The role of conventionality and design in children's function judgments about malfunctioning artifacts.

This study investigated the individual influences of conventionality and designer's intent on function judgments of possibly malfunctioning artifacts. Children aged 4 and 5 years and 6 to 8 years were presented with stories about an artifact with two equally plausible functions, one labeled as either conventional or designed. Subsequently, a character attempted to use the artifact for the cued function, which resulted in either malfunction or successful use. The children's task was to identify the real function of the artifact. When the use attempt succeeded, 4- and 5-year-olds preferred conventional functions to the alternative (but did not show a clear preference between design functions and the alternative), and 6- to 8-year-olds preferred conventional and designed functions to the alternative. In case of malfunction, children's choices were at chance, where the effect of either conventional or design cues was less salient. This contrasts with a baseline condition where children avoided the malfunctioning alternatives. Presenting additional cues about an artifact's function can affect function judgments in cases of malfunction.

Clarifying status of DNNs as models of human vision.

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

Systemic Disease Associations in a Cohort of Hispanic Patients with Scleritis.

(1) Purpose: A patient with scleritis may have an associated systemic disease, which is often autoimmunological and seldom infectious in origin. The data regarding such associations in Hispanic populations are scarce. Therefore, we evaluated the clinical characteristics and systemic-disease associations of a cohort of Hispanic patients with scleritis. (2) Methods: A retrospective review of the medical records (January 1990-July 2021) of two private uveitis practices in Puerto Rico was performed. Clinical characteristics and systemic-disease associations observed either at presentation or diagnosed as a consequence of the initial workup were recorded. (3) Results: A total of 178 eyes of 141 patients diagnosed with scleritis were identified. An associated autoimmune disease was present in 33.3% of the patients (rheumatoid arthritis, 22.7%; Sjögren's syndrome, 3.5%; relapsing polychondritis, 2.8%; sarcoidosis, 1.4%; systemic lupus erythematosus, 1.4%; and systemic vasculitis, 0.7%). An associated infectious disease was present in 5.7% of the patients (2.13%, syphilis; 1.41%, herpes simplex; 1.14%, herpes zoster; and 0.71%, Lyme disease). One patient had all-trans retinoic-acid-associated scleritis. Statistical analysis revealed that patients with nodular anterior scleritis were less likely to have an associated immune-mediated disease (OR: 0.21; p = 0.011). (4) Conclusion: Rheumatoid arthritis was the most common systemic autoimmune disease association, while syphilis was the most common infectious disease associated with scleritis patients. Our study suggests that patients with nodular scleritis have a lower risk of having an associated immune-mediated disease.

Aflibercept as a Treatment for Secondary Central Serous Chorioretinopathy in a Patient With Myasthenia Gravis.

We report on a case of central serous chorioretinopathy (CSCR) secondary to chronic steroid use that showed sustained improvement when treated with an aflibercept intravitreal injection. A 44-year-old woman presented with decreased visual acuity of the left eye (OS). The patient had a recent history of myasthenia gravis and was being treated with systemic corticosteroids and immunosuppressants. At presentation, her visual acuity was 20/80 OS; an examination (using fluorescein angiography) of the left fundus revealed a serous retinal detachment of the posterior pole that extended to the mid-periphery and multiple areas of leakage, which findings were consistent with CSCR. The patient also had a history of unresolved strabismic amblyopia in her right eye. The patient's CSCR was managed with one injection of intravitreal aflibercept (2 mg/0.05 mL). One month following treatment, her visual acuity improved to 20/20 OS, and the serous retinal detachment had resolved. Ten months following treatment, an examination revealed a sustained improvement, with a visual acuity of 20/20 OS. Concomitantly, the patient's amblyopic eye revealed an improved visual acuity of 20/20. Our case suggests that some cases of secondary CSCR may respond to treatment with intravitreal aflibercept. This case also suggests that the CSCR imposed a unique form of occlusion therapy that helped improve the amblyopia of the contralateral eye in this adult patient.

Deep problems with neural network models of human vision.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

Can deep convolutional neural networks support relational reasoning in the same-different task?

Same-different visual reasoning is a basic skill central to abstract combinatorial thought. This fact has lead neural networks researchers to test same-different classification on deep convolutional neural networks (DCNNs), which has resulted in a controversy regarding whether this skill is within the capacity of these models. However, most tests of same-different classification rely on testing on images that come from the same pixel-level distribution as the training images, yielding the results inconclusive. In this study, we tested relational same-different reasoning in DCNNs. In a series of simulations we show that models based on the ResNet architecture are capable of visual same-different classification, but only when the test images are similar to the training images at the pixel level. In contrast, when there is a shift in the testing distribution that does not change the relation between the objects in the image, the performance of DCNNs decreases substantially. This finding is true even when the DCNNs' training regime is expanded to include images taken from a wide range of different pixel-level distributions or when the model is trained on the testing distribution but on a different task in a multitask learning context. Furthermore, we show that the relation network, a deep learning architecture specifically designed to tackle visual relational reasoning problems, suffers the same kind of limitations. Overall, the results of this study suggest that learning same-different relations is beyond the scope of current DCNNs.

A theory of relation learning and cross-domain generalization.

People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the Learning and Inference with Schemas and Analogy (LISA; Hummel & Holyoak, 1997, 2003) and Discovery of Relations by Analogy (DORA; Doumas et al., 2008) models of relational inference and learning. The resulting model learns both the content and format (i.e., structure) of relational representations from nonrelational inputs without supervision, when augmented with the capacity for reinforcement learning it leverages these representations to learn about individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference. We demonstrate the capacity of the model to learn structured relational representations from a variety of simple visual stimuli, and to perform cross-domain generalization between video games (Breakout and Pong) and between several psychological tasks. We demonstrate that the model's trajectory closely mirrors the trajectory of children as they learn about relations, accounting for phenomena from the literature on the development of children's reasoning and analogy making. The model's ability to generalize between domains demonstrates the flexibility afforded by representing domains in terms of their underlying relational structure, rather than simply in terms of the statistical relations between their inputs and outputs. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Microsatellite Instability-High, Malignant Insulinoma With Brain Metastasis.

Insulinomas are the most common type of functional pancreatic neuroendocrine tumor. Although insulinomas usually are noninvasive or benign, 10% are deemed invasive or malignant. The pathologic mechanisms that lead to the malignant phenotype are not well elucidated. In this case report, we present a patient with stage 4 malignant insulinoma with metastasis to the liver, bone, and brain. Genetic analysis of the tumor showed that the tumor was mismatch-repair deficient and had a high rate of microsatellite instability. There was loss of MLH1- and PMS2-encoded protein expression, and MLH1 and MEN1 variants were identified. Notably, the liver metastasis showed considerable tumor heterogeneity (well differentiated) compared with the brain metastasis (poorly differentiated).

Inference and coherence in causal-based artifact categorization.

In four experiments, we tested conditions under which artifact concepts support inference and coherence in causal categorization. In all four experiments, participants categorized scenarios in which we systematically varied information about artifacts' associated design history, physical structure, user intention, user action and functional outcome, and where each property could be specified as intact, compromised or not observed. Consistently across experiments, when participants received complete information (i.e., when all properties were observed), they categorized based on individual properties and did not show evidence of using coherence to categorize. In contrast, when the state of some property was not observed, participants gave evidence of using available information to infer the state of the unobserved property, which increased the value of the available information for categorization. Our data offers answers to longstanding questions regarding artifact categorization, such as whether there are underlying causal models for artifacts, which properties are part of them, whether design history is an artifact's causal essence, and whether physical appearance or functional outcome is the most central artifact property.