Negative affordance effect: automatic response inhibition triggered by handle orientation of non-target object.
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ABSTRACT:
SUBMITTER: Vainio L
PROVIDER: S-EPMC8475350 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This study is devoted to investigating mechanisms that inhibit habituated response associated with affordance of a non-target while executing action directed to a target. In four experiments, a paradigm was used that required a rapid left- or right-hand response according to the direction of the target arrow presented simultaneously or in close temporal proximity to a non-target whose handle position afforded grasping with the left or right hand. In general, responding was decelerated and more erroneous when the handle position was compatible with the responding hand. This effect of response inhibition was removed when the delay between the non-target offset and target onset was longer than 200 ms, and reversed into response facilitation when the target onset was delayed for 400-600 ms. The study suggests that processes that control withholding habitual response associated with affordance of a non-target utilise response inhibition mechanisms overlapping with those involved in behavioural control of the stop-signal task. This response inhibition is triggered automatically and directly by affordance of a non-target without preceding response excitation associated with this affordance cue.
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Project description:While affordance detection and Human-Object interaction (HOI) detection tasks are related, the theoretical foundation of affordances makes it clear that the two are distinct. In particular, researchers in affordances make distinctions between J. J. Gibson's traditional definition of an affordance, “the action possibilities” of the object within the environment, and the definition of a telic affordance, or one defined by conventionalized purpose or use. We augment the HICO-DET dataset with annotations for Gibsonian and telic affordances and a subset of the dataset with annotations for the orientation of the humans and objects involved. We then train an adapted Human-Object Interaction (HOI) model and evaluate a pre-trained viewpoint estimation system on this augmented dataset. Our model, AffordanceUPT, is based on a two-stage adaptation of the Unary-Pairwise Transformer (UPT), which we modularize to make affordance detection independent of object detection. Our approach exhibits generalization to new objects and actions, can effectively make the Gibsonian/telic distinction, and shows that this distinction is correlated with features in the data that are not captured by the HOI annotations of the HICO-DET dataset.
| S-EPMC9923013 | biostudies-literature
Project description:We live in a right-hander's world. Although left-handers become accustomed to using right-handed devices, an underlying preference for objects that afford the dominant hand could remain. We employed eye tracking while left- and right-handed participants viewed advertisements for everyday products. Participants then rated aesthetic appeal, purchase intention, and perceived value. Left-handed participants found advertisements for products that more easily afforded them action to be more aesthetically appealing. They also indicated greater future purchase intention for products that were oriented towards the left hand, and gave these products a higher perceived value. Eye tracking data showed that object handles attracted attention, and were also able to retain participants' attention. Further, across multiple eye movement measures, our data show that participant eye movements were altered by the orientation of the handle, such that this side of the image was examined earlier and for longer, regardless of handedness. Left-handers' preferences might be stronger because they are more aware of object orientation, whereas right-handers do not experience the same difficulties. These findings highlight intrinsic differences in the way in which we perceive objects and our underlying judgments about those products, based on handedness.
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Project description:Similar to specific natural language instructions, intention-related natural language queries also play an essential role in our daily life communication. Inspired by the psychology term "affordance" and its applications in Human-Robot interaction, we propose an object affordance-based natural language visual grounding architecture to ground intention-related natural language queries. Formally, we first present an attention-based multi-visual features fusion network to detect object affordances from RGB images. While fusing deep visual features extracted from a pre-trained CNN model with deep texture features encoded by a deep texture encoding network, the presented object affordance detection network takes into account the interaction of the multi-visual features, and reserves the complementary nature of the different features by integrating attention weights learned from sparse representations of the multi-visual features. We train and validate the attention-based object affordance recognition network on a self-built dataset in which a large number of images originate from MSCOCO and ImageNet. Moreover, we introduce an intention semantic extraction module to extract intention semantics from intention-related natural language queries. Finally, we ground intention-related natural language queries by integrating the detected object affordances with the extracted intention semantics. We conduct extensive experiments to validate the performance of the object affordance detection network and the intention-related natural language queries grounding architecture.
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Project description:Contour interpolation automatically binds targets with distractors to impair multiple object tracking (Keane, Mettler, Tsoi, & Kellman, 2011). Is interpolation special in this regard or can other features produce the same effect? To address this question, we examined the influence of eight features on tracking: color, contrast polarity, orientation, size, shape, depth, interpolation, and a combination (shape, color, size). In each case, subjects tracked 4 of 8 objects that began as undifferentiated shapes, changed features as motion began (to enable grouping), and returned to their undifferentiated states before halting. We found that intertarget grouping improved performance for all feature types except orientation and interpolation (Experiment 1 and Experiment 2). Most importantly, target-distractor grouping impaired performance for color, size, shape, combination, and interpolation. The impairments were, at times, large (>15% decrement in accuracy) and occurred relative to a homogeneous condition in which all objects had the same features at each moment of a trial (Experiment 2), and relative to a "diversity" condition in which targets and distractors had different features at each moment (Experiment 3). We conclude that feature-based grouping occurs for a variety of features besides interpolation, even when irrelevant to task instructions and contrary to the task demands, suggesting that interpolation is not unique in promoting automatic grouping in tracking tasks. Our results also imply that various kinds of features are encoded automatically and in parallel during tracking.
| S-EPMC3901520 | biostudies-literature
Project description:Quickly and accurately extracting information about a touched object's orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile system's capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3° in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system.
| S-EPMC5922971 | biostudies-literature
Project description:Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells' size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.
| S-EPMC8629215 | biostudies-literature
Project description:PurposeTo accurately record the movements of a hand-held target together with the smooth pursuit eye movements (SPEMs) elicited with video-oculography (VOG) combined with deep learning-based object detection using a single-shot multibox detector (SSD).MethodsThe SPEMs of 11 healthy volunteers (21.3 ± 0.9 years) were recorded using VOG. The subjects fixated on a moving target that was manually moved at a distance of 1 m by the examiner. An automatic recording system was developed using SSD to predict the type and location of objects in a single image. The 400 images that were taken of one subject using a VOG scene camera were distributed into 2 groups (300 and 100) for training and validation. The testing data included 1100 images of all subjects (100 images/subject). The method achieved 75% average precision (AP75) for the relationship between the location of the fixated target (as calculated by SSD) and the position of each eye (as recorded by VOG).ResultsThe AP75 for all subjects was 99.7% ± 0.6%. The horizontal and vertical target locations were significantly and positively correlated with each eye position in the horizontal and vertical directions (adjusted R2 ≥ 0.955, P < 0.001).ConclusionsThe addition of SSD-driven recording of hand-held target positions with VOG allows for quantitative assessment of SPEMs following a target during an SPEM test.Translational relevanceThe combined methods of VOG and SSD can be used to detect SPEMs with greater accuracy, which can improve the outcome of clinical evaluations.
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Project description:3D objects (artifacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities or affordances (action possibilities) that it should provide, known as functional requirements. Today, designing 3D object models is still a slow and difficult activity, with few Computer-Aided Design (CAD) tools capable to explore the design solution space. The purpose of this study is to explore shape generation conditioned on desired affordances. We introduce an algorithm for generating voxelgrid object shapes which afford the desired functionalities. We follow the principle form follows function, and assume that object forms are related to affordances they provide (their functions). First, we use an artificial neural network to learn a function-to-form mapping from a dataset of affordance-labeled objects. Then, we combine forms providing one or more desired affordances, generating an object shape expected to provide all of them. Finally, we verify in simulation whether the generated object indeed possesses the desired affordances, by defining and executing affordance tests on it. Examples are provided using the affordances contain-ability, sit-ability, and support-ability.
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Project description:In order to acquire information concerning the geometry and material of handheld objects, people tend to execute stereotypical hand movement patterns called haptic Exploratory Procedures (EPs). Manual annotation of haptic exploration trials with these EPs is a laborious task that is affected by subjectivity, attentional lapses, and viewing angle limitations. In this paper we propose an automatic EP annotation method based on position and orientation data from motion tracking sensors placed on both hands and inside a stimulus. A set of kinematic variables is computed from these data and compared to sets of predefined criteria for each of four EPs. Whenever all criteria for a specific EP are met, it is assumed that that particular hand movement pattern was performed. This method is applied to data from an experiment where blindfolded participants haptically discriminated between objects differing in hardness, roughness, volume, and weight. In order to validate the method, its output is compared to manual annotation based on video recordings of the same trials. Although mean pairwise agreement is less between human-automatic pairs than between human-human pairs (55.7% vs 74.5%), the proposed method performs much better than random annotation (2.4%). Furthermore, each EP is linked to a specific object property for which it is optimal (e.g., Lateral Motion for roughness). We found that the percentage of trials where the expected EP was found does not differ between manual and automatic annotation. For now, this method cannot yet completely replace a manual annotation procedure. However, it could be used as a starting point that can be supplemented by manual annotation.
| S-EPMC4319767 | biostudies-literature