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1. The role of category- and exemplar-specific experience in ensemble processing of objects

   https://doi.org/10.3758/s13414-020-02162-4  

   Cha, Oakyoon; Blake, Randolph; Gauthier, Isabel (January 2020, Attention, Perception, & Psychophysics)

   null (Ed.)

   People can relatively easily report summary properties for ensembles of objects, suggesting that this information can enrich visual experience and increase the efficiency of perceptual processing. Here, we ask whether the ability to judge diversity within object arrays improves with experience. We surmised that ensemble judgments would be more accurate for commonly experienced objects, and perhaps even more for objects of expertise like faces. We also expected improvements in ensemble processing with practice with a novel category, and perhaps even more with repeated experience with specific exemplars. We compared the effect of experience on diversity judgments for arrays of objects, with participants being tested with either a small number of repeated exemplars or with a large number of exemplars from the same object category. To explore the role of more prolonged experience, we tested participants with completely novel objects (random-blobs), with objects familiar at the category level (cars), and with objects with which observers are experts at subordinate-level recognition (faces). For objects that are novel, participants showed evidence of improved ability to distribute attention. In contrast, for object categories with long-term experience, i.e., faces and cars, performance improved during the experiment but not necessarily due to improved ensemble processing. Practice with specific exemplars did not result in better diversity judgments for all object categories. Considered together, these results suggest that ensemble processing improves with experience. However, the role of experience is rapid, does not rely on exemplar-level knowledge and may not benefit from subordinate-level expertise. 

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2. The Vanderbilt Holistic Processing Tests for Novel Objects: Validation in Novice Participants

   Chua, K.W.; Gauthier, I. (January 2020, Journal of expertise)

   null (Ed.)

   In recent work, the Vanderbilt Holistic Processing Tests for novel objects (VHPT-NOs), were used to show that holistic processing for artificial objects increased as a function of parametric variation of experience. Here, novices are tested on the VHPT-Nos to address two questions. First, does the test detect any level of holistic processing for novel objects in novices? Second, how is part matching performance on this test related to object recognition ability, as measured by the Novel Object Memory Test (NOMT)? In a high-powered study, we provide substantial evidence of no holistic processing on the VHPT-NO in novices, including for arguably facelike symmetrical Greebles. Evidence of no correlations between measures of holistic processing suggests that these indices can be considered free of influences from domain-general selective attention. In contrast, overall performance in part matching in the VHPT-NO shows shared variance across categories, which we postulate is related to object recognition. A second study provides direct evidence that part matching measures to a large extent the same ability as whole object learning on the NOMT. Our results suggest that any holistic processing measured in the VHPT-NOs will not be contaminated by domain-general effects and can be considered entirely due to experience with a category. The VHPT-NO will therefore be useful in further examination of how different aspects of experience contribute to the development of holistic processing. 

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3. Domain-specific experience determines individual differences in holistic processing

   https://doi.org/10.1037/xge0000628  

   Chua, K.-W.; Gauthier, I. (January 2020, Journal of experimental psychology)

   null (Ed.)

   Holistic processing refers to the processing of objects as wholes rather than in a piecemeal, part-based fashion. Despite a suggested link between expertise and holistic processing, the role of experience in determining holistic processing of both faces and objects has been questioned. Here, we combine an individual differences approach with an experimental training study and parametrically manipulate experience with novel objects to examine the determinants of holistic processing. We also measure object-recognition ability. Our results show that although domain-general visual ability is a predictor of the ability to match object parts, it is the amount of experience people have individuating objects of a category that determines the extent to which they process new objects of this category in a holistic manner. This work highlights the benefits of dissociating the influences of domain-general ability and domain-specific experience, typically confounded in measures of performance or “expertise.” Our findings are consistent with those in recent work with faces showing that variability specific to experience is a better predictor of domain-specific effects than is variability in performance. We argue that individual differences in holistic processing arise from domain-specific experience and that these effects are related to similar effects of experience on other measures of selective attention. 

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4. Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview Study

   https://doi.org/10.2196/29368  

   Schueller, Stephen M; Neary, Martha; Lai, Jocelyn; Epstein, Daniel A (January 2021, JMIR Mental Health)

   Background Supporting mental health and wellness is of increasing interest due to a growing recognition of the prevalence and burden of mental health issues. Mood is a central aspect of mental health, and several technologies, especially mobile apps, have helped people track and understand it. However, despite formative work on and dissemination of mood-tracking apps, it is not well understood how mood-tracking apps used in real-world contexts might benefit people and what people hope to gain from them. Objective To address this gap, the purpose of this study was to understand motivations for and experiences in using mood-tracking apps from people who used them in real-world contexts. Methods We interviewed 22 participants who had used mood-tracking apps using a semistructured interview and card sorting task. The interview focused on their experiences using a mood-tracking app. We then conducted a card sorting task using screenshots of various data entry and data review features from mood-tracking apps. We used thematic analysis to identify themes around why people use mood-tracking apps, what they found useful about them, and where people felt these apps fell short. Results Users of mood-tracking apps were primarily motivated by negative life events or shifts in their own mental health that prompted them to engage in tracking and improve their situation. In general, participants felt that using a mood-tracking app facilitated self-awareness and helped them to look back on a previous emotion or mood experience to understand what was happening. Interestingly, some users reported less inclination to document their negative mood states and preferred to document their positive moods. There was a range of preferences for personalization and simplicity of tracking. Overall, users also liked features in which their previous tracked emotions and moods were visualized in figures or calendar form to understand trends. One gap in available mood-tracking apps was the lack of app-facilitated recommendations or suggestions for how to interpret their own data or improve their mood. Conclusions Although people find various features of mood-tracking apps helpful, the way people use mood-tracking apps, such as avoiding entering negative moods, tracking infrequently, or wanting support to understand or change their moods, demonstrate opportunities for improvement. Understanding why and how people are using current technologies can provide insights to guide future designs and implementations. 

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5. Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

   https://doi.org/10.1109/ICRA48891.2023.10161284  

   Chen, Yiye; Lin, Yunzhi; Xu, Ruinian; Vela, Patricio A. (May 2023, International Conference on Robotics and Automation)

   The success of 6-DoF grasp learning with point cloud input is tempered by the computational costs resulting from their unordered nature and pre-processing needs for reducing the point cloud to a manageable size. These properties lead to failure on small objects with low point cloud cardinality. Instead of point clouds, this manuscript explores grasp generation directly from the RGB-D image input. The approach, called Keypoint-GraspNet (KGN), operates in perception space by detecting projected gripper keypoints in the image, then recovering their SE(3) poses with a PnP algorithm. Training of the network involves a synthetic dataset derived from primitive shape objects with known continuous grasp families. Trained with only single-object synthetic data, Keypoint-GraspNet achieves superior result on our single-object dataset, comparable performance with state-of-art baselines on a multi-object test set, and outperforms the most competitive baseline on small objects. Keypoint-GraspNet is more than 3x faster than tested point cloud methods. Robot experiments show high success rate, demonstrating KGN's practical potential. 

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