Reviewerly/exHarmony huggingface dataset:
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 September 2023; 128 (5): 393–394. doi: https://doi.org/10.1352/1944-7558-128.5.393 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Resúmenes al Español

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Resúmenes al Español. Am J Intellect Dev Disabil 1 September 2023; 128 (5): 395–396. doi: https://doi.org/10.1352/1944-7558-128.5.395 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
The semantic distance between a linguistic prime and a natural scene target predicts reaction times in a visual search experiment

Does reading a semantically similar sentence description of a scene make you faster at subsequently detecting that scene in a visual search task? How does this vary across individuals when everyone has different conceptual knowledge? To investigate the former, 95 subjects were asked to identify a target natural scene in a visual search experiment where the display of a target scene and five semantically related distractors was preceded by a sentence prime. Every scene from our stimuli set was sampled as a target under three conditions: the prime was either the same as the target, halfway between the target and the semantically farthest item from the target, or the semantically farthest item from the target. The semantic distances between each pair of items were averaged Euclidean distances from the multi-arrangements (MA) task collected as part of the Natural Scenes Dataset (NSD). A subset of 27 subjects also completed MA on both the linguistic primes and scene targets, allowing us to use the idiosyncratic distances as predictors of each subject’s RTs. A generalised linear mixed effects model (GLMM) revealed that after removing the zero-distance condition (prime same as the target) the averaged NSD distances (n = 84, slope = .953, t = 5.229, p < .001) do indeed predict the RTs. Strikingly, the strongest effect emerged when the idiosyncratic distances from the captions MA (n = 26, slope = 1.165, t = 3.105, p < .01) were used as the predictors. These findings are significant in at least two major respects: firstly, linguistic primes promote visual targets the more semantically related they are. Secondly, this seems to be closely linked to the subject-specific similarity judgements which extends previous knowledge of semantic priming and opens a discussion as to what level the linguistic input intertwines with one's visual representations.
Unsupervised feature selection methods for modeling human similarity judgments with deep neural networks

When people compare two objects that belong to the same category, they rely on visual dimensions that may be irrelevant to objects in another category. For instance, texture may be more important for distinguishing between different fruits than between different cars. Deep convolutional neural networks (DNN) learn visual features that can be used to approximate human similarity judgments (HSJ). Current understanding of the relation between DNN features and HSJ is based on models that consider all DNN features for computing pairwise similarity. Recent reweighting approaches use HSJ to supervise the learning of weights assigned to each feature, but similarly assign weights to all features. These may be computational and theoretical limitations: we hypothesized that when modeling the representational geometry of any given domain, many DNN features will not encode relevant information at all, and their removal may even improve the predictions of HSJ. We used unsupervised methods to identify those DNN features which do not contribute to approximating HSJ. We used 6 datasets, each containing 120 natural images. For each set of images separately, we considered less-relevant features as ones with: large percentage of zero activations, low standard deviation, low entropy, or high collinearity with other features. Our results show that: 1) For all datasets, up to 90% of features could be pruned without a meaningful drop in the predictions of HSJ, and for some datasets we observed slight but statistically significant improvements; 2) Pruning introduced slight and gradual increasing changes in the representational geometry of the embedding space; 3) The dominant latent dimension of the pruned embeddings was sufficient for ordering categories on a similarity continuum. In summary, while irrelevant features are common in images of natural categories, they are weakly informative when modeling HSJ, and in some cases their removal can produce better approximations of human representational geometry.
Pengaruh Interaksi Sosial dengan Hasil Belajar IPS Siswa Kelas V MIN 12 Kota Medan

This study examines the effect of social interaction on social studies teaching results for fifth grade students at Min 12 Medan City. The definition of social interaction is the relationship that occurs from an individual to another individual, each of which can influence one another. The ability to interact is something that every student needs to have because humans are said to be social beings and require interaction. High interaction will result in teaching results on social studies material for grade 5, and the higher will be the analysis technique carried out with descriptive statistical analysis. The hypothesis is tested using collarational analysis through the formula on the moment product by utilizing Excel. . From trials conducted on 15 question items, it was found that each of these questions could be used as a questionnaire to find out the interactions that occurred between students because they had gone through validation both in the form of items and content by utilizing the formula from the moment product and testing the reliability of the instrument. One of the important things to do before taking action is to test it in the form of linearity of normality and correlation between products. The analysis is carried out using a simple linear regression analysis technique through the t test and the coefficient of determination. Through the research that has been done at MIN 12 Medan city, the regression equation is obtained with the magnitude Y = 51.592 + 0.537 X. Based on the t test, it is found that tcount = 3.480. Degrees of freedom dk = 27 and a significant level of 5%, so that the price ttable = 1.703 is obtained. Through calculations obtained tcount&gt; ttable (3.480 &gt; 1.703) so that towards Ho experienced rejection As for Aa experienced acceptance from this study showed the results of the influence of social interaction on students related to teaching outcomes in social studies subjects at MIN 12 Medan city on the coefficient of determination the magnitude is 0.310 with the understanding that the average student's ability to carry out social interactions contributes to the results of teaching them with a magnitude of 31.0% from the regression equation Y = 51.592 + 0.537 X The remaining 69.0% is determined by other factors. &#x0D; Keywords: Student, Social Interaction, Learning Outcomes, Social Science
Artifact magnification on deepfake videos increases human detection and subjective confidence

Deefake videos, where faces are modified using deep learning, are becoming easier to make and more convincing, creating online security risks. Machine learning models are being developed to detect deepfakes, but human users often disregard model predictions (Groh et al, 2021). Here, we explore a novel visual indicator called Deepfake Caricatures, in which video artifacts are magnified, and show that it is both effective and subjectively convincing. In Experiment 1, participants viewed silent videos of people, and indicated whether each was real or fake. Deepfakes were either non-signaled, or signaled with the Caricature technique (between-subjects, N=180). Detectability was measured across conditions known to affect visual search and signal detection performance: a Baseline condition (E1a, 50% prevalence, no time limit), Low Prevalence (E1b, 20% prevalence), Time Pressure (E1c, 2 second time limit), and Noisy Visual Input (E1d, video compression noise). Across all conditions, sensitivity was much higher for Caricatures than non-signaled deepfakes (E1a: t = 30.08, p<0.001; E1b: t = 32.28, p<0.001; E1c: t = 39.62, p<0.001; E1d: t=30.58, p<0.001 ; unpaired t-tests), showing that the Caricatures manipulation is effective. Additionally, we compared the detectability of non-signaled deepfakes across experiments, and found that all conditions significantly reduced deepfake detection relative to Baseline, suggesting that the present literature overestimates deepfake detectability in the wild. In Experiment 2, participants (N = 298) viewed videos and rated their confidence that they were real or fake using a slider interface, then viewed a model’s prediction displayed either as text or in Caricatures form, and finally were allowed to update their responses. Participants updated their responses more often following a Caricature than a text-based indicator (t=4.30, p<0.001), showing that Caricatures is a more convincing visual indicator than text.
SimpleXR: An open-source Unity toolbox for simplifying vision research using augmented and virtual reality

Extended reality (XR) is a powerful tool for human behavioral research. The ability to create 3D visual scenes and measure responses to arbitrary visual stimuli enables the behavioral researcher to test hypotheses in a well-controlled environment. However, software packages such as SteamVR, OpenXR, and ARKit have been developed for game designers rather than behavioral researchers. While Unity is considered the most beginner-friendly platform, barriers still exist for inexperienced programmers. Toolboxes such as VREX and USE have focused on simplifying experimental design and remote data collection, but no tools currently exist to help with all aspects of an experiment. To address this challenge, we have developed SimpleXR (sXR), an open-source Unity package that allows for creating complex experiments with relatively little code. The toolbox contains a plethora of tools that are particularly useful for the visual sciences, such as creating dynamic scenes, randomizing object locations, accessing eye-tracker data, and applying full-screen shader effects (e.g., blurring, gaze-contingent scotomas, edge detection) either in virtual reality (VR) or to the pass-through camera for augmented reality (AR) tasks. sXR also provides one-line commands for interacting with virtual objects, displaying stimuli and instructions, using timers, and much more. Additionally, it automatically switches between desktop and immersive VR modes. sXR creates separate user interfaces for the experimenter and participant, allowing the experimenter to track performance and monitor for anomalies. By using Unity’s Universal Rendering Pipeline, sXR allows researchers to develop across platforms, including VR headsets, AR glasses, and smartphones. sXR is freely available at github.com/unity-sXR/sXR.
A novel eye-tracking paradigm to investigate the focus of object-based attention

We use object-based attention in our daily lives to selectively attend to and interact with task-relevant objects in our environment. Previous research has suggested that common neural mechanisms serve to guide eye movements and covert shifts of spatial attention, but might eye movements be also used to determine the focus of object-based attention? We investigated this question by developing a novel eye-tracking paradigm, wherein two objects followed pseudo-randomly generated trajectories while remaining partially overlapping. At random times, either object would be briefly spatially distorted. In separate blocks, participants had to attend to face only, flower only, or to both objects to perform a change detection task while free-viewing eye movements were monitored. We hypothesized that the selectivity of object-based attention would be reflected by the correlation strength between eye position and stimulus location. Using a sliding-window correlation analysis (N=11), we found that gaze trajectories were highly correlated with the trajectory of the task-relevant stimulus (mean r = 0.7061), weakly correlated with that of the irrelevant stimulus (mean r = 0.1612), and intermediately correlated with both stimuli in the attend-both condition (mean r = 0.4278). Behavioral performance also revealed a two-object cost, with higher accuracy for single-object tracking than for the attend-both condition (mean face accuracy, 79% vs. 57%; mean flower accuracy, 66% vs. 42%). To measure the precision with which gaze following was indicative of the attentional focus, we binned behavioral accuracy based on average correlation of each trial (10 trials/bin). This analysis revealed that trials with higher stimulus-gaze correlations had better behavioral performance for the attended stimulus. In the attend-both condition, the degree of gaze following for each of the two stimuli was also predictive of detection accuracy (p < .05). Overall, we demonstrate a promising, novel method to evaluate the trial-by-trial focus of object-based attention using gaze-tracking.
The statistics of infants’ natural visual experience are shaped by motor development.

During the first two years of life, human infants develop the ability to coordinate their head/eye movements and move through the world, first by rolling, then crawling and eventually walking. These changing motor abilities have an impact on their visual experience. We examined how the statistics of visual experience change over the course of motor and visual development. We analyzed video data gathered from cameras mounted on the heads of infants (N=89, age=1 to 26 months) in their home environment. Image statistics were calculated for each video frame, including RMS contrast, orientation, spatial frequency, and contrast energy. We also measured the frequency of large-head-movements. Using deep learning, and epi-polar geometry, we reconstructed the angular camera movement between video frames. We treat these estimates as an approximation of the head movements. Most notably, between 1 and 11 months, there was an increase in the occurrence of large-head-movements, followed by a slow decrease through 26 months. The global RMS contrast was uniform across age groups, but the concentration of RMS contrast towards the center of the video frames was more pronounced in older infants, suggesting that older infants select high-contrast information as they become more capable of manipulating their bodies and their environment. We saw no consistent difference in the orientation content across age. This was counter to our hypothesis that the typical biases (horizontal/vertical) in orientation statistics in natural images would emerge/tighten as infants developed postural stability. However, 26 months is still relatively early in the development of walking and postural control. Future work will include comparable data from older children or adults to determine if the orientation content changes with this increased stability or if the variability due to infant postural instability is small relative to the natural variability of orientation content due to the self- motion observed in adults.
Stable individual differences in gaze behavior reflect unique conceptual priority maps

No two individuals’ gaze patterns during naturalistic viewing are identical: we each prioritize attending to different features when exploring a real-world environment. Recent studies have shown that gaze patterns are not only explained by attention to semantic content (e.g., phone, car) but also abstract conceptual information (e.g., for sale; being repaired) that can be modeled using computational language models. Are there unique patterns in individuals’ gaze toward conceptual information as they attend in real-world environments, and are these patterns stable over time for an individual? Here, we tested whether gaze patterns in conceptual space can individuate viewers across testing days, thus reflecting attentional priorities that are trait-, rather than state-like. We measured participants’ (N = 29) visual attention while they explored real-world photospheres in VR, across two sessions (Day 1: N = 60; Day 2: N = 40). We characterized the conceptual information in each scene by decomposing photospheres into a set of overlapping tiles and obtaining context-informed verbal descriptions from online raters. Tile descriptions were then transformed into sentence-level embeddings using a computational language model (BERT), capable of capturing context-dependent conceptual meaning (e.g., river bank vs. financial bank). To characterize the conceptual information a participant prioritized across all photospheres, we fit a model between this conceptual feature space and gaze behavior for each participant and testing session. We assessed whether individual conceptual priorities are stable and individuating across sessions by calculating the correlation between feature weights modeled for each participant’s two sessions. We find that conceptual feature weights were stable (r = 0.72) and individuating across days: within-subject feature weight correlation was higher, on average, than between-subject correlation (t(28) = 2.4, p = 0.03). Our results suggest that individual gaze patterns reflect high-level conceptual information seeking and individuating, trait-like priorities within conceptual space that persist across viewing sessions.
Reconstruction-guided attention improves the object recognition robustness of neural networks

Many visual phenomena suggest that humans use top-down generative or reconstructive processes to create visual percepts (e.g., imagery, object completion, pareidolia), but little is known about the role reconstruction plays in robust object recognition. We built an iterative encoder-decoder network that generates an object reconstruction and uses it as top-down attentional feedback to route the most relevant spatial and feature information to feed-forward object recognition processes. We tested this model using the challenging out-of-distribution object recognition dataset, MNIST-C (handwritten digits under corruptions) and IMAGENET-C (real-world objects under corruptions). Our model showed strong generalization performance against various image corruptions and significantly outperformed other feedforward convolutional neural network models (e.g., ResNet) on both datasets. Our model’s robustness was particularly pronounced under high levels of distortions, where it showed a maximum 20% accuracy improvement from the baseline model in the maximally noisy conditions in IMAGENET-C. Ablation studies further reveal two complementary roles of spatial and feature-based attention in robust object recognition, with the former largely consistent with spatial masking benefits in the attention literature (the reconstruction serves as a mask) and the latter mainly contributing to the model’s inference speed (i.e., number of time steps to reach a certain confidence threshold) by reducing the space of possible object hypotheses. Finally, the proposed model also yields high behavioral correspondence with humans, which are evaluated by the correlation between human and model’s response time (Spearman’s r=0.36, p<.001) and the types of error made. By infusing an AI model with a powerful attention mechanism, we show how reconstruction-based feedback can be used to explore the role of generation in human visual perception.
‘Visual verbs’: Dynamic event types (such as twisting vs. rotating) are extracted quickly and spontaneously during visual perception

The underlying units of visual representation often transcend lower-level properties, for example when we see objects in terms of a small number of generic stimulus types (e.g. animals, plants, faces, etc.). There has been much less attention, however, to the possibility that we also represent dynamic information in terms of a small number of primitive *event types* — such as twisting, rotating, bouncing, rolling, etc. (In models that posit a “language of vision”, these would be the foundational visual *verbs*.) We explored the possibility that such ‘event type’ representations are formed quickly and spontaneously during visual perception — even when they are entirely task-irrelevant. We did so by exploiting the phenomenon of *categorical perception* — wherein the differences between two stimuli are more readily noticed when they are represented in terms of different underlying categories. Observers simply viewed pairs of images or animations (presented very briefly, one at a time), and reported for each pair whether they were the same or different in any way. Cross-Type changes involved switches in the underlying event type (e.g. a towel being *twisted* in someone’s hands, replaced by a towel being *rotated* in someone’s hands), while Within-Type changes maintained the same event type (e.g. a towel being more or less twisted in someone’s hands). Critically, this distinction was always task-irrelevant, and Within-Type changes were always objectively greater in magnitude than were Cross-Type changes. Nevertheless, Cross-Type changes were much more readily noticed. And additional controls confirmed that such effects could not be explained by appeal to lower-level stimulus differences (such as the different hand positions involved in twisting vs. rotating). This spontaneous perception of a potentially continuous range of stimuli in terms of a smaller set of primitive “visual verbs” might promote both generalization and prediction about how events are likely to unfold.
Does your old clutter measure spark joy?

Clutter can degrade performance at many visual tasks. Though the degree to which this is true depends on the task, task-independent measures of clutter have proven useful for replacing notions of set-size for natural scenes, and for predicting subjective clutter judgments, search performance, eye movements, and visibility of artifacts. Rosenholtz, Li, & Nakano (2007) proposed several clutter measures: The Feature Congestion measure derives from the suggestion that clutter makes it difficult to add a salient, easy-to-find item; higher feature variance means more clutter. On the other hand, the Subband Entropy clutter measure follows from the intuition (and observations) that the more cluttered an image the more bits one needs to encode it. However, a prominent factor in the degradation in performance due to clutter has to do with peripheral crowding. Peripheral vision pools texture statistics over sizable regions that grow with eccentricity (Balas et al., 2009; Freeman & Simoncelli, 2011). The degradation of performance due to clutter may therefore depend on the cost of that summarization. This intuition connects to the aforementioned clutter measures, since crowding affects ease of search (e.g. saliency), and one might reasonably associate an entropy measure with the cost of summarizing a patch. This work develops a new multifeatured clutter measure. It can be foveated, i.e. for known fixation it depends on eccentricity. Rather than measuring simple subband entropy, it depends on the texture statistics hypothesized to underlie peripheral crowding, such as the joint statistics of magnitude of oriented subbands. This measure has advantages over previous clutter measures; for example, it marks some textures as relatively uncluttered, in line with predictions of texture statistic models of peripheral vision.