Neural correlates of span capacity during visual discrimination under varying cognitive demands

Abstract

This study examined the neural correlates of individual differences in span capacity during a visual discrimination task under varying cognitive demands. Thirty-six participants (ages 19–33) completed span tasks to assess cognitive capacity and were categorized into high- and low-span groups. Behavioral results showed that reaction times (RTs) increased and accuracy decreased with task difficulty, with individuals of higher span capacity exhibiting faster RTs and greater accuracy across conditions. Whole-brain analysis revealed distinct activation patterns: individuals with higher span capacity demonstrated more localized activation in anterior brain regions during complex tasks, while those with lower span capacity exhibited broader activation in posterior areas. The most demanding condition accentuated these differences, with higher-span individuals showing stronger BOLD responses in both anterior and posterior regions, whereas low-span capacity exhibited broader activation in posterior areas, suggesting more widespread neural engagement. Searchlight-based multivoxel pattern analysis further confirmed these group differences. Additionally, regression analyses, rather than the median-split approach, produced similar results. These findings reveal that neural activation patterns under cognitive load vary with individual differences in span capacity, pointing to distinct patterns of neural activity related to cognitive demands.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-17003-3.

Introduction

In everyday life, we regularly engage in visual discrimination tasks, where we differentiate between stimuli based on features such as color, shape, or motion. These tasks can vary in complexity, demanding more or less cognitive resources depending on the situation. While the general mechanisms involved in visual discrimination have been well studied, there is a growing interest in how individual differences in span capacity—the ability to maintain and manipulate information in working memory—may interact with how we process visual stimuli under different cognitive demands.

Span capacity has been shown to relate to individual differences in cognitive performance, including tasks involving attention, memory, and decision-making^1,2. However, it remains unclear how these differences manifest at the neural level, particularly during tasks that require varying degrees of cognitive effort. It is likely that individuals with higher span capacity will exhibit different patterns of brain activation than those with lower capacity, especially when performing tasks with increased difficulty. For example, individuals with higher span capacity may rely on more localized neural networks for processing complex information, while those with lower span capacity may show broader, more diffuse brain activation patterns as they recruit additional neural resources to manage the increased demands^3–7.

The prefrontal cortex, particularly regions such as the dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC), plays a key role in higher-order cognitive control and task performance under varying difficulty levels^8,9. Activation in these areas is often associated with maintaining focus, managing attention, and integrating information. As task difficulty increases, individuals with higher span capacity may tend to show more localized activation in frontal regions, while those with lower span capacity might exhibit greater activation across both frontal and posterior regions, possibly reflecting broader recruitment under high demands.

The aim of this study is to investigate the neural correlates of individual differences in span capacity during a visual discrimination task with varying cognitive demands. Participants completed span tasks adopted from Stone and Towse (2015) to assess their cognitive capacity, allowing us to categorize them into high- and low-span groups¹⁰. Using fMRI, we examined the patterns of brain activation during the visual discrimination task, which involved systematically varying task difficulty. We hypothesized that individuals with higher span capacity would show more localized activation in anterior brain regions, particularly in the DLPFC, while those with lower span capacity would display broader activation across posterior and lateral regions, especially under higher task difficulty.

To clarify the relationship between individual differences in span capacity and neural activation under cognitive load, this study examines the neural representational patterns associated with span capacity using a visual discrimination task adapted from Crittenden and Duncan (2014)¹¹. This approach provides a framework for linking individual variation in cognitive capacity with neural activation patterns.

Materials and methods

Participants

We recruited 36 young participants from southern Taiwan through advertisements on the Internet and bulletin boards. We conducted a power analysis (G*Power 3.1.9.7¹², power = 0.95, effect size f = 0.25, α = 0.05, within-between subjects’ design, correlation among repeat measures = 0.5). The analysis indicated that a sample size of 36 participants would be sufficient to detect an estimated medium effect size. All participants were right-handed and without evidence of neurological or psychiatric disorders based on self-reports. This study was reviewed and approved by the Human Research Ethics Committee at National Cheng Kung University (NCKU), Tainan, Taiwan, R.O.C., authorized by the Ministry of Education, Taiwan. All experimental procedures and the informed consent were obtained from all the participants and were approved under Approval No. NCKU HREC-E-112-120-2. All research was performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Upon completion of all experiments — including computerized span tasks conducted outside the MRI scanner and a visual discrimination task performed inside the MRI scanner — participants received compensation of 1,500 New Taiwan Dollars (NTD). Detailed demographic characteristics are presented in Table 1.

Table 1.

Demographic information of the participants.

	High Span	Low Span	paired t (p)
Age (years)	24.61 ± 3.82	23.07 ± 2.65	0.17
Education (years)	16.50 ± 1.82	15.72 ± 0.96	0.12
Complex span task performance (FTA)	120.06 ± 18.44	74.50 ± 16.01	< 0.001

Note: FTA: Full-trial accuracy.

Span tasks outside an MRI scanner

We assessed participants’ span capacity (individual differences) using computerized span tasks developed by Stone and Towse (2015) in JAVA¹³. The tasks included three complex span tasks and corresponding simple span tasks in both the verbal and visuo-spatial domains.

Verbal domain

Operation span task (complex span)

The Operation Span task was chosen as the complex span task for the verbal domain (see Supplementary Figure S1). This task involved a repetitive sequence of memory and processing components. In each trial, participants were presented with an integer to memorize and recall in its original serial position at the end of the trial. Following each memory element (the integer), there was a processing phase where participants encountered a mathematical operation, such as ‘9 + 9 = 27’. They had to determine whether the presented answer was correct. Digits and operations were generated randomly for each trial, with digits ranging from 1 to 99. Each operation had an equal 50% chance of being correct, and the types of operations (multiplication, division, addition, subtraction) each had a 25% probability, ensuring a diverse range of operation types requiring both correct and incorrect responses. Digit span (simple span). Digit span corresponded to the simple span task for Operation Span. Essentially, it was Operation Span without the processing phase. Participants only needed to remember the digits and recall them in sequence at the end of the trial.

Visuo-spatial domain

Symmetry span task (complex span)

The Symmetry Span task is a type of visuo-spatial complex span task where participants were required to recall grid locations in a 4 × 4 grid in the correct serial order (see Supplementary Figure S2). Following the presentation of each To-Be-Remembered (TBR) grid, participants engaged in a processing operation where they judged whether the presented pattern was symmetrical along the vertical axis, using the left/right arrow keys. Patterns were displayed on an 8 × 8 grid for this assessment. The recall phase began once the required number of storage-processing elements had been completed for a trial. Participants were prompted to recall by presenting them with the 4 × 4 grid, allowing them to click on the boxes in the order they remembered seeing them. Upon selection, a box turned blue, helping participants keep track of their responses. Matrix span (simple span). The matrix span task served as the simple span counterpart to the symmetry span task. Its procedure closely mirrored that of the symmetry span task, except for omitting the processing element.

Rotation span task (complex span)

The Rotation Span task was another visuo-spatial complex span task (see Supplementary Figure S3). The To-Be-Remembered (TBR) stimuli consisted of arrows characterized by two features: length (long or short) and angle of rotation (0°, 45°, 90°, 135°, 180°, 225°, 270°, or 315°). Participants were tasked with remembering these arrows presented in their correct serial order during the storage phase. In this complex span task, the processing operation involved presenting participants with a letter (F, G, or R) that could appear in its standard form or as a mirror image, and it could also be rotated at one of the 45-degree angles. Participants had to mentally rotate the image to determine whether the letter was presented normally or as a mirror image, using the left/right keys for their judgment. During the recall phase, participants were shown a 2 × 8 grid displaying all 16 possible arrows. The top row displayed all the long arrows, while the bottom row displayed all the short arrows. Participants used the mouse to select the arrows they recalled seeing in the correct sequential order. Arrow span (simple span). The processing phase was omitted in the arrow span task, which served as the memory span equivalent to the rotation span task. Consequently, the arrow span task focused on the participant’s ability to remember the arrows in their correct serial positions.

Visual discrimination tasks with difficulty manipulation in an MRI scanner

Participants engaged in a visual discrimination task featuring four distinct conditions, as outlined in Fig. 1. The task was programmed using OpenSesame¹⁴. Inside the MRI scanner, the stimulus display was projected onto a mirror affixed to the head coil. The task design was adapted from difficulty manipulations reported by Crittenden and Duncan (2014). These tasks varied in cognitive demands, ranging from simple perceptual discrimination to complex rule-based tasks¹¹.

Fig. 1.

Task conditions: In each scenario, participants were required to respond to the position of the shorter line using either four (conditions 4 L, FD, and MS) or eight (condition 8 L) alternative response buttons. Icons below each example display indicate the correct response for that display. The conditions are as follows: (a) 4 lines (4 L) condition, where participants respond to the position of the shorter line among four lines; (b) 8 lines (8 L) condition, where participants respond to the position of the shorter line among eight lines; (c) fine discrimination (FD) condition, where participants make a more precise response to the shorter line among four lines with smaller differences in length; and (d) mapping switch (MS) condition, where participants respond to the shorter line among four lines with reversed stimulus-response mapping. Arrows show the correct key presses for each target stimulus (shorter line) position.

Each trial began with a uniform gray screen in the baseline 4-line (4 L) condition. Participants were required to select the one odd line out of four based on length, considered the baseline or least demanding condition involving simple perceptual discrimination. At the start of each trial, a small fixation cross appeared at the center of the screen for 200 ms. Subsequently, four vertical lines were briefly presented (100 ms), aligned along the middle of the screen with their midpoints distributed symmetrically on either side of the fixation cross (total width 8.3° visual angle). Among these lines, three were equal in length (13.4°), while the fourth was consistently 50% shorter (6.7°). Participants indicated the position of the shorter line by pressing the corresponding key on an 8-button response box (e.g., Fig. 1, leftmost line shortest, response with the left middle finger). Responses were recorded only within the time window of the fixation cross, which persisted for 1000 ms after the lines disappeared from the display. Following a response, there was a jittered interstimulus interval of 500 to 1500 ms before the onset of the subsequent trial.

The remaining conditions exhibited similarities with some alterations as described below. In the 8 L condition, similar to the 4 L condition but with eight lines instead of four, two additional vertical lines were presented on either side of the original four lines (total display width 16.7°; see Fig. 1). This required participants to use each hand’s little and ring fingers for a response. The 8 L condition increased the perceptual load by doubling the number of lines, thereby raising the cognitive demand compared to the 4 L condition.

In the fine discrimination (FD) condition, similar to the 4 L condition, there were still four lines, but the shortest line was reduced to only 10% shorter than the other three lines (see Fig. 1). Participants had to discriminate between lines of very similar lengths, necessitating fine perceptual judgments. This condition heightened the cognitive demand further by requiring precise discrimination of line lengths, adding to the perceptual load.

In the mapping switch (MS) condition, the stimulus-response mapping was modified from the natural one to the alternative illustrated in Fig. 1. Participants were still required to discriminate between lines of very similar lengths, making fine perceptual judgments. This condition was considered the most demanding, as participants had to select the right-most odd line but press the left-most button. This complex rule introduced higher-order cognitive processing involving working memory and response inhibition. It placed significant demands on executive functions and was generally regarded as the most challenging^15,16. Participants practiced this condition until their accuracy rate reached 70% or higher.

Trials were grouped into blocks, each dedicated to one task condition. Each block displayed a schematic similar to Fig. 1 indicating the upcoming condition in the middle of the screen for 2,000 ms. Following the cue, there was a 3,800 ms pause before the onset of the first trial. Each block consisted of 8 trials, with a total duration of 18,400 milliseconds. There was a 10-second interval between blocks. To maintain task engagement, the accuracy of each block was displayed at the end.

The experiment was divided into three scanning sessions, each separated by a 30-second break. Within each session, there were 20 task blocks, comprising five blocks for each of the four conditions (4 L, 8 L, FD, and MS). The sequence of blocks was arranged in a pseudorandom order.

Behavioral data analysis

Span task performance

For each span task, we calculated their Full-Trial Accuracy (FTA) score. In the case of simple span tasks, points were awarded only when all Target-to-Be-Remembered (TBR) stimuli within a trial were correctly recalled. The score for each trial was determined by the loading of that particular trial, and the sum of scores across all trials constituted the FTA score for that task. In the case of complex span tasks, the calculation method was similar to that of simple span tasks, with the distinction that points were awarded only for trials where the response to the processing component was correct.

Grouping participants based on a median split of the FTA score

Our study employed the complex span task total scores to perform a median split, grouping participants into high-span and low-span groups based on individual differences in span task performances. This decision was based on the higher complex span scores compared to simple span scores, making them more effective for distinguishing individual differences in span capacity. However, we acknowledge that this approach may inadvertently capture differences in perceptual abilities, as evidenced by significant differences in simple span performance between groups (p <.005; see Results). Complex span tasks, which involve both storage and processing components, are known to be more sensitive measures of working memory capacity compared to simple span tasks that only require storage¹⁷. Given that our participant sample consisted of young adults, the complex span tasks were particularly effective in capturing individual differences in working memory capacity, which is critical for examining PFC activation patterns under varying cognitive demands^18,19.

Additionally, using the median split method allowed for a clear and balanced division of participants into two groups, facilitating the analysis of how these differences associate with neural activation patterns during task performance²⁰. Specifically, using a median split ensured an equal distribution of participants into high- and low-span groups, facilitating balanced and well-built statistical comparisons. This approach also mitigated potential biases that could arise from uneven group sizes, enhancing the validity and reliability of our findings²¹. By employing a median split on complex span scores, we aimed to provide a clear delineation of how varying levels of individual differences impact neural activation patterns and cognitive control processes. However, we acknowledge that treating individual differences as a continuous covariate in a regression analysis might offer greater statistical power and a more profound understanding of its relationship with PFC activation. To address this, we conducted an additional regression analysis using complex span FTA scores as a continuous covariate, confirming that the observed patterns of PFC activation remained significant (p <.001; see Figure S4 in Supplementary Information).

Visual discrimination task performance

Behavioral performance (reaction time [RT] and accuracy) was measured separately for each of the four conditions (4 L, 8 L, FD, MS). Subsequently, we conducted a one-way analysis of variance (ANOVA) on the four conditions to determine if the behavioral performance replicated previous findings reported by Duncan and colleagues¹¹. To test the hypothesis regarding whether individual differences would be associated with primary contrasts between conditions of interest in task performance, we initially compared the 8 L, FD, and MS conditions with the 4 L condition to identify any increase in reaction time (RT) and/or decrease in accuracy resulting from manipulation difficulty. Tasks with higher task demands, such as increased perceptual load in the 8 L condition, finer discrimination in the FD condition, or complex rule mapping in the MS condition, require greater working memory resources. High-span individuals typically exhibit superior cognitive control in these contexts²². In contrast, low-span individuals exhibited broader neural recruitment, which may reflect the additional engagement of brain regions under increased task demands²³.

Subsequently, we contrasted the conditions of interest. We used the 4 L condition as a baseline to evaluate how the 8 L, FD, and MS conditions demanded greater performance costs. The three contrast pairs were as follows:

8 L–4 L (Perceptual Load): The primary difference lies in the number of items to be processed. The 8 L condition has double the items of the 4 L condition, increasing perceptual load and cognitive demand.

FD-4 L (Precision of Discrimination): While both conditions involve selecting an odd line, the FD condition requires finer perceptual judgments, thereby increasing cognitive load compared to the broader discrimination required in the 4 L condition.

MS-4 L (Complexity of Rule Application): The MS condition introduces a complex rule that reverses the typical response mapping, significantly increasing cognitive demands compared to the straightforward perceptual discrimination in the 4 L condition.

We then employed mixed-design repeated-measures 2 (high- and low-span groups) x 3 (paired-contrasts: 8 L–4 L, FD-4 L, MS-4 L) ANOVAs on RT and accuracy, respectively, to examine the effects of the three contrasted conditions and determine if they interacted across two groups of individuals with high versus low span performances. Following the initial statistical testing, post hoc analyses were conducted using the Holm correction to further explore and compare the significant differences identified among the conditions.

Imaging acquisition and analysis for the visual discrimination task

The imaging data was gathered utilizing a General Electric (GE) Discovery MR750 3 Tesla scanner (General Electric Medical Systems, Milwaukee, USA) equipped with a 32-channel receive-only phased-array head coil at the Mind Research Imaging Center, National Cheng Kung University. High-resolution structural images were obtained using a fast-SPGR sequence comprising 166 axial slices (TR/TE/flip angle 7.6 ms/3.3 ms/12°; field of view (FOV) 22.4 × 22.4 cm²; matrix size 224 × 224; slice thickness 1 mm). Functional EPI images were acquired through an interleaved T2* weighted gradient-echo planar imaging (EPI) pulse sequence (TR/TE/flip angle, 2000 ms/30 ms/78°; matrix size, 64 × 64; FOV, 22 × 22 cm²; slice thickness, 3 mm; voxel size, 3.4375 × 3.4375 × 3 mm). Each run comprised 368 volumes, with the initial eight being dummy scans discarded to mitigate T1 equilibrium effects.

fMRI imaging preprocessing

Functional imaging data were analyzed using the FMRIB Software Library (FSL)²⁴ software. The analysis process comprised several specific steps at the 1 st level: Initially, preprocessing involved correcting head motion artifacts using the Motion Correction FMRIB’s Linear Image Registration Tool (MCFLIRT)^25–27. Subsequently, the brain extraction tool eliminated non-brain tissue from the preprocessed MR images (BET²⁷. The FSL Motion Outliers tool^25,27 accessible at https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLMotionOutliers, was then employed to detect outlier volumes based on frame displacement between volumes (exceeding the 75th percentile + 1.5 times the interquartile range). The results of this process were utilized to reduce the influence of those volumes in subsequent analyses. Individual brain functional images underwent registration to the high-resolution T1 structural image via linear transformation, followed by registration of the individual structural image to the standard MNI152 template via linear transformation²⁸. The first-level General Linear Model (GLM) in FEAT tool^29–31 was then established, incorporating a 9 mm full-width half-maximum (FWHM) Gaussian kernel for spatial smoothing.

Statistical analysis: fMRI blocked analyses for the visual discrimination task

A general linear model (GLM) was used to estimate parameter values reflecting the mean difference between experimental conditions of the visual discrimination task. Contrasts were performed to identify the regions recruited more for the 8 L, FD, and MS conditions relative to the 4 L condition.

The start time of each condition block’s stimulus to the endpoint of the block was captured and used to generate the onset file. The onset files for the four conditions were incorporated into the model as EVs (explanatory variables) and convolved with the double gamma hemodynamic response function. The six head motion parameters and the motion outlier data obtained in the previous step were included as covariates in the model for control.

The second-level analysis integrated data from the three runs, and the results of the three paired-contrasts (8 L–4 L, FD-4 L, MS-4 L) obtained at the first level were averaged separately.

To investigate our hypotheses regarding the high- and low-span groups, the subject-level files of the two groups were compared by averaging them separately according to the three paired-contrasts. We further examined the significant clusters for the combinations with simple main effects based on the ANOVA results of behavioral data, separately contrasting high span > low span and low span > high span.

Whole-brain univariate analysis

All group-level analyses involved computing the activation level across the whole brain region for each participant and submitting each of those to a group-level t-test, treating the participant as a random effect. We identified clusters of activity that were significant at a cluster-level rate of 0.01, using a 3.1 z-threshold to define contiguous clusters³². Subsequently, the estimated significance level of each cluster (derived from Gaussian Random Field theory) was compared with the probability threshold³³. To account for potential confounding factors, weperformed a partial regression analysis to control for gender effects between the high- and low-span groups. This step aimed to isolate the specific contributions of individual differences observed in neural activation patterns between the two groups.

fMRI preprocessing and GLM for multivoxel pattern analysis (MVPA)

The fMRI data were preprocessed using Statistical Parametric Mapping (SPM) 12^34,35 implemented in MATLAB (The MathWorks, Inc., Natick, MA). The preprocessing steps included slice-time correction and realignment to correct head motion using a rigid-body transformation³⁶. The T1 image was co-registered to the mean EPI image, and then both the T1 image and functional volumes were normalized to the MNI template. All images were resliced to a 2 × 2 × 2 mm voxel size, resulting in a data cube of 79 × 95 × 79 voxels. The onset files, marked with the start times for three runs and four conditions, were input into the GLM model. Head motion parameters obtained from realignment were included as regressors. After preprocessing, we obtained the beta maps and SPM.mat files for subsequent MVPA analysis.

MVPA

MVPA was conducted using the Decoding Toolbox (TDT, version 3.999 F) implemented in MATLAB, following standard preprocessing procedures. A whole-brain searchlight analysis was performed to identify regions whose activation patterns allowed classification between higher difficulty conditions and the 4 L condition used as the baseline³⁷. The input data for the classifier were the beta maps obtained from preprocessing and GLM analysis, normalized to MNI standard space. For every voxel in the brain, a sphere with a radius of 5 voxels centered on that voxel was used to train and test a linear support vector machine (SVM) using leave-one-run-out cross-validation within each participant^38,39. The classification accuracy of each sphere was assigned to the center voxel, resulting in a subject-level accuracy map. Accuracy maps were then entered into group-level analysis to identify regions where decoding accuracy was significantly above chance (50%). To ensure independence between training and testing phases, cross-validation was performed within each participant’s functional space. This method avoids assumptions of voxel-wise anatomical correspondence across participants^38,40.

Results

Participants

A total of 36 participants (17 males and 19 females) were recruited for the study. Their mean age was 23.8 years (age range: 19–33 years, SD = 3.34), and their mean education year was 16.11 years (range: 13–20 years, SD = 1.49). As aforementioned, participants were further classified into two groups based on their performance scores derived from their FTA on complex span tasks (See detailed analyses in the sections below).

Behavioral data for span tasks

The simple span includes digit span, matrix span, and arrow span, with their respective average scores being 31.56 (SD = 9.33), 56.47 (SD = 12.77), and 20.56 (SD = 9.50). The complex span includes operation span, symmetry span, and rotation span, with their respective average scores being 31.86 (SD = 8.62), 44.64 (SD = 15.60), and 20.78 (SD = 14.30).

We combine the scores of digit span, matrix span, and arrow span to form the simple span FTA score, and we aggregate the FTA scores of operation span, symmetry span, and rotation span to create the complex span FTA score. The average simple span FTA score was 108.58 (SD = 23.04), and the average complex span FTA score was 97.28 (SD = 28.69). Due to the greater range of the complex span FTA score compared to the simple span FTA score, complex span scores are better suited for distinguishing individual differences.

In the high-span group, the average simple span FTA score is 119.33 (SD = 19.27), and the average complex span FTA score is 120.06 (SD = 18.44).

In the low-span group, the average simple span FTA score is 97.83 (SD = 21.85), and the average complex span FTA score is 74.50 (SD = 16.01).

Behavior data for visual discrimination task

The initial analysis involved conducting a one-way repeated-measures ANOVA with four conditions.

RT

Median RTs are shown in Fig. 2A. A one-way repeated-measures ANOVA revealed a significant main effect of the difficulty condition (F(3, 105) = 220.688, p <.001, η² = 0.863). Post hoc tests, conducted with Holm correction for multiple comparisons, indicated specific differences among the conditions. 4 L condition significantly differed from 8 L (t(35) = −9.820, p <.001), FD (t(35) = −10.810, p <.001) and MS (t(35) = −25.482, p <.001). 8 L condition also significantly differed from MS (t(35) = −15.662, p <.001). There was also a significant difference between FD and MS conditions (t(35) = −14.672, p <.001). There was no significant difference between the 8 L and FD conditions. Mean median RT for the 4 L, 8 L, FD, and MS conditions were 418.66 ms, 516.11 ms, 525.94 ms, and 671.54 ms, respectively.

Fig. 2.

Boxplots for Reaction time (A; left panel) and accuracy (B; right panel) for the four conditions (4 L, 8 L, FD, and MS) across all participants. Bar = min/max.

Accuracy

The accuracy rates for the four conditions are depicted in Fig. 2B. A one-way repeated-measures ANOVA revealed a significant main effect of difficulty condition (F(3, 105) = 78.76, p <.001, η² = 0.692). Post hoc tests, conducted with Holm correction for multiple comparisons, indicated specific differences among the conditions. 4 L condition significantly differed from 8 L (t(35) = 2.57, p =.023), FD (t(35) = 4.73, p <.001) and MS (t(35) = 14.37, p <.001). 8 L condition also significantly differed from FD (t(35) = 2.16, p =.033) and MS (t(35) = 11.81, p <.001). There was also a significant difference between FD and MS conditions (t(35) = 9.64, p <.001). Mean accuracies for the 4 L, 8 L, FD, and MS conditions were 96%, 92.5%, 89.6%, and 76.3%, respectively.

The second set of analysis involved two groups of span performances and the three paired contrasts conditions

RT

Figure 3A shows the mean RTs for the three paired-contrast conditions across the two span groups. The results of a 2 × 3 mixed-design repeated-measures ANOVA revealed a significant main effect of paired-contrast conditions (F(2, 68) = 153.46, p <.001, η_p² = 0.819). Post hoc tests, conducted with Holm correction for multiple comparisons, indicated specific differences among the conditions. 8 L–4 L condition significantly differed from MS-4 L (t(35) = −15.64, p <.001). FD-4 L also significantly differed from MS-4 L (t(35) = −14.65, p <.001). There was no significant difference between the 8 L–4 L and FD-4 L conditions.

Fig. 3.

Boxplots for Reaction time (A; left panel) and accuracy (B; right panel) for the three paired-contrast conditions for the high- and low-span groups, respectively. Bar = min/max.

Although there was no significant main effect of span groups, F(1, 34) = 3.89, p =.057, η_p² = 0.103, there was a significant interaction effect between span groups and paired-contrast conditions, F(2, 68) = 5.10, p =.009, η_p² = 0.130, indicating that the relationship between group and condition varied significantly across the three contrasted conditions. Further analysis showed no significant span group effect in the paired-contrast conditions of 8 L–4 L and FD-4 L, but a significant simple main effect was found in the MS-4 L condition (F(1,102) = 12.32, p <.001)⁴⁰.

Accuracy

Figure 3B depicts the mean accuracy for the three paired-contrast conditions across the two span groups. The results of a 2 × 3 mixed-design repeated-measures ANOVA revealed a significant main effect of paired-contrast conditions (F(2, 68) = 67.98, p <.001, η_p² = 0.667). Post hoc tests, conducted with Holm correction for multiple comparisons, indicated specific differences among the paired-contrast conditions. The 8 L–4 L contrasted condition significantly differed from FD-4 L (t(35) = 2.01, p =.049) and MS-4 L (t(35) = 10.95, p <.001). Additionally, FD-4 L significantly differed from MS-4 L (t(35) = 8.95, p <.001).

There was also a significant main effect of span group (F(1, 34) = 10.40, p =.003, η_p² = 0.234). However, there was no significant interaction effect between the span groups and the three contrasted conditions, F(2, 34) = 1.14, p =.33, η_p² = 0.032.

fMRI results for visual discrimination task

Whole-brain univariate analysis of paired-contrast results

To identify brain regions sensitive to increases in different forms of difficulty, we conducted three paired contrasts comparing 8 L > 4 L, FD > 4 L, and MS > 4 L across all participants. Figure 4 shows the resulting contrast images, evaluated at a threshold of p <.01, with a 3.1 z-threshold used to define contiguous clusters³² (see Supplementary Table S1).

Fig. 4.

Mean activation of BOLD signal changes across contrasts between conditions of interest across all participants.

Each of the contrasts (see Supplementary Table S1) revealed extensive activation across the frontoparietal cortex. Parietal activations associated with the 8 L condition were more widespread and slightly more anterior than those observed for FD and MS in both hemispheres. Across all three conditions, activation was observed bilaterally around the intraparietal sulcus (IPS), involving the inferior parietal and angular gyrus regions. On the medial surface of the PFC, near the middle cingulum, all three contrasts showed regions of significant activity, with the most extensive activation observed for MS and the most restricted for 8 L. Additionally, the peak medial PFC activation for 8 L was more caudal compared to FD and MS.

Joint activation for all three paired contrasts was also observed bilaterally in the anterior insula/frontal operculum, with activation being most restricted for the 8 L > 4 L contrast. Interestingly, the analysis revealed distinct activity patterns for the three conditions in the lateral frontal lobe. Under the 8 L > 4 L contrast in the left frontal lobe, significant activation clusters were mainly concentrated around the precentral gyrus, with an additional considerable cluster in the middle frontal region (−31, 36, 27). Under the FD > 4 L contrast, an activation cluster was found near the frontal operculum and insula (32, 24, −8). For the MS > 4 L contrast, widespread activation was observed, starting from the frontal pole and extending along the ventral margin of the frontal lobe to the inferior and middle frontal regions.

Compared to the left frontal lobe, the clusters in the right frontal lobe under the 8 L > 4 L contrast were primarily around the precentral gyrus, with a larger activation cluster near the middle frontal region (36, 38, 19) than observed on the left side. Interestingly, under the MS > 4 L contrast, the pattern in the right frontal lobe closely mirrored that observed on the left side. Additionally, the significant clusters in the right frontal lobe under the FD > 4 L contrast almost overlapped with those observed under the MS > 4 L contrast.

Whole-brain univariate analysis of paired-contrast results between high vs. low span groups

Of primary interest, we compared three paired-contrast conditions (i.e., 8 L–4 L, FD-4 L, MS-4 L) between high and low groups. Based on the results of the 2-way mixed-design ANOVA, we observed that only the contrast of MS-4 L exhibited significant group differences, but not the other contrasts. Therefore, we reported the results for the MS-4 L contrast condition as follows (see Fig. 5).

Fig. 5.

In the whole-brain analysis comparing high-span and low-span groups in the MS-4 L contrast condition, red clusters indicate regions where BOLD responses were greater in the low-span group than in the high-span group, while green clusters represent regions where BOLD responses were greater in the high-span group than in the low-span group.

In the MS > 4 L contrast, Clusters associated with the higher span group predominantly showed more localized activity in anterior cortical regions, whereas clusters in the low-span group were broader and more posterior (see Fig. 5). The high-span group exhibited stronger BOLD responses in the left orbital frontal gyrus (−20, 30, −26; t(34) = 11.1; p <.001), left posterior cingulate (−8, −46, 0; t(34) = 5.35; p <.001), and right superior frontal gyrus (2, 22, 66; t(34) = 6.37; p =.011). Conversely, clusters where the low-span group > high-span group (see Fig. 5) were primarily located in more posterior regions such as the middle frontal and precentral areas. The low span group exhibited stronger BOLD responses in the right middle frontal gyrus (50, 44, 26; t(34) = 11.7; p <.001), left angular gyrus (−34, −64, 28; t(34) = 6.32; p <.001), and right inferior temporal gyrus (60, −48, −24; t(34) = 4.56; p =.024).

MVPA searchlight whole-brain analysis results between high- and low-span groups

The MVPA searchlight whole-brain analysis revealed significant brain regions where pattern discriminability distinguished task conditions (MS vs. 4 L) between high- and low-span groups. The low-span group exhibited widespread discriminative patterns across both anterior and posterior brain regions, consistent with a more distributed neural representation. In contrast, the high-span group showed more localized pattern separability, primarily in anterior regions, reflecting more focused neural engagement. Notably, in the MS–4 L comparison, the high-span group displayed concentrated and lateralized discriminative information, while the low-span group maintained a broader spatial distribution of separability extending into posterior regions. These findings highlight the distinct neural patterns associated with individual differences in span, with the high-span group demonstrating peak classification accuracy of 70.37% in anterior regions during the MS condition. This suggests differences in the spatial distribution of voxel-wise pattern separability associated with span capacity, potentially reflecting group-level variation in how task-relevant information is represented across these regions.

Discussion

This study specifically utilized span tasks to operationalize individual differences because span tasks provide a sensitive and direct measure of measure of an individual’s cognitive processing abilities. Span tasks effectively capture variations in individuals’ abilities to manage cognitive load and attention demands, which are crucial for understanding performance differences under task manipulations of varying complexity. These observations showed that participants with high span outperformed those with low span across all conditions, with particularly marked differences in complex tasks requiring rule-based stimulus-response mappings. Functional imaging revealed that high-span individuals were associated with more focused and localized patterns of activation in anterior PFC regions, particularly during tasks with increased cognitive demands. In contrast, low-span individuals showed broader and more diffuse activation across PFC regions, potentially indicating a distributed engagement of neural activity. These observations underscore how individual differences in span capacity modulate both behavioral performance and PFC activation patterns, particularly under increasing task demands.

The current behavioral results showed that RTs increased, and accuracy decreased across different levels of task difficulty, consistent with previous findings reported by Crittenden and Duncan (2014)¹¹. Specifically, the most challenging condition, the mapping switch (MS) condition, elicited the longest RTs and lowest accuracy, followed by the fine discrimination (FD) and eight-line (8 L) conditions, relative to the baseline four-line (4 L) condition. These results aligned with studies on cognitive control and working memory^1,22. For instance, Engle (2002) demonstrated that tasks requiring greater cognitive control and working memory resources typically result in longer RTs and lower accuracy, particularly under high cognitive load (e.g., FD-4 L and MS-4 L)¹. While the 4 L, 8 L, and FD conditions incrementally increase in perceptual and attentional demands, the MS condition introduces a qualitatively different type of challenge that significantly elevates the cognitive load. Specifically, the progression from 4 L to MS can be seen as a shift from perceptual loads to cognitive control. For perceptual loads, increasing the number of items from four to eight raises the perceptual load and attentional demands from 4 L to 8 L conditions. Requiring fine discrimination further enhances perceptual difficulty, necessitating more precise and focused attention from 8 L to FD conditions. The shift from FD to MS introduces reverse stimulus-response mapping, adding a layer of cognitive control, response inhibition, and working memory load. This aligns with Kane and Engle’s (2002) findings that individuals with higher span manages cognitive demands more efficiently, leading to faster RTs and higher accuracy²². These results are consistent with studies indicating that individuals with higher span perform better on tasks requiring greater cognitive control and complex problem-solving abilities^1,18. Our results resonate with the work of Conway, Kane, and Engle (2003), who found that individuals with higher span score is associated with superior performance in tasks requiring complex cognitive processes⁴¹. Overall, the gradient of performance from the 8 L–4 L to the MS-4 L condition also reflects the increasing cognitive demands of the tasks, where more difficult tasks necessitate greater engagement of anterior PFC regions involved in abstract reasoning and complex control processes^42–47.

The current fMRI results replicated those reported by Crittenden and Duncan (2014)¹¹. Significant activations were observed in the bilateral DLPFC in the MS-4 L contrast condition, which was associated with the largest increase in RT values. In the FD-4 L contrast condition, significant activation was observed in the right prefrontal cortex. This is consistent with research suggesting that memory span differences are more pronounced during tasks that require higher cognitive demands⁴⁸. In the more challenging contrasts of the FD and MS conditions compared to the 4 L condition, significant activations were observed in the anterior prefrontal cortex (APFC), with many overlapping regions between them. These findings reinforce the notion that high-span individuals show more focused PFC engagement to manage task complexity, while low-span individuals exhibit broader activation patterns, which may be interpreted as a wider engagement of brain regions in response to increased cognitive demands, aligning with prior research on task difficulty and cognitive load^22,49.

The whole-brain analysis indicated differential activation patterns between high-span and low-span groups, providing evidence for the interaction between span groups and the observed patterns of the PFC. In the high-span group, we observed a reliance on more anterior regions, particularly the frontal pole and medial prefrontal cortex. This finding is consistent with aspects of both the multiple-demand and, to some extent, hierarchical models of cognitive control^43,44,50 which posit that higher cognitive demands involve anterior PFC regions capable of handling abstract and complex cognitive functions. Specific regions with stronger BOLD responses in the high-span group included the left orbital frontal gyrus and right superior frontal gyrus. Conversely, the low-span group exhibited broader network activation, particularly in more posterior regions, such as the middle frontal and precentral areas. These findings align with theories suggesting that individuals with lower span recruit broader and more posterior PFC regions to manage increased task complexity.

The differential activation patterns and performance metrics observed between high and low-span groups underscore the importance of considering individual differences in span task performances when examining cognitive performance and neural mechanisms. These findings contribute to our understanding of how individual differences relate to task performance and patterns of PFC activation under varying cognitive demands^51,52. Studies have suggested that individuals with high span capacity exhibit more precise neural activation, particularly in the PFC. For instance, McNab and Klingberg (2008) found that high-span individuals display focused activation in the PFC during tasks requiring complex cognitive control, correlating with better performance and fewer errors⁵³. This focused activation enables high-span individuals to allocate cognitive resources more effectively, managing complex tasks without unnecessary brain region engagement⁵⁴. This pattern supports the view that individuals with higher cognitive abilities exhibit more focused brain activation during demanding tasks⁵⁵. In our study, high-span individuals exhibited prominent anterior PFC activation during complex visual discrimination, suggesting more selective engagement of task-relevant regions. In contrast, low-span individuals showed broader PFC activation, including more posterior regions, indicating less focal neural recruitment under increased cognitive demands⁵⁶.

The MVPA results (see Fig. 6) revealed significant differences in distributed neural patterns between groups, suggesting that individual differences are associated with distinct neural representational patterns under cognitive task demands. Using a threshold based on cross-validation accuracy scores, the analysis identified brain regions where voxel-wise patterns reliably distinguished task conditions. For the low-span group, MS-4 L searchlights revealed widespread decoding clusters in the inferior and superior frontal gyri, demonstrating distributed prefrontal pattern information. Conversely, the high-span group showed highly focal classification in the frontal pole and orbitofrontal cortex, reflecting more localized encoding of task-related information within the prefrontal cortex. This pattern aligns with the observation that high-span individuals tend to exhibit more focused neural activity, potentially processing cognitive tasks with more contained activation in regions like the anterior PFC²². Notably, under the MS-4 L condition, the high-span group showed significant lateralization^57,58with pronounced activation in the APFC and frontal pole, consistent with specialized cognitive processing in these anterior brain regions^11,59. The low-span group, however, displayed more widespread activations, possibly reflecting a more diffuse distribution of task-related information across brain regions to address higher cognitive demands^23,53. These findings support the idea that span capacity influences the spatial organization of task-relevant neural representations ^61–63. Notably, lateralized activation in the high-span group during the MS condition reflects a distinct pattern of neural engagement under increased cognitive load^60–62.

Fig. 6.

Searchlight-based MVPA Results. Span, working memory capacity derives from span task performance; MVPA, multivoxel pattern analysis; 4 L, 4-letter condition; MS, mapping switch condition. (A) 72.22% classification accuracy occurred in the right inferior frontal gyrus, pars triangularis, with 71.29% in the superior frontal gyrus. (B) 68.51% classification accuracy was recorded in the frontal pole, and 70.37% in the frontal orbital cortex. These percentages reflect the MVPA’s ability to distinguish between the different task conditions based on the multivoxel patterns of brain activity within these regions.

Limitations of the study

While our findings provide valuable insights into the interaction between individual differences and the observed patterns in PFC, several limitations should be acknowledged. First, our sample size, although comparable to similar studies, may limit the generalizability of our findings. Future research should aim to replicate these results with larger and more diverse samples to strengthen the reliability of the conclusions^60,61. Second, while we employed well-established tasks to measure individual differences and task difficulty, other cognitive and neural factors not accounted for in this study may also play significant roles. Future research should incorporate additional measures, such as fluid intelligence and executive function, to provide a more comprehensive understanding of cognitive control⁶². While our primary analyses used a median split on complex span scores, these differences suggest that observed PFC activation patterns may not be exclusively attributable to individual differences.

Conclusions

In summary, our findings demonstrate that individual differences in span capacity are associated with both behavioral performance and neural activation patterns under varying task demands. High-span individuals showed more selective and anterior-focused activation, particularly under complex task conditions, while low-span individuals exhibited broader activation patterns, potentially reflecting greater recruitment of posterior regions under increased cognitive demands. These results underscore the importance of considering span capacity when modeling individual variability in cognitive control and brain function. As such, individual differences in working memory capacity should be systematically incorporated into theoretical models of executive control to better account for variability in cognitive flexibility and performance.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Z.F.Y and S.H. wrote the main manuscript text and Z.F.Y and M.H.Y collected and analyzed the data. S.H. supervised the project and secured the funding. All authors reviewed the manuscript.

Funding

This work was supported by the National Science and Technology Council (NSTC), Taiwan, for financially supporting this research (Grant Numbers: 112-2410-H-006-090-MY3, 113-2321-B-006-014, 113-2410-H-006-097-MY3, and 114-2321-B-006-015).

Data availability

Data are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.