COBERTURA DE EQUIPOS ELECTRÓNICOS

The cognitive stages defining memory processing are typically divided into encoding, maintenance and retrieval (Baddeley, 2001). The study described in the previous chapter provided a direct measure of the encoding and retrieval processes, by including load and an explicit judgment for both processes. However, it is possible that even in the case of successful perceptual encoding failures in the comparison between target and probe may be due to a loss or overwriting of information during retention (Shapiro, Arnell, & Raymond, 1997), which could make memory representations more vulnerable at the retrieval stage (Galli, 2014; Park, Min, & Lee, 2010; Théau, 2012).

Therefore, this study aimed at specifically investigating the maintenance period, with the purpose of elucidating the role of accurate perceptual discrimination on recognition, obtained by comparing the electrophysiological correlates when maintaining memory representations differing in stimulus complexity (load).

Maintenance involves distributed neural regions primarily in the frontal and parietal lobes, and is thought to result from the synchronization of neuronal assembles across a number of different frequencies (Gevins et al., 1997; Jensen & Tesche, 2002; Raghavachari et al., 2001).

Previous studies have demonstrated the involvement of theta (4-7Hz), alpha (8-12 Hz), beta (13-30 Hz) and gamma (above (13-30 Hz) during the maintenance of memory representations (Herrmann, Munk, & Engel, 2004; Jensen & Colgin, 2007; Raghavachari et al., 2001; Tallon-Baudry, Mandon, Freiwald, & Kreiter, 2004).

Specifically ‘load’ – which is usually described as the number of stimuli in the initial sample array – is typically associated with theta power, although the direction of this relationship in terms of polarity has been controversial (Jensen & Tesche, 2002; Onton et al., 2005; Raghavachari et al., 2001). Indeed, some studies have reported higher theta power for increased load, in accordance with the view that higher frontal theta reflects enhanced attention (Bruneau, Roux, Guérin, Garreau, & Lelord, 1993; Deiber et al., 2007; Gevins et al., 1997).

Conversely, other studies have found a decrease of theta power with memory load increase (Babiloni et al., 2004; Bastiaansen, Posthuma, Groot, & De Geus, 2002; Pan, Tan, Gao, Li, &

Wang, 2018). Increased alpha and decreased beta activity also reflect increased short-term memory load (Jensen, Gelfand, Kounios, & Lisman, 2002; Pesonen, Hämäläinen, & Krause, 2007).

Non-human primate studies showed that the association between maintenance and subsequent performance accuracy corresponds to increased synchronization in the beta range

Human studies, on the other hand, report a detectable difference in the power of gamma oscillations related to the number of hits versus misses in the encoding phases of a memory task (Park et al., 2010).

Although these studies identified the neural features of successful short-term memory task performance by investigating the maintenance period, their focus has been on the memory judgement, neglecting the role of accurate perceptual discrimination and its potential effect on load processing following encoding.

In order to address this question, this study used the same task as in Chapter 3, differing only in the level of load (two instead of three levels). Participants were still required to retain binding information (location and orientation) of briefly presented visual stimuli in the form of Gabor patches, to explicitly discriminate them and then compare them to a probe, after a short interval.

This study had three aims: 1) replicate the results reported in Chapter 3; 2) investigate the neural signatures of the load effect during the maintenance of successfully encoded and subsequently retrieved stimuli; 3) if present, assess whether these could be predicted from resting state neural activity prior the task execution. In the context of past electrophysiological studies, a theta increment associated with load was observed when stimuli were presented sequentially (mostly using the Sternberg task whereby participants decide whether a probe item belongs to a sequence of previously presented stimuli) (Sternberg, 1966) and/or when exploring episodic memory (Jensen & Tesche, 2002; Klimesch, Doppelmayr, Pachinger, et al., 1997; Onton et al., 2005; Raghavachari et al., 2001). Conversely, a recent study using a delayed match-to-sample task and manipulating the length of the delay period, showed a decrease of theta power with the increase of time length (Pan et al., 2018). This novel finding was interpreted as the effect of items being encoded and maintained spatially rather than temporally, which is known to affect

theta increase with the increment of the memorized items (Bastiaansen et al., 2002; S. Palva, Kulashekhar, Hamalainen, & Palva, 2011; Roberts, Hsieh, & Ranganath, 2013)

In light of this, we reasoned that maintenance could be characterized by a decrement of theta power as the stimuli here were presented simultaneously and with a constant set size.

Alternatively, if high load corresponded to the most challenging condition at the behavioural level, then theta power may increase following past observations that this corresponds to an increment of attentional effort (Bruneau et al., 1993; Deiber et al., 2007; Klimesch, Vogt, et al., 1999).

To investigate the extent to which the neural processes underlying maintenance also promote successful memory performance, we investigated the association between theta frequency recorded at rest (prior to task execution) and subsequent performance.

4.2 METHODS

4.2.1PARTICIPANTS

Thirty-two right-handed students from Goldsmiths, University of London (8 males, mean age: 26.4 ±4.3) with normal or corrected to normal vision provided written informed consent to volunteer to the study, which was approved by the Ethics Committee of the Department of Psychology at Goldsmiths.

Three participants were excluded due to technical issues during EEG data recording, therefore the final sample was of 29 participants (7: males, mean age: 26.5 ±4.6).

4.2.2STIMULI AND TASK

The stimuli and experimental task were identical to the one used in Chapter 3 except for the use of only two rather than three load levels, high and low. Therefore, for each of three investigated factors, the similarity between stimuli in terms of degrees of orientation, was either the same (‘low’ load) or slightly differing (‘high load’) (see section 3.2.2 p.48 for similar approach). We did not include a third load level as we wanted to focus on the load conditions that proved to be the most challenging in our previous study.

There were 144 trials for each gradient of difficulty and each judgment, with 72 trials for each possible combination, resulting in a total of 288 for the entire experimental session, divided in 4 blocks.

4.2.3EXPERIMENTAL PROCEDURE

Participants were seated in a dark room at approximately 57 cm from the computer monitor, and were asked to fixate at the centre of the monitor in order to better discriminate the orientation of the two stimuli.

In contrast to the previous study (Chapter 3), participants completed a practice block of 20 trials, where feedback was provided for both the perceptual and memory judgments. After the practice session, participants were instructed to initiate the experiment which took about 45 minutes, with breaks between the self-timed blocks.

Electrophysiological activity was recorded for five minutes prior to task execution and during the whole task.

4.2.4THRESHOLDING

The same thresholding procedure used in Chapter 3 was used for this study.

4.2.5BEHAVIOURAL D^ATA A^NALYSIS

The same approach for behavioural data analysis used in Chapter 3 was also applied here, except that no generalized mixed model was used. This is because only two contrasts (high and low) were available for this study, which is below the minimum requirement for mixed models.

4.2.6ELECTROPHYSIOLOGICAL D^ATA A^NALYSIS

A time-frequency transformation was computed to examine whether there was any systematic variation in energy at different frequencies in the EEG signal during the maintenance interval (900 ms before probe presentation). As our main interest was restricted to this interval, time frequency representations of individual trials were calculated using Morlet wavelet analysis with a wavelet number of cycles that linearly increased from 3 to 8 depending on the frequency range, time-locked to the memory probe presentation.

Trials were then averaged for each perceptual load condition and normalised to the entire trial epoch as baseline period (-3500ms to 1000ms). We reasoned that since the physical characteristics of the visual stimuli (the fixation dot during the maintenance interval and the Gabor patch during the probe presentation) were identical to those during the remainder of the trial, the main neural difference between these periods are likely to correspond to memory maintenance. For the statistical analyses, non-parametric cluster permutation was used (Maris

& Oostenveld, 2007) (for details on this method, see Chapter 2).