Infant number perception is a topic that has been studied for many years, but many questions remain regarding what cues infants use to make these discriminations, when and how these abilities develop, and what systems are responsible for infants’ number processing. In the domain of small number perception (quantities less than four), researchers have studied the effects of continuous extent on infants’ number discrimination (Clearfield & Mix, 1999; 2001). While evidence exists that infants can use continuous extent to make discriminations, it is not clear how much influence continuous extent has on infants’ behavior in these tasks. Another issue that has not been thoroughly addressed is the role of featural information in number discrimination. Few studies exist in which featural information is manipulated so that this issue can be addressed. The current study was designed to address these issues as well as to study infant number discrimination from a developmental perspective across several ages. Infants, aged 9-, 11-, and 13-months, completed a categorization task in which they were habituated to pictures of objects (e.g. bowl, tree, shoe) in either groups of two or groups of three. They saw four different sets of objects throughout habituation. In the test phase, infants saw both new and old objects in both groups of two and three. The 9-month-olds discriminated number independent of whether the object was familiar or novel. In contrast, the 11- month-olds appeared to discriminate between the familiar and novel objects. And, the 13-month-olds exhibited a combination of these two patterns; they discriminated between the familiar and novel object when the number of objects was familiar, but not when the number of objects was novel. These data suggest that number is an easily abstracted construct and that early number representations do not contain any featural information. As infants get older, they begin to incorporate featural information into their representations, but they do so in a step-wise fashion, as demonstrated by the 13-month-olds. Therefore, featural information does not appear to be important for small number discrimination at early ages, but infants do begin to integrate featural information as they develop. / text

During normal development, face processing involves a gradual shift from a featurally oriented style to a mature configural style by adolescence. This shift may coincide with increased right hemispheric dominance for faces supporting configural processing. Previous studies suggest that individuals diagnosed with ASD continue to process faces using individual parts and features into adulthood. This continued bias may be due to deficits in configural processing abilities. The current study investigated measures of functional connectivity during featural and configural processing of faces in broad autism phenotype sibling (ASD-sibs) children compared to age, sex, and handedness matched normal developing (ND) controls and in children diagnosed with an Autism Spectrum Disorder compared to ASD-matched ND controls. Results indicate that children with ASD and ASD-sibs were capable of performing configural processing tasks at similar performance levels to those of ND children. Additionally, patterns of functional network connectivity for configural processing in ASD-sibs were similar to those observed in ND controls. Few network-wide hemispheric differences emerged between groups. While behavioral performance and overall network-wide patterns of connectivity suggest a face processing network that is capable of supporting configural processing in ASD and ASD-sibs, abnormalities were observed in specific regions. The amygdala and fusiform face area showed fewer interactions with the rest of the face processing network in ASD children compared to ND during configural, but not featural processing. Additionally, hemispheric comparisons show greater differences between ASD and ND controls in the right fusiform face area. The ability of these regions to communicate with other regions in the face network could be important for social motivation and attention during configural processing. Interestingly, network connectivity in ASD children during passive viewing of faces, objects, and textures without featural or configural manipulations showed a more functionally integrated, and less segregated network with a lower “wiring cost” during non-face conditions compared to ND children. ASD-sibs may demonstrate a similar milder pattern.

Many important questions remain unanswered regarding how we recognize faces. Methodological inconsistencies have contributed to confusion regarding these questions, especially those surrounding three purported face processing mechanisms—featural, configural, and holistic—and the extent to which each play a role in face recognition. The work presented here aims to 1) empirically test the assumption that several face recognition tasks index the same underlying construct(s), and 2) contribute data to a number of ongoing debates concerning the reliability and validity of various methods for assessing integrative (i.e., holistic and/or configural) aspects of face processing. Experiment 1 tested the assumption that various tasks purporting to measure integrative face processing index the same construct(s). It is important to test this assumption because if these tasks are in fact measuring different things, then researchers should cease interpreting them as interchangeable measures. Using a within-subjects design (N = 223) we compared performance—as reflected by accuracy and reaction time measures, as well as two types of difference scores—across four of the most commonly used integrative face processing tasks: The Partial Composite Face Effect Task, the Face Inversion Effect Task, the Part Whole Effect Task, and the Configural/Featural Difference Detection Task. Analyses showed that within-task correlations were much stronger than those between-tasks. This suggests that the four conditions within each task are measuring something in common; In contrast, low correlations across tasks suggest that each is measuring something unique. This in turn suggests these tasks should not be seen as assessing the same integrative face-processing construct. Exploratory factor analyses corroborated the correlation data, finding that performance on most conditions loaded onto a single factor in unrotated solutions, but onto separate factors in direct oblimin-rotated solutions. In Experiment 2, we investigated the question of whether integrative face processing performance is related to face recognition ability. We did this by assessing the degree to which results from four widely-used integrative face processing tasks correlate with a measure of general face recognition ability, The Cambridge Face Memory Test (CFMT). The four integrative processing tasks used in this study only partly overlapped those from in Experiment 1. They were: The Complete Composite Face Effect Task, the Partial Composite Face Effect Task, the Part Whole Effect Task, and the Configural/Featural Difference Detection Task. As with Experiment 1, we used a within-subjects design (N = 260) and analyzed a variety of performance variables across these tasks. Analyses demonstrated low to moderate positive correlations between performance on the task conditions and performance on the CFMT. This suggests that the constructs the tasks reflect do contribute to face recognition ability to a modest degree. These analyses also replicated parts of Experiment 1, showing weak correlations between tasks. Also similar to Experiment 1, factor analyses generally revealed task conditions loading onto a common first factor in the unrotated factor matrix, but loading separately in the rotated factor solution. In addition to providing evidence regarding the nature of integrative face processing tasks, the data presented here speak to a number of other questions in this domain. For instance, they contribute to the debate regarding which kinds of difference scores (subtraction-based or regression-based) are more reliable, as well as the reliability of the various tasks used to investigate integrative face processing. In addition, the data inform the debate over whether the Complete or the Partial version of the Composite Face Effect Task is the superior measure of integrative face processing. In summary, the studies presented here indicate that the previous literature in face recognition needs to be interpreted with care, with an eye to differences in methodology and the problems of low measurement reliability. The various methods used to investigate integrative face processing are not assessing the same thing and cannot be taken as reflecting the same underlying construct.