Static Tactical Diagrams and Imagination: Differential Effects on Novice and Expert Handball Players

Introduction

In team sports such as handball, players must constantly adapt to complex and rapidly changing tactical situations (Espoz-Lazo & Hinojosa-Torres, 2025). Understanding tactical systems (sets of coordinated movements between teammates designed to exploit defensive weaknesses) requires players to mentally represent spatial and temporal relationships between players, the ball, and the opponents (Espoz-Lazo & Hinojosa-Torres, 2025; Gréhaigne & Godbout, 1995; Vilar et al., 2012). Coaches often communicate such systems through diagrams or verbal explanations. Athletes are then expected to transform these patterns into coherent mental representations that can later be recalled and executed under pressure (Ben Chikha et al., 2024). However, the comprehension and memorization of tactical plays represent a demanding cognitive process, particularly when the material is complex or abstract (Khacharem et al., 2013a). Therefore, researchers in sport pedagogy and cognitive psychology have long sought instructional methods that can optimize the mental acquisition of tactical knowledge while respecting the cognitive limits of the learner (Khacharem, 2017; Peráèek & Peráèková, 2018).

One instructional approach that has received considerable attention is the use of mental imagery, defined as the internal generation or recreation of perceptual experiences in the absence of direct sensory input (Murphy & Martin, 2002). In sport contexts, imagery allows athletes to simulate movements, sequences, or strategies, thereby strengthening the underlying cognitive representations that support performance (Frank & Schack, 2017). Traditionally, imagery has been used to enhance motor skills or confidence (Callow et al., 2001; Cumming & Ste-Marie, 2001), but a growing body of research emphasizes its relevance for tactical learning, that is, the understanding and retention of collective game patterns (Frank et al., 2018; Munroe-Chandler et al., 2005). This form of cognitive general imagery allows players to mentally rehearse complex game situations involving multiple interacting elements, including teammates, opponents, and the ball.

Within the framework of Cognitive Load Theory (CLT; Sweller, 2012, 2019), imagination is conceptualized more specifically as the mental reproduction of procedures or conceptual relations in the absence of continuous external guidance (Cooper et al., 2001; Leahy & Sweller, 2005; Sweller, 2012). In this perspective, imagination constitutes a structured generative learning activity rather than unconstrained or creative ideation. In the present study, imagination is operationalized as the guided internal dynamic rehearsal of tactical sequences depicted in static diagrams. Such processing requires learners to transform externally presented spatial configurations into internally simulated action sequences, a mechanism grounded in visuospatial mental imagery and internal visualization processes (Cohen & Hegarty, 2007). This interpretation aligns with theoretical accounts emphasizing the role of mental simulation in the construction of structured cognitive representations of action (Frank & Schack, 2017; Jeannerod, 2001).

From a cognitive load perspective, the potential benefits of imagination can be understood in terms of schema construction. CLT distinguishes among three types of cognitive load: intrinsic load, which relates to the inherent complexity of the material; extraneous load, which results from ineffective instructional design; and germane load, which refers to the cognitive resources devoted to schema construction (Paas et al., 1994; Sweller, 1994). By encouraging learners to mentally elaborate relationships among informational elements, imagination may promote germane processing and facilitate the integration of tactical structures into long-term memory (Fiorella & Mayer, 2016; Hsu et al., 2014). This phenomenon, commonly referred to as the imagination effect, predicts improved learning outcomes when learners actively generate internal representations rather than passively observe instructional material (Cooper et al., 2001).

However, the effectiveness of imagination is not universal. Numerous studies have shown that its benefits depend critically on the learner’s prior knowledge (Ginns et al., 2003; Leahy & Sweller, 2005; Mguidich et al., 2024a). Novices often struggle to integrate new information because they lack stable schemas to support the generation of mental images (Mguidich et al., 2024a, 2024b). For them, imagination can impose a high intrinsic cognitive load, leading to poorer performance compared with simpler study instructions. In contrast, experts—who possess well-developed domain-specific schemas—can exploit imagination efficiently to elaborate and automate knowledge structures (Mguidich et al., 2024a, 2024b). This shift in effectiveness across levels of expertise is referred to as the expertise-reversal effect (Kalyuga, 2007). In the context of declarative tactical learning, this effect implies that instructional techniques beneficial for novices (e.g., direct observation or study) may become redundant or even counterproductive for experts, and vice versa (Mguidich et al., 2025a).

Empirical research in sports learning has provided substantial support for this notion. Mguidich et al. (2025b) demonstrated that when basketball tactics were delivered through auditory instruction, imagination improved immediate performance among expert players but hindered performance in novices. In addition, subsequent work confirmed that the imagination effect and its reversal are moderated not only by expertise but also by the modality of instructional material. For example, Mguidich et al. (2025b) reported that experts benefited most when imagery scripts were presented aurally, whereas novices improved after a delay particularly when the scripts were presented in written form.

Taken together, these findings indicate that imagination-based learning is a multifaceted cognitive process whose efficiency depends on the interaction between instructional design, expertise, and presentation format. Despite this theoretical and empirical background, several important questions remain unresolved. Most previous studies have used verbal or auditory materials describing game plays, thereby requiring participants to construct the entire spatial configuration mentally (Mguidich et al., 2025a, 2025b). Although this approach ensures ecological relevance, it also imposes high transient information load because the elements of the tactical sequence disappear as the narration proceeds (Khacharem et al., 2020). Under such transient conditions, imagination may overburden working memory, particularly for novices (Leahy & Sweller, 2011). Conversely, when the learning material is visually stable, such as in a static diagram that remains on the screen, learners can process the elements at their own pace, revisit relations between players, and organize the information spatially before simulating the sequence (Khacharem et al., 2020). The availability of a permanent visual support may thus reduce extraneous load and transform imagination into a more accessible and effective learning strategy even for inexperienced learners.

From a spatial cognition perspective, static tactical diagrams constitute forms of external visualization that learners must transform into internal dynamic representations to achieve meaningful understanding (Cohen & Hegarty, 2007). Although experimental research has predominantly focused on dynamic or verbally supported instructional formats (Rekik et al., 2020), static visualizations remain widely used in applied coaching practice across invasion team sports. For example, in football and basketball, coaches routinely rely on tactical boards, schematic diagrams, and printed playbooks to externalize spatial-temporal relations and collective organization during briefings and debriefings (Ben Chikha et al., 2023, 2024; Vorstandlechner et al., 2017). This applied coaching practice is reflected in our own experimental paradigms, which were explicitly designed to reproduce diagram-supported tactical communication (e.g., Ben Chikha et al., 2023; 2024; Khacharem, 2017). Beyond these empirical investigations, pedagogical research on team sports has long emphasized the instructional value of schematic representations of game configurations. In particular, Gréhaigne and colleagues (Gréhaigne et al., 2005; Gréhaigne & Godbout, 1995) describe diagrams of prototypical configurations as key tools for supporting the construction of tactical knowledge through the identification, discussion, and stabilization of meaningful spatial relations. Thus, despite the dominance of dynamic formats in controlled laboratory studies, static tactical representations continue to constitute a core cognitive and communicative scaffold in coaching contexts. Yet, empirical research on how imagination interacts with such static materials remains scarce. From the standpoint of generative learning theory (Fiorella & Mayer, 2016), imagination in this context could function as an active elaboration process. By mentally animating the static representation, learners engage in selection, organization, and integration of information; three processes essential for deep understanding.

Investigating such mechanisms often requires highly controlled experimental settings in which extraneous influences can be minimized. Laboratory-based studies allow researchers to isolate specific instructional processes, such as imagination versus passive study, while controlling presentation format, exposure time, and task demands. Although such conditions inevitably simplify the complexity of real sporting environments, they provide an important first step in identifying underlying cognitive mechanisms before examining their effectiveness in more ecologically valid training contexts. The present study therefore aims to extend previous findings by examining the effects of imagination-based learning compared to a traditional study-based approach in the acquisition of representational tactical knowledge from static diagrams. Specifically, it investigates whether the effectiveness of imagination varies as a function of learners’ expertise level (novice vs. expert). Based on prior research on the imagination effect, cognitive load theory, and expertise reversal effect, two main hypotheses were formulated. Although prior studies often reported greater benefits of imagination for experts in transient verbal or auditory formats, we hypothesized that the stable visual support provided by static diagrams may alter this pattern and make imagination particularly beneficial for novices. In contrast, experts were expected to perform equally well in both conditions, reflecting efficient schema-based processing.

Methods

Statistical Analyses

The data were analyzed using mixed design ANOVAs, in which learning condition (imagination vs. study) was a within-subjects factor and expertise level (expert vs. novice) was a between-subjects factor. These analyses aimed to examine both the main effects of instructional condition and expertise, as well as their interaction effects, on the study’s dependent variables (reconstruction performance, reconstruction time, task difficulty, mental effort, and self-efficacy).

For all statistical tests, the significance threshold was set at p < .05. Partial eta squared (η_p²) values were reported to estimate effect sizes for each main and interaction effect, with benchmarks of 0.01, 0.06, and 0.14 corresponding to small, medium, and large effects, respectively. When significant interactions were identified, Bonferroni post-hoc analyses were carried out to compare groups pairwise. In addition, Cohen’s d was computed to quantify the magnitude of significant mean differences, with 0.20, 0.50, and 0.80 interpreted as small, medium, and large effects, respectively. All data analyses were performed using Statistica software.

Results

Means and standard deviations are reported in Table 1.

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Table 1.

Means (Standard Deviations) Concerning Recall Reconstruction Score and Time, Perceived difficulty, Mental Effort, and Self-Efficacy for Each Condition and Across Different Levels of Expertise

​	Study	Imagination
Novice
Recall reconstruction score (%)	41.36 (13.76)	58.84 (16.79)
Recall reconstruction time (min)	2.93 (0.19)	2.61 (0.69)
Perceived difficulty	7.33 (1.41)	6.66 (1.03)
Mental effort	7.38 (1.24)	7.78 (0.94)
Self-efficacy	4.17 (1.76)	5 (1.64)
Expert
Recall reconstruction score (%)	79.28 (9.41)	81.06 (9.71)
Recall reconstruction time (s)	2.82 (0.3)	2.48 (0.47)
Perceived difficulty	6.56 (0.7)	6.83 (0.99)
Mental effort	5.94 (0.64)	7.44 (1.20)
Self-efficacy	7.06 (1.47)	6.72 (1.18)

Discussion

The purpose of the present study was to examine the effect of imagination-based learning, compared with a traditional study-based approach, on the acquisition of representational tactical knowledge from static diagrams. Additionally, the study investigated whether this effect varied as a function of learners’ level of expertise.

The main findings showed that novices achieved significantly higher reconstruction scores in the imagination condition than in the study condition, indicating that imagination instructions improved their reconstruction performance. Contrary to much of the previous literature on imagination and tactical learning, the present findings indicated that novices encoded and recalled the tactical sequences more effectively through imagination than through traditional study (Mguidich et al., 2024a). Previous research in sport contexts, such as in basketball tactical learning, typically reported the opposite pattern, showing that imagery benefits experts more than novices, particularly when materials were presented through text or aurally (Mguidich et al., 2024b, 2025a). These results have generally been explained by the expertise-reversal effect derived from cognitive load theory (Kalyuga, 2007; Sweller, 2012, 2019), suggesting that imagination places excessive demands on novices who lack well-developed schemas to integrate new information (Mguidich et al., 2024b).

The present findings extend this theoretical perspective by indicating that the effectiveness of imagination-based learning is not determined solely by learner expertise but is critically moderated by the representational characteristics of instructional materials. One interpretation of this divergence lies in the representational properties of the instructional format. Static diagrams provide continuous visual access to spatial relations, thereby reducing the need for learners to internally reconstruct transient information (Ng et al., 2013). From a cognitive load perspective, such stability may lower extraneous load and enable imagination processes to function as constructive mechanisms that support schema formation rather than as additional sources of cognitive burden (Sweller, 1994). In this context, imagination may facilitate the organization and integration of tactical elements into coherent mental representations, especially for learners with limited prior knowledge. This interpretation aligns with generative learning theory, which emphasizes the role of active cognitive processing in promoting meaningful learning (Fiorella & Mayer, 2016).

Complementary theoretical perspectives further support this account. Research on long-term working memory suggests that the availability of stable visual structures may facilitate chunking processes, allowing learners to encode complex relational patterns more efficiently (Ericsson & Kintsch, 1995). Similarly, dual-coding theory implies that combining externally presented spatial representations with internally generated dynamic imagery may strengthen multimodal memory traces, thereby enhancing recall accuracy (Paivio, 1991). Taken together, these perspectives suggest that representational stability may transform imagination from a cognitively demanding activity into a generative mechanism that supports learning efficiency.

Subjective measures provide additional insight into the underlying mechanisms. Although novices reported lower perceived task difficulty in the imagination condition, their reported mental effort did not significantly differ between instructional modes. This pattern may reflect changes in processing fluency rather than reductions in overall cognitive resource investment. In other words, imagination may have improved learners’ appraisal of task complexity without substantially altering the cognitive demands of the task (Sweller, 2012). The absence of changes in self-efficacy further suggests that performance gains were primarily driven by cognitive rather than motivational factors (Fiorella & Mayer, 2016).

In contrast, for experts, results showed no significant difference in reconstruction performance between imagination and study conditions, and their reconstruction time, perceived task difficulty, mental effort, and self-efficacy were similarly unaffected by instructional strategy. Experts appear capable of extracting tactical structure efficiently regardless of learning mode. This pattern aligns with cognitive load theory predictions: once learners possess well-developed domain schemas, both imagination and study can activate relevant long-term memory structures with relatively low cognitive demands (Mguidich et al., 2024a, 2024b). Experts’ ability to chunk complex spatial and temporal relations into meaningful units likely reduces the marginal benefits of additional generative prompts (Ericsson & Kintsch, 1995).

Previous studies in sport imagery have often reported stronger benefits of imagination for experts, particularly when materials are presented dynamically or when performance is assessed immediately or after a delay (Mguidich et al., 2025a). The absence of such an advantage in the present study may be attributable to the static diagram format, which may have rendered both learning modes equally compatible with experts’ pre-existing schemas. In structured visual contexts, studying diagrams may spontaneously elicit imagery processes among experts, thereby limiting the additional benefits of explicit imagination instructions. The slightly shorter reconstruction times observed under imagination may nonetheless reflect enhanced retrieval fluency, even when accuracy approaches ceiling levels.

Experts also reported significantly higher self-efficacy than novices, a pattern consistently observed in tactical learning research (Mguidich et al., 2024b). This elevated self-efficacy likely reflects accumulated domain knowledge and experience, enabling experts to approach both instructional conditions with greater confidence and autonomy. Overall, these findings suggest that experts’ performance is governed by efficient schema activation and metacognitive regulation, rendering imagination-based and study-based learning similarly effective under conditions of representational stability.