Age-Related Changes in Visual Spatial Working Memory Cognits: Frontal-Parietal EEG Coherence During Delay

This study explored changes in scalp electrophysiology across two Working Memory (WM) tasks and two age groups. Continuous electroencephalography (EEG) was recorded from 18 healthy adults (18-34 years) and 12 healthy adolescents (14-17) during the performance of two Oculomotor Delayed Response (ODR) WM tasks; (i.e. eye movements were the metric of motor response). Delay-period, EEG data in the alpha frequency was sampled from anterior and parietal scalp sites to achieve a general measure of frontal and parietal activity, respectively. Frontal-parietal, alpha coherence was calculated for each participant for each ODR-WM task. Coherence significantly decreased in adults moving across the two ODR tasks, whereas, coherence significantly increased in adolescents moving across the two ODR tasks. The effects of task in the adolescent and adult groups were large and medium, respectively. Within the limits of this study, the results provide empirical support that WM development during adolescence include complex, qualitative, change.

A domain-general perspective on medial frontal brain activity during performance monitoring

Activity of the medial frontal cortex (MFC) has been implicated in attention regulation and performance monitoring. The MFC is thought to generate several event-related potential (ERPs) components, known as medial frontal negativities (MFNs), that are elicited when a behavioural response becomes difficult to control (e.g., following an error or shifting from a frequently executed response). The functional significance of MFNs has traditionally been interpreted in the context of the paradigm used to elicit a specific response, such as errors. In a series of studies, we consider the functional similarity of multiple MFC brain responses by designing novel performance monitoring tasks and exploiting advanced methods for electroencephalography (EEG) signal processing and robust estimation statistics for hypothesis testing. In study 1, we designed a response cueing task and used Independent Component Analysis (ICA) to show that the latent factors describing a MFN to stimuli that cued the potential need to inhibit a response on upcoming trials also accounted for medial frontal brain responses that occurred when individuals made a mistake or inhibited an incorrect response. It was also found that increases in theta occurred to each of these task events, and that the effects were evident at the group level and in single cases. In study 2, we replicated our method of classifying MFC activity to cues in our response task and showed again, using additional tasks, that error commission, response inhibition, and, to a lesser extent, the processing of performance feedback all elicited similar changes across MFNs and theta power. In the final study, we converted our response cueing paradigm into a saccade cueing task in order to examine the oscillatory dynamics of response preparation. We found that, compared to easy pro-saccades, successfully preparing a difficult anti-saccadic response was characterized by an increase in MFC theta and the suppression of posterior alpha power prior to executing the eye movement. These findings align with a large body of literature on performance monitoring and ERPs, and indicate that MFNs, along with their signature in theta power, reflects the general process of controlling attention and adapting behaviour without the need to induce error commission, the inhibition of responses, or the presentation of negative feedback.

Charting developmental EEG gamma changes in the auditory modality /

The oscillation of neuronal circuits reflected in the EEG gamma frequency may be fundamental to the perceptual process referred to as binding (the integration of various thoughts and perceptions into a coherent picture). The aim of our study was to expand our knowledge of the developmental course ofEEG gamma in the auditory modality. 2 We investigated EEG 40 Hz gamma band responses (35.2 to 43.0 Hz) using an auditory novelty oddball paradigm alone and with a visual-number-series distracter task in 208 participants as a function of age (7 years to adult) at 9 sites across the sagital and lateral axes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4). Gamma responses were operationally defined as change in power or a change in phase synchrony level from baseline within two time windows. The evoked gamma response was defined as a significant change from baseline occurring between 0 to 150 ms after stimulus onset; the induced gamma response was measured from 250 to 750 ms after stimulus onset. A significant evoked gamma band response was found when measuring changes in both power and phase synchrony. The increase in both measures was maximal at frontal regions. Decreases in both measures were found when participants were distracted by a secondary task. For neither measure were developmental effects noted. However, evoked gamma power was significantly enhanced with the presentation of a novel stimulus, especially at the right frontal site (F4); frontal evoked gamma phase synchrony also showed enhancement for novel stimuli but only for our two oldest age groups (16-18 year olds and adults). Induced gamma band responses also varied with task-dependent cognitive stimulus properties. In the induced gamma power response in all age groups, target stimuli generated the highest power values at the parietal region, while the novel stimuli were always below baseline. Target stimuli increased induced synchrony in all regions for all participants, but the novel stimulus selectively affected participants dependent on their age and gender. Adult participants, for example, exhibited a reduction in gamma power, but an increase in synchrony to the novel stimulus within the same region. Induced gamma synchrony was more sensitive to the gender of the participant than was induced gamma power. While induced gamma power produced little effects of age, gamma synchrony did have age effects. These results confirm that the perceptual process which regulates gamma power is distinct from that which governs the synchronization for neuronal firing, and both gamma power and synchrony are important factors to be considered for the "binding" hypothesis. However, there is surprisingly little effect of age on the absolute levels of or distribution of EEG gamma in the age range investigated.

A microanalysis of EMG and EEG changes during the sleep onset period (SOP) : a theoretical investigation with practical applications /

The present study has both theoretical and practical aspects. The theoretical intent of the study was to closely examine the relationship between muscle activity (EMG) and EEG state during the process of falling asleep. Sleep stages during sleep onset (SO) have been generally defined with regards to brain wave activity (Recht schaff en & Kales (1968); and more precisely by Hori, Hayashi, & Morikawa (1994)). However, no previous study has attempted to quantify the changes in muscle activity during this same process. The practical aspect of the study examined the reliability ofa commercially developed wrist-worn alerting device (NovAlert™) that utilizes changes in muscle activity/tension in order to alert its user in the event that he/she experiences reduced wakefulness that may result in dangerous consequences. Twelve female participants (aged 18-42) sp-ent three consecutive nights in the sleep lab ("Adaptation", "EMG", and "NOVA" nights). Each night participants were given 5, twenty-minute nap opportunities. On the EMG night, participants were allowed to fall asleep freely. On the NOV A night, participants wore the Nov Alert™ wrist device that administered a Psychomotor Vigilance Test (PVT) when it detected that muscle activity levels had dropped below baseline. Nap sessions were scored using Hori's 9-stage scoring system (Hori et aI, 1994). Power spectral analyses (FFT) were also performed. Effects ofthe PVT administration on EMG and EEG frequencies were also examined. Both chin and wrist EMG activity showed reliable and significant decline during the early stages ofHori staging (stages HO to H3 characterized by decreases in alpha activity). All frequency bands studied went through significant changes as the participants progressed through each ofHori's 9 SO stages. Delta, theta, and sigma activity increased later in the SO continuum while a clear alpha dominance shift was noted as alpha activity shifted from the posterior regions of the brain (during Hori stages HO to H3) to the anterior portions (during Hori stages H7 to H9). Administration of the PVT produced significant increases in EMG activity and was effective in reversing subjective drowsiness experienced during the later stages of sleep onset. Limitations of the alerting effects of the PVTs were evident following 60 to 75 minutes of use in that PVTs delivered afterwards were no longer able to significantly increase EMG levels. The present study provides a clearer picture of the changes in EMG and EEG during the sleep onset period while testing the efficacy of a commercially developed alerting device. EMG decreases were found to begin during Hori stage 0 when EEG was - dominated by alpha wave activity and were maximal as Hori stages 2 to 5 were traversed (coincident with alpha and beta activity). This signifies that EMG decrements and the loss of resting alpha activity are closely related. Since decreased alpha has long been associated with drowsiness and impending sleep, this investigation links drops in muscle tone with sleepiness more directly than in previous investigations. The EMG changes were reliably demonstrated across participants and the NovAlert™ detected the EMG decrements when Hori stage 3 was entered. The alerting vibrations produced by the NovAlert™ occurred early enough in the SO process to be of practical importance as a sleepiness monitoring and alerting device.

A comparison of electrophysiological changes during the sleep onset period of psychophysiological insomniacs, psychiatric insomniacs and normal sleepers

The EEG of the sleep onset period of psychophysiological insomniacs, psychiatric insomniacs and controls was compared using power spectral analysis (FFT). Eighteen drug-free subjects were equally divided into three groups according to their responses in the Brock Sleep and Insomnia Questionnaire, the Minnesota Multiphasic Personality Inventory and the Sleep Disorders Questionnaire. Group 1 consisted of psychophysiological insomniacs, group 2 included insomniacs with an indication of psychiatric disturbances, and group 3 was a control group. EEG, EOG and EMG were recorded for two consecutive nights. Power spectral analysis (FFT) of EEG at C4 from the sleep onset period (defined as lights out to the first five minutes of stage 2) was performed on all standard frequency bands, delta: .5-4 Hz; theta: 4-8 Hz; alpha: 8-12 Hz; sigma: 12-15 Hz beta: 15-25 Hz. Psychophysiological insomniacs had less alpha during wakefulness than the other two groups and did not show the dramatic drop in alpha across the sleep onset period, which characterizes normal sleep. They also had less delta, especially during stage 2 on night 2. They also showed less delta in the last quartile of the chronological analysis of the sleep onset period. Psychiatric insomniacs showed lower relative beta power values overall while psychophysiological insomniacs showed higher relative beta power values during wakefulness. This microanalysis 11 confirms that the sleep onset period is generally similar for psychiatric insomniacs and normal sleepers. This may be due to the sample of psychiatric insomniacs being heterogeneous or may reflect a sleep onset system that is essentially intact. Psychophysiological insomniacs have higher cortical arousal during the sleep onset period than do the psychiatric insomniacs and the controls. Clear differences in the sleep onset period of psychophysiological insomniacs exist. The dramatic changes in power values in these two groups are not seen in the psychophysiological insomniacs, which may make the discrimination between wakefulness and sleep more difficult.

The role of daytime napping in sleepiness and cognitive function in 24-70 year olds /

Daytime napping improves well-being and performance for young adults. The benefits of napping in older adults should be investigated because they have fragmented nocturnal sleep, cognitive declines, and more opportunity to nap. In addition, experience with napping might influence the benefits of napping. Study 1 examined the role of experience with napping in young adults. Habitual (n = 23) and non-habitual nappers (n = 16) were randomly assigned to a 20-minute nap or a 20- minute reading condition. Both groups slept the same according to macro architecture. However, microarchitecture showed greater theta, alpha, and beta power during Stage 1, and greater delta, alpha, and sigma power during Stage 2 for habitual nappers, for the most part indicating better sleep. Both groups felt less sleepy after the nap. P2 latency, reflecting information processing, decreased after the nap for habitual nappers, and after the control condition for non-habitual nappers. In sum, both groups who slept felt better, but only the habitual nappers who napped gained a benefit in terms of information processing. Based on this outcome, experience with napping was investigated in Study 2. Study 2 examined the extent to which daytime napping enhanced cognition in older adults, especially frontal lobe function. Cognitive deficits in older adults may be due to sleep loss and age-related decline in brain functioning. Longer naps were expected to provide greater improvement, particularly for older adults, by reducing sleep pressure. Thirty-two adults, aged 24-70 years, participated in a repeated measures dose-response manipulation of sleep pressure. Twenty- and sixty-minute naps were compared to a no-nap condition in three age groups. Mood, subjective sleepiness, reaction time, working memory, 11 novelty detection, and waking electro physiological measures were taken before and after each condition. EEG was also recorded during each nap or rest condition. Napping reduced subjective sleepiness, improved working memory (serial addition / subtraction task), and improved attention (reduced P2 amplitude). Physiological sleepiness (i.e., waking theta power) increased following the control condition, and decreased after the longer nap. Increased beta power after the short nap, and seen with older adults overall, may have reflected increased mental effort. Older adults had longer latencies and smaller amplitudes for several event-related potential components, and higher beta and gamma power. Following the longer nap, gamma power decreased for older adults, but increased for young adults. Beta and gamma power may represent enhanced alertness or mental effort. In addition, Nl amplitude showed that benefits depend on the preceding nap length as well as age. Since the middle group had smaller Nl amplitudes following the short nap and rest condition, it is possible that they needed a longer nap to maintain alertness. Older adults did not show improvements to Nl amplitude following any condition; they may have needed a nap longer than 60 minutes to gain benefits to attention or early information processing. Sleep characteristics were not related to benefits of napping. Experience with napping was also investigated. Subjective data confirmed habitual nappers were happier to nap, while non-habitual nappers were happier to stay awake, reflecting self-identified napping habits. Non-habitual nappers were sleepier after a nap, and had faster brain activity (i.e., heightened vigilance) at sleep onset. These reasons may explain why non-habitual nappers choose not to nap.

Subjective, behavioural and electrophysiological measurements of the time-course and intensity of sleep inertia after a full night of sleep /

Several recent studies have described the period of impaired alertness and performance known as sleep inertia that occurs upon awakening from a full night of sleep. They report that sleep inertia dissipates in a saturating exponential manner, the exact time course being task dependent, but generally persisting for one to two hours. A number of factors, including sleep architecture, sleep depth and circadian variables are also thought to affect the duration and intensity. The present study sought to replicate their findings for subjective alertness and reaction time and also to examine electrophysiological changes through the use of event-related potentials (ERPs). Secondly, several sleep parameters were examined for potential effects on the initial intensity of sleep inertia. Ten participants spent two consecutive nights and subsequent mornings in the sleep lab. Sleep architecture was recorded for a fiiU nocturnal episode of sleep based on participants' habitual sleep patterns. Subjective alertness and performance was measured for a 90-minute period after awakening. Alertness was measured every five minutes using the Stanford Sleepiness Scale (SSS) and a visual analogue scale (VAS) of sleepiness. An auditory tone also served as the target stimulus for an oddball task designed to examine the NlOO and P300 components ofthe ERP waveform. The five-minute oddball task was presented at 15-minute intervals over the initial 90-minutes after awakening to obtain six measures of average RT and amplitude and latency for NlOO and P300. Standard polysomnographic recording were used to obtain digital EEG and describe the night of sleep. Power spectral analyses (FFT) were used to calculate slow wave activity (SWA) as a measure of sleep depth for the whole night, 90-minutes before awakening and five minutes before awakening.

The effects of simple procedural, cognitive procedural and declarative learning on sleep /

Sleep spindles have been found to increase following an intense period of learning on a combination of motor tasks. It is not clear whether these changes are task specific, or a result of learning in general. The current study investigated changes in sleep spindles and spectral power following learning on cognitive procedural (C-PM), simple procedural (S-PM) or declarative (DM) learning tasks. It was hypothesized that S-PM learning would result in increases in Sigma power during Non-REM sleep, whereas C-PM and DM learning would not affect Sigma power. It was also hypothesized that DM learning would increase Theta power during REM sleep, whereas S-PM and C-PM learning would not affect Theta power. Thirty-six participants spent three consecutive nights in the sleep laboratory. Baseline polysomnographic recordings were collected on night 2. Participants were randomly assigned to one of four conditions: C-PM, S-PM, DM or control (C). Memory task training occurred on night 3 followed by polysomnographic recording. Re-testing on respective memory tasks occurred one-week following training. EEG was sampled at 256Hz from 16 sites during sleep. Artifact-free EEG from each sleep stage was submitted to power spectral analysis. The C-PM group made significantly fewer errors, the DM group recalled more, and the S-PM improved on performance from test to re-test. There was a significant night by group interaction for the duration of Stage 2 sleep. Independent t-tests revealed that the S-PM group had significantly more Stage 2 sleep on the test night than the C group. The C-PM and the DM group did not differ from controls in the duration of Stage 2 sleep on test night. There was no significant change in the duration of slow wave sleep (SWS) or REM sleep. Sleep spindle density (spindles/minute) increased significantly from baseline to test night following S-PM learning, but not for C-PM, DM or C groups. This is the first study to have shown that the same pattern of results was found for spindles in SWS. Low Sigma power (12-14Hz) increased significantly during SWS following S-PM learning but not for C-PM, DM or C groups. This effect was maximal at Cz, and the largest increase in Sigma power was at Oz. It was also found that Theta power increased significantly during REM sleep following DM learning, but not for S-PM, C-PM or C groups. This effect was maximal at Cz and the largest change in Theta power was observed at Cz. These findings are consistent with the previous research that simple procedural learning is consolidated during Stage 2 sleep, and provide additional data to suggest that sleep spindles across all non-REM stages and not just Stage 2 sleep may be a mechanism for brain plasticity. This study also provides the first evidence to suggest that Theta activity during REM sleep is involved in memory consolidation.

Sleep and information processing in individuals who have sustained a traumatic brain injury

Individuals who have sustained a traumatic brain injury (TBI) often complain of t roubl e sleeping and daytime fatigue but little is known about the neurophysiological underpinnings of the s e sleep difficulties. The fragile sleep of thos e with a TBI was predicted to be characterized by impairments in gating, hyperarousal and a breakdown in sleep homeostatic mechanisms. To test these hypotheses, 20 individuals with a TBI (18- 64 years old, 10 men) and 20 age-matched controls (18-61 years old, 9 men) took part in a comprehensive investigation of their sleep. While TBI participants were not recruited based on sleep complaint, the fmal sample was comprised of individuals with a variety of sleep complaints, across a range of injury severities. Rigorous screening procedures were used to reduce potential confounds (e.g., medication). Sleep and waking data were recorded with a 20-channel montage on three consecutive nights. Results showed dysregulation in sleep/wake mechanisms. The sleep of individuals with a TBI was less efficient than that of controls, as measured by sleep architecture variables. There was a clear breakdown in both spontaneous and evoked K-complexes in those with a TBI. Greater injury severities were associated with reductions in spindle density, though sleep spindles in slow wave sleep were longer for individuals with TBI than controls. Quantitative EEG revealed an impairment in sleep homeostatic mechanisms during sleep in the TBI group. As well, results showed the presence of hyper arousal based on quantitative EEG during sleep. In wakefulness, quantitative EEG showed a clear dissociation in arousal level between TBls with complaints of insomnia and TBls with daytime fatigue. In addition, ERPs indicated that the experience of hyper arousal in persons with a TBI was supported by neural evidence, particularly in wakefulness and Stage 2 sleep, and especially for those with insomnia symptoms. ERPs during sleep suggested that individuals with a TBI experienced impairments in information processing and sensory gating. Whereas neuropsychological testing and subjective data confirmed predicted deficits in the waking function of those with a TBI, particularly for those with more severe injuries, there were few group differences on laboratory computer-based tasks. Finally, the use of correlation analyses confirmed distinct sleep-wake relationships for each group. In sum, the mechanisms contributing to sleep disruption in TBI are particular to this condition, and unique neurobiological mechanisms predict the experience of insomnia versus daytime fatigue following a TBI. An understanding of how sleep becomes disrupted after a TBI is important to directing future research and neurorehabilitation.

Electrophysiological constituents of the P100 and N170 ERP complex: De-constructing of face processing using independent component analysis and robust estimation.

The initial timing of face-specific effects in event-related potentials (ERPs) is a point of contention in face processing research. Although effects during the time of the N170 are robust in the literature, inconsistent effects during the time of the P100 challenge the interpretation of the N170 as being the initial face-specific ERP effect. The interpretation of the early P100 effects are often attributed to low-level differences between face stimuli and a host of other image categories. Research using sophisticated controls for low-level stimulus characteristics (Rousselet, Husk, Bennett, & Sekuler, 2008) report robust face effects starting at around 130 ms following stimulus onset. The present study examines the independent components (ICs) of the P100 and N170 complex in the context of a minimally controlled low-level stimulus set and a clear P100 effect for faces versus houses at the scalp. Results indicate that four ICs account for the ERPs to faces and houses in the first 200ms following stimulus onset. The IC that accounts for the majority of the scalp N170 (icNla) begins dissociating stimulus conditions at approximately 130 ms, closely replicating the scalp results of Rousselet et al. (2008). The scalp effects at the time of the P100 are accounted for by two constituent ICs (icP1a and icP1b). The IC that projects the greatest voltage at the scalp during the P100 (icP1a) shows a face-minus-house effect over the period of the P100 that is less robust than the N 170 effect of icN 1 a when measured as the average of single subject differential activation robustness. The second constituent process of the P100 (icP1b), although projecting a smaller voltage to the scalp than icP1a, shows a more robust effect for the face-minus-house contrast starting prior to 100 ms following stimulus onset. Further, the effect expressed by icP1 b takes the form of a larger negative projection to medial occipital sites for houses over faces partially canceling the larger projection of icP1a, thereby enhancing the face positivity at this time. These findings have three main implications for ERP research on face processing: First, the ICs that constitute the face-minus-house P100 effect are independent from the ICs that constitute the N170 effect. This suggests that the P100 effect and the N170 effect are anatomically independent. Second, the timing of the N170 effect can be recovered from scalp ERPs that have spatio-temporally overlapping effects possibly associated with low-level stimulus characteristics. This unmixing of the EEG signals may reduce the need for highly constrained stimulus sets, a characteristic that is not always desirable for a topic that is highly coupled to ecological validity. Third, by unmixing the constituent processes of the EEG signals new analysis strategies are made available. In particular the exploration of the relationship between cortical processes over the period of the P100 and N170 ERP complex (and beyond) may provide previously unaccessible answers to questions such as: Is the face effect a special relationship between low-level and high-level processes along the visual stream?

Electrophysiological investigations of the timing of face processing

As important social stimuli, faces playa critical role in our lives. Much of our interaction with other people depends on our ability to recognize faces accurately. It has been proposed that face processing consists of different stages and interacts with other systems (Bruce & Young, 1986). At a perceptual level, the initial two stages, namely structural encoding and face recognition, are particularly relevant and are the focus of this dissertation. Event-related potentials (ERPs) are averaged EEG signals time-locked to a particular event (such as the presentation of a face). With their excellent temporal resolution, ERPs can provide important timing information about neural processes. Previous research has identified several ERP components that are especially related to face processing, including the N 170, the P2 and the N250. Their nature with respect to the stages of face processing is still unclear, and is examined in Studies 1 and 2. In Study 1, participants made gender decisions on a large set of female faces interspersed with a few male faces. The ERP responses to facial characteristics of the female faces indicated that the N 170 amplitude from each side of the head was affected by information from eye region and by facial layout: the right N 170 was affected by eye color and by face width, while the left N 170 was affected by eye size and by the relation between the sizes of the top and bottom parts of a face. In contrast, the P100 and the N250 components were largely unaffected by facial characteristics. These results thus provided direct evidence for the link between the N 170 and structural encoding of faces. In Study 2, focusing on the face recognition stage, we manipulated face identity strength by morphing individual faces to an "average" face. Participants performed a face identification task. The effect of face identity strength was found on the late P2 and the N250 components: as identity strength decreased from an individual face to the "average" face, the late P2 increased and the N250 decreased. In contrast, the P100, the N170 and the early P2 components were not affected by face identity strength. These results suggest that face recognition occurs after 200 ms, but not earlier. Finally, because faces are often associated with social information, we investigated in Study 3 how group membership might affect ERP responses to faces. After participants learned in- and out-group memberships of the face stimuli based on arbitrarily assigned nationality and university affiliation, we found that the N170 latency differentiated in-group and out-group faces, taking longer to process the latter. In comparison, without group memberships, there was no difference in N170 latency among the faces. This dissertation provides evidence that at a neural level, structural encoding of faces, indexed by the N170, occurs within 200 ms. Face recognition, indexed by the late P2 and the N250, occurs shortly afterwards between 200 and 300 ms. Social cognitive factors can also influence face processing. The effect is already evident as early as 130-200 ms at the structural encoding stage.

The Role of Sleep in the Selective Reconsolidation of Declarative Memories

While sleep has been shown to be involved in memory consolidation and the selective enhancement of newly acquired memories of future relevance (Wilhelm, et al., 2011), limited research has investigated the role of sleep or future relevance in processes of memory reconsolidation. The current research employed a list-method directed forgetting procedure in which participants learned two lists of syllable pairs on Night 1 and received directed forgetting instructions on Night 2. On Night 2, one group (Labile; n = 15) received a memory reactivation treatment consisting of reminders designed to return memories of the learned lists to a labile state. A second group (Stable, n = 16) received similar reminders designed to leave memories of the learned lists in their stable state. No differences in forgetting were found across the two lists or groups. However, a negative correlation between frontal delta (1 – 4 Hz) electroencephalographic (EEG) power during Early Stage 2 non-rapid eye movement (NREM) sleep and forgetting of to-beremembered material was found exclusively in the Labile group (r = -.61, p < .05). Further, central theta (4 – 8 Hz ) EEG power during rapid eye movement (REM) sleep was found to correlate with directed forgetting exclusively in the Labile group (r = .81, p < .001) and total forgetting in the Stable group (r = .50, p < .05). These observed relationships support the proposed hypothesis suggesting that sleep processes are involved in the reconsolidation of labile memories, and that this reconsolidation may be selective for memories of future relevance. A role for sleep in the beneficial reprocessing of memories through the selective reconsolidation of labile memories in NREM sleep and the weakening of memories in REM sleep is discussed.

Blocking Top-down awareness of Kanizsa Figures: An ERP Investigation into Bottom-up Processing of Illusory Contours

Neural models of the processing of illusory contour (ICs) diverge from one another in terms of their emphasis on bottom-up versus top-down constituents. The current study uses a dichoptic fusion paradigm to block top-down awareness of ICs in order to examine possible bottom-up effects. Group results indicate that the N170 ERP component is particularly sensitive to ICs at central occipital sites when top-down awareness of the stimulus is permitted. Furthermore, single-subject statistics reveal that the IC N170 ERP effect is highly variable across individuals in terms of timing and topographical spread. The results suggest that the ubiquitous N170 effect to ICs found in the literature depends, at least in part, on participants’ awareness of the stimulus. Therefore a strong bottom-up model of IC processing at the time of the N170 is unlikely.