Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

Perception of visual stimuli improves with training, but improvements are specific for trained stimuli rendering the development of generic training programs challenging. It remains unknown to which extent training of low-level visual features transfers to high-level visual perception, and whether this is accompanied by neuroplastic changes. The current event-related potential (ERP) study showed that training-induced increased sensitivity to a low-level feature, namely low spatial frequency (LSF), alters neural processing of this feature in high-level visual stimuli. Specifically, neural activity related to face processing (N170), was decreased for low (trained) but not high (untrained) SF content in faces following LSF training. These novel results suggest that: (1) SF discrimination learning transfers from simple stimuli to complex objects; and that (2) training the use of specific SF information affects neural processing of facial information. These findings may open up a new avenue to improve face recognition skills in individuals with atypical SF processing, such as in cataract or Autism Spectrum Disorder (ASD).

Rapidly decoding the emotional content of a face is an important skill for successful social behavior. Several Event Related brain Potential (ERP) have indicated that emotional expressions already influence brain activity as early as 100 ms. Some studies hypothesized that this early brain response to fear depends on coarse-magnocellular inputs, which are primarily driven by Low Spatial Frequency (LSF) cues. Until now however, evidence is inconclusive probably due to the divergent methods used to match luminance and contrast across spatial frequencies and emotional stimuli. In the present study, we measured ERPs to LSF and HSF faces with fearful or neutral expressions when contrast and luminance was matched across SF or not. Our findings clearly show that fearful facial expressions increases the amplitude of P1 (only for contrast-luminance equated images) and N170 in comparison to neutral faces but only in LSF faces, irrespective of contrast or luminance equalization, further suggesting that LSF information plays a crucial role in the early brain responses to fear. Furthermore, we found that, irrespective of luminance or contrast equalization, N170 occurred faster when perceiving LSF faces than HSF faces, again emphasizing the primacy of LSF processing in early face perception.

Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, & Kemner Infancy, 20, 601–633, 2015; Wass, Forssman, & Leppänen Infancy, 19, 427–460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, & Smith Behavior Research Methods, 47, 53–72, 2015). Here we introduce a fixation detection algorithm—identification by two-means clustering (I2MC)—built specifically for data across a wide range of noise levels and when periods of data loss may occur. We evaluated the I2MC algorithm against seven state-of-the-art event detection algorithms, and report that the I2MC algorithm’s output is the most robust to high noise and data loss levels. The algorithm is automatic, works offline, and is suitable for eye-tracking data recorded with remote or tower-mounted eye-trackers using static stimuli. In addition to application of the I2MC algorithm in eye-tracking research with infants, school children, and certain patient groups, the I2MC algorithm also may be useful when the noise and data loss levels are markedly different between trials, participants, or time points (e.g., longitudinal research).

These results plead against the notion that the abnormal gaze behavior in everyday life is due to the presence of facial stimuli per se. Furthermore, the absence of a face orientation effect in autistic children might be a reflection of a lack of holistic processing of human faces in autism.

A problem in eyetracking research is choosing areas of interest (AOIs): Researchers in the same field often use widely varying AOIs for similar stimuli, making cross-study comparisons difficult or even impossible. Subjective choices while choosing AOIs cause differences in AOI shape, size, and location. On the other hand, not many guidelines for constructing AOIs, or comparisons between AOI-production methods, are available. In the present study, we addressed this gap by comparing AOI-production methods in face stimuli, using data collected with infants and adults (with autism spectrum disorder [ASD] and matched controls). Specifically, we report that the attention-attracting and attention-maintaining capacities of AOIs differ between AOI-production methods, and that this matters for statistical comparisons in one of three groups investigated (the ASD group). In addition, we investigated the relation between AOI size and an AOI’s attention-attracting and attention-maintaining capacities, as well as the consequences for statistical analyses, and report that adopting large AOIs solves the problem of statistical differences between the AOI methods. Finally, we tested AOI-production methods for their robustness to noise, and report that large AOIs—using the Voronoi tessellation method or the limited-radius Voronoi tessellation method with large radii—are most robust to noise. We conclude that large AOIs are a noise-robust solution in face stimuli and, when implemented using the Voronoi method, are the most objective of the researcher-defined AOIs. Adopting Voronoi AOIs in face-scanning research should allow better between-group and cross-study comparisons.

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A popular goal in psychological science is to understand human cognition and behavior in the 'real-world.' In contrast, researchers have typically conducted their research in experimental research settings, a.k.a. the 'psychologist's laboratory.' Critics have often questioned whether psychology's laboratory experiments permit generalizable results. This is known as the 'real-world or the lab'-dilemma. To bridge the gap between lab and life, many researchers have called for experiments with more 'ecological validity' to ensure that experiments more closely resemble and generalize to the 'real-world.' However, researchers seldom explain what they mean with this term, nor how more ecological validity should be achieved. In our opinion, the popular concept of ecological validity is ill-formed, lacks specificity, and falls short of addressing the problem of generalizability. To move beyond the 'real-world or the lab'-dilemma, we believe that researchers in psychological science should always specify the particular context of cognitive and behavioral functioning in which they are interested, instead of advocating that experiments should be more 'ecologically valid' in order to generalize to the 'realworld.' We believe this will be a more constructive way to uncover the context-specific and context-generic principles of cognition and behavior.