Adding years to your life (or at least looking like it): a simple normalization underlies adaptation to facial age

Clinical trial design in the era of precision medicine

Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.

What happens to our representation of identity as familiar faces age? Evidence from priming and identity aftereffects

Matching identity in images of unfamiliar faces is error prone, but we can easily recognize highly variable images of familiar faces – even images taken decades apart. Recent theoretical development based on computational modelling can account for how we recognize extremely variable instances of the same identity. We provide complementary behavioural data by examining older adults’ representation of older celebrities who were also famous when young. In Experiment 1, participants completed a long‐lag repetition priming task in which primes and test stimuli were the same age or different ages. In Experiment 2, participants completed an identity after effects task in which the adapting stimulus was an older or young photograph of one celebrity and the test stimulus was a morph between the adapting identity and a different celebrity; the adapting stimulus was the same age as the test stimulus on some trials (e.g., both old) or a different age (e.g., adapter young, test stimulus old). The magnitude of priming and identity after effects were not influenced by whether the prime and adapting stimulus were the same age or different age as the test face. Collectively, our findings suggest that humans have one common mental representation for a familiar face (e.g., Paul McCartney) that incorporates visual changes across decades, rather than multiple age‐specific representations. These findings make novel predictions for state‐of‐the‐art algorithms (e.g., Deep Convolutional Neural Networks).

Diversity and spatiotemporal dynamics of fungal communities in the rhizosphere soil of Lycium barbarum L.: a new insight into the mechanism of geoherb formation

Lycium barbarum L. is a well-known traditional geoherb in Ningxia, China. The fruits of L. barbarum contain several dietary constituents, and thus, they exert many beneficial effects on human health. However, a few studies have been conducted on the geoherb L. barbarum and its rhizosphere soil fungal community. In this study, we determined the physicochemical properties and fungal community structure of rhizosphere soil of L. barbarum from three regions of China, namely Ningxia (NX), Qinghai (QH), and Xinjiang (XJ), during three development stages of L. barbarum. Soil pH varied between 7.56 and 8.60 across the three regions, indicating that alkaline soil is conducive to the growth of L. barbarum. The majority of soil properties in NX, an authentic geoherb-producing area, were substantially inferior to those in XJ and QH during all three developmental stages. Total sugar, polysaccharide (LBP), and flavonoid contents were the highest in wolfberry fruits from NX. High-throughput sequencing showed that the abundance of the soil fungal population in NX was higher than that in QH and XJ during the flowering and fruiting stage and summer dormant stage. Moreover, the soil fungal diversity increased with the development of wolfberry. Ascomycota and Mortierellomycota were the predominant phyla in the rhizosphere fungal communities in all samples. Redundancy analysis showed a significant correlation of the soil-available phosphorus and LBP of wolfberry fruits with the fungal community composition. The characteristics of rhizosphere fungal communities determined in the present study provide insights into the mechanism of geoherb formation in NX wolfberry.

Spontaneous recovery of adaptation aftereffects of natural facial categories

Adaptation to a natural face attribute such as a happy face can bias the perception of a subsequent face in this dimension such as a neutral face. Such face adaptation aftereffects have been widely found in many natural facial categories. However, how temporally tuned mechanisms could control the temporal dynamics of natural face adaptation aftereffects remains unknown. To address the question, we used a deadaptation paradigm to examine whether the spontaneous recovery of natural facial aftereffects would emerge in four natural facial categories including variable categories (emotional expressions in Experiment 1 and eye gaze in Experiment 2) and invariable categories (facial gender in Experiment 3 and facial identity in Experiment 4). In the deadaptation paradigm, participants adapted to a face with an extreme attribute (such as a 100% angry face in Experiment 1) for a relatively long duration, and then deadapted to a face with an opposite extreme attribute (such as a 100% happy face in Experiment 1) for a relatively short duration. The time courses of face adaptation aftereffects were measured using a top-up manner. Deadaptation only masked the effects of initial longer-lasting adaptation, and the spontaneous recovery of adaptation aftereffects was observed at the post-test stage for all four natural facial categories. These results likely indicate that the temporal dynamics of adaptation aftereffects of natural facial categories may be controlled by multiple temporally tuned mechanisms.

Two sources of bias explain errors in facial age estimation

Accurate age estimates underpin our everyday social interactions, the provision of age-restricted services and police investigations. Previous work suggests that these judgements are error-prone, but the processes giving rise to these errors are not understood. Here, we present the first systematic test of bias in age estimation using a large database of standardized passport images of heterogeneous ages (n = 3948). In three experiments, we tested a range of perceiver age groups (n = 84), and found average age estimation error to be approximately 8 years. We show that this error can be attributed to two separable sources of bias. First, and accounting for the vast majority of variance, our results show an assimilative serial dependency whereby estimates are systematically biased towards the age of the preceding face. Second, younger faces are generally perceived to be older than they are, and older faces to be younger. In combination, these biases account for around 95% of variance in age estimates. We conclude that perception of age is modulated by representations that encode both a viewer's recent and normative exposure to faces. The finding that age perception is subject to strong top-down influences based on our immediate experience has implications for our understanding of perceptual processes involved in face perception, and for improving accuracy of age estimation in important real-world tasks.

Model Fitting Versus Curve Fitting: A Model of Renormalization Provides a Better Account of Age Aftereffects Than a Model of Local Repulsion

Recently, we proposed that the aftereffects of adapting to facial age are consistent with a renormalization of the perceived age (e.g., so that after adapting to a younger or older age, all ages appear slightly older or younger, respectively). This conclusion has been challenged by arguing that the aftereffects can also be accounted for by an alternative model based on repulsion (in which facial ages above or below the adapting age are biased away from the adaptor). However, we show here that this challenge was based on allowing the fitted functions to take on values which are implausible and incompatible across the different adapting conditions. When the fits are constrained or interpreted in terms of standard assumptions about normalization and repulsion, then the two analyses both agree in pointing to a pattern of renormalization in age aftereffects.

Absence of Sex-Contingent Gaze Direction Aftereffects Suggests a Limit to Contingencies in Face Aftereffects

Face aftereffects (e.g., expression aftereffects) can be simultaneously induced in opposite directions for different face categories (e.g., male and female faces). Such aftereffects are typically interpreted as indicating that distinct neural populations code the categories on which adaptation is contingent, e.g., male and female faces. Moreover, they suggest that these distinct populations selectively respond to variations in the secondary stimulus dimension, e.g., emotional expression. However, contingent aftereffects have now been reported for so many different combinations of face characteristics, that one might question this interpretation. Instead, the selectivity might be generated during the adaptation procedure, for instance as a result of associative learning, and not indicate pre-existing response selectivity in the face perception system. To alleviate this concern, one would need to demonstrate some limit to contingent aftereffects. Here, we report a clear limit, showing that gaze direction aftereffects are not contingent on face sex. We tested 36 young Caucasian adults in a gaze adaptation paradigm. We initially established their ability to discriminate the gaze direction of male and female test faces in a pre-adaptation phase. Afterwards, half of the participants adapted to female faces looking left and male faces looking right, and half adapted to the reverse pairing. We established the effects of this adaptation on the perception of gaze direction in subsequently presented male and female test faces. We found that adaptation induced pronounced gaze direction aftereffects, i.e., participants were biased to perceive small gaze deviations to both the left and right as direct. Importantly, however, aftereffects were identical for male and female test faces, showing that the contingency of face sex and gaze direction participants experienced during the adaptation procedure had no effect.

Facial Age Aftereffects Provide Some Evidence for Local Repulsion (But None for Re-Normalisation)

Face aftereffects can help adjudicate between theories of how facial attributes are encoded. O'Neil and colleagues (2014) compared age estimates for faces before and after adapting to young, middle-aged or old faces. They concluded that age aftereffects are best described as a simple re-normalisation—e.g. after adapting to old faces, all faces look younger than they did initially. Here I argue that this conclusion is not substantiated by the reported data. The authors fit only a linear regression model, which captures the predictions of re-normalisation, but not alternative hypotheses such as local repulsion away from the adapted age. A second concern is that the authors analysed absolute age estimates after adaptation, as a function of baseline estimates, so goodness-of-fit measures primarily reflect the physical ages of test faces, rather than the impact of adaptation. When data are re-expressed as aftereffects and fit with a nonlinear “locally repulsive” model, this model performs equal to or better than a linear model in all adaptation conditions. Data in O'Neil et al. do not provide strong evidence for either re-normalisation or local repulsion in facial age aftereffects, but are more consistent with local repulsion (and exemplar-based encoding of facial age), contrary to the original report.