La parte principal de la clase (45 minutos)

As was mentioned previously, another aim of this thesis is to explore the factors underlying the RP and the RN (Experiment 1 and Experiment 2 in Chapter 3). These two components have been suggested to be markers of different processing stages of perceptual reversals. However, several factors have been identified that bring into question the nature of these two ERP components. These factors have been shown to influence the latency, amplitude and occurrence of these components. Therefore, in

order to investigate the underlying mechanisms of perceptual reversals, we chose to investigate whether these two ERP components are related to perceptual reversal processing or to other factors.

Event related potentials (ERPs) as brain responses to certain cognitive, sensory and motor events, can serve as neural correlates of perceptual processing. ERPs are calculated by trial-averaging stimulus-evoked EEG signals. The ERP technique,

according to Luck (2014), is more appropriate for answering some questions than others. Due to their high temporal resolution, ERPs provide a moment-by-moment measure of activity from before the stimulus and extending past the response. Thus, ERPs could help shed light on perceptual dynamics before and after stimulus presentation. Moreover, ERPs help determine which process is influenced by experimental

manipulation (Luck, 2014). This means that ERPs can be useful in determining what stage(s) of processing are influenced (or not) by a certain experimental manipulation. The change in latency, amplitude and lateralization of an ERP could be indicative of what brain processes are involved in the experimental paradigm. Moreover, according to Luck (2014), ERP recordings provide a much richer data set, often making it clear that a given experimental manipulation could influence more than one ERP component and that a given pattern of behavior might be caused by different mechanisms in different experiments (e.g. different manipulations of attention influence different ERP

components; Luck & Hillyard, 2000; Luck & Vecera, 2002).

For instance, Pitts et al. (2008) compared ERPs associated with voluntary perceptual reversals (i.e. inducing a perceptual reversal while perceiving an ambiguous Necker Lattice) to those associated with involuntary reversals (passively observing the

Necker Lattice) and found differences in the latencies, topographies and amplitudes of these components. Here, the ERP results reveal that although the behavior is the same (experience of perceptual reversals), the underlying mechanisms differ based on the experimental manipulation associated with that behavior (voluntary vs. involuntary reversals). This is not unique to ERP analyses. For instance, an fMRI study by Sterzer & Kleinschmidt (2007) showed an increased activation with endogenous motion reversals in the right inferior frontal cortex but not with the exogenous ones although the behavior observed and reported is the same (i.e. reversals).

The features and characteristics of ERP analyses (although not all unique to ERP analyses) taken together will help us identify what factors influence the changes we might observe in the RP and the RN in our experiments (Experiments 1 & 2). However, we cannot use this type of analysis in order to identify what spatio-temporal profile is most predictive of perceptual reversals and percept choice in ambiguous figure perception.

2.3.1.1. ‘What are ERPs bad for?’ (Luck, 2014).

As was mentioned previously, the ERP technique is well suited for answering questions of time when the components are time-locked to an event (i.e. stimulus onset). However, it is also bad for answering others (Luck, 2014). The waveforms recorded on the scalp represent the sum of several underlying components making it difficult to decompose this mixture into individual underlying sources (i.e., the superposition problem where multiple components are superimposed onto the same waveform) arising from specific brain areas.

Another limitation of this type of analysis is that some cognitive processes may not have distinct ERP components. This is because ERPs are measurable only when a certain set of criteria are met. For instance, a large number of neurons must be activated at the same time or the individual neurons must have similar orientations (Buzsaki et al., 2012; Luck, 2014). Moreover, to use the ERP technique, it is also necessary to have measurable events that can be used as time-locking points. This is a problem for experimental paradigms where the stimulus is presented in a continuous manner whereby the events are time-locked to response. As was discussed previously, with the current temporal resolution of the manual response paradigm, this type of backward averaging obliterates the presence of ERPs that are identified in experiments using the Onset Paradigm. In addition to that, non-time-locked activity is averaged out and effects that are observed using different methods of analyses (e.g. time-frequency analysis) are not observed in the ERP analyses.

Another limitation of ERPs, which would make it difficult to explore the pre- (Experiments 3 & 4) and post-stimulus (Experiments 1 & 2) activity and answer the questions posed in this thesis is that ERPs involve averaging across trials. This leads to the averaging out of activity that is not strictly time-locked, therefore reducing the ability of the analysis to take advantage of activity in single trials. This means that ERP analyses have reduced information, which may be relevant for explaining the trial-to- trial variations in perception in perceptual reversal paradigms. Moreover, the univariate nature of typical ERP analyses means that activity is typically only considered at a few localized positions on the scalp. For instance, the RN is typically quantified by

perceptual reversals suggest that mechanisms from across the brain may be involved. If this is the case, then the activity that distinguishes between reversal and stable trials may be better indicated by the pattern of activity across the entire scalp. Alternative analyses are required to investigate this quantitatively. Therefore, in order to address this issue, I apply a different type of analysis known as Multi-Variate Pattern Analysis (MVPA). The latter gives us a way to pick up on broad patterns of activity in raw EEG data and incorporates information about the spatial pattern of activity at each time point during each trial. This preserves activity, which may be lost in ERP analyses.