Figure 2. A: Mean percentage of correct responses as a
function of object configuration (grouped, partially grouped, and
ungrouped) for the color and orientation changes (solid and dashed
lines, respectively). B: Mean percentage of correct responses as a
function of change type (color and orientation) in the partially grouped
triangle condition. Accuracies in B are plotted separately for trials on
which the probe was one of the three pacmen that gave rise to the
illusory triangle (inside), or, respectively, on which the probe was one
of the three non-grouped pacmen (outside). Error bars denote the 95%
(within-subject) confidence interval.
A subsequent analysis examined whether change detection performance was
influenced by the probe location in partially grouped displays (with
triangle groupings). Figure 2B presents the percentage of
correct responses for color and orientation changes, separately for
trials on which the probe was presented at one of the three pacman
locations that formed the illusory triangle (inside) and, respectively,
trials on which the probe appeared at one of the three other,
“non-grouped” pacmen (outside). A corresponding two-way
repeated-measures ANOVA of the accuracies, with the factors Change Type
(color, orientation) and Probe Location (inside, outside), revealed both
main effects to be significant: Change Type, F (1, 23) = 15.96,p < .001, ηp2= .41; and Probe Location, F (1, 23) = 10.09, p = .004,ηp2 = .31. Accuracies were
higher for color changes (68%) than for orientation changes (64%),
mirroring the analysis described above. In addition, the accuracies were
increased when the probe was presented inside the partially grouped
triangle (68%) as compared to an outside location (64%). The
Change-Type × Probe-Location interaction was not significant, F(1, 23) = 0.55, p = .47,ηp2 = .02,BF10 = 0.34. Thus, the behavioral results
directly replicate our previous findings (Chen et al., 2021a) and show
an object-benefit for both grouping-relevant and -irrelevant features.
Moreover, a final analysis was performed which computed an overall
estimate of VWM capacity K (Cowan, 2001) in order to determine how the
change in grouping strength across our stimulus configurations affected
the capacity estimate. Each individual’s memory capacity was computed
using Cowan’s formula: K = (H – FA) × N, where K is the memory
capacity, H is the observed hit rate, FA the false alarm rate and N the
number of (pacman) items presented. The resulting capacities for
orientation and color change trials were then combined to yield an
”overall” capacity estimate for a given configuration. Next, a one-way
repeated-measures ANOVA was performed on the mean K estimates, which
(again) revealed a reliable effect of Object Configuration, F (2,
46) = 70.97, p < .001,ηp2 = .76. The K estimates were
largest for the grouped configuration (5.5), intermediate for partially
grouped configuration (3.8), and smallest for the ungrouped
configuration (3.0; all p’ s < .001,dz s > 0.65, for the pairwise
comparisons between configurations). This shows that grouping can lead
to a substantial enhancement of the overall VWM capacity beyond the
usual capacity estimates of around 3-4 items (Luck & Vogel, 1997).
ERP data. The corresponding ERP waves at
parieto-occipital electrodes (averaged across electrodes PO3, PO4, PO7,
PO8, O1, and O2) for the different object configurations are plotted inFigure 3A . Visual inspection of the ERP waves suggests that
major differences between the different object configurations occurred
in the PPC, N1pc, N2pc, and CDA components. For analysis, we examined
these amplitude variations across conditions separately for each
component in a series of one-way repeated-measures ANOVAs with the
within-subject factor Object Configuration (ungrouped, partially
grouped, and grouped; see also Figure 3B ).
The ANOVA of the mean PPC amplitudes revealed the Object-Configuration
effect to be significant, F (1.43, 32.78) = 9.56, p =
.002, ηp2 = .29: there was a
graded difference across object configurations, with the positive
deflection being largest for the ungrouped (0.89 µV), intermediate for
partially grouped (0.72 µV), and smallest for the grouped (0.50 µV)
configurations (all p’ s < .008,dz s > 0.53, for the pairwise
comparisons between configurations).
The analysis of the N1pc also yielded a significant Configuration
effect, F (1.47, 33.83) = 5.08, p = .019,ηp2 = .18, with a larger
negativity for the grouped (-0.29 µV) as compared to the ungrouped (0.03
µV, p = .006, dz = 0.55) and partially
grouped (-0.11 µV, p = .004, dz = 0.58)
configurations, but no reliable difference between ungrouped and
partially grouped configurations (p = .12,dz = 0.25, BF10 = 0.73).
For the N2pc, the Configuration effect was again significant, F(2, 46) = 10.07, p < .001,ηp2 = .31, due to more
negative-going amplitudes for the grouped (-0.95 µV) as compared to the
ungrouped (-0.56 µV, p < .001,dz = 0.74) and partially grouped (-0.61 µV,p = .001, dz = 0.69) configurations, but
no significant difference between ungrouped and partially grouped
configurations (p = .26, dz = 0.13,BF10 = 0.38).
Finally, the analysis of the CDA amplitudes also yielded an effect of
Object Configuration, F (2, 46) = 3.57, p = .036,ηp2 = .13. As depicted inFigure 3B , the mean CDA amplitude was more negative for the
grouped (-1.26 µV) as compared to the ungrouped (-1.08 µV, p =
.01, dz = .51) and partially grouped (-1.15 µV,p = .046, dz = .36) configuration. There
was again no reliable difference between ungrouped and partially grouped
configurations (p = .16, dz = .21,BF10 = 0.56).
The result patterns of the PPC, N1pc, N2pc, and CDA thus mirror (at
least to a large extent) the pattern of behavioral performance,
evidencing an effect of Object Configuration, which was driven
particularly by the fully grouped star object. Of note, a graded
improvement in VWM performance with an increase in grouping strength
(across all three configurations) was already evident at early stages of
perceptual processing, namely, in the PPC component.
Moreover, the CDA results essentially mirrored the estimated VWM
capacity scores (see above), thus supporting the view that the CDA
corresponds to the number of effectively remembered items. In addition,
the findings are also compatible with the view that the generation of a
global shape (in Kanizsa figures) requires additional mnemonic
resources, and this increase in the mnemonic activity may likewise be
reflected in the increased negativity of the CDA.
Finally, additional correlational analyses between the individual
behavioral performance and the corresponding ERP amplitudes revealed
significant negative relationships for the PPC components in the grouped
and partially grouped configurations for orientation changes (grouped:r = -0.47, p = .01; partially grouped: r = -0.36,p = .04; see Figure 3C ), that is, the PPC amplitude
scaled with behavioral performance for the grouped (and partially
grouped) memory configurations. The correlations thus show that larger
performance benefits for the (partially) grouped memory configurations
were associated with less positive PPC amplitude deflections. No other
significant correlations between behavioral performance and ERP
components were revealed. Statistical significance of the correlation
coefficient was determined by comparing the observed correlations with
results derived from 20000 permutations of the two variables, thus
excluding the influence from any outliers in the data.