Time-frequency results and neural origins of gamma oscillations. I firstly tested the
modulation of location on gamma power lateralization (contralateral relative to ipsilateral site to the probe hemifield) at the sensor level when the participants compared the incoming probe item with the WM contents. Four contrasts were conducted: (1) grouped-shared-feature vs. one-shared-feature, (2) grouped-unshared-featured vs.
one-unshared-feature, (3) grouped-shared-feature vs. separated-feature, and (4) grouped-unshared-feature vs. separated-feature. I observed significant effects of sensors in all contrasts. Significant effects of sensors were observed over the posterior parietal sensors from 580 to 612 ms for grouped-shared-feature vs. one-shared-feature trials (corrected p
= .013), over the frontal and parietal sensors from 500 to 530 ms for grouped-unshared-feature vs. one-unshared-feature trials (corrected p = .033), over the frontal and parietal sensors from 500 to 600 ms for grouped-shared-feature vs.
separated-feature trials (corrected p = .03), and over the frontal and parietal sensors from 520 to 550 ms for grouped-unshared-feature vs. separated-feature trials (corrected p = .024).
The beamformer source analysis showed stronger neural responses for in gamma rhyme in the right parietal cortex by contrasting left hemifield with right hemifield for both shared location and unshared location trials. These results are illustrated in Figure 7.
Figure 7. Sensor space and cortical source of gamma oscillation results. (a) Scalp
topography of the difference in gamma lateralization between grouped-shared and one-shared trials. The gamma lateralization over frontal and parietal areas was observed when compared grouped-feature trials to one-feature trials in shared and unshared conditions respectively. (b) The gamma source results on grouped-shared, grouped-unshared, one-shared, and one-unshared trials respectively. The DICS beamformer analysis showed stronger gamma only in grouped-shared and grouped-unshared conditions.
Event-related fields and ROI source analyses. Finally, I tested the neural correlates of
feature coactivation effect (Saiki, 2016) in the posterior brain regions and the difference in neural activity among three two-feature conditions (i.e. grouped-shared, grouped-unshared, and separated trials) during the WM-perceptual comparison. These results are illustrated in Figure 8. The mean ERFs were extracted from the posterior sensors (see Figure 8 for an example). A 2 (time window: 250-400 ms, 400-550 ms) x 3 (two-feature condition) repeated-measures ANOVA was then conducted. There was a significant main effect of two-feature condition, F (2,34) = 11.61, p < .001, and a significant interaction between time window and condition, F (2,34) = 3.19, p = .05. This interaction arose from greater activity on separated trials relative to both grouped-shared (p = .002) and grouped-unshared (p
< .001) during the late time window (400-550 ms). However, no difference in activity was observed during the early time window. The source analysis using the LCMV beamformer showed greater neural responses in the right parietal cortex for the separated trials relative to grouped-shared and grouped-unshared trials respectively. The ROI-based analyses in the right parietal and visual ROIs confirmed these results (Figure 9).
Figure 8. Sensor-space ERF results on grouped-shared, grouped-unshared, and separated
trials. The gray bar and black bar indicated the early and late time window in the analysis.
(a) Topography of the difference between separated and grouped trials from 428 to 460 ms.
(b) The power value of each condition was extracted from the sensor in the blue square.
The activity from 400 to 550 ms in separated condition was significantly higher than those in grouped-shared and grouped-unshared conditions.
Figure 9. Source and ROI-based results of ERF. The black arrows indicate the differential
effect between separated and grouped conditions. (a) The source-level ERFs in left and right posterior parietal cortex show that there is greater activity on separated trials relative to grouped-shared and grouped-unshared trials. (b) The ERF in the bilateral visual ROI from the visual localizer task showed that there are also greater activities on separated trials relative to grouped-shared and grouped-unshared trials.
Discussion
The purpose of this study was to investigate whether the feature-bound representations can be influenced by the location during the WM-perceptual comparisons. The behavioral results showed that participants responded faster on grouped trials versus one-feature trials for both shared-location and unshared-location. The RMI test also indicated that feature binding could occur independently of the location factor. Significant gamma activity was found in the parietal cortex for feature-bound representations regardless of the locations.
Finally, the ERF data confirmed the effects of feature-bound in the posterior parietal and visual areas in both shared-location and unshared-location conditions. Together, these results provide further evidence in behavioural and neural correlates of WM-perceptual comparisons and suggest that feature-bound representations are not necessarily modulated by their locations. unshared-location trials. However, these results were inconsistent with the predictions by the object file theory (Kahneman et al., 1992; Noles, Scholl, & Mitroff, 2005). The object file account proposed that feature integration may occur when the probe was present at the
same location as the memory items. The RT RMI tests in the current study did not fully support the object file account (see Saiki, 2016 for a similar finding).
I speculate that the inconsistent results between my study and the object file account may arise from the feature types. In the current study, RMI tests showed that feature coactivation (letter and color) occurred in all two-feature conditions – not only grouped trials but also separated trials. However, Saiki (2016) reported the feature coactivation (shape and color) effect on grouped trials only. The effects of the one-feature and two-feature object were also different in location-sharing. For example, the location benefit in RT was observed on shape-match trials (Saiki, 2016) and on letter-match trials (the current study), not on color-match trials in both studies. Since previous studies have shown that WM performance may vary across stimulus types (Alvarez & Cavanagh, 2004;
Treisman & Zhang, 2006), I suggest that feature types could influence the degree of features binding during WM-perceptual comparisons. Future work should systematically manipulate the feature types and test how they affect the features binding in WM.
The discrepancy from the current findings and Saiki’s (2016) work may also result from the existence of non-target items. In the current study, I showed one probe item and a filler (i.e.
a percentage sign) at the same time in boxes in each hemifield. This manipulation allows controlling the physical inputs from both visual hemifields across all feature types.
However, the filler in the opposite hemifield may be likely to affect the allocation of attention on the target probe. Moreover, color and letter may reflect different levels of familiarity or superiority in real life (Mcclelland & Rumelhart, 1981). For example, if participants adopt a verbal strategy to perform the task, letter may cause stronger
interference with color in our case but shape may cause less interference with color. In sum, these factors may result in inconsistencies between the current results and others.
Importantly, I showed stronger lateralized gamma power with increased activity contralateral to the attended location and decreased activity contralateral to the unattended location for grouped object relative to single feature and for both shared and unshared locations. Similar modulations on lateralized gamma power were also found when I compared grouped-shared trials relative to separated trials and grouped-unshared trials relative to separated trials respectively. The results suggest that the gamma oscillations play an important role in feature integration in WM.
Studies in humans and monkeys have showed that increased activity in the gamma-band is related to attentional and mnemonic processes (Bauer, Oostenveld, Peeters, & Fries, 2006;
Brovelli, Lachaux, Kahane, & Boussaoud, 2005; Buzsáki & Wang, 2012; Jensen, Kaiser, &
Lachaux, 2007; Tallon-Baudry, Bertrand, Peronnet, & Pernier, 1998). For example, Fries, Reynolds, Rorie, and Desimone (2001) demonstrated that selective attention can influence the patterns of oscillatory synchrony between neural populations across different cortical areas with the enhancement of high-frequency oscillations (i.e. gamma) in occipital cortex.
They showed that when attended items were represented by neurons that were able to communicate on such a fast temporal scale during gamma-frequency range, their impact was enhanced, leading to a more efficient selection of behaviorally relevant stimulus (see also Womelsdorf, Fries, Mitra, & Desimone, 2006). Moreover, other studies also found strong gamma activity during encoding and maintenance of WM (Mainy et al., 2007;
Sauseng & Klimesch, 2008; Tallon-Baudry et al., 1998). My results in lateralized gamma power are consistent with these findings.
The source results also suggest that gamma oscillation in parietal cortex may provide a putative mechanism for top-down control on feature binding. For example, an MEG study by Morgan et al. (2011) demonstrated greater gamma activity in the parietal cortex when colors and angles were integrated relative to single features during WM maintenance. A recent study using brain stimulation technique (e.g. transcranial alternative current stimulation) applied gamma frequency, theta frequency, and sham stimulations over the temporal and parietal cortex when participants were being performed a WM task with the manipulation of feature-only and color-shape binding (Tseng et al., 2016). They demonstrated that the anti-phase gamma stimulation had an influence on binding trials.
Together, these results suggest that gamma oscillation is critical in the comparison process, particular in matching top-down (WM contents) and bottom-up (perceptual) information (Herrmann, Munk, & Engel, 2004).
While I did not observe any modulatory effect in early visual areas in the gamma band (Magazzini & Singh, 2018; Wilson, McDermott, Mills, Coolidge, & Heinrichs-Graham, 2018), the ERF results assured the feature binding effects in posterior parietal and early visual areas, indicating stronger activity for separated features relative to grouped-shared features and grouped-unshared features. To further examine the time course of ERF data, I divided the time-series into early and late time windows. This ERF analysis showed that the differential effects among two grouped feature conditions and separated feature conditions only occur in the late time window (400 ms – 550 ms). These results also support a recently
described load-dependent effect (e.g. N3rs) related to searching within WM (Kuo, Rao, Lepsien, & Nobre, 2009; Nobre, Griffin, & Rao, 2008). In their studies, the amplitude of the N3rs increase monotonically with the increasing load of retrospective search from within WM representations. The amplitude of the N3rs carried according to the degrees of feature coactivation, being smaller for grouped-shared features and grouped-unshared features compared to separated features. The N3rs therefore reflects time-consuming evaluation of the degrees of feature coactivation for putative probe items. Together with behavioral and gamma oscillation data, the ERF results also support the feature-bound representation independent to location congruency.
In conclusion, the current results from the behavioral and MEG data suggest that the location may have no or little influence on feature-bound representations during the comparisons of WM representation with perceptual information. These findings are compatible with previous findings that parietal cortex supports integration of visual features (Shadlen & Movshon, 1999; Shafritz et al., 2002). This study provides novel evidence in behavioral modeling and the oscillatory mechanisms which underlie the comparison of feature-bound and feature-unbound representations during the comparisons between WM and perception and the comparison process is associated with the gamma oscillation.
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