Neural Correlate of Filtering of Irrelevant Information from Visual Working Memory

In a dynamic environment stimulus task relevancy could be altered through time and it is not always possible to dissociate relevant and irrelevant objects from the very first moment they come to our sight. In such conditions, subjects need to retain maximum possible information in their WM until it is clear which items should be eliminated from WM to free attention and memory resources. Here, we examined the neural basis of irrelevant information filtering from WM by recording human ERP during a visual change detection task in which the stimulus irrelevancy was revealed in a later stage of the task forcing the subjects to keep all of the information in WM until test object set was presented. Assessing subjects' behaviour we found that subjects' RT was highly correlated with the number of irrelevant objects and not the relevant one, pointing to the notion that filtering, and not selection, process was used to handle the distracting effect of irrelevant objects. In addition we found that frontal N150 and parietal N200 peak latencies increased systematically as the amount of irrelevancy load increased. Interestingly, the peak latency of parietal N200, and not frontal N150, better correlated with subjects' RT. The difference between frontal N150 and parietal N200 peak latencies varied with the amount of irrelevancy load suggesting that functional connectivity between modules underlying fronto-parietal potentials vary concomitant with the irrelevancy load. These findings suggest the existence of two neural modules, responsible for irrelevant objects elimination, whose activity latency and functional connectivity depend on the number of irrelevant object

Configural and analytical processing of familiar and unfamiliar objects

Configural processing could develop for non-face visual objects as one becomes familiar with those objects through repeated exposure. To explore the role of familiarity in object recognition, we studied the effect of adaptation to a visual object (adapting stimulus) on the identification performance of other objects (test stimulus) while adapting and test stimuli were exactly the same, shared parts or were completely different. We used a subset of English alphabets (p, q, d and b) as familiar objects and an unfamiliar set of symbols constructed from same parts but with different configurations. Adaptation to a member of each set led to a lower identification performance for that object in a crowding paradigm. Adaptation to each member of the unfamiliar set resulted in decreased identification performance for the same object and those members of the set that shared parts with the adapting stimulus. But no such transfer of adaptation was observed for the familiar set. Our results support the notion that processing of object parts plays an important role in the recognition of unfamiliar objects while recognition of familiar objects is mainly based on configural processing mechanisms

The difference between peak latencies of frontal N150 and parietal N200.

<p>The interval between the latency of these two ERP components decreased when the number of irrelevant objects increased (Pearson test of correlation, r = −0.872, <i>p</i> = 0.01). Symbols and lines represent are similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003282#pone-0003282-g006" target="_blank">figure 6</a>.</p

The relation between the parietal N200 peak latencies recorded in left (a) and right (b) hemispheres and the participants' RT.

<p>Parietal N200 peak latency in right hemisphere showed better correlation to participants' RT. Each symbol depicts values related to whole report (asterisk) and partial report trials (square) and the lines regression line.</p

The relation between participants' RT and the number of (a) relevant and (b) irrelevant objects.

<p>Pearson test of correlation showed that there was a significant correlation between participants' RT and the number of relevant (r = −0.204, <i>p</i><0.001) and irrelevant (r = 0.401, <i>p</i><0.001) objects. Importantly, subsequent Pearson test for comparing two correlation coefficients yielded that participants RT is significantly better correlated to irrelevant objects number (<i>p</i><0.01). Open and closed squares correspond to whole report and partial report trials, respectively. Error bars represent one standard error.</p

The relation between the parietal N200 (a, c) and frontal N150 (b, d) peak latencies and the participants' RT.

<p>Plots represent the ERP activities (left) and the corresponding scatter plots of their peak times (right). In both brain regions the peak latency of the ERP components increased with increasing the number of irrelevant objects and correlated with participants' RT. In scatter plots asterisks demonstrate values corresponding to whole report (WR) trials and squares depict values of partial report (PR) trials. The numbers close to each square show the amount of irrelevant load in each PR trial. Colour legends are the same for the scatter plots and the ERP plot. Lines in part c and d demonstrate the regression line.</p

Participants' response accuracy (a) and RT (b) in different experimental conditions.

<p>Participants' performance declined as the number of sample objects increased (<i>p</i><0.001) while participants' RT was mainly affected by the trial type (<i>p</i><0.001). In both graphs open and closed squares correspond to whole report and partial report trials respectively. Error bars represent one standard error.</p

Early (first 400ms) ERP activities recorded in frontal (a) and parietal (c) leads after test onset.

<p>Activity mappings also demonstrate distribution of frontal (b) and parietal (d) activities during 130–150ms and 190–220ms after the test onset respectively. These timings correspond to averaged frontal N150 and parietal N200 peak latencies. Blue and red lines correspond to whole report (WR) and partial report (PR) trials, respectively. Pink bar in both graphs represents test stimulus presentation time. Gray areas depict the period with a significant difference between ERP area under curve of WR and PR trials (<i>p</i><0.05).</p