding. Therefore, across two experiments we investigated if this cross-modal ensemble capacity limit could be explained by individual differences in VWM processing. To test this, observers performed both an ensemble task and a VWM task, and we determined 1) how much information they integrated into their ensemble percepts, and 2) how much information they remembered from those displays. Interestingly, we found that individual differences in VWM capacity did not explain differences in performance on the ensemble coding task (i.e., high-capacity individuals did not have significantly higher “ensemble abilities” than low-capacity individuals). While our data cannot definitively state whether or not VWM is necessary to perform the ensemble task, we conclude that it is certainly not sufficient to support this cognitive process. We speculate that the capacity limit may be explained by 1) a bottleneck at the perceptual stage (i.e., a failure to process multiple visual features across multiple items, as there are no singular features that convey taste), or 2) the interaction of multiple cognitive systems (e.g., VWM, gustatory working memory, long term memory). Our results highlight the importance of examining ensemble perception across multiple sensory and cognitive domains to provide a clearer picture of the mechanisms underlying everyday behavior.

The neurocognitive role of working memory load when Pavlovian motivational control affects instrumental learning.

Research suggests that a fast, capacity-limited working memory (WM) system and a slow, incremental reinforcement learning (RL) system jointly contribute to instrumental learning. Thus, situations that strain WM resources alter instrumental learning: under WM loads, learning becomes slow and incremental, the reliance on computationally efficient learning increases, and action selection becomes more random. It is also suggested that Pavlovian learning influences people's behavior during instrumental learning by providing hard-wired instinctive responses including approach to reward predictors and avoidance of punishment predictors. However, it remains unknown how constraints on WM resources affect instrumental learning under Pavlovian influence. Thus, we conducted a functional magnetic resonance imaging (fMRI) study (N = 49) in which participants completed an instrumental learning task with Pavlovian-instrumental conflict (the orthogonalized go/no-go task) both with and without extra WM load. Behavioral and computational modeling analyses revealed that WM load reduced the learning rate and increased random choice, without affecting Pavlovian bias. Model-based fMRI analysis revealed that WM load strengthened RPE signaling in the striatum. Moreover, under WM load, the striatum showed weakened connectivity with the ventromedial and dorsolateral prefrontal cortex when computing reward expectations. These results suggest that the limitation of cognitive resources by WM load promotes slow and incremental learning through the weakened cooperation between WM and RL; such limitation also makes action selection more random, but it does not directly affect the balance between instrumental and Pavlovian systems.

31 Examining the Mechanisms of Verbal Working Memory Capacity Consumption in Monolingual Spanish-Speaking Individuals

Objective:Converging evidence across languages suggests that the word length effect (WLE; rate of number of syllables, phonemes, or pronunciation times per word) significantly contributes to estimates of verbal working memory (WM) capacity limits in the storage phase, but not in the manipulation phase (i.e., word length effect decay), of WM. Direct examination of the WLE on verbal WM performance within monolingual Spanish-speakers has not been reported. We investigated the psychophysical mechanisms of capacity consumption in Spanish-speakers across three syllabic word length rates to clarify the relative contributions of the WLE to storage (digit span forward) versus manipulation (digit span backward) memory phases within one language of monolingual speakers.Participants and Methods:Monolingual Spanish-speaking adults (N = 84) born in Latin American countries and age 18-65 completed testing over Zoom. Inclusion criteria required proficiency in the Spanish-language; exclusion criteria were bilingualism, multilingualism, TONI-4 IQ < 85, or history of head injury/LOC. A within-group design measured the WLE across three cognitive load conditions in the forward and backward directions of the digit span test varying in Spanish syllabic word length: the Mexican WAIS-IV Digit Span Test (“Standard Load”), and two modified measures with either a ∼20% decrease (“Low Load”) or ∼20% increase (“High Load”) in total syllables/digit relative to the Standard Load.Results:A reverse WLE was observed on syllable accuracy percentage task performance (p < 0.01), such that longer word length led to higher capacity limits during storage WM. A WLE, not decay, was found on both raw score (p < .001) and syllable accuracy percentage (p < 0.01) task performances during manipulation WM, where longer word length led to lower capacity limits.Conclusions:The reverse WLE was attributed to higher-order, executive-function cognitive strategies (such as chunking) that superseded negative word length effects. A larger syllabic discrepancy during manipulation WM could have superseded executive-function strategies, rendering a traditional WLE. Our study contributed more precise capacity estimates and clearer understanding of successful WM performance within monolingual, Latin American-born Spanish-speakers, helping to reduce cultural disparities in neurocognitive and neuropsychological research. Future studies may extend these findings to examine how WM capacity resources can be harnessed to improve memory strategies in clinically-applied settings with Spanish-speaking populations.

Remembering in the Digital World: Autobiographical Memory in Social Media

The use of social media continues to increase in modern cultures in recent years. This new context leads to creating a virtual self, which somewhat differs from the real self. Further, social relations are set and maintained predominantly in this new context. There is no doubt that social media does not only affect individuals' social relations but also their cognitive skills by pulling them into new situations that they are unfamiliar with. In this new context, content coded in social media can be remembered in real life, and content coded in real life can be remembered in social media. This new context, where mostly real-life stories are shared, is likely to strongly affect the autobiographical memory processes of individuals. Past research suggests that social media affects autobiographical memory processes both directly (phenomenological characteristics, functions) and indirectly through cognitive processes (eg, attention, working memory, transitive memory). Studies about its indirect effects indicate that people with limited attention and working memory capacity have difficulty processing the high number of stimuli offered by social media. Given that the long-term memory capacity is not limited, it is plausible to expect that some of the contents are encoded into the memory; however, problems may occur in the storage and retrieval processes. Studies about the direct effect of social media show that the phenomenological characteristics (number of details, accuracy, emotional content) and functions (self, social, directing, therapeutic) of events experienced or shared on social media differ from real-life events. This theoretical review discusses the effects of social media use on cognitive processes related to memory processes and more specifically on the phenomenological and functional characteristics of autobiographical memory. To recognize and prevent potential psychological issues that may emerge in relation to this new setting, it appears essential to comprehend how social media affects autobiographical memory, which is essential for self-perception.

Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference

Learning new rules rapidly and effectively via instructions is ubiquitous in our daily lives, yet the underlying cognitive and neural mechanisms are complex. Using functional magnetic resonance imaging we examined the effects of different instructional load conditions (4 vs. 10 stimulus-response rules) on functional couplings during rule implementation (always 4 rules). Focusing on connections of lateral prefrontal cortex (LPFC) regions, the results emphasized an opposing trend of load-related changes in LPFC-seeded couplings. On the one hand, during the low-load condition LPFC regions were more strongly coupled with cortical areas mostly assigned to networks such as the fronto-parietal network and the dorsal attention network. On the other hand, during the high-load condition, the same LPFC areas were more strongly coupled with default mode network areas. These results suggest differences in automated processing evoked by features of the instruction and an enduring response conflict mediated by lingering episodic long-term memory traces when instructional load exceeds working memory capacity limits. The ventrolateral prefrontal cortex (VLPFC) exhibited hemispherical differences regarding whole-brain coupling and practice-related dynamics. Left VLPFC connections showed a persistent load-related effect independent of practice and were associated with ‘objective’ learning success in overt behavioral performance, consistent with a role in mediating the enduring influence of the initially instructed task rules. Right VLPFC's connections, in turn, were more susceptible to practice-related effects, suggesting a more flexible role possibly related to ongoing rule updating processes throughout rule implementation.

Partial recall: Implications for the discrete slot limit of working memory capacity.

The nature of working memory capacity (WMC) has been a highly contested topic among cognitive scientists. Some advocate for the discrete nature of this construct, fixed to a set number of independent slots, each capable of storing a single chunk of bound information. Others advocate for a continuous limit, guided by a pool of immediately available resources spent across the to-be-remembered items. To understand the nature of WMC, it was first essential to separate capacity from other factors, such as performance consistency, which may impact overall WM performance. Recent work by Schor et al., (2020, Psychonomic Bulletin & Review, 27[5], 1006-1013) has provided a method for separating these constructs within a single visual array task. The present study used this statistical model to extract partial information, defined as accurate recall of a correct color, but not location, at a rate greater than expected through guessing. The successful memory of this information would demonstrate that capacity does not rely on the existence of empty slots, which discrete slot model advocates argue, are necessary for successful storage and recall of items. The present study found that participants were able to successfully recall partial information at a rate significantly greater than chance, but not beyond the individual working memory capacity limit. These findings help provide additional support for the discrete resource slot model, while simultaneously casting doubt on its strong object slot model alternative.

Two types of redundancy in multimedia learning: a literature review.

Regarding the redundancy effect in multimedia learning environments, more consistency is needed in the theoretical assumptions and investigation of this effect. Current research lacks a comprehensive account of different redundant scenarios in which materials facilitate or inhibit learning and provides little conceptual guidance on how learning processes are affected by different types of redundancy. Theoretical assumptions refer to redundancy as a contentual overlap of information provided by the learning material; in this case, processing duplicated information strains the learners' limited cognitive capacities. Other assumptions refer to the role of processing limitations in working memory channels, including separate processing for visual and verbal information. In this case, an ineffective combination of sources leads to an overload of the limited working memory capacity. This paper reviews empirical research on the redundancy effect (63 studies) and classifies two types of redundancy: (1) content redundancy, and (2) working memory channel redundancy. From an instructional psychology perspective, the analyses reveal four different implementations of redundant scenarios: (1) adding narration to visualizations, (2) adding written text to visualizations, (3) adding written text to narration, and (4) adding written text to narrated visualizations. Regarding the effects of the two redundancy types within these scenarios, analyses indicate positive effects of content redundancy (affected by learners' prior knowledge), negative effects of working memory channel redundancy (regarding visualizations and written text), and positive effects of working memory channel redundancy (regarding narration and written text). Moreover, results point to factors that might moderate the effect of redundancy and illustrate interactions with existing multimedia effects. Overall, this review provides an overview of the state of empirical research and reveals that the consideration of both redundancy types provides further explanations in this field of research.

Investigating the impact of self-pacing on the L2 listening performance of young learner candidates with differing L1 literacy skills

In online environments, listening involves being able to pause or replay the recording as needed. Previous research indicates that control over the listening input could improve the measurement accuracy of listening assessment. Self-pacing also supports the second language (L2) comprehension processes of test-takers with specific learning difficulties (SpLDs) or, more specifically, of learners with reading-related learning difficulties who might have slower processing speed and limited working memory capacity. Our study examined how L1 literacy skills influence L2 listening performance in the standard single-listening and self-paced administration mode of the listening section of the Test of English as a Foreign Language (TOEFL) Junior Standard test. In a counterbalanced design, 139 Austrian learners of English completed 15 items in a standard single-listening condition and another 15 in a self-paced condition. L1 literacy skills were assessed via a standard reading, non-word reading, word-naming, and non-word repetition test. Generalized Linear Mixed-Effects Modelling revealed that self-pacing had no statistically significant effect on listening scores nor did it boost the performance of test-takers with lower L1 literacy scores indicative of reading-related SpLDs. The results indicate that young test-takers might require training in self-pacing or that self-paced conditions may need to be carefully implemented when they are offered to candidates with SpLDs.

Cognitive Load Theory on Virtual Mathematics Laboratory: Systematic Literature Review

The primary goal of cognitive load theory is to improve the learning of complex cognitive tasks by transforming current scientific knowledge on how cognitive structures and processes are organized into guidelines for instructional design. Cognitive load theory assumes that the bottleneck for acquiring new secondary biological knowledge is the limited working memory capacity. In the ideal situation, the working memory resources required for learning do not exceed the available resources. Despite this, in reality, there will often be a high cognitive load, or even ”overload,” for two reasons. First, dealing with interactive information elements in complex cognition imposes a high intrinsic working memory load. Second, learners also have to use working memory resources for activities that are extraneous to performing and learning tasks, that is, activities that are not productive for learning. Virtual Laboratory is a form of animation that can visualize abstract phenomena or complex experiments in natural laboratories to increase learning activities and develop problem-solving skills. A virtual math laboratory was created to optimize dual coding memory, namely verbal and audio learning. The investigation tracked the approved reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) guidelines, illustrating the outcomes of the literature searches and articles selection process. It is used to provide that the selection process is replicable and transparent. We accomplished a computerized bibliometric analysis from 2002-2022 for articles retrieved from the SCOPUS database. Data were collected in July 2022.
 Keywords: cognitive load theory, virtual laboratory, mathematics education

Developments in Transcranial Magnetic Stimulation to Study Human Cognition.

The study of human cognition through neuroscience has benefitted from a constant development and transformation of associated technology and analysis. Taking working memory (WM) as an example, and also as the area in which Mark Stokes has demonstrated this, it is a field that has often found itself right at the forefront of cognitive neuroscience, with a fascinating progression of theoretical insight and development as a consequence (see D'Esposito and Postle [2014] and Postle [2006] for thorough reviews of the field). Often, this innovation and discovery has produced theoretical upheaval, such as understanding the relationship/dependence/separation between attention and WM (Nobre & Stokes, 2011), challenging the limited capacity of WM (Bays & Husain, 2008), and the role of pFC in WM (Stokes, 2015) to name a few. (At this stage, the reader may have noted the involvement of Mark Stokes in several of these upheavals. It is not likely to be coincidence.)Throughout this journey over these last decades, TMS has been present, providing causal results to complement correlational neuroimaging, and behavioral studies. Despite its theoretical strengths as a class of research tools, the practical challenges and theoretical unknowns regarding physiological effects have meant that TMS has not always lived up to its promise as a “causal silver bullet” that can confirm correlational findings (Parkin, Ekhtiari, & Walsh, 2015; Siebner, Hartwigsen, Kassuba, & Rothwell, 2009).1 The future of TMS is, however, looking decidedly up (again). Advances in concurrent invasive recordings to visualize neuronal responses to TMS have provided invaluable in vivo evidence on the acute effects of stimulation (Allen, Pasley, Duong, & Freeman, 2007; Moliadze, Zhao, Eysel, & Funke, 2003). In humans, combining TMS with neuroimaging has also given the field a boost, showing effects across the brain (Bergmann et al., 2021), and on electrophysiological markers such as ERPs (Taylor, Walsh, & Eimer, 2008) and oscillations (Thut & Miniussi, 2009). Thus, as this commentary will argue, a crucial way forward for investigating cognition through neuroscience will be through innovative multimodal experimental approaches that can produce convergent evidence in one shot; examples will follow here as to how this approach can drive the field forward, with the focus being on TMS, given its superior spatial and temporal resolution compared with transcranial electrical stimulation.A game-changer for TMS has been in its simultaneous combination with fMRI and EEG. Although these innovations were demonstrated decades ago—for TMS-fMRI: Bohning et al. (1998); TMS-EEG: Ilmoniemi et al. (1997)—their progression from niche technical feats to essential contributors to cognitive neuroscience is coming to fruition. Given that one can now acquire reliable, well-supported systems for TMS-fMRI and TMS-EEG, the next step is underway in which advanced experimental and analysis approaches will answer questions that would otherwise be difficult to address.The first growth area of cognitive TMS research is in the combination of TMS-fMRI with functional and effective connectivity analysis approaches; TMS can be applied to intervene with brain networks necessary for behavior and causally establish the necessity of associated network nodes and communication pathways. Although there exist many sophisticated methods for establishing connectivity to uncover brain networks through advanced mathematical and statistical algorithms, they all provide indirect “best current estimates” (Johansen-Berg, 2013); what remains to be established is whether such connections exist beyond the statistical level and TMS is arguably the best placed to accomplish this. Nee and D'Esposito (2017) provided a valuable demonstration of what a TMS-fMRI-effective connectivity—here, dynamic causal modeling—approach can contribute theoretically. The use of TMS to causally test dynamic causal modelings has been suggested previously (Hartwigsen et al., 2015; Bestmann & Feredoes, 2013), and perturbation of a network with TMS has also been proposed in the context of network control theory (Medaglia, Pasqualetti, Hamilton, Thompson-Schill, & Bassett, 2017). A recent study by Sydnor et al. (2022) is a clear indication of the strengths of TMS-fMRI-perturbation, in which single pulses of TMS to ventrolateral pFC produced fMRI signal changes in the amygdala, the magnitude of which were predicted by the density of the white matter fiber pathway between the TMS target and amygdala. One could envisage a similar approach in which multiple lines of evidence including structure and function are combined to reveal patterns of connectivity for different types of behavioral tasks and/or different task stages. In the context of WM, such an approach may contribute to the ongoing debate on the nature of communication between sensory, parietal, and prefrontal cortex during WM maintenance (Gayet, Paffen, & Van der Stigchel, 2018; Scimeca, Kiyonaga, & D'Esposito, 2018; Xu, 2017), or the involvement of, and interactions between, more established networks such as the default mode and frontoparietal networks during WM (Murphy, Bertolero, Papadopoulos, Lydon-Staley, & Bassett, 2020).In addition to brain networks, the cognitive neuroscience field is interested in what information is being coded by brain regions engaged for a behavior. Here, decoding techniques such as multivoxel pattern analysis (MVPA) have proven to be useful (e.g., Woolgar, Jackson, & Duncan, 2016; Postle, 2015), and recently, they have been combined with fMRI (Jackson, Feredoes, Rich, Lindner, & Woolgar, 2021) and EEG (Rose et al., 2016) to provide answers to otherwise difficult-to-resolve questions, with the bonus of causal inference. Jackson et al. (2021) addressed a long-standing debate on relevant information representation across the brain in the face of irrelevant information. In this study, concurrent TMS-fMRI (i.e., TMS applied online during task performance in the MRI scanner) data were analyzed with MVPA, to show causally that the multiple demand network (Duncan, 2010) enhances relevant information rather than suppresses irrelevant information. Specifically, right dorsolateral pFC (DLPFC) TMS disrupted task performance, resulting in a decrease in the decoding of relevant information across the multiple demand network, whereas irrelevant information decoding was not significantly modulated by disruptive TMS. MVPA provided a unique way in which to view the effects of TMS, that is, on information coding, rather than the magnitude of the BOLD response and which in this case was critical for testing the impact of DLPFC on the same information being represented under different behavioral contexts.Rose et al. (2016) combined TMS with EEG (with fMRI informing the location of the cortical TMS target) to test a cognitive account of WM in which information can be held in different states depending on task demands (Oberauer, 2009). A latent, undecodable WM item (rendered thus because it was not required during a specific epoch of the task) could be “reactivated” with a single TMS “ping.”2 Specifically, the inactive item could be decoded for a brief period post-TMS, indicating that it had been brought into a different physiological state by TMS such that it could be detected with MVPA. This result marries very well with multiple-states theoretical accounts of WM (Stokes, 2015; Oberauer, 2009; Cowan, 2005), and demonstrates the unique ability of a TMS ping to access an otherwise inaccessible brain state.Combining TMS with neuroimaging has taken a further, dramatic step with simultaneous TMS-fMRI-EEG. While a technically extremely challenging feat, this approach is arguably a “holy grail” for some in the brain stimulation field, providing a bridge between the spatial and temporal, to capture oscillatory activity changes across the whole brain in response to perturbation with TMS. Proof of concept was demonstrated by Peters et al. (2013), with a cognitive application published more recently (Peters et al., 2020) in which right dorsal premotor cortex was stimulated with a short burst of below motor-threshold TMS and the state of pre-TMS alpha and low beta oscillations were examined to determine how trial-by-trial fluctuations influenced the effect of TMS throughout a functional motor network. TMS-induced signal propagation, as indexed by the fMRI response throughout the cortical–subcortical network of interest, was impeded by stronger pre-TMS alpha power, in line with alpha serving a suppressive function in terms of information transmission. The promise of this technique is in its ability to probe dynamic brain states, including those which exist during attention, memory, or cognitive control (Sack, 2022), and it is certainly in line with views such as those put forward by, for example, Mark Stokes, in which multiple brain areas combine while maintaining a highly dynamic state, to achieve flexible behavior (Stokes & Duncan, 2014). TMS-fMRI-EEG would very much be in the “watch this exciting space closely” category.Computational modeling of behavior can now also be added as an experimental approach benefitting from the addition of TMS. Again, relying on the perturbation logic, the spread of the consequences of TMS throughout hidden layers can provide a causal missing link, particularly if applied to existing TMS data (e.g., TMS-behavioral or TMS-neuroimaging data). Very recent work (Whyte, 2022) has taken this exact approach, applying the WM model of Manohar, Pertzov, and Husain (2017) to explain the behavioral and TMS effects on fMRI decoding of stimuli of Jackson et al. (2021). According to the model, stimuli and associated response rules are coded through rapid synaptic changes. Modeling a short TMS train to DLPFC as a brief excitatory input, the effect on the model was to produce a disruption of conjunction units coding task rules, assumed to be in the multiple demand network (Woolgar, Hampshire, Thompson, & Duncan, 2011). The results of the modeling could explain the fMRI decoding and behavioral results reported by Jackson et al. (2021). This plastic attractor model provides an account of DLPFC that permits adaptive, flexible behavior to select task-relevant stimulus information and to produce appropriate motor actions in response. The added value of TMS here was to provide a controlled perturbation of the network with behavioral and neural outcomes, with the computational model revealing the otherwise hidden underlying mechanisms. It is a fruitful area of future research, given that at some stage, computational models of brain and behavior will need causal verification in the same way as for mathematically derived functional brain networks, as described above.A final area that will be discussed here, for future cognitive TMS studies to pursue more ardently, is combining of patterned stimulation protocols with neuroimaging. There are a range of patterns in which TMS can be applied, producing complex physiological effects such as spike timing-dependent plasticity, to investigate cortico-cortico connectivity, for example, cortico-cortical paired associative stimulation (ccPAS; Romei, Thut, & Silvanto, 2016; Rizzo et al., 2009). Casula, Pellicciari, Picazio, Caltagirone, and Koch (2016) applied a ccPAS protocol to investigate the direction of communication between key nodes of the frontoparietal network, DLPFC, and posterior parietal cortex. Concurrent EEG (in the absence of behavior) showed an increase in high frontal gamma activity following DLPFC-parietal ccPAS, with the reverse when parietal cortex was stimulated first. The timing of these changes also fit with a GABA-dependent mechanism, suggesting a change in the amount of GABA available to DLPFC pyramidal neurons that generated the EEG signal (Casula et al., 2016). The interesting confluence of findings from this study can certainly motivate similar studies combining neuroimaging, and perhaps also neuropharmacology to generate unique research questions regarding the neural pathways and mechanisms underlying cognition (Pitcher, Parkin, & Walsh, 2021; Romei et al., 2016).The clear improvements in the field of combining TMS with neuroimaging and analysis, such as developments in hardware (Navarro de Lara et al., 2015), understanding the effects of TMS at the level of brain regions (Tik et al., 2019) and neuronally (Romero, Davare, Armendariz, & Janssen, 2019), and modeling of TMS effects (Shirinpour et al., 2021), all contribute to increasing the accuracy of TMS for causally perturbing brain and behavior. There are more sophisticated questions being asked by this field, producing more sophisticated evidence, compared with the “early days” of cognitive TMS. It is indeed an exciting time to be in the noninvasive neurostimulation field, with its potential to have a central role in informing cognitive neuroscientific theory growing with each publication.The author is supported by a Royal Society Leverhulme Trust Senior Research Fellowship.Reprint requests should be sent to Eva Feredoes, School of Psychology and Clinical Language Sciences, University of Reading, Earley Gate, Reading, RG2 7AG, UK, or via e-mail: e.a.feredoes@reading.ac.uk.Retrospective analysis of the citations in every article published in this journal from 2010 to 2021 reveals a persistent pattern of gender imbalance: Although the proportions of authorship teams (categorized by estimated gender identification of first author/last author) publishing in the Journal of Cognitive Neuroscience (JoCN) during this period were M(an)/M = .407, W(oman)/M = .32, M/W = .115, and W/W = .159, the comparable proportions for the articles that these authorship teams cited were M/M = .549, W/M = .257, M/W = .109, and W/W = .085 (Postle and Fulvio, JoCN, 34:1, pp. 1–3). Consequently, JoCN encourages all authors to consider gender balance explicitly when selecting which articles to cite and gives them the opportunity to report their article’s gender citation balance. The authors of this article report its proportions of citations by gender category to be as follows: M/M = .615; W/M = .256; M/W = .077; W/W = .051.

The contribution of episodic long-term memory to working memory for bindings

The present experiments support two conclusions about the capacity limit of working memory (WM). First, they provide evidence for the Binding Hypothesis, WM capacity is limited by interference between bindings but not items. Second, they show that episodic LTM contributes substantially to binding memory when the capacity of WM is stretched to the limit by larger set sizes. We tested immediate memory for sets of word-picture pairs. With increasing set size, memory for bindings declined more precipitously than memory for items, as predicted from the binding hypothesis. Yet, at higher set sizes performance was more stable than expected from a capacity limited memory, suggesting a contribution of episodic long-term memory (LTM) to circumvent the WM capacity limit. In support of that hypothesis, we show a double dissociation of contributions of WM and episodic LTM to binding memory: Performance at set sizes larger than 3 was specifically affected by proactive interference – but were immune to influences from a distractor-filled delay. In contrast, performance at set size 2 was unaffected by proactive interference but harmed by a distractor-filled delay.

Under-resourced or overloaded? Rethinking working memory deficits in developmental language disorder.

Dominant theoretical accounts of developmental language disorder (DLD) commonly invoke working memory capacity limitations. In the current report, we present an alternative view: That working memory in DLD is not under-resourced but overloaded due to operating on speech representations with low discriminability. This account is developed through computational simulations involving deep convolutional neural networks trained on spoken word spectrograms in which information is either retained to mimic typical development or degraded to mimic the auditory processing deficits identified among some children with DLD. We assess not only spoken word recognition accuracy and predictive probability and entropy (i.e., predictive distribution spread), but also use mean-field-theory based manifold analysis to assess; (a) internal speech representation dimensionality and (b) classification capacity, a measure of the networks' ability to isolate any given internal speech representation that is used as a proxy for attentional control. We show that instantiating a low-level auditory processing deficit results in the formation of internal speech representations with atypically high dimensionality, and that classification capacity is exhausted due to low representation separability. These representation and control deficits underpin not only lower performance accuracy but also greater uncertainty even when making accurate predictions in a simulated spoken word recognition task (i.e., predictive distributions with low maximum probability and high entropy), which replicates the response delays and word finding difficulties often seen in DLD. Overall, these simulations demonstrate a theoretical account of speech representation and processing deficits in DLD in which working memory capacity limitations play no causal role. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Tense and agreement in Moroccan Arabic agrammatism: Evidence for task effects

ABSTRACT Background Cross-linguistic studies on agrammatism have provided evidence that tense inflection is severely impaired while agreement inflection is relatively preserved (Friedmann & Grodzinsky 1997, Benedet et al., 1998). More recent evidence has suggested that the tense deficit is central, affecting comprehension as well. To account for this pattern of performance, a number of hypotheses have been suggested, making different predictions about the nature of the tense deficit in agrammatism. Objectives The purpose of this study was (1) to examine the centrality of a tense-agreement dissociation in Moroccan Arabic agrammatism, and (2) to determine task effects on agrammatic performance. Methods We presented results from a picture description, two sentence completion and a grammaticality judgement task with five Moroccan Arabic-speaking agrammatic patients and five matched neurologically normal adults. Results Control participants performed at ceiling across all the experimental tasks used. PWAA revealed different patterns of performance. Findings from both picture description and sentence completion tasks indicated a dissociation between tense and agreement production, with tense being more error-prone than agreement. The tense-agreement dissociation found in production extended to the comprehension modality in the sense that patients were less sensitive to tense than to agreement violations. Findings indicated task effects on performance with patients performing differently across different experimental tasks. Conclusions Findings of the present study argue in disfavour of a structural or representational account of the tense deficit in agrammatism. The interaction between task effects and tense accuracy suggests that the deficit can best be characterized in processing terms, particularly being a manifestation of limited working memory capacity.

Intentional remembering and intentional forgetting in working and long-term memory.

We show that the intention to remember information substantially improves recall of that information when maintained in working memory (WM), whereas it does not have the same effect on maintenance in episodic long-term memory (LTM). In eight experiments, participants processed lists of words according to a semantic orienting task in three instruction conditions: to remember, to forget, or an incidental-memory baseline with no instruction. The first two experiments showed better memory for intentional remembering compared to incidental memory for a typical test of WM, but not for a typical test of episodic LTM. The subsequent six experiments determined which of three variables distinguishing typical WM and episodic-LTM tests-delay of test, list length, and proactive interference-are responsible for this difference. The intention to remember improved free-recall performance for short lists (close to the capacity limit of WM), most strongly when tested immediately, and only in the presence of proactive interference. This result supports a functional distinction between WM and episodic LTM: Whereas episodic LTM keeps a nonselective record of experiences for future use, WM holds selectively only the information relevant for the current goal. In addition, we found a beneficial effect of intentional remembering on memory for the list position of words, which was obtained for longer lists and regardless of the delay of testing, probably reflecting episodic LTM. The instruction to forget had no effect compared to the incidental baseline, questioning the assumption of a targeted forgetting process on memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Comparison of Resting-State Brain Activation between Healthy Normal and Low Auditory-Verbal Working Memory Capacity Participants

Working memory (WM) capacity is the ability to maintain attention and store information briefly in the mind. However, each individual has a limited WM capacity that varies from one person to another. An individual can be categorized as having either normal or low WM capacity. This study aimed to evaluate and compare brain activations of healthy individuals with low and normal auditory-verbal WM capacity. A total of 39 healthy male young adults were recruited from local universities for this study. They were categorized into the normal and low auditory-verbal WM capacity group based on their score in the Malay Version of Auditory Verbal Learning Test (MVAVLT). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. The functional data were analyzed using Statistical Parametric Mapping (SPM) and Wake Forest University (WFU) Pickatlas softwares. Brain activations and resting-state amplitude fluctuation (rsAF) were contrasted between groups to determine whether there were any significant differences caused by the different auditory-verbal WM capacity. The findings indicated that the low auditory-verbal WM capacity group showed significantly higher cortical activations in the left lingual gyrus, bilateral middle temporal gyrus, left calcarine, left superior frontal gyrus, and left precuneus as compared to normal auditory-verbal WM capacity group. It is suggested that the higher activation of these brain areas in low verbal-auditory WM capacity participants was attributed to the lower neural adaptability of the brain at rest.

Children's long-term retention is directly constrained by their working memory capacity limitations.

We explored the causal role of individual and age-related differences in working memory (WM) capacity in long-term memory (LTM) retrieval. Our sample of 160 participants included 120 children (6-13-years old) and 40 young adults (18-24 years). Participants performed a WM task with images of unique everyday items, presented at varying set sizes. Subsequently, we tested participants' LTM for items from the WM task. Using these measures, we estimated the ratio at which items successfully held in WM were recognized in LTM. While WM and LTM generally improved with age, the ability to transfer information from WM to LTM appeared consistent between age groups. Moreover, individual differences in WM capacity appeared to predict LTM encoding. Overall, these results suggested that LTM performance was constrained by experimental, individual, and age-related WM limitations. We discuss the theoretical and practical implications of this WM-to-LTM bottleneck.