An appropriate use of iMap produces correct statistical results: a reply to McManus (2013) "iMAP and iMAP2 produce erroneous statistical maps of eye-movement differences"

Eye movements while judging faces for trustworthiness and dominance

Past studies examining how people judge faces for trustworthiness and dominance have suggested that they use particular facial features (e.g. mouth features for trustworthiness, eyebrow and cheek features for dominance ratings) to complete the task. Here, we examine whether eye movements during the task reflect the importance of these features. We here compared eye movements for trustworthiness and dominance ratings of face images under three stimulus configurations: Small images (mimicking large viewing distances), large images (mimicking face to face viewing), and a moving window condition (removing extrafoveal information). Whereas first area fixated, dwell times, and number of fixations depended on the size of the stimuli and the availability of extrafoveal vision, and varied substantially across participants, no clear task differences were found. These results indicate that gaze patterns for face stimuli are highly individual, do not vary between trustworthiness and dominance ratings, but are influenced by the size of the stimuli and the availability of extrafoveal vision.

iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-of-the-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple between- and within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance. Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.

Super-Memorizers Are Not Super-Recognizers

Humans have a natural expertise in recognizing faces. However, the nature of the interaction between this critical visual biological skill and memory is yet unclear. Here, we had the unique opportunity to test two individuals who have had exceptional success in the World Memory Championships, including several world records in face-name association memory. We designed a range of face processing tasks to determine whether superior/expert face memory skills are associated with distinctive perceptual strategies for processing faces. Superior memorizers excelled at tasks involving associative face-name learning. Nevertheless, they were as impaired as controls in tasks probing the efficiency of the face system: face inversion and the other-race effect. Super memorizers did not show increased hippocampal volumes, and exhibited optimal generic eye movement strategies when they performed complex multi-item face-name associations. Our data show that the visual computations of the face system are not malleable and are robust to acquired expertise involving extensive training of associative memory.

Differences in Looking at Own- and Other-Race Faces Are Subtle and Analysis-Dependent: An Account of Discrepant Reports

The Other-Race Effect (ORE) is the robust and well-established finding that people are generally poorer at facial recognition of individuals of another race than of their own race. Over the past four decades, much research has focused on the ORE because understanding this phenomenon is expected to elucidate fundamental face processing mechanisms and the influence of experience on such mechanisms. Several recent studies of the ORE in which the eye-movements of participants viewing own- and other-race faces were tracked have, however, reported highly conflicting results regarding the presence or absence of differential patterns of eye-movements to own- versus other-race faces. This discrepancy, of course, leads to conflicting theoretical interpretations of the perceptual basis for the ORE. Here we investigate fixation patterns to own- versus other-race (African and Chinese) faces for Caucasian participants using different analysis methods. While we detect statistically significant, though subtle, differences in fixation pattern using an Area of Interest (AOI) approach, we fail to detect significant differences when applying a spatial density map approach. Though there were no significant differences in the spatial density maps, the qualitative patterns matched the results from the AOI analyses reflecting how, in certain contexts, Area of Interest (AOI) analyses can be more sensitive in detecting the differential fixation patterns than spatial density analyses, due to spatial pooling of data with AOIs. AOI analyses, however, also come with the limitation of requiring a priori specification. These findings provide evidence that the conflicting reports in the prior literature may be at least partially accounted for by the differences in the statistical sensitivity associated with the different analysis methods employed across studies. Overall, our results suggest that detection of differences in eye-movement patterns can be analysis-dependent and rests on the assumptions inherent in the given analysis.

Novel word learning: An eye-tracking study. Are 18-month-old late talkers really different from their typical peers?

Infants, 18-24 months old who have difficulty learning words compared to their peers are often referred to as "late talkers" (LTs). These children are at risk for continued language delays as they grow older. One critical question is how to best identify which LTs will have language disorders, such as Specific Language Impairment (SLI) at school age, in order to maximize the opportunity for early and appropriate intervention and support. Recent research suggests that LTs are not only slower to learn and speak words than their peers, but are also slower to recognize and interpret known words in real time. This investigation examined online moment-by-moment processing of novel word learning in 18-month-olds. A low vocabulary, late talking group (LT, N=14) and an age and cognitive-level matched typical group (TYP, N=14) of infants participated in an eye-tracked novel word learning task and completed standardized testing of vocabulary and cognitive ability. Infants were trained on two novel word-picture pairs and then were tested using an adaptation of the looking while listening paradigm. Results suggest that there are differences between groups in the time-course of looking to the novel target picture during testing. These findings suggest that LTs and typical infants developed strong enough representations to recognize novel words using traditional measures of accuracy and reaction time, however interesting group differences emerge when using additional fine-grained processing measures. Implications for differences in emerging knowledge and learning patterns are discussed.

LEARNING OUTCOMES

The reader will be able to understand many benefits of using eye-tracking methods to study young infant and toddler populations with and without language disorders. Readers will learn that examining moment-by-moment time course of novel word learning allows additional insight into different learning patterns. Finally, readers should understand the data from this article suggest late talkers may have different emerging representations of novel words than their typical peers, which may contribute to their difficulty learning new words.