Human Visual Pathways for Action Recognition Versus Deep Convolutional Neural Networks: Representation Correspondence in Late But Not Early Layers.

Deep convolutional neural networks (DCNNs) have attained human-level performance for object categorization and exhibited representation alignment between network layers and brain regions. Does such representation alignment naturally extend to other visual tasks beyond recognizing objects in static images? In this study, we expanded the exploration to the recognition of human actions from videos and assessed the representation capabilities and alignment of two-stream DCNNs in comparison with brain regions situated along ventral and dorsal pathways. Using decoding analysis and representational similarity analysis, we show that DCNN models do not show hierarchical representation alignment to human brain across visual regions when processing action videos. Instead, later layers of DCNN models demonstrate greater representation similarities to the human visual cortex. These findings were revealed for two display formats: photorealistic avatars with full-body information and simplified stimuli in the point-light display. The discrepancies in representation alignment suggest fundamental differences in how DCNNs and the human brain represent dynamic visual information related to actions.

Cognitive complexity explains processing asymmetry in judgments of similarity versus difference.

Human judgments of similarity and difference are sometimes asymmetrical, with the former being more sensitive than the latter to relational overlap, but the theoretical basis for this asymmetry remains unclear. We test an explanation based on the type of information used to make these judgments (relations versus features) and the comparison process itself (similarity versus difference). We propose that asymmetries arise from two aspects of cognitive complexity that impact judgments of similarity and difference: processing relations between entities is more cognitively demanding than processing features of individual entities, and comparisons assessing difference are more cognitively complex than those assessing similarity. In Experiment 1 we tested this hypothesis for both verbal comparisons between word pairs, and visual comparisons between sets of geometric shapes. Participants were asked to select one of two options that was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard; on ambiguous trials, one option was more featurally similar to the standard, whereas the other was more relationally similar. Given the higher cognitive complexity of processing relations and of assessing difference, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was replicated using more complex story stimuli (Experiment 2). We showed that this pattern can be captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments.

Reproductive factors and risk of cardiovascular diseases and all-cause and cardiovascular mortality in American women: NHANES 2003-2018.

BACKGROUND: The evidence regarding the association of reproductive factors with cardiovascular diseases (CVDs) is limited. AIMS: To investigate the relationship of reproductive factors with the risk of CVDs, as well as all-cause and cardiovascular mortality. METHODS: This study included 16,404 adults with reproductive factors from the National Health and Nutrition Examination Survey (NHANES) and followed up until 31 December 2019. Logistic models and restricted cubic spline models were used to assess the association of reproductive factors with CVDs. COX proportional hazards models and restricted cubic spline models, with adjustment for potential confounding, were employed to analyze the relation between reproductive factors and cardiovascular and all-cause death. RESULTS: There is a nonlinear relationship between age at menarche and CVDs. Age at menopause ≤ 11(OR 1.36, 95% CI 1.10-1.69) was associated with an increased risk of CVDs compared to ages 12-13 years. Age at Menopause ≤ 44 (OR 1.69, 95% CI 1.40-2.03) was associated with increased CVDs compared to age 35-49 years. Number of pregnancies ≥ 5(OR 1.26, 95% CI 1.02-1.55) was associated with an increased risk of CVDs compared to one pregnancy. In continuous variable COX regression models, a later age at menopause (HR 0.98, 95% CI 0.97-0.99) and a longer reproductive lifespan (HR 0.98, 95% CI 0.97-0.99) were associated with a decreased risk of all-cause death. A later age at menopause (HR 0.98, 95% CI 0.97-0.99) and a longer reproductive lifespan (HR 0.98, 95% CI 0.97-0.99) were associated with a decreased risk of cardiac death. CONCLUSIONS: Female reproductive factors are significant risk factors for CVDs American women.

Individual differences and motor planning influence self-recognition of actions.

Although humans can recognize their body movements in point-light displays, self-recognition ability varies substantially across action types and participants. Are these variations primarily due to an awareness of visually distinct movement patterns, or to underlying factors related to motoric planning and/or individual differences? To address this question, we conducted a large-scale study in self-action recognition (N = 101). We motion captured whole-body movements of participants who performed 27 different actions across action goals and degree of motor planning. After a long delay period (~ 1 month), participants were tested in a self-recognition task: identifying their point-light action amongst three other point-light actors performing identical actions. We report a self-advantage effect from point-light actions, consistent with prior work in self-action recognition. Further, we found that self-recognition was modulated by the action complexity (associated with the degree of motor planning in performed actions) and individual differences linked to motor imagery and subclinical autism and schizotypy. Using dynamic time warping, we found sparse evidence in support of visual distinctiveness as a primary contributor to self-recognition, though speed distinctiveness negatively influenced self-recognition performance. Together, our results reveal that self-action recognition involves more than an awareness of visually distinct movements, with important implications for how the motor system may be involved.

Association Between Systemic Immune Inflammation Index and Diabetes Mellitus in the NHANES 2003-2018 Population.

Objects: This study aimed to explore the association between the Systemic Immune-Inflammation Index (SII) and diabetes mellitus (DM) and to assess its influence on the prognosis of the DM and no-DM groups. Methods: The study used data from the National Health and Nutrition Examination Survey; 9643 participants were included. Logistic regression analysis was employed to evaluate connections between SII and DM. We used the Cox proportional hazards model, restricted cubic spline, and Kaplan-Meier curve to analyze the relationship between SII and mortality. Results: The logistic regression analysis indicated that a significant increase in the likelihood of developing DM with higher SII levels (odds ratio, 1.31; 95% CI, 1.09-1.57, P = .003). The Cox model showed that there is a positive association between increased SII and higher all-cause mortality. The hazard ratios for SII were 1.53 (1.31, 1.78), 1.61 (1.31, 1.98), and 1.41 (1.12, 1.78) in the total, DM and non-DM groups, respectively. We observed a linear correlation between SII and all-cause mortality in DM participants, whereas non-DM participants and the total population showed a nonlinear correlation. Conclusion: Elevated SII levels are linked to an augmented risk of DM. Those with DM and higher SII levels demonstrated an elevated risk of mortality.

Patterns of saliency and semantic features distinguish gaze of expert and novice viewers of surveillance footage.

When viewing the actions of others, we not only see patterns of body movements, but we also "see" the intentions and social relations of people. Experienced forensic examiners - Closed Circuit Television (CCTV) operators - have been shown to convey superior performance in identifying and predicting hostile intentions from surveillance footage than novices. However, it remains largely unknown what visual content CCTV operators actively attend to, and whether CCTV operators develop different strategies for active information seeking from what novices do. Here, we conducted computational analysis for the gaze-centered stimuli captured by experienced CCTV operators and novices' eye movements when viewing the same surveillance footage. Low-level image features were extracted by a visual saliency model, whereas object-level semantic features were extracted by a deep convolutional neural network (DCNN), AlexNet, from gaze-centered regions. We found that the looking behavior of CCTV operators differs from novices by actively attending to visual contents with different patterns of saliency and semantic features. Expertise in selectively utilizing informative features at different levels of visual hierarchy may play an important role in facilitating the efficient detection of social relationships between agents and the prediction of harmful intentions.