Naturalistic actions modulate Working Memory prioritization in immersive virtual reality

Naturalistic actions modulate Working Memory prioritization in immersive virtual reality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Naturalistic actions modulate Working Memory prioritization in immersive virtual reality Estela Caballero-Picazo, Francisco Rocabado, José Antonio Hinojosa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8946771/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Human cognition is inherently action-oriented, enabling flexible interaction with dynamic environments. Working Memory (WM) links perception and action by maintaining task-relevant representations. Although evidence indicates that action planning can influence what information is maintained in WM, traditional laboratory paradigms often rely on simplified response demands that only partially capture real-world perception–action coupling. Here, we investigated whether the nature of actions modulate the prioritization of sensory representations in WM. Using a virtual reality task, participants responded either via a simple button press or by performing a more naturalistic object movement, while a spatial cue indicated the item most likely to be probed (pre-cued or retro-cued). Accuracy was analyzed using generalized linear mixed-effects models. Naturalistic actions yielded higher accuracy than button presses. Critically, this advantage was confined to validly cued items. Together, these findings suggest that the action associated with a representation plays a constitutive role in WM prioritization, such that prioritization cannot be fully characterized independently of its action context. More broadly, the study highlights immersive virtual reality as a valuable tool for investigating working memory under ecologically grounded yet experimentally controlled conditions, enabling the characterization of functional properties that would be difficult to capture with traditional laboratory paradigms. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Working memory Action–perception coupling Working Memory prioritization Virtual reality Retro-cueing Pre-cueing Figures Figure 1 Figure 2 Introduction Humans adapt to their environment by dynamically adjusting their behavior. Cognitive processes enable individuals to effectively interact and manipulate their environment by guiding behavior, and, as a result, facilitating problem solving and goal achievement. Therefore, cognition is considered to be inherently action-oriented (Allport, 1987; Ballard et al., 1997; Engel et al., 2013; Glenberg et al., 2013; Neumann, 1987; Neumann & Prinz, 1990), with an increasing number of studies linking cognition and action through bidirectional influences (Boettcher et al., 2021; Heuer et al., 2017; Trentin et al., 2023; Wykowska et al., 2009). This is not surprising, since, as ambulatory beings, we need a system, prospective in nature, that allows us to select and retain visual representations that are relevant to guide our future behavior beyond the immediate moment (van Ede, 2020). Working Memory (WM) has been proposed to be the interface that links perception and action, allowing us to keep pace with the changing environmental demands (Heuer et al., 2017; Nobre, 2022; Olivers & Roelfsema, 2020; van Ede, 2020; van Ede et al., 2021; van Ede & Nobre, 2023). Working memory (WM) enables the temporary maintenance and manipulation of information relevant to task goals (Baddeley, 1992; Heuer et al., 2020; van Ede et al., 2019). Despite the importance that actions have in guiding what is relevant to maintain in WM for future behavior, traditionally, WM research has mainly focused on the mechanisms involved in the maintenance of sensory information, until recently overlooking its prospective and functional nature (Heuer et al., 2020; Olivers & Van der Stigchel, 2020). In recent years there has been an increasing interest in studying how actions influence what is encoded, prioritized or retrieved (Olivers & Roelfsema, 2020; van Ede, 2020). In this regard, performing eye movements and/or preparing or executing manual movements toward a spatial location (Hanning & Deubel, 2018; Heuer et al., 2017; Hanning et al., 2016; Heuer & Schubö, 2017; Ohl & Rolfs, 2017, 2018 Trentin et al., 2023) during the maintenance period of a WM task enhances performance for the WM item that matches the location of the action target (Hanning & Deubel, 2018; Hanning et al., 2016; Ohl & Rolfs, 2017, 2018). In the same vein, Heuer and Schubö (2017) showed that planning grasping movements after encoding leads to the prioritization of perceptual features of items congruent with the type of action to be executed (e.g., prioritizing size when planning a grasping movement). However, although involuntary memory facilitation was observed in that study, actions did not hold any instrumental value for the WM task, which makes it challenging to draw definitive conclusions about how WM representations support prospective actions. In natural environments, action and cognitive operations are inherently bound. In this context, some studies have directly explored how actions are encoded along with WM sensory representations (Olivers & Van der Stigchel, 2020; van Ede, 2020). Such research tracks the time course of motor planning within WM using EEG to monitor both the temporal dynamics of encoding of the item's visual location and its associated prospective manual action (Boettcher et al., 2021; van Ede et al., 2019; Nasrawi et al., 2023). These studies revealed early prospective action encoding alongside detailed sensory information, suggesting that dual coding could make memories more robust and more resistant to later interferences (Boettcher et al., 2021). Additional support for the functional role of action in WM comes from recent work by Trentin et al. (2023), who explored how memory prioritization is affected when an action is coupled with a WM representation. The study showed that direct planning of a grasping movement on a previously encoded item enhanced the sensory memory representation, promoting its prioritization over other equally task-relevant representations not linked to any prospective action. It has been proposed that WM prioritization emerges from an attentional bias produced by the recurrent feedback mechanisms connecting an item in WM to a specific action plan (Olivers & Roelfsema, 2020). This account aligns with new theorical proposals on how top-down mechanisms operate within WM to select relevant representations (van Ede, 2017). The study of top-down processes within WM has generated numerous behavioral and neuronal findings that resemble those observed for external attention. Specifically, retrospectively orienting attention to a WM representation enhances behavioral performance for the retro-cued item, and elicits brain oscillatory patterns associated with spatial attention (Poch et al., 2014, 2017; Souza & Oberauer, 2016; van Ede & Nobre, 2023). Although research on external and internal selective attention points to similar underlying mechanisms, a crucial finding has emerged that must be taken into account when developing models of WM prioritization. Both behavioral and oscillatory evidence indicate that sustained internal attention is not required to produce the retro-cue benefit (Macedo-Pascual et al., 2023; Myers et al., 2017; Poch et al., 2017). Once a retro-cue is fully processed, attention can shift away from the cued item to another task (Gao et al., 2022; Makovski & Pertzov, 2015) or WM representation (Rerko et al., 2014; Souza & Oberauer, 2016), as evidenced by the absence of behavioral costs for the retro-cued item and the lack of sustained neural activity related to spatial attention (Myers et al., 2018; van Ede et al., 2017; van Ede & Nobre, 2023). These findings, along with the observation of brain preparatory motor activity following retro-cues (Chatham et al., 2014; Schneider, 2017), have led to the proposal of a two-step WM prioritization model (van Ede, 2017). According to this model, once the sensory properties of the cued item are selected, the WM representation is transformed into an action-oriented format (van Ede 2017), and sustained attention is no longer necessary. This prioritization approach aligns with WM models that propose three distinct representational states in WM (Oberauer, 2013): (1) the activated portion of long-term memory (LTM), (2) the region of direct access, and (3) the focus of attention. Based on the presented action-oriented WM framework, prioritized representations would be transferred to the focus of attention by reformatting them into an action-oriented code. In the current landscape, there are still only a limited number of laboratory tasks designed to investigate the role of prospective action encoding alongside detailed visual information. To date, most studies have investigated WM-action interactions in the context of eye movements, which mainly relate to spatial locations, or simple manual actions like button presses (Olivers & Roelfsema, 2020), dial rotations (van Ede et al., 2019), joystick manipulations or touchscreen taps (Trentin et al., 2023) that do not require full-body movements. While executing a saccade or a simple manual action, such as pressing a button, may involve minimal effort in the laboratory, directing an action toward a target in a natural context usually requires coordinating head, arm, and other body movements, as well as retaining detailed visuo-spatial information necessary for movement planning in real-world environments (Draschkow et al., 2021). Consequently, from an ecological WM–action perspective, it is necessary to adapt WM paradigms, introducing more complex and coordinated body movements, to account for the real demands of action in everyday WM tasks (Fanuel et al., 2020; Kourtesis et al., 2025). Virtual Reality (VR) enables the assessment of working memory in ecologically valid and standardized environments, while overcoming limitations associated to measuring natural behavior in the laboratory (Fooken et al., 2023; Kourtesis et al., 2025; Mancuso et al., 2024). VR enables the creation of immersive, interactive, and controllable three-dimensional environments, in which participants can naturally explore, manipulate, and integrate visual and motor information (Kourtesis et al., 2020). Research using VR has demonstrated a strong convergence with traditional neuropsychological assessments (Kourtesis et al., 2020; Mancuso et al., 2024), establishing it as an efective tool for engaging and evaluating cognitive processes (Chawoush et al., 2023; Draschkow et al., 2022; Kourtesis et al., 2025). Moreover, VR provides a more ecologically valid representation of real-world cognitive demands and ensures higher data reliability (Kourtesis & MacPherson, 2020; Mancuso et al., 2024). Although in recent years, interest in using VR to study memory processes has increased (Mancuso et al., 2024) only few studies have specifically applied this innovative technology to exploring the mechanisms of WM (Draschkow et al., 2022; Fanuel et al., 2020). In this study, we aimed to gain a better understanding of action-oriented WM representations by exploring how the nature and demands of actions influence maintenance and prioritization processes in WM. Building on the discussion above, it is plausible that WM prioritization could be influenced by the complexity of action plans (Draschkow et al., 2021; Kourtesis et al., 2025). To investigate this, we designed a WM VR experiment to manipulate the action plan linked to WM representations. These actions could either involve a button press (laboratory response setting) or a spatial displacement of an object (naturalistic response setting). Importantly, since our focus was on WM prioritization, a cue indicating which item was more likely to be tested was presented either before the encoding array in the pre-cue condition or during the maintenance period in the retro-cue condition. Given the proposal that WM prioritization results from a recurrent feedback mechanism linking a sensory representation to a specific action, we hypothesized that the strength of this connection would be influenced by the nature and complexity of the planned and executed action. Additionally, although some authors suggest that action-sensory coupling occurs at the start of the encoding phase, this has so far only been demonstrated for a single item or sequential items, which might actually reflect the sensory-action coupling underlying WM prioritization. In our design, the cue was presented either before encoding (pre-cue), such that the encoding and selection of one out of four items occurred simultaneously, or during the maintenance period (retro-cue), such that the encoding of all four items and the subsequent selection of one of them were dissociated in time. Rather than focusing exclusively on the well-established performance differences between pre-cue and retro-cue paradigms, the critical contrast in the present study concerns the cue-validity cost associated with unattended items. If working memory representations were encoded in a dual format from the outset, even when multiple items are presented simultaneously, then action complexity should confer a comparable behavioral advantage to unattended representations in the retro-cue condition, where selection occurs after encoding. By contrast, such an effect would not be expected in the pre-cue condition, in which unattended items are not anticipated to benefit from action-related modulation. Under this account, one would therefore predict a three-way interaction between Response Modality, Cue Validity, and Cue Timing. Methods Participants Eighty volunteers (sixty one females) aged 18–35 years (M = 23.90; SD = 4.12) participated in the study in exchange for monetary compensation. All participants were right-handed, with normal or corrected vision, without psychiatric or neurological disorders, and were not under any pharmacological treatment that caused mnemic disturbances. An approximate sample size was calculated a priori using G*Power 3.1.9.7 software (Faul et al., 2007 ). Although the primary analyses were conducted using generalized linear mixed-effects models (GLMMs), the ANOVA provides a reasonable approximation for estimating the required number of participants, as the fixed effects in both designs are conceptually comparable. In order to achieve a statistical power of 0.8 and to detect a mean effect size of 0.25, the necessary sample size was determined to be 82 participants. Each participant signed an informed consent form, specifying the characteristics and procedures of the study, in accordance with the Helsinki declaration (1991). All experimental procedures were approved by a local ethics committee. Materials and Apparatus Eleven models of abstract 3D objects were acquired from the Sketchfab platform ( https://sketchfab.com/ ) for the task. All objects were the same color and size (Fig. 1 B) and were aligned perpendicular to the plane, without any angular rotation. The experimental scenario was built from an open-access 3D model imported from the Sketchfab platform. The environment consisted of a 3D showroom model of rectangular configuration with brick walls, wooden floor, central table and overhead lighting (Fig. 1 C). This environment was selected for its quality and realism, to facilitate a sense of presence for the participants. The size of the elements and the lighting of the room were modified to adapt the model to the needs of the experiment and to improve the perception of realism and immersion. A 3x3 grid with nine white squares, on which the task objects were presented, was placed over the central table of the room. In addition, a black 3D panel was placed on the front wall of the room to display various items of information about the experiment. All modifications to the 3D environment were made with Vizard 7 inspector (Worldviz, 2023). The virtual reality environment and scripts were programmed and reproduced in Vizard 7 (Worldviz, 2023), a Python-based software (Python v. 3.13.2; Python Software Foundation, https://www.python.org/ ). This software and all scripts were run on a high-performance MSI gaming computer equipped with a 12th Gen Intel(R) Core (TM) i7-12700KF (3.61 GHz), Windows 11 Pro (64-bit) operating system, 32 GB of RAM, and an NVIDIA GeForce RTX 3070 graphics card. In this study, we employed the full HTC Vive Pro PC virtual reality system. Participants were equipped with the head-mounted display (HMD) of this system for immersion in the virtual world and with two HTC Vive Pro controllers for executing and recording their responses. The HMD had two OLED screens with a resolution of 2880 × 1600 pixels (1440 × 1600 per eye), a refresh rate of 90 Hz, and a field of view of 110 degrees vertically and 100° horizontally. The automatic eye tracking calibration interface was used in this system, allowing the interocular distance between participants to be adjusted and ensuring the headset was positioned correctly. In turn, SteamVR's Motion Smoothing system for HTC Vive Pro was disabled in order to guarantee a constant frame refresh rate during the task. The positions of the headset and controllers were tracked with submillimeter accuracy using infrared pulses projected by four Lighthouse base stations (each located in a corner of the room), captured by 32 sensors in the HMD and 24 in each controller. To interact with the experimental task, participants used the trigger button on each of the wireless controllers, activating it with their index finger. With these buttons, participants could give a Yes or No response (by pressing the trigger on the right or left controller, respectively) or manipulate the object by holding down the button to pick up the object and releasing it to release it. Experimental Task The experiment employed a 2 × 2 × 2 mixed factorial design. Cue Timing (Pre-cue vs. Retro-cue) was manipulated between participants. Response Modality (Button Press vs. Hand Movement) was administered in counterbalanced blocks within participants, and Cue Validity (Valid vs. Invalid) was manipulated on a trial-by-trial basis. At the beginning of each block, the response modality was indicated by projecting an image of an eye (Button Press) or a hand grasping an object (Hand Movement) on the front wall of the virtual environment. After the image disappeared, a three-second countdown signaled the start of the block. Participants completed a training phase consisting of 20 practice trials (10 per response modality), during which feedback (correct/incorrect) and time-out warnings were provided. Feedback was not presented during the experimental phase. The experimental session consisted of six blocks of 36 trials each (three Button Press and three Hand Movement blocks), presented in randomized order. Short breaks of up to five minutes were allowed between blocks if needed. The experimental task was a spatial cueing paradigm implemented in two conditions, pre-cue and retro-cue, as illustrated in Fig. 1 A. Each trial began with the presentation of a 3 × 3 grid (nine squares) displayed on the table for 500 ms. In both conditions, four objects drawn from a set of eleven were presented, each occupying a different grid location. Participants were instructed to encode both the identity and the spatial location of each object. Object identity and spatial positions were randomized across trials. A spatial memory cue, consisting of one grid square turning green, was presented for 250 ms and indicated the item that was most likely to be tested. Participants were informed that the cue was valid on 70% of trials and invalid on 30% of trials, with trial types randomly distributed. In the pre-cue condition, the cue appeared before stimulus encoding. After the initial grid display (500 ms), the cue was presented for 250 ms, followed by the presentation of the four objects for 1000 ms. The objects then disappeared and were followed by a retention interval of 2000 ms. Subsequently, the memory probe was presented for a maximum of 3000 ms or until a response was made. After the response, the grid disappeared for a 1000 ms intertrial interval before the onset of the next trial. In the retro-cue condition, stimulus encoding preceded cue presentation. After the initial grid display (500 ms), the four objects were presented for 2000 ms, followed by a first retention interval of 750 ms. The retro-cue was then presented for 250 ms, followed by a second retention interval of 2000 ms. The memory probe was subsequently presented for a maximum of 3000 ms or until a response was made. After the response, the grid disappeared during the intertrial interval before the next trial began. At test, a previously occupied location was highlighted in black and one of the four encoded objects was displayed. Participants judged whether the object matched the item originally presented at that location. Response modality depended on the block condition. In the Button Press condition, the object appeared on the highlighted square and participants responded YES or NO by pressing the trigger button on the right or left controller, respectively. In the Hand Movement condition, the object appeared to the right of the grid together with a tray. Participants grasped the object and placed it either on the highlighted square (match) or on the tray (non-match). In both response modalities, target-present trials occurred in 50% of trials. Statistical analyses Statistical analyses were performed using Jamovi (version 2.6), an open-source statistical software (The jamovi project, 2025 , https://www.jamovi.org ) based on the R programming language (Version 4.4.1, R Core Team, 2024). We analyzed accuracy (dependent variable) using generalized linear mixed models (Baayen et al., 2008 ) with the GAMLj module (Version 3.6.1, Gallucci, 2019 ) in Jamovi and the lme4 package in R to fit the data (Bates et al., 2018 ). Finally, to characterize the source of significant interactions, planned pairwise comparisons were conducted using Bonferroni correction. Accuracy data were analyzed using a generalized linear mixed-effects model with a binomial distribution and a logit link function (correct = 1, incorrect = 0). Fixed effects included Response Modality (Button Press vs. Hand Movement), Cue Validity (Valid vs. Invalid), Cue Timing (Pre-cue vs. Retro-cue), and their interactions. The random-effects structure included by-subject random intercepts (Var = 0.11, SD = 0.33, ICC = 0.033) and random slopes for Cue Validity (Var = 0.40, SD = 0.63) and Response Modality (Var = 0.0007, SD = 0.03) within Subject. The model specification was: Accuracy ~ Response Modality × Cue Validity × Cue Timing + (1 + Cue Validity + Response Modality | Subject ). The model converged and showed no evidence of overdispersion (χ² = 0.968). Results The accuracy levels based on the proportion of correct answers in each experiment and the type of condition and trial are shown in Table 1 . Although descriptive statistics are reported as mean accuracy proportions per condition, all inferential analyses were conducted on trial-level binary data. Table 1 Accuracy levels (correct proportion) in each condition and type of trial Hand Movement Botton press Valid Invalid Valid Invalid M SD M SD M SD M SD Pre-cue 0.88 0.07 0.65 0.12 0.85 0.09 0.64 0.11 Retro-cue 0.84 0.06 0.71 0.11 0.80 0.09 0.68 0.10 Accuracy in the memory task was analyzed using a generalized linear mixed-effects model to examine the effects of different experimental factors on working memory performance. A significant main effect of Response Modality (B = 0.146, SE = 0.040, z = 3.624, p < .001) indicated differences in accuracy between response modes. On average, accuracy was higher in the Hand Movement condition (M = 0.81, SD = 0.39) than in the Button Press condition (M = 0.78, SD = 0.41). The results also revealed a significant main effect of Cue Validity (B = 1.055, SE = 0.082, z = 12.881, p < .001), indicating better performance on valid trials (M = 0.84, SD = 0.36) compared with invalid trials (M = 0.67, SD = 0.47). The main effect of Cue Timing was not significant (B = − 0.112, SE = 0.085, z = − 1.312, p = .190), indicating no overall differences between the pre-cue and retro-cue paradigms. A significant interaction was observed between Response Modality and Cue Validity (B = 0.179, SE = 0.081, z = 2.214, p = .027), indicating that the effect of response modality on accuracy depended on cue validity. Post hoc analyses revealed that accuracy was significantly higher for the Hand Movement than for the Button Press response in valid trials (OR = 0.79, SE = 0.042, z = − 4.44, p < .001), whereas no significant difference between response modalities was observed in invalid trials (OR = 0.95, SE = 0.060, z = − 0.85, p = 1.000, Bonferroni-corrected). A significant Cue Validity × Cue Timing interaction (B = − 0.644, SE = 0.163, z = − 3.946, p < .001) indicated that pre-cues yielded higher accuracy than retro-cues in the valid condition (SE = 0.203, z = 3.30, p = .006), whereas this difference was not observed in invalid trials (SE = 0.083, z = − 2.05, p = .241, Bonferroni-corrected). The interaction between Response Modality and Cue Timing was not significant (B = 0.030, SE = 0.081, z = 0.368, p = .713), suggesting that the effect of response modality on performance was consistent across cue timing conditions. Finally, the three-way interaction among Response Modality, Cue Validity, and Cue Timing was also not statistically significant (B = − 0.125, SE = 0.162, z = − 0.770, p = .442). Figure 2 shows the estimated marginal means of accuracy derived from the generalized linear mixed-effects model for each combination of Cue Validity, Response Modality, and Cue Timing, providing a visual summary of the effects reported above. Discussion The aim of this study was to determine whether the nature and complexity of actions modulate the prioritization of sensory representations in working memory (WM), and whether such modulation differs when prioritization occurs at encoding versus during maintenance. To this end, we developed a virtual reality WM task in which we manipulated the action plan associated with each representation. Depending on the condition, participants either responded by pressing a button (a typical laboratory response) or by moving an object in space (a more naturalistic response). Additionally, since our focus was on WM prioritization, a cue indicating the item most likely to be probed was presented either before the encoding array (pre-cue condition) or during the maintenance period (retro-cue condition). This design allowed us to dissociate situations in which selection and encoding occur simultaneously from those in which multiple items are first encoded and selection takes place only after maintenance has begun, thereby providing a direct test of whether action-related modulation depends on the timing and functional state of prioritization. Among the results obtained, we observed higher memory performance when the task required executing a more complex and naturalistic action. This finding is consistent with previous work suggesting that action planning can interact with the maintenance of sensory information in working memory, potentially contributing to more functionally relevant representations (Boettcher et al., 2021 ; Olivers & Roelfsema, 2020 ; van Ede et al., 2017 , 2020 ). Rather than directly demonstrating a specific sensorimotor coupling mechanism, the present result indicates that the characteristics of the planned and executed action can modulate behavioral performance in a WM task. This modulation may reflect differences in how representations are functionally engaged or utilized when an upcoming action is more naturalistic or motorically demanding, a possibility that we further qualify when considering the interaction with cue validity below. From a functional perspective, more naturalistic actions might place different demands on the coordination between perception and action than simple laboratory responses, which may influence how task-relevant information is engaged during working memory performance. Consistent with this view, a growing body of evidence indicates that action planning and execution can modulate WM maintenance. For example, preparing eye or hand movements toward a spatial location enhances memory performance for items at the movement target (Hanning & Deubel, 2018 ; Hanning et al., 2016 ; Ohl & Rolfs, 2017 , 2018 ), and planning specific manual actions biases the maintenance of perceptual features toward those that are functionally relevant for the upcoming movement (Heuer & Schubö, 2017 ). Together, these findings suggest that the interaction between action and WM is not merely epiphenomenal but can shape how representations are selected and functionally organized depending on behavioral demands. One alternative explanation for the observed advantage of the more naturalistic response condition is that it reflects differences in the amount of encoding resources allocated to each response modality, such that increased action planning demands promote deeper perceptual encoding at the expense of the remaining representations (Bays & Husain, 2008; Cowan, 2010 ). Under this account, improved performance in valid trials in the more naturalistic condition should be accompanied by a corresponding cost for unattended items, resulting in poorer performance in invalid trials relative to the laboratory response condition. However, the Response Modality × Cue Validity interaction showed that performance differences were confined to valid trials, whereas invalid trials showed comparable accuracy across modalities. This pattern argues against an explanation based on differential allocation of encoding resources and instead points to a modulation of how prioritized representations are utilized once selected. Another alternative explanation is that the observed performance differences reflect factors related to response execution rather than memory representations, such as differences in the ease, reliability, or motor demands of the response modality itself (e.g., accidental trigger presses or controller handling). If this were the case, differences between response modalities should be observed independently of cue validity, as execution-related factors would affect both valid and invalid trials to a similar extent. However, because the divergence between response modalities emerged exclusively in valid trials, this account appears unlikely. Instead, the pattern suggests that response modality interacts with processes specifically engaged when a representation is prioritized, rather than reflecting a general response execution advantage. In addition to the effects related to response modality, the present results also replicate a robust main effect of Cue Validity, with higher accuracy for validly cued items relative to invalid trials. This pattern is well established in the literature and reflects the role of top-down attentional mechanisms in selecting and prioritizing task-relevant representations within working memory (Griffin & Nobre, 2003 ; Nobre et al., 2004 ; Gazzaley & Nobre, 2012 ; Heuer & Rolfs, 2022 ; Heuer & Schubö, 2016 , 2017 ; Heuer et al., 2017 , 2020 ; Poch et al., 2014 ; Poth, 2020 ; Souza & Oberauer, 2016 ; van Ede et al., 2020 ). Retrospective and prospective cues allow attentional resources to be directed toward the most behaviorally relevant representation, leading to enhanced accessibility, protection from interference, and improved behavioral performance. Current theoretical accounts propose that this prioritization process is driven by top-down control mechanisms that dynamically regulate the representational state of items in working memory (Oberauer, 2013 ; van Ede et al., 2017 ). Within this framework, cued representations gain privileged access to the focus of attention or to a state of heightened functional relevance, whereas non-cued items remain in a less accessible representational state. The present findings are fully consistent with this general account of attentional prioritization in working memory. Importantly, the fact that response-related effects emerged only for prioritized items can be interpreted in light of models proposing that working memory prioritization emerges from an attentional bias driven by recurrent feedback mechanisms linking a representation in WM to a specific action plan (Olivers & Roelfsema, 2020 ; van Ede, 2017; van Ede & Nobre, 2023 ; Heuer et al., 2017 ). In this context, the present results suggest that the coupling between perceptual representations and action-related processes may be modulated by the complexity and naturalness of the action once a representation has been selected. Previous studies have suggested that action-related information may be encoded early during stimulus processing, potentially in parallel with sensory representations (e.g., Heuer et al., 2017 ; Trentin et al., 2023 ; van Ede et al., 2019 ). However, this evidence has largely been obtained in paradigms involving single items or sequential displays, making it difficult to determine whether such action-related coding reflects an automatic process applied to all encoded representations, or instead emerges as a consequence of selection and prioritization. The present design allows this issue to be addressed by dissociating encoding and selection in time. In the retro-cue condition, multiple items are encoded before one of them is selected, whereas in the pre-cue condition selection and encoding coincide. If action-related coding were established automatically for all items during encoding, independent of selection, one would expect response-modality effects to also emerge for non-cued items in the retro-cue condition, resulting in a three-way interaction between Response Modality, Cue Validity, and Cue Timing. Contrary to this prediction, our data did not reveal such a three-way interaction, so there is no evidence at present to support motor coding for simultaneously presented items. Instead, the data revealed the expected main effect of Cue Timing, with better performance in the pre-cue than in the retro-cue condition, a pattern that has been widely reported in the literature (Griffin & Nobre, 2003 ; Nobre et al., 2004 ). In addition, a significant Cue Validity × Cue Timing interaction indicated that the pre-cue advantage was present for cued items but absent for non-cued items. This pattern is also consistent with representational state models of working memory (Oberauer, 2013 ; van Ede, 2017). In the retro-cue condition, the absence of advance information requires multiple items to be maintained in comparable functional states until selection occurs, whereas in the pre-cue condition early selection promotes the cued representation into a privileged state from the outset. Accordingly, non-cued items show limited sensitivity to cue timing manipulations, consistent with the absence of pre–retro differences observed for invalid trials, while cued items benefit from early prioritization. Together, these findings suggest that action-related coupling does not arise indiscriminately at the time of encoding for all representations, but rather depends on the selection and prioritization of a specific item. In conclusion, the present findings indicate that the type of action associated with a working memory representation plays an important role in how that representation is functionally prioritized, suggesting that prioritization does not yield a uniform representational benefit but depends on its coupling with action-related processes. In line with previous work showing that action planning can bias the prioritization of memory representations (e.g., Trentin et al., 2023 ), the present results extend this framework by demonstrating that the nature and complexity of the action context modulate the functional quality of prioritized representations. From this perspective, the focus of attention in working memory should not be conceptualized as a fixed representational state, but rather a flexible state whose properties are shaped by prospective behavioral demands. More broadly, these findings highlight the importance of adopting action-oriented and ecologically grounded approaches to the study of working memory, using methodological tools that capture the real demands of perception–action coupling beyond traditional laboratory paradigms (Fanuel et al., 2020 ; Kourtesis et al., 2025 ). In the present study, virtual reality provided a controlled yet ecologically valid environment for investigating these mechanisms (Kourtesis et al., 2025 ; Mancuso et al., 2024 ). Despite the still limited number of studies applying immersive technologies to working memory research (Draschkow et al., 2022 ; Fanuel et al., 2020 ), the current results contribute to growing evidence supporting VR as a suitable platform for examining how action, attention, and memory interact in more naturalistic settings, showing convergent patterns with previous behavioral work on action–memory interactions (Heuer et al., 2017 , 2020 ; Heuer & Schubö, 2017 ; van Ede et al., 2019 , 2020 ; Trentin et al., 2023 ; Hanning & Deubel, 2018 ). Declarations Funding: This work was funded by the Ministerio de Ciencia, Innovación y Universidades under grant PID2022-143111NB-I00, grant PID2021-125842NB-I00 and grant PID2024-158143NB-I00 from Ministerio de Ciencia e Innovación. Author Contributions: E..C.-P.: Software, Investigation, Formal Analysis, Writing; F.R.: Software; J.A.H.: Conceptualization, Writing, Supervision, Funding acquisition. C.P.: Conceptualization, Methodology, Writing, Supervision, Project Administration, Funding acquisition. All authors have read and agreed to the submitted version of the manuscript. Conflicts of interest/Competing interests: The authors have no relevant financial or non-financial interests to disclose. Ethics approval: This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Universidad Antonio de Nebrija (Madrid, Spain). Consent to participate: Informed consent was obtained from all individual participants included in the study. Availability of data: The datasets generated during the current study will be made available upon acceptance. Code availability: Custom code used for analyzing the datasets and for stimuli presentation during the current study are available from the corresponding author on reasonable request. References Allport, D. A. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Editor invited by journal 06 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 02 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8946771","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604007848,"identity":"1ac9a88c-afd6-4e12-a213-90051e864f4f","order_by":0,"name":"Estela Caballero-Picazo","email":"","orcid":"","institution":"Universidad Complutense de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Estela","middleName":"","lastName":"Caballero-Picazo","suffix":""},{"id":604007849,"identity":"17cb5c90-c645-4c69-865f-16911879330b","order_by":1,"name":"Francisco Rocabado","email":"","orcid":"","institution":"Universidad Nebrija","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Rocabado","suffix":""},{"id":604007850,"identity":"fe68bb9f-0fcb-4012-a5e1-527faff76691","order_by":2,"name":"José Antonio Hinojosa","email":"","orcid":"","institution":"Universidad Complutense de Madrid","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Antonio","lastName":"Hinojosa","suffix":""},{"id":604007851,"identity":"b0ef390e-ce8f-40c9-829e-8ee401eadd32","order_by":3,"name":"Claudia Poch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYJACZoYKBgYDBsYGAxK0nCFZC2MbSAuxgH9288HPhfPuyJlLH24o+MFgY09Qi8SdY8nSM7c9M7bsS2ww7GFIS2wgpMVAIseMmXfb4cQNZ4B+4WE4nEDQFoiWOYfrQVoM/zD8J+wwiJaGwwkGQC3GPAwHGAk6DOwXnmOHDXf2ALXIGCQT9gs4xHhqDsub87A/M3xTYUfYYQwSCCabAXGxg6SF+QExGkbBKBgFo2DkAQBRcTgMa7nNBAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Nebrija","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Poch","suffix":""}],"badges":[],"createdAt":"2026-02-23 11:54:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8946771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8946771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104546912,"identity":"da4d0bf9-ea64-45e3-8f6d-218c8480a03d","added_by":"auto","created_at":"2026-03-13 07:30:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1826231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design, stimuli, and virtual environment. \u003c/strong\u003e(A) Schematic illustration of the 2 × 2 × 2 factorial design, crossing Response Modality (Hand Movement vs. Button Press), Cue Timing (Pre-cue vs. Retro-cue), and Cue Validity (Valid vs. Invalid). In the Hand Movement condition, participants responded by physically moving the target object within the virtual environment, whereas in the Button Press condition responses were executed via controller button presses. In the Pre-cue condition, the spatial cue indicating the most likely target location was presented before stimulus encoding; in the Retro-cue condition, the cue was presented during the maintenance interval. Cue Validity indicates whether the probed location corresponded to the cued location (Valid) or to a non-cued location (Invalid).\u003cstrong\u003e \u003c/strong\u003e(B) Set of three-dimensional abstract objects used as memory stimuli. Objects were matched in size and color and varied only in shape.\u003cstrong\u003e \u003c/strong\u003e(C) View of the immersive virtual reality environment in which the task was performed, depicting the experimental room, central table, and display panel used for stimulus presentation and task instructions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8946771/v1/0f9662f0a2637d8e4ff154dd.png"},{"id":104546911,"identity":"60564095-6f2a-41df-9dd7-edaac5e5e37c","added_by":"auto","created_at":"2026-03-13 07:30:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234657,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted accuracy (estimated marginal means) as a function of Cue Validity (Invalid vs. Valid), Response Modality (Button press vs. Hand movement), and Cue Timing (Pre-cue vs. Retro-cue). Points represent model-based estimated marginal means derived from the generalized linear mixed-effects model, back-transformed to the probability scale. Error bars indicate 95% confidence intervals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8946771/v1/160500a093222978e21f9a93.png"},{"id":104546915,"identity":"3cdc0022-5032-453b-a3a2-3de70a47b25b","added_by":"auto","created_at":"2026-03-13 07:30:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3757259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8946771/v1/daa03758-ac75-4070-956d-dfd4fbea2cff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Naturalistic actions modulate Working Memory prioritization in immersive virtual reality","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHumans adapt to their environment by dynamically adjusting their behavior. Cognitive processes enable individuals to effectively interact and manipulate their environment by guiding behavior, and, as a result, facilitating problem solving and goal achievement. \u0026nbsp;Therefore, cognition is considered to be inherently action-oriented (Allport, 1987; Ballard et al., 1997; Engel et al., 2013; Glenberg et al., 2013; Neumann, 1987; Neumann \u0026amp; Prinz, 1990), with an increasing number of studies linking cognition and action through bidirectional influences (Boettcher et al., 2021; Heuer et al., 2017; Trentin et al., 2023; Wykowska et al., 2009). This is not surprising, since, as ambulatory beings, we need a system, prospective in nature, that allows us to select and retain visual representations that are relevant to guide our future behavior beyond the immediate moment (van Ede, 2020). Working Memory (WM) has been proposed to be the interface that links perception and action, allowing us to keep pace with the changing environmental demands (Heuer et al., 2017; Nobre, 2022; Olivers \u0026amp; Roelfsema, 2020; van Ede, 2020; van Ede et al., 2021; van Ede \u0026amp; Nobre, 2023).\u003c/p\u003e\n\u003cp\u003eWorking memory (WM) enables the temporary maintenance and manipulation of information relevant to task goals (Baddeley, 1992; Heuer et al., 2020; van Ede et al., 2019). Despite the importance that actions have in guiding what is relevant to maintain in WM for future behavior, traditionally, WM research has mainly focused on the mechanisms involved in the maintenance of sensory information, until recently overlooking its prospective and functional nature (Heuer et al., 2020; Olivers \u0026amp; Van der Stigchel, 2020). In recent years there has been an increasing interest in studying how actions influence what is encoded, prioritized or retrieved (Olivers \u0026amp; Roelfsema, 2020; van Ede, 2020). In this regard, performing eye movements and/or preparing or executing manual movements toward a spatial location (Hanning \u0026amp; Deubel, 2018; Heuer et al., 2017; Hanning et al., 2016; Heuer \u0026amp; Schub\u0026ouml;, 2017; Ohl \u0026amp; Rolfs, 2017, 2018 Trentin et al., 2023) during the maintenance period of a WM task enhances performance for the WM item that matches the location of the action target (Hanning \u0026amp; Deubel, 2018; Hanning et al., 2016; Ohl \u0026amp; Rolfs, 2017, 2018). In the same vein, Heuer and Schub\u0026ouml; (2017) showed that planning grasping movements after encoding leads to the prioritization of perceptual features of items congruent with the type of action to be executed (e.g., prioritizing size when planning a grasping movement). However, although involuntary memory facilitation was observed in that study, actions did not hold any instrumental value for the WM task, which makes it challenging to draw definitive conclusions about how WM representations support prospective actions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn natural environments, action and cognitive operations are inherently bound. In this context, some studies have directly explored how actions are encoded along with WM sensory representations (Olivers \u0026amp; Van der Stigchel, 2020; van Ede, 2020). Such research tracks the time course of motor planning within WM using EEG to monitor both the temporal dynamics of encoding of the item\u0026apos;s visual location and its associated prospective manual action (Boettcher et al., 2021; van Ede et\u0026nbsp;al., 2019; Nasrawi et al., 2023). These studies revealed early prospective action encoding alongside detailed sensory information, suggesting that dual coding could make memories more robust and more resistant to later interferences (Boettcher et al., 2021). Additional support for the functional role of action in WM comes from recent work by Trentin et al. (2023), who explored how memory prioritization is affected when an action is coupled with a WM representation. The study showed that direct planning of a grasping movement on a previously encoded item enhanced the sensory memory representation, promoting its prioritization over other equally task-relevant representations not linked to any prospective action. It has been proposed that WM prioritization emerges from an attentional bias produced by the recurrent feedback mechanisms connecting an item in WM to a specific action plan (Olivers \u0026amp; Roelfsema, 2020). This account aligns with new theorical proposals on how top-down mechanisms operate within WM to select relevant representations (van Ede, 2017). The study of top-down processes within WM has generated numerous behavioral and neuronal findings that resemble those observed for external attention. Specifically, retrospectively orienting attention to a WM representation enhances behavioral performance for the retro-cued item, and elicits brain oscillatory patterns associated with spatial attention (Poch et\u0026nbsp;al., 2014, 2017; Souza \u0026amp; Oberauer, 2016; van Ede \u0026amp; Nobre, 2023). Although research on external and internal selective attention points to similar underlying mechanisms, a crucial finding has emerged that must be taken into account when developing models of WM prioritization. Both behavioral and oscillatory evidence indicate that sustained internal attention is not required to produce the retro-cue benefit (Macedo-Pascual et\u0026nbsp;al., 2023; Myers et\u0026nbsp;al., 2017; Poch et\u0026nbsp;al., 2017). Once a retro-cue is fully processed, attention can shift away from the cued item to another task (Gao et\u0026nbsp;al., 2022; Makovski \u0026amp; Pertzov, 2015) or WM representation (Rerko et\u0026nbsp;al., 2014; Souza \u0026amp; Oberauer, 2016), as evidenced by the absence of behavioral costs for the retro-cued item and the lack of sustained neural activity related to spatial attention (Myers et\u0026nbsp;al., 2018; van Ede et\u0026nbsp;al., 2017; van Ede \u0026amp; Nobre, 2023). These findings, along with the observation of brain preparatory motor activity following retro-cues (Chatham et\u0026nbsp;al., 2014; Schneider, 2017), have led to the proposal of a two-step WM prioritization model (van Ede, 2017). According to this model, once the sensory properties of the cued item are selected, the WM representation is transformed into an action-oriented format (van Ede 2017), and sustained attention is no longer necessary. This prioritization approach aligns with WM models that propose three distinct representational states in WM (Oberauer, 2013): (1) the activated portion of long-term memory (LTM), (2) the region of direct access, and (3) the focus of attention. Based on the presented action-oriented WM framework, prioritized representations would be transferred to the focus of attention by reformatting them into an action-oriented code.\u003c/p\u003e\n\u003cp\u003eIn the current landscape, there are still only a limited number of laboratory tasks designed to investigate the role of prospective action encoding alongside detailed visual information. To date, most studies have investigated WM-action interactions in the context of eye movements, which mainly relate to spatial locations, or simple manual actions like button presses (Olivers \u0026amp; Roelfsema, 2020), dial rotations (van Ede et al., 2019), joystick manipulations or touchscreen taps (Trentin et al., 2023) that do not require full-body movements. While executing a saccade or a simple manual action, such as pressing a button, may involve minimal effort in the laboratory, directing an action toward a target in a natural context usually requires coordinating head, arm, and other body movements, as well as retaining detailed visuo-spatial information necessary for movement planning in real-world environments (Draschkow et al., 2021). Consequently, from an ecological WM\u0026ndash;action perspective, it is necessary to adapt WM paradigms, introducing more complex and coordinated body movements, to account for the real demands of action in everyday WM tasks (Fanuel et al., 2020; Kourtesis et al., 2025). Virtual Reality (VR) enables the assessment of working memory in ecologically valid and standardized environments, while overcoming limitations associated to measuring natural behavior in the laboratory (Fooken et al., 2023; Kourtesis et al., 2025; Mancuso et al., 2024). VR enables the creation of immersive, interactive, and controllable three-dimensional environments, in which participants can naturally explore, manipulate, and integrate visual and motor information (Kourtesis et al., 2020). Research using VR has demonstrated a strong convergence with traditional neuropsychological assessments (Kourtesis et al., 2020; Mancuso et al., 2024), establishing it as an efective tool for engaging and evaluating cognitive processes (Chawoush et al., 2023; Draschkow et al., 2022; Kourtesis et al., 2025). Moreover, VR provides a more ecologically valid representation of real-world cognitive demands and ensures higher data reliability (Kourtesis \u0026amp; MacPherson, 2020; Mancuso et al., 2024). Although in recent years, interest in using VR to study memory processes has increased (Mancuso et al., 2024) only few studies have specifically applied this innovative technology to exploring the mechanisms of WM (Draschkow et al., 2022; Fanuel et al., 2020).\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to gain a better understanding of action-oriented WM representations by exploring how the nature and demands of actions influence maintenance and prioritization processes in WM. Building on the discussion above, it is plausible that WM prioritization could be influenced by the complexity of action plans (Draschkow et al., 2021; Kourtesis et al., 2025). To investigate this, we designed a WM VR experiment to manipulate the action plan linked to WM representations. These actions could either involve a button press (laboratory response setting) or a spatial displacement of an object (naturalistic response setting). Importantly, since our focus was on WM prioritization, a cue indicating which item was more likely to be tested was presented either before the encoding array in the pre-cue condition or during the maintenance period in the retro-cue condition. Given the proposal that WM prioritization results from a recurrent feedback mechanism linking a sensory representation to a specific action, we hypothesized that the strength of this connection would be influenced by the nature and complexity of the planned and executed action. Additionally, although some authors suggest that action-sensory coupling occurs at the start of the encoding phase, this has so far only been demonstrated for a single item or sequential items, which might actually reflect the sensory-action coupling underlying WM prioritization. In our design, the cue was presented either before encoding (pre-cue), such that the encoding and selection of one out of four items occurred simultaneously, or during the maintenance period (retro-cue), such that the encoding of all four items and the subsequent selection of one of them were dissociated in time. Rather than focusing exclusively on the well-established performance differences between pre-cue and retro-cue paradigms, the critical contrast in the present study concerns the cue-validity cost associated with unattended items. If working memory representations were encoded in a dual format from the outset, even when multiple items are presented simultaneously, then action complexity should confer a comparable behavioral advantage to unattended representations in the retro-cue condition, where selection occurs after encoding. By contrast, such an effect would not be expected in the pre-cue condition, in which unattended items are not anticipated to benefit from action-related modulation. Under this account, one would therefore predict a three-way interaction between Response Modality, Cue Validity, and Cue Timing.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eEighty volunteers (sixty one females) aged 18\u0026ndash;35 years (M\u0026thinsp;=\u0026thinsp;23.90; SD\u0026thinsp;=\u0026thinsp;4.12) participated in the study in exchange for monetary compensation. All participants were right-handed, with normal or corrected vision, without psychiatric or neurological disorders, and were not under any pharmacological treatment that caused mnemic disturbances. An approximate sample size was calculated a priori using G*Power 3.1.9.7 software (Faul et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Although the primary analyses were conducted using generalized linear mixed-effects models (GLMMs), the ANOVA provides a reasonable approximation for estimating the required number of participants, as the fixed effects in both designs are conceptually comparable. In order to achieve a statistical power of 0.8 and to detect a mean effect size of 0.25, the necessary sample size was determined to be 82 participants. Each participant signed an informed consent form, specifying the characteristics and procedures of the study, in accordance with the Helsinki declaration (1991). All experimental procedures were approved by a local ethics committee.\u003c/p\u003e \u003cp\u003eMaterials and Apparatus\u003c/p\u003e \u003cp\u003eEleven models of abstract 3D objects were acquired from the Sketchfab platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sketchfab.com/\u003c/span\u003e\u003cspan address=\"https://sketchfab.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the task. All objects were the same color and size (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and were aligned perpendicular to the plane, without any angular rotation. The experimental scenario was built from an open-access 3D model imported from the Sketchfab platform. The environment consisted of a 3D showroom model of rectangular configuration with brick walls, wooden floor, central table and overhead lighting (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This environment was selected for its quality and realism, to facilitate a sense of presence for the participants. The size of the elements and the lighting of the room were modified to adapt the model to the needs of the experiment and to improve the perception of realism and immersion. A 3x3 grid with nine white squares, on which the task objects were presented, was placed over the central table of the room. In addition, a black 3D panel was placed on the front wall of the room to display various items of information about the experiment. All modifications to the 3D environment were made with Vizard 7 inspector (Worldviz, 2023).\u003c/p\u003e \u003cp\u003eThe virtual reality environment and scripts were programmed and reproduced in Vizard 7 (Worldviz, 2023), a Python-based software (Python v. 3.13.2; Python Software Foundation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org/\u003c/span\u003e\u003cspan address=\"https://www.python.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This software and all scripts were run on a high-performance MSI gaming computer equipped with a 12th Gen Intel(R) Core (TM) i7-12700KF (3.61 GHz), Windows 11 Pro (64-bit) operating system, 32 GB of RAM, and an NVIDIA GeForce RTX 3070 graphics card.\u003c/p\u003e \u003cp\u003eIn this study, we employed the full HTC Vive Pro PC virtual reality system. Participants were equipped with the head-mounted display (HMD) of this system for immersion in the virtual world and with two HTC Vive Pro controllers for executing and recording their responses. The HMD had two OLED screens with a resolution of 2880 \u0026times; 1600 pixels (1440 \u0026times; 1600 per eye), a refresh rate of 90 Hz, and a field of view of 110 degrees vertically and 100\u0026deg; horizontally. The automatic eye tracking calibration interface was used in this system, allowing the interocular distance between participants to be adjusted and ensuring the headset was positioned correctly. In turn, SteamVR's Motion Smoothing system for HTC Vive Pro was disabled in order to guarantee a constant frame refresh rate during the task. The positions of the headset and controllers were tracked with submillimeter accuracy using infrared pulses projected by four Lighthouse base stations (each located in a corner of the room), captured by 32 sensors in the HMD and 24 in each controller. To interact with the experimental task, participants used the trigger button on each of the wireless controllers, activating it with their index finger. With these buttons, participants could give a Yes or No response (by pressing the trigger on the right or left controller, respectively) or manipulate the object by holding down the button to pick up the object and releasing it to release it.\u003c/p\u003e \u003cp\u003eExperimental Task\u003c/p\u003e \u003cp\u003eThe experiment employed a 2 \u0026times; 2 \u0026times; 2 mixed factorial design. Cue Timing (Pre-cue vs. Retro-cue) was manipulated between participants. Response Modality (Button Press vs. Hand Movement) was administered in counterbalanced blocks within participants, and Cue Validity (Valid vs. Invalid) was manipulated on a trial-by-trial basis. At the beginning of each block, the response modality was indicated by projecting an image of an eye (Button Press) or a hand grasping an object (Hand Movement) on the front wall of the virtual environment. After the image disappeared, a three-second countdown signaled the start of the block. Participants completed a training phase consisting of 20 practice trials (10 per response modality), during which feedback (correct/incorrect) and time-out warnings were provided. Feedback was not presented during the experimental phase. The experimental session consisted of six blocks of 36 trials each (three Button Press and three Hand Movement blocks), presented in randomized order. Short breaks of up to five minutes were allowed between blocks if needed.\u003c/p\u003e \u003cp\u003eThe experimental task was a spatial cueing paradigm implemented in two conditions, pre-cue and retro-cue, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Each trial began with the presentation of a 3 \u0026times; 3 grid (nine squares) displayed on the table for 500 ms. In both conditions, four objects drawn from a set of eleven were presented, each occupying a different grid location. Participants were instructed to encode both the identity and the spatial location of each object. Object identity and spatial positions were randomized across trials. A spatial memory cue, consisting of one grid square turning green, was presented for 250 ms and indicated the item that was most likely to be tested. Participants were informed that the cue was valid on 70% of trials and invalid on 30% of trials, with trial types randomly distributed.\u003c/p\u003e \u003cp\u003eIn the pre-cue condition, the cue appeared before stimulus encoding. After the initial grid display (500 ms), the cue was presented for 250 ms, followed by the presentation of the four objects for 1000 ms. The objects then disappeared and were followed by a retention interval of 2000 ms. Subsequently, the memory probe was presented for a maximum of 3000 ms or until a response was made. After the response, the grid disappeared for a 1000 ms intertrial interval before the onset of the next trial. In the retro-cue condition, stimulus encoding preceded cue presentation. After the initial grid display (500 ms), the four objects were presented for 2000 ms, followed by a first retention interval of 750 ms. The retro-cue was then presented for 250 ms, followed by a second retention interval of 2000 ms. The memory probe was subsequently presented for a maximum of 3000 ms or until a response was made. After the response, the grid disappeared during the intertrial interval before the next trial began.\u003c/p\u003e \u003cp\u003eAt test, a previously occupied location was highlighted in black and one of the four encoded objects was displayed. Participants judged whether the object matched the item originally presented at that location. Response modality depended on the block condition. In the Button Press condition, the object appeared on the highlighted square and participants responded YES or NO by pressing the trigger button on the right or left controller, respectively. In the Hand Movement condition, the object appeared to the right of the grid together with a tray. Participants grasped the object and placed it either on the highlighted square (match) or on the tray (non-match). In both response modalities, target-present trials occurred in 50% of trials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using Jamovi (version 2.6), an open-source statistical software (The jamovi project, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org\u003c/span\u003e\u003cspan address=\"https://www.jamovi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on the R programming language (Version 4.4.1, R Core Team, 2024). We analyzed accuracy (dependent variable) using generalized linear mixed models (Baayen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) with the GAMLj module (Version 3.6.1, Gallucci, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in Jamovi and the lme4 package in R to fit the data (Bates et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, to characterize the source of significant interactions, planned pairwise comparisons were conducted using Bonferroni correction.\u003c/p\u003e \u003cp\u003eAccuracy data were analyzed using a generalized linear mixed-effects model with a binomial distribution and a logit link function (correct\u0026thinsp;=\u0026thinsp;1, incorrect\u0026thinsp;=\u0026thinsp;0). Fixed effects included Response Modality (Button Press vs. Hand Movement), Cue Validity (Valid vs. Invalid), Cue Timing (Pre-cue vs. Retro-cue), and their interactions. The random-effects structure included by-subject random intercepts (Var\u0026thinsp;=\u0026thinsp;0.11, SD\u0026thinsp;=\u0026thinsp;0.33, ICC\u0026thinsp;=\u0026thinsp;0.033) and random slopes for Cue Validity (Var\u0026thinsp;=\u0026thinsp;0.40, SD\u0026thinsp;=\u0026thinsp;0.63) and Response Modality (Var\u0026thinsp;=\u0026thinsp;0.0007, SD\u0026thinsp;=\u0026thinsp;0.03) within Subject. The model specification was: \u003cem\u003eAccuracy\u0026thinsp;~\u0026thinsp;Response Modality \u0026times; Cue Validity \u0026times; Cue Timing +\u003c/em\u003e (1\u0026thinsp;\u003cem\u003e+\u0026thinsp;Cue Validity\u0026thinsp;+\u0026thinsp;Response Modality | Subject\u003c/em\u003e). The model converged and showed no evidence of overdispersion (χ\u0026sup2; = 0.968).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe accuracy levels based on the proportion of correct answers in each experiment and the type of condition and trial are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Although descriptive statistics are reported as mean accuracy proportions per condition, all inferential analyses were conducted on trial-level binary data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy levels (correct proportion) in each condition and type of trial\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHand Movement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eBotton press\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eInvalid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eInvalid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetro-cue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccuracy in the memory task was analyzed using a generalized linear mixed-effects model to examine the effects of different experimental factors on working memory performance. A significant main effect of Response Modality (B\u0026thinsp;=\u0026thinsp;0.146, SE\u0026thinsp;=\u0026thinsp;0.040, z\u0026thinsp;=\u0026thinsp;3.624, p \u0026lt; .001) indicated differences in accuracy between response modes. On average, accuracy was higher in the Hand Movement condition (M\u0026thinsp;=\u0026thinsp;0.81, SD\u0026thinsp;=\u0026thinsp;0.39) than in the Button Press condition (M\u0026thinsp;=\u0026thinsp;0.78, SD\u0026thinsp;=\u0026thinsp;0.41). The results also revealed a significant main effect of Cue Validity (B\u0026thinsp;=\u0026thinsp;1.055, SE\u0026thinsp;=\u0026thinsp;0.082, z\u0026thinsp;=\u0026thinsp;12.881, p \u0026lt; .001), indicating better performance on valid trials (M\u0026thinsp;=\u0026thinsp;0.84, SD\u0026thinsp;=\u0026thinsp;0.36) compared with invalid trials (M\u0026thinsp;=\u0026thinsp;0.67, SD\u0026thinsp;=\u0026thinsp;0.47). The main effect of Cue Timing was not significant (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.112, SE\u0026thinsp;=\u0026thinsp;0.085, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.312, p = .190), indicating no overall differences between the pre-cue and retro-cue paradigms.\u003c/p\u003e \u003cp\u003eA significant interaction was observed between Response Modality and Cue Validity (B\u0026thinsp;=\u0026thinsp;0.179, SE\u0026thinsp;=\u0026thinsp;0.081, z\u0026thinsp;=\u0026thinsp;2.214, p = .027), indicating that the effect of response modality on accuracy depended on cue validity. Post hoc analyses revealed that accuracy was significantly higher for the Hand Movement than for the Button Press response in valid trials (OR\u0026thinsp;=\u0026thinsp;0.79, SE\u0026thinsp;=\u0026thinsp;0.042, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.44, p \u0026lt; .001), whereas no significant difference between response modalities was observed in invalid trials (OR\u0026thinsp;=\u0026thinsp;0.95, SE\u0026thinsp;=\u0026thinsp;0.060, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.85, p\u0026thinsp;=\u0026thinsp;1.000, Bonferroni-corrected).\u003c/p\u003e \u003cp\u003eA significant Cue Validity \u0026times; Cue Timing interaction (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.644, SE\u0026thinsp;=\u0026thinsp;0.163, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.946, p \u0026lt; .001) indicated that pre-cues yielded higher accuracy than retro-cues in the valid condition (SE\u0026thinsp;=\u0026thinsp;0.203, z\u0026thinsp;=\u0026thinsp;3.30, p = .006), whereas this difference was not observed in invalid trials (SE\u0026thinsp;=\u0026thinsp;0.083, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.05, p = .241, Bonferroni-corrected).\u003c/p\u003e \u003cp\u003eThe interaction between Response Modality and Cue Timing was not significant (B\u0026thinsp;=\u0026thinsp;0.030, SE\u0026thinsp;=\u0026thinsp;0.081, z\u0026thinsp;=\u0026thinsp;0.368, p = .713), suggesting that the effect of response modality on performance was consistent across cue timing conditions. Finally, the three-way interaction among Response Modality, Cue Validity, and Cue Timing was also not statistically significant (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.125, SE\u0026thinsp;=\u0026thinsp;0.162, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.770, p = .442). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the estimated marginal means of accuracy derived from the generalized linear mixed-effects model for each combination of Cue Validity, Response Modality, and Cue Timing, providing a visual summary of the effects reported above.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to determine whether the nature and complexity of actions modulate the prioritization of sensory representations in working memory (WM), and whether such modulation differs when prioritization occurs at encoding versus during maintenance. To this end, we developed a virtual reality WM task in which we manipulated the action plan associated with each representation. Depending on the condition, participants either responded by pressing a button (a typical laboratory response) or by moving an object in space (a more naturalistic response). Additionally, since our focus was on WM prioritization, a cue indicating the item most likely to be probed was presented either before the encoding array (pre-cue condition) or during the maintenance period (retro-cue condition). This design allowed us to dissociate situations in which selection and encoding occur simultaneously from those in which multiple items are first encoded and selection takes place only after maintenance has begun, thereby providing a direct test of whether action-related modulation depends on the timing and functional state of prioritization. Among the results obtained, we observed higher memory performance when the task required executing a more complex and naturalistic action. This finding is consistent with previous work suggesting that action planning can interact with the maintenance of sensory information in working memory, potentially contributing to more functionally relevant representations (Boettcher et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Olivers \u0026amp; Roelfsema, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van Ede et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Rather than directly demonstrating a specific sensorimotor coupling mechanism, the present result indicates that the characteristics of the planned and executed action can modulate behavioral performance in a WM task.\u003c/p\u003e \u003cp\u003eThis modulation may reflect differences in how representations are functionally engaged or utilized when an upcoming action is more naturalistic or motorically demanding, a possibility that we further qualify when considering the interaction with cue validity below. From a functional perspective, more naturalistic actions might place different demands on the coordination between perception and action than simple laboratory responses, which may influence how task-relevant information is engaged during working memory performance. Consistent with this view, a growing body of evidence indicates that action planning and execution can modulate WM maintenance. For example, preparing eye or hand movements toward a spatial location enhances memory performance for items at the movement target (Hanning \u0026amp; Deubel, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hanning et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ohl \u0026amp; Rolfs, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and planning specific manual actions biases the maintenance of perceptual features toward those that are functionally relevant for the upcoming movement (Heuer \u0026amp; Schub\u0026ouml;, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Together, these findings suggest that the interaction between action and WM is not merely epiphenomenal but can shape how representations are selected and functionally organized depending on behavioral demands.\u003c/p\u003e \u003cp\u003eOne alternative explanation for the observed advantage of the more naturalistic response condition is that it reflects differences in the amount of encoding resources allocated to each response modality, such that increased action planning demands promote deeper perceptual encoding at the expense of the remaining representations (Bays \u0026amp; Husain, 2008; Cowan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Under this account, improved performance in valid trials in the more naturalistic condition should be accompanied by a corresponding cost for unattended items, resulting in poorer performance in invalid trials relative to the laboratory response condition. However, the Response Modality \u0026times; Cue Validity interaction showed that performance differences were confined to valid trials, whereas invalid trials showed comparable accuracy across modalities. This pattern argues against an explanation based on differential allocation of encoding resources and instead points to a modulation of how prioritized representations are utilized once selected.\u003c/p\u003e \u003cp\u003eAnother alternative explanation is that the observed performance differences reflect factors related to response execution rather than memory representations, such as differences in the ease, reliability, or motor demands of the response modality itself (e.g., accidental trigger presses or controller handling). If this were the case, differences between response modalities should be observed independently of cue validity, as execution-related factors would affect both valid and invalid trials to a similar extent. However, because the divergence between response modalities emerged exclusively in valid trials, this account appears unlikely. Instead, the pattern suggests that response modality interacts with processes specifically engaged when a representation is prioritized, rather than reflecting a general response execution advantage.\u003c/p\u003e \u003cp\u003eIn addition to the effects related to response modality, the present results also replicate a robust main effect of Cue Validity, with higher accuracy for validly cued items relative to invalid trials. This pattern is well established in the literature and reflects the role of top-down attentional mechanisms in selecting and prioritizing task-relevant representations within working memory (Griffin \u0026amp; Nobre, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Nobre et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gazzaley \u0026amp; Nobre, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heuer \u0026amp; Rolfs, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Heuer \u0026amp; Schub\u0026ouml;, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Heuer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Poch et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Poth, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Souza \u0026amp; Oberauer, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van Ede et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Retrospective and prospective cues allow attentional resources to be directed toward the most behaviorally relevant representation, leading to enhanced accessibility, protection from interference, and improved behavioral performance. Current theoretical accounts propose that this prioritization process is driven by top-down control mechanisms that dynamically regulate the representational state of items in working memory (Oberauer, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; van Ede et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within this framework, cued representations gain privileged access to the focus of attention or to a state of heightened functional relevance, whereas non-cued items remain in a less accessible representational state. The present findings are fully consistent with this general account of attentional prioritization in working memory. Importantly, the fact that response-related effects emerged only for prioritized items can be interpreted in light of models proposing that working memory prioritization emerges from an attentional bias driven by recurrent feedback mechanisms linking a representation in WM to a specific action plan (Olivers \u0026amp; Roelfsema, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van Ede, 2017; van Ede \u0026amp; Nobre, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Heuer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this context, the present results suggest that the coupling between perceptual representations and action-related processes may be modulated by the complexity and naturalness of the action once a representation has been selected.\u003c/p\u003e \u003cp\u003ePrevious studies have suggested that action-related information may be encoded early during stimulus processing, potentially in parallel with sensory representations (e.g., Heuer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Trentin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; van Ede et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, this evidence has largely been obtained in paradigms involving single items or sequential displays, making it difficult to determine whether such action-related coding reflects an automatic process applied to all encoded representations, or instead emerges as a consequence of selection and prioritization. The present design allows this issue to be addressed by dissociating encoding and selection in time. In the retro-cue condition, multiple items are encoded before one of them is selected, whereas in the pre-cue condition selection and encoding coincide. If action-related coding were established automatically for all items during encoding, independent of selection, one would expect response-modality effects to also emerge for non-cued items in the retro-cue condition, resulting in a three-way interaction between Response Modality, Cue Validity, and Cue Timing. Contrary to this prediction, our data did not reveal such a three-way interaction, so there is no evidence at present to support motor coding for simultaneously presented items. Instead, the data revealed the expected main effect of Cue Timing, with better performance in the pre-cue than in the retro-cue condition, a pattern that has been widely reported in the literature (Griffin \u0026amp; Nobre, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Nobre et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In addition, a significant Cue Validity \u0026times; Cue Timing interaction indicated that the pre-cue advantage was present for cued items but absent for non-cued items. This pattern is also consistent with representational state models of working memory (Oberauer, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; van Ede, 2017). In the retro-cue condition, the absence of advance information requires multiple items to be maintained in comparable functional states until selection occurs, whereas in the pre-cue condition early selection promotes the cued representation into a privileged state from the outset. Accordingly, non-cued items show limited sensitivity to cue timing manipulations, consistent with the absence of pre\u0026ndash;retro differences observed for invalid trials, while cued items benefit from early prioritization. Together, these findings suggest that action-related coupling does not arise indiscriminately at the time of encoding for all representations, but rather depends on the selection and prioritization of a specific item.\u003c/p\u003e \u003cp\u003eIn conclusion, the present findings indicate that the type of action associated with a working memory representation plays an important role in how that representation is functionally prioritized, suggesting that prioritization does not yield a uniform representational benefit but depends on its coupling with action-related processes. In line with previous work showing that action planning can bias the prioritization of memory representations (e.g., Trentin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the present results extend this framework by demonstrating that the nature and complexity of the action context modulate the functional quality of prioritized representations. From this perspective, the focus of attention in working memory should not be conceptualized as a fixed representational state, but rather a flexible state whose properties are shaped by prospective behavioral demands.\u003c/p\u003e \u003cp\u003eMore broadly, these findings highlight the importance of adopting action-oriented and ecologically grounded approaches to the study of working memory, using methodological tools that capture the real demands of perception\u0026ndash;action coupling beyond traditional laboratory paradigms (Fanuel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kourtesis et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the present study, virtual reality provided a controlled yet ecologically valid environment for investigating these mechanisms (Kourtesis et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mancuso et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite the still limited number of studies applying immersive technologies to working memory research (Draschkow et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fanuel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the current results contribute to growing evidence supporting VR as a suitable platform for examining how action, attention, and memory interact in more naturalistic settings, showing convergent patterns with previous behavioral work on action\u0026ndash;memory interactions (Heuer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Heuer \u0026amp; Schub\u0026ouml;, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; van Ede et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trentin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hanning \u0026amp; Deubel, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was funded by the Ministerio de Ciencia, Innovaci\u0026oacute;n y Universidades under grant PID2022-143111NB-I00, grant PID2021-125842NB-I00 and grant PID2024-158143NB-I00 from Ministerio de Ciencia e Innovaci\u0026oacute;n.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e E..C.-P.: Software, Investigation, Formal Analysis, Writing; F.R.: Software; J.A.H.: Conceptualization, Writing, Supervision, Funding acquisition. C.P.: Conceptualization, Methodology, Writing, Supervision, Project Administration, Funding acquisition. All authors have read and agreed to the submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Universidad Antonio de Nebrija (Madrid, Spain).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data:\u003c/strong\u003e The datasets generated during the current study will be made available upon acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e Custom code used for analyzing the datasets and for stimuli presentation during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllport, D. 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Psychol. Hum. Percept. Perform.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e (6), 1755\u0026ndash;1769. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0016798\u003c/span\u003e\u003cspan address=\"10.1037/a0016798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Working memory, Action–perception coupling, Working Memory prioritization, Virtual reality, Retro-cueing, Pre-cueing","lastPublishedDoi":"10.21203/rs.3.rs-8946771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8946771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman cognition is inherently action-oriented, enabling flexible interaction with dynamic environments. Working Memory (WM) links perception and action by maintaining task-relevant representations. Although evidence indicates that action planning can influence what information is maintained in WM, traditional laboratory paradigms often rely on simplified response demands that only partially capture real-world perception\u0026ndash;action coupling. Here, we investigated whether the nature of actions modulate the prioritization of sensory representations in WM. Using a virtual reality task, participants responded either via a simple button press or by performing a more naturalistic object movement, while a spatial cue indicated the item most likely to be probed (pre-cued or retro-cued). Accuracy was analyzed using generalized linear mixed-effects models. Naturalistic actions yielded higher accuracy than button presses. Critically, this advantage was confined to validly cued items. Together, these findings suggest that the action associated with a representation plays a constitutive role in WM prioritization, such that prioritization cannot be fully characterized independently of its action context. More broadly, the study highlights immersive virtual reality as a valuable tool for investigating working memory under ecologically grounded yet experimentally controlled conditions, enabling the characterization of functional properties that would be difficult to capture with traditional laboratory paradigms.\u003c/p\u003e","manuscriptTitle":"Naturalistic actions modulate Working Memory prioritization in immersive virtual reality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 07:30:17","doi":"10.21203/rs.3.rs-8946771/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-21T06:36:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T20:38:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T11:11:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151318959168367910190790453142223951034","date":"2026-03-26T13:15:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126325218246041838975592784113859526700","date":"2026-03-22T13:55:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114242451585058170588512204320541047234","date":"2026-03-06T12:27:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T11:09:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T08:57:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T08:31:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T18:50:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-02T11:11:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76aa21ee-fa24-4b94-bf76-78eb34d9d655","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64276664,"name":"Biological sciences/Neuroscience"},{"id":64276665,"name":"Biological sciences/Psychology"},{"id":64276666,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-18T13:40:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 07:30:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8946771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8946771","identity":"rs-8946771","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}