Ensemble coding of faces occurs in children and develops dissociably from coding of individual faces

Ensemble coding of facial identity is robust, but may not contribute to face learning

Ensemble coding - the rapid extraction of a perceptual average - has been proposed as a potential mechanism underlying face learning. We tested this proposal across five pre-registered experiments in which four ambient images of an identity were presented in the study phase. In Experiments 1 and 2a-c, participants were asked whether a test image was in the study array; these experiments examined the robustness of ensemble coding. Experiment 1 replicated ensemble coding in an online sample; participants recognize images from the study array and the average of those images. Experiments 2a-c provide evidence that ensemble coding meets several criteria of a possible learning mechanism: It is robust to changes in head orientation (± 60o), survives a short (30s) delay, and persists when images of two identities are interleaved during the study phase. Experiment 3 examined whether ensemble coding is sufficient for face learning (i.e., facilitates recognition of novel images of a target identity). Each study array comprised four ambient images (variability + average), a single image, or an average of four images (average only). Participants were asked whether a novel test image showed the identity from a study array. Performance was best in the four-image condition, with no difference between the single-image and average-only conditions. We conclude that ensemble coding of facial identity is robust but that the perceptual average per se is not sufficient for face learning.

From Pictures to the People in Them: Averaging Within-Person Variability Leads to Face Familiarization

Familiar faces can be confidently recognized despite sometimes radical changes in their appearance. Exposure to within-person variability-differences in facial characteristics over successive encounters-contributes to face familiarization. Research also suggests that viewers create mental averages of the different views of faces they encounter while learning them. Averaging over within-person variability is thus a promising mechanism for face familiarization. In Experiment 1, 153 Canadian undergraduates (88 female; age: M = 21 years, SD = 5.24) learned six target identities from eight different photos of each target interspersed among 32 distractor identities. Face-matching accuracy improved similarly irrespective of awareness of the target's identity, confirming that target faces presented among distractors can be learned incidentally. In Experiment 2, 170 Canadian undergraduates (125 female; age: M = 22.6 years, SD = 6.02) were tested using a novel indirect measure of learning. The results show that viewers update a mental average of a person's face as it becomes learned. Our findings are the first to show how averaging within-person variability over time leads to face familiarization.

A Model of Scientific Data Reasoning

Data reasoning is an essential component of scientific reasoning, as a component of evidence evaluation. In this paper, we outline a model of scientific data reasoning that describes how data sensemaking underlies data reasoning. Data sensemaking, a relatively automatic process rooted in perceptual mechanisms that summarize large quantities of information in the environment, begins early in development, and is refined with experience, knowledge, and improved strategy use. Summarizing data highlights set properties such as central tendency and variability, and these properties are used to draw inferences from data. However, both data sensemaking and data reasoning are subject to cognitive biases or heuristics that can lead to flawed conclusions. The tools of scientific reasoning, including external representations, scientific hypothesis testing, and drawing probabilistic conclusions, can help reduce the likelihood of such flaws and help improve data reasoning. Although data sensemaking and data reasoning are not supplanted by scientific data reasoning, scientific reasoning skills can be leveraged to improve learning about science and reasoning with data.

Emotion matters: Face ensemble perception is affected by emotional states

Ensemble coding is the ability of the visual system to extract a summary statistic from a set of stimuli. For example, observers often spontaneously extract an average face identity from a set of faces. Ensemble coding is known to operate in the frame of a distributed/global attention model. Because both attention and holistic processing are modulated by emotion - where positive emotions broaden the scope of attention and facilitate global processing, whereas negative emotions narrow the scope of attention and promote local processing - the current research explored whether emotional states could affect visual averaging of multiple face identities. Participants completed an ensemble-coding task before and after their emotion was induced via film clips. In the ensemble-coding task, a set of four face identities was shown briefly, followed by a probe face. Participants judged whether the probe face was presented in the preceding set. Evidence for ensemble coding was indexed by responses that treated an average face of the preceding set as a member of that set. The results showed that the tendency to choose this average was modulated by emotional states. Visual averaging increased after seeing positive film clips, but decreased after seeing negative film clips. These results support Frederickson's broaden-and-built theory, and extended its application to ensemble perception.

Combining Wearable Devices and Mobile Surveys to Study Child and Youth Development in Malawi: Implementation Study of a Multimodal Approach

BACKGROUND

Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children's health and development outcomes more integrally from multiple perspectives.

OBJECTIVE

The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency.

METHODS

We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study.

RESULTS

During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks.

CONCLUSIONS

This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development.

Self-Construal Priming Modulates Ensemble Perception of Multiple-Face Identities

This study explored the modulatory role of independent/interdependent self-construal on ensemble perception. Two experiments were conducted to study the effect of self-construal on ensemble coding of multiple-face identities (Experiment 1) and dot size (Experiment 2) separately. Before the implicit ensemble perception task, participants in both experiments were either primed with independent or interdependent self-construal via a well-validated pronoun circle task, in which they were exposed to either singular ("I," "me," and "my") or plural ("We," "us," and "our") pronouns in essays. The results showed that interdependent self-construal (vs. independent self-construal) featured as global processing and emphasizing interconnectedness with others enhanced the ensemble coding of high-level features (e.g., identity in Experiment 1) but not of low-level features (e.g., size in Experiment 2). To the best of our knowledge, this study was the first to investigate the role of self-construal on ensemble representations. In sum, the results of the current study supported the domain-specific mechanism of ensemble perception on one hand, and extended the effect of self-construal on single face recognition to multiple face recognition on the other hand.

Memory of Ensemble Representation Was Independent of Attention

The hierarchical view of working memory suggested that object ensemble could also be stored into working memory by treating ensemble properties as single "unit." However, it remains unclear whether ensemble representation in working memory is vulnerable to attention demanding. The present study designed a dual-task paradigm constituting of a memory retaining task and an attention-demanding arrow flanker task. Participants were firstly presented an array (4 or 9) of facial images with neutral expressions and then shown a left- or right-orientated arrow surrounded by four congruent or incongruent oriented arrows or short lines. Participants judged the orientation of the target arrow and then indicated whether a probe facial image was present or absent in the preceding facial array. The probe face consisted of four conditions: (1) a morphed average face of prior face set, (2) a morphed average face of another face set, (3) an exemplar face of prior set, and (4) an exemplar face of another face set. Results confirmed that participants implicitly coded the average facial image of preceding set and retained in working memory. More importantly, the memory representation of ensemble property (e.g., average facial identity) was independent of flanker type. In sum, this study provided further evidence of the hierarchical view of working memory and suggested that attention was not a pre-requisite for the retaining of ensemble properties in working memory.

Precise Ensemble Face Representation Given Incomplete Visual Input

Humans can recognize faces in the presence of environmental noise. Here, we explore whether ensemble perception of faces is similarly robust. Is summary statistical information available from crowds of faces that are visually incomplete? Observers viewed sets of faces varying in identity or expression and adjusted a test face to match the perceived average. In one condition, faces amodally completed behind horizontal bars. In another condition, identical facial information was presented, but in the foreground (i.e., face parts appeared on fragmented strips in front of a background). Baseline performance was determined by performance on sets of fully visible faces. The results revealed that the ensemble representation of amodally completing sets was significantly better than the fragmented sets and marginally worse than in the fully visible condition. These results suggest that some ensemble information is available given limited visual input and supports a growing body of work suggesting that ensembles may be represented in the absence of complete visual information.

Becoming Familiar With a Newly Encountered Face: Evidence of an Own-Race Advantage

Adults' ability to match identity in images of unfamiliar faces is impaired for other- compared with own-race faces; their ability to match identity in images of familiar faces is independent of face race. Exposure to within-person variability in appearance plays a key role in face learning. Past research suggests that children need exposure to higher levels of variability than adults to learn a new face-a difference that has been attributed to experience. We predicted that adults' limited experience with other-race faces would result in their needing exposure to higher levels of variability when learning other- compared with own-race faces. We introduced adults to four new identities (two own-race; two other-race) in one of the three conditions: a single image, a low-variability video (filmed on 1 day), or a high-variability video (filmed across 3 days). Adults' ability to recognize new instances of learned identities improved in the low-variability condition for own-race faces but only in the high-variability condition for other-race faces. We discuss learning mechanisms that might drive this difference-a difference we attribute to experience.

Getting to know you: The development of mechanisms underlying face learning

Nearly every study investigating the development of face recognition has focused on the ability to tell people apart using one or two tightly controlled images to represent each identity. Such research ignores the challenge of recognizing the same person despite variability in appearance. Whereas natural variation in appearance makes unfamiliar faces difficult to recognize, by 6 years of age people easily recognize multiple images of familiar faces. Two mechanisms are proposed to underlie the process by which adults become familiar with newly encountered faces. We provide the first examination of the development of these mechanisms during childhood (6-11 years). In Experiment 1, we examined children's (6- to 10-year-olds') and adults' ability to engage in ensemble coding-the ability to rapidly extract an average representation of an identity from several instances. In Experiment 2, we examined children's ability to use within-person variability in appearance to recognize novel instances of a newly encountered identity. We created a child-friendly perceptual matching task, and the number of images to which participants were exposed varied across targets. Although children were less accurate than adults overall in Experiment 2, we found no age-related improvement in either ensemble coding or the ability to benefit from exposure to within-person variability in appearance when learning a new face, suggesting that both abilities are developed by 6 years of age. We discuss the implications of these findings for understanding the nature of mechanisms underlying face learning and other developmental processes such as language and music.