title: Understanding age-related differences in the content and temporal dynamics of visual information processing
Creators: name: Gilman, Hannah Lois
Publisher: name: University of Glasgow
Year: 2019
Subjects: subject: N170, ageing, face perception, EEG, mutual information, bubbles.

description: The N170, a negative amplitude peak occurring at approximately 170 ms poststimulus onset, is an event-related potential (ERP) component observed during electroencephalography (EEG) recordings that preferentially responds to faces compared to other objects (Bentin, Allison, Puce, Perez, &amp; McCarthy, 1996; Bötzel, Schulze, &amp; Stodieck, 1995). EEG research has suggested that the N170 may be modulated by the eye region, however this has received much debate (e.g. Bentin et al., 1996; Eimer,1998; Taylor, Itier, Allison, &amp; Edmonds, 2001). Most recently, Rousselet, Ince, Rijsbergen, &amp; Schyns, (2014) used Gaussian apertures (‘bubbles’) (Gosselin &amp; Schyns, 2001) in a reverse correlation experiment to demonstrate that increased visibility of the contralateral eye leads to larger and earlier N170s in a face versus noise detection task. However, these results may be explained by the phenomenon of ‘left gaze bias’ – a preferential looking towards the left visual field. To understand if contralateral eye sensitivity can be explained by a non-feature specific attentional bias to the left, in the first study (Chapter 2) we investigated contralateral eye sensitivity to faces of different image sizes in a face versus noise detection task. Using reverse correlation and Mutual Information (MI) we found that contralateral eye sensitivity is size tolerant, suggesting that contralateral eye sensitivity does reflect feature encoding rather than a general left attentional bias. Next we wanted to address whether eye coding precedes other feature encoding in a more heterogeneous face set. The traditional ‘bubbles’ technique relies on stimuli being spatially aligned i.e. the eyes, nose, mouth of all images in the stimulus set to be in comparable positions for averaging bubble-masks across stimuli. To overcome this limitation, we used an adaption of BubbleWarp (Gill, DeBruine, Jones, &amp; Schyns, 2015) a new technique outlined in Chapter 3, to retrospectively ‘warp’ Gaussian bubble masks to an average face image. Using this new technique, in Chapter 4, we tested the assumption that contralateral eye sensitivity preceded sensitivity to other facial features, specifically the mouth, in a gender and expressive versus non-expressive (EXNEX) categorisation task in young adult participants. Using MI onset analysis, we found idiosyncratic differences in MI onsets suggesting preferential encoding of the eye before other facial features for ~65 % of participants. This revealed that whilst there is an eye 3 coding preference, there is not a constraint to encoding the contralateral eye before other facial features. Aging is marked by a decline in processing speed (Salthouse, 1996) and previous work has suggested that whilst older adults process the contralateral eye in face versus noise detection tasks, this processing is weaker and delayed compared to younger adults (Jaworska, 2017). In Chapter 5 using the same task as in Chapter 4, we quantified age-related differences in feature processing speed by calculating 50 % integration times in younger and older participants. There was a ~20 ms delay in eye encoding for older compared to younger adults. We found a 9 ms delay in mouth encoding in the gender task and no differences in mouth processing speed in the EXNEX task. This suggests that there was not a general, uniform delay in processing speed of all facial features across tasks. Overall, our results demonstrate for the first time that 1) contralateral eye sensitivity is tolerant to changes in stimulus size, stimulus set, task demands and age, 2) contralateral eye sensitivity preferentially precedes sensitivity to the mouth but is not a prerequisite in gender or EXNEX categorisation tasks and 3) older adults process the same facial feature information as younger adults, but feature coding is not uniformly delayed compared to younger adults.