Influence of chess expertise on visual versus spatial aspects of working memory
To investigate the influence of chess expertise on visual and spatial aspects of working memory, a 2 × 2 × 2 (Skill [Expert, Novice] × Stimuli [Chess, Non-chess] × Feature-change [Identity, Location]) mixed-model analysis of variance (ANOVA) was conducted.Footnote 1 We found significant main effects of Skill, F(1,27) = 28.17, p < .01, Stimuli, F(1,27) = 7.14, p = .01, Feature-change, F(1,27) = 68.39, p < .01. This suggests that the chess experts outperformed the novices overall in this VSWM task. Moreover, chess stimuli facilitated easier change detection than novel shapes in both experts and novices, and that across both groups of participants, Location-change was easier to detect than Identity-change. Skill was found to interact marginally with Stimuli, F(1,27) = 3.11, p = .09, with the expertise advantage exaggerated with chess stimuli. However, Skill did not interact with Feature-change, F(1,27) = 0.63, p = .43, suggesting that although the experts outperformed the novices at the Identity-change condition, the degree to which the Location-change condition was advantageous over Identity-change condition was the same in chess experts and in novices. The three-way interaction between Skill, Stimuli, and Feature-change was also significant, F(1,27) = 14.9, p < .01. A visual inspection of these data revealed that chess experts exhibited a strong advantage over novices not only in all trials with the chess stimuli, but also in the Location-change trials with non-chess stimuli, but not in the Identity-change trials with non-chess stimuli (see Fig. 2). Outside of the aforementioned interactions with Skill, the two-way interaction between the Stimuli × Feature-change interaction was also found to be significant in this analysis, F(1,27) = 16.18, p < .01, suggesting that Location-change detection was equally good for both chess and non-chess stimuli, whereas the Identity-change detection was easier for chess stimuli.
Influence of chess expertise on visual versus spatial aspects of working memory: Automatic processing versus controlled processing
In order to investigate whether these expertise advantage in VSWM varies with near- automatic processing inside the FoA versus controlled processing entailed for items outside the FoA, we conducted three separate 2 × 2 × 2 ANOVAs (Skill [Expert, Novice] × Stimuli [Chess, Non-chess] × Feature-change [Identity, Location]); one each for Setsize 1, Setsize 2–3, and Setsize 5–8. As discussed above, Setsize 4 was not considered for these individual analyses as it could not be assumed to be reliably within the working-memory capacity or outside the working-memory capacity (Basak & Verhaeghen, 2003; Todd & Marois, 2005). Full reports of each of these analyses can be found in Table 1.
For Setsize 1, the main effects of Skill, F(1,27) = 10.24, p < .01, and Feature-change, F(1,27) = 6.77, p = .01, were significant, suggesting that the chess experts outperformed the novices and that Location-change was easier to detect than Identity-change. The main effect of Stimuli was not significant (see Table 1). Interestingly, no significant interactions between Skill and other variables were observed, indicating that the chess experts outperformed novices on all four conditions for items in FoA (see Fig. 3a). These results contradict the overall findings, where chess experts did not show an advantage over novices in Identity-change of novel shapes.
For the Setsize 2–3, all main effects were significant: Skill, F(1,27) = 35.63, p < .01; Stimuli, F(1,27) = 6.65, p = .02; Feature-change, F(1,27) = 41.39, p < .01. Although Skill × Feature-change interaction was not significant, F(1,27) = .02, p = .88, Skill significantly interacted with Stimuli, F(1,27) = 5.61, p = .03, reflecting the selective expertise advantage with chess-like stimuli within working-memory capacity. The three-way Skill × Stimuli × Feature-change interaction was also significant, F(1,27) = 3.68, p = .01, showing similar patterns to that of the overall dataset (compare Fig. 3b with Fig. 2).
For Setsize 5–8, ANOVAs again revealed the significant main effects of Skill, F(1,27) = 23.09, p < .01, Stimuli, F(1,27) = 48.61, p < .01, and Feature-change, F(1,27) = 107.61, p < .01. The Skill × Stimuli interaction was not significant, F(1,27) = 3.2, p = .09. Importantly, unlike other set-sizes, the two-way Skill × Feature-change interaction was significant, F(1,27) = 12.07, p < .01. Inspection of the data (Fig. 3c) revealed that experts demonstrated a selective advantage of discriminability in Location-change trials, but only for processing outside the WM capacity. Additionally, the Skill × Stimuli × Feature-change interaction was found to be significant, F(1,27) = 18.51, p < .01. This result is similar to that of Setsize 2–3, suggesting that when encoding Setsize supersedes FoA capacity of one item, experts failed to exhibit the domain-general benefits to early processing of visual identity of novel stimuli in VSWM, although domain-general benefits to spatial processing were still observed.
Is the enhanced visuo-spatial capacity of chess experts disrupted by dual feature monitoring?
To assess the potential interaction between the attentional control processes (Selective Attention and Divided Attention) and chess experts’ advantage in processing of visuo-spatial stimuli, we next conducted a Skill [Expert, Novice] × Attention [Single, Dual] ANOVA. The main effect of Skill was significant, F(1,27) = 28.17, p < .01, but the main effect of Attention was not, F(1,27) = 2.68, p = .11. However, Skill × Attention interaction was significant, F(1,27) = 4.1, p = .05, with experts demonstrating a greater advantage over novices for Single Attention compared to Dual Attention trials (see Fig. 4).
As in the previous analyses, we conducted three Skill × Expertise ANOVAs, one each for Setsize 1, Setsize 2–3, and Setsize 5–8, in order to determine how the observed Skill × Attention interaction manifests at different levels of controlled processing. At Setsize 1, a significant main effect of skill was observed, F(1,27) = 10.24, p < .01, but neither the main effect of Attention, F(1,27) = 3.93, p = .06, nor the Skill × Attention interaction, F(1,27) <.01, p = .97, reached significance. At Setsize 2–3, both main effects [Skill F(1,27) = 35.63, p < .01; Attention F(1,27) = 14.16, p < .01] and the Skill × Attention interaction, F(1,27) = 7.24, p = .01, were significant. For Setsize 5–8, both main effects demonstrated significance [Skill F(1,27) = 23.09, p < .01; Attention F(1,27) = 14.16, p < .01], but there was no interaction between Skill and Attention, F(1,27) = 0.2, p = .66. These results demonstrate a selective advantage in chess experts for single-attention processing outside of the focus of attention but within semi-automatized processing, i.e., within working-memory capacity.
Is the enhanced visuo-spatial capacity of chess experts affected by detection of simultaneous feature changes under dual monitoring conditions?
Our earlier analysis demonstrated that experts possess a distinct advantage in processing Location-change over novices, even though both groups performed better when asked to process location changes compared to changes in identity. However, that analysis did not address the question of whether participants may be processing individual stimuli as whole objects or are selectively processing each aspect of the stimuli separately – it is plausible that differences between experts and novices in Location-change trials is not due to enhanced spatial processing in experts, but due to a fundamental difference in how experts process a visuo-spatial stimulus compared to the novices. In order to examine this in detail, we conducted a 2 × 2 × 3 (Skill [Expert, Novice] × Stimuli [Chess, Non-chess] × Change_type [Identity-change, Location-change, Both-change]) mixed-model ANOVA for the Dual Attention blocks only. Crucially, Both-change trials were included as a third level in the previously described Feature-change variable (here called “Change_type”) that had only included Identity-change and Location-change trials. Analysis of all three types of changes that is only possible in the Dual Attention condition will allow us to determine experts and novices differed in how they processed simultaneous changes in both features versus processing changes to either feature individually. All main effects were significant; Skill, F(1,27) = 15.22, p < .01; Stimuli, F(1,27) = 5.5, p = .03; and Change_type, F(2,54) = 54.93, p < .01. In terms of two-way interactions, neither interaction with Skill demonstrated significance [Skill × Stimuli, F(1,27) = 3.96, p = .06.; Skill × Change_type, F(2,54) = .04, p = .96], while the Stimuli × Change_type interaction did, F(2,54) = .04, p = .96. Finally, the three-way Skill × Stimuli × Change_type demonstrated significance, F(2,54) = 4.22, p = .02.
Post hoc comparisons, using Bonferroni corrections, for Change_type variable demonstrated that d’ for Identity-change was significantly lower than for Location-change trials (Mean Difference = -.72; p < .01) and Both-change trials (Mean Difference = -.81; p < .01), whereas performance for Location-change and Both-change trials did not significantly differ, (Mean Difference = -.1; p = .63, see Fig. 6). These results demonstrate that, across both skill groups, trials in which the identity of the stimuli changed were easier than location-change only trials. Additionally, as performance for Location-change and Both-change was nearly identical, we can conclude that performance in the Both-change trials was driven by participant attention to the location feature of the stimuli.
Chess expertise advantages in a standard visual change detection task
To test the generalizability of chess expertise advantage to a standard VSWM task, a 2 × 2 × 2 (Skill [Expert, Novice], Attention [Single Attention, Dual Attention], and Feature-change [Identity, Location]) mixed-model ANOVA was conducted on data from the Board-Absent set. We observed just a main effect of Feature-change, F(1,36) = 8.43, p = .01. Neither main effect of Skill, F(1,37) = .94, p = .34, nor its interaction with other variables [Skill × Attention, F(1,36) = .18, p = .67; Skill × Feature-change, F(1,36) = .21 p = .65] were significant.
These results from this baseline board-absent task are contrary to the results from our previous analyses, where experts demonstrated enhanced discriminability for all conditions, with the exception of identity-change trials with novel stimuli. This observed difference could be due to the lack of the 8 × 8 chess-board structure in this experiment. Fluency in binding chess stimuli to this chess-board structure could explain the relatively higher performance of chess experts on tasks that have involved randomized piece configurations, as well as performance with novel stimuli presented on such a structure. To investigate this possibility, we compared the data from the Board-Absent set with comparable trials collected from grid-present blocks using abstract stimuli, specifically those of Setsize 4. This allowed us to directly compare performance in trials in which the chessboard was present, and those for which it was absent.
Effect of presence of chess board on expertise advantage for abstract, non-chess stimuli
To investigate the effect of the chessboard display on expert visual processing, we conducted a Skill [Expert, Novice] × Board [Board-present, Board-absent] × Attention [Single Attention, Dual Attention] × Feature-change [Identity, Location] mixed-model ANOVA. This analysis revealed significant main effects of Skill, F(1,32) = 7.55, p = 0.01, Board, F(1,32) = 7.96, p = .01, and Feature-change, F(1,32) = 59.9, p < .01, as well as a significant Skill by Board interaction, F(1,32) = 4.98, p = .03, with experts demonstrating a selective advantage when the board was present (Fig. 5a). Additionally, a significant Skill by Feature-change interaction was also observed, F(1,32) = 7.17, p = .01, with experts demonstrating a selective advantage for Location-change trials, as seen in previous analyses. This advantage was limited to the presence of the 8 × 8 chess board (Fig. 5b). Finally, a significant four-way interaction between all factors was significant, F(1,32) = 4.66, p = .04. A visual inspection of the data (see Fig. 7) reveals that experts exhibited a specific advantage in terms of d’ on Single Attention Location-change trials when a board was present, highlighting the specificity of the expertise effect in this circumstance.