Memory & Cognition最新文献

Boundary contraction and extension are two types of scene transformations that occur in memory. In extension, viewers extrapolate information beyond the edges of the image, whereas in contraction, viewers forget information near the edges. Recent work suggests that image composition influences the direction and magnitude of boundary transformation. We hypothesize that selective attention at encoding is an important driver of boundary transformation effects, selective attention to specific objects at encoding leading to boundary contraction. In this study, one group of participants (N = 36) memorized 15 scenes while searching for targets, while a separate group (N = 36) just memorized the scenes. Both groups then drew the scenes from memory with as much object and spatial detail as they could remember. We asked online workers to provide ratings of boundary transformations in the drawings, as well as how many objects they contained and the precision of remembered object size and location. We found that search condition drawings showed significantly greater boundary contraction than drawings of the same scenes in the memorize condition. Search drawings were significantly more likely to contain target objects, and the likelihood to recall other objects in the scene decreased as a function of their distance from the target. These findings suggest that selective attention to a specific object due to a search task at encoding will lead to significant boundary contraction.

Graphical perception is an important part of the scientific endeavour, and the interpretation of graphical information is increasingly important among educated consumers of popular media, who are often presented with graphs of data in support of different policy positions. However, graphs are multidimensional and data in graphs are comprised not only of overall global trends but also local perturbations. We presented a novel function estimation task in which scatterplots of noisy data that varied in the number of data points, the scale of the data, and the true generating function were shown to observers. 170 psychology undergraduates with mixed experience of mathematical functions were asked to draw the function that they believe generated the data. Our results indicated not only a general influence of various aspects of the presented graph (e.g., increasing the number of data points results in smoother generated functions) but also clear individual differences, with some observers tending to generate functions that track the local changes in the data and others following global trends in the data.

Searching within natural scenes can induce incidental encoding of information about the scene and the target, particularly when the scene is complex or repeated. However, recent evidence from attribute amnesia (AA) suggests that in some situations, searchers can find a target without building a robust incidental memory of its task relevant features. Through drawing-based visual recall and an AA search task, we investigated whether search in natural scenes necessitates memory encoding. Participants repeatedly searched for and located an easily detected item in novel scenes for numerous trials before being unexpectedly prompted to draw either the entire scene (Experiment 1) or their search target (Experiment 2) directly after viewing the search image. Naïve raters assessed the similarity of the drawings to the original information. We found that surprise-trial drawings of the scene and search target were both poorly recognizable, but the same drawers produced highly recognizable drawings on the next trial when they had an expectation to draw the image. Experiment 3 further showed that the poor surprise trial memory could not merely be attributed to interference from the surprising event. Our findings suggest that even for searches done in natural scenes, it is possible to locate a target without creating a robust memory of either it or the scene it was in, even if attended to just a few seconds prior. This disconnection between attention and memory might reflect a fundamental property of cognitive computations designed to optimize task performance and minimize resource use.

As psychological research embraces more naturalistic questions and large-scale analytic methods, drawing has emerged as an exciting tool for studying cognition. Drawing provides rich information about how we view the world, ranging from largely veridical perceptual representations to abstracted meta-cognitive representations. Drawing also requires the integration of multiple processes (e.g., vision, memory, motor learning), and experience with drawing can have an impact on such processes. As a result, drawing presents several interesting cognitive questions, while also providing a way to gain insight into a multitude of others. This Special Issue features 25 cutting-edge studies utilizing drawing to reveal discoveries transversing fields in psychology. These diverse studies investigate drawing across children, young adults, older adults, and special populations such as individuals with blindness, anterograde amnesia, apraxia, and semantic dementia. These studies detail new discoveries about the mechanisms underlying memory, attention, mathematical reasoning, and other cognitive processes. They employ a range of methods including psychophysical experiments, deep learning, and neuroimaging. Finally, many of these studies cover topics about the impact of drawing as a process on other cognitive processes, including how drawing expertise impacts other processes like visual memory or spatial abilities. Overall, this collection of studies paves the way for an exciting future of drawing as a commonplace tool used by psychologists to understand complex phenomena.

Gestalt psychologists posited that we always organize our visual input in the best way possible under the given conditions. Both weakening or removing unnecessary details (i.e., leveling) and exaggerating distinctive features (i.e., sharpening) can contribute to achieving a better organization. When is a feature leveled or sharpened, however? We investigated whether the importance of a feature for discrimination among alternatives influences which organizational tendency occurs. Participants were simultaneously presented with four figures composed of simple geometrical shapes, and asked to reconstruct one of these figures in such a way that another participant would be able to recognize it among the alternatives. The four figures differed either qualitatively or only quantitatively (i.e., far or close context). Regarding quantitative differences, two feature dimensions were varied, with one manifesting a wider range of variability across the alternatives than the other. In case of a smaller variability range, the target figure was either at the extreme of the range or had an in-between value. As expected, the results indicated that sharpening occurred more often for the feature with an extreme value, for the feature exhibiting more variability, and for the features of figures presented in the close context, than for the feature with a non-extreme value, exhibiting less variability, or in the far context. In line with Metzger's (1941) definition of prägnant Gestalts, the essence of a Gestalt is context-dependent, and this will influence whether leveling or sharpening of a feature will lead to the best organization in the specific context.

Understanding normal probability distributions is a crucial objective in mathematics and statistics education. Drawing upon cognitive psychology research, this study explores the use of drawings and visualizations as effective scaffolds to enhance students' comprehension. Although much research has documented the helpfulness of drawing as a research tool to reveal students' knowledge states, its direct utility in advancing higher-order cognitive processes remains understudied. In Study 1, qualitative methods were utilized to identify common misunderstandings among students regarding canonical depictions of the normal probability distribution. Building on these insights, Study 2 experimentally compared three instructional videos (static slides, dynamic drawing, and dynamic drawings done by a visible hand). The hand drawing video led to better learning than the other versions. Study 3 examined whether the benefits from observing a hand drawing could be reproduced by a dynamic cursor moving around otherwise static slides (without the presence of a hand). Results showed no significant learning difference between observing a hand drawing and a moving cursor, both outperforming a control. This research links the cognitive process of drawing with its educational role and provides insights into its potential to enhance memory, cognition, and inform instructional methods.

Previous studies have demonstrated that engaging in graphomotor activity for creating graphemes can enhance their subsequent visual discrimination. This suggests a positive influence of the motor system on visual learning. However, existing studies have emphasized the dominant hand, which is superiorly dexterous in fine-motor movements. This near-exclusive focus prompts the inquiry of whether the observed perceptual facilitation is a general characteristic of the motor system, or specific to pathways controlling the skilled over-trained dominant hand. Furthermore, the mechanistic underpinning of visual facilitation from graphomotor training (i.e., the individual contribution of motor activity, temporal evolution of the visual trace, variability of visual output) remain unclear. To address these questions, we assessed visual discrimination capabilities of healthy right-handed participants (N = 60) before and after graphomotor or visual training. Contrary to our initial expectation, graphomotor engagement with the non-dominant hand did not yield additional benefits to visual learning beyond those attainable through visual training alone. Moreover, graphomotor training with the non-dominant hand resulted in visual discrimination improvements comparable to those of dominant hand training, despite the inherent differences between hands in motor performance and in the amount of improvement in shape tracing throughout training. We conclude that the motor components of graphomotor activity may not be critical for visual learning of shapes through tracing activity. Instead, our results are in agreement with the symbolic theoretical account, suggesting that basic shape features required for discrimination can be acquired through visual inspection alone, providing a perspective on the improvements observed in prior studies.

Many objects and materials in our environment are subject to transformations that alter their shape. For example, branches bend in the wind, ice melts, and paper crumples. Still, we recognize objects and materials across these changes, suggesting we can distinguish an object's original features from those caused by the transformations ("shape scission"). Yet, if we truly understand transformations, we should not only be able to identify their signatures but also actively apply the transformations to new objects (i.e., through imagination or mental simulation). Here, we investigated this ability using a drawing task. On a tablet computer, participants viewed a sample contour and its transformed version, and were asked to apply the same transformation to a test contour by drawing what the transformed test shape should look like. Thus, they had to (i) infer the transformation from the shape differences, (ii) envisage its application to the test shape, and (iii) draw the result. Our findings show that drawings were more similar to the ground truth transformed test shape than to the original test shape-demonstrating the inference and reproduction of transformations from observation. However, this was only observed for relatively simple shapes. The ability was also modulated by transformation type and magnitude but not by the similarity between sample and test shapes. Together, our findings suggest that we can distinguish between representations of original object shapes and their transformations, and can use visual imagery to mentally apply nonrigid transformations to observed objects, showing how we not only perceive but also 'understand' shape.

Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perception. Contrasting five contemporary DNNs, we evaluated how well each explains human similarity judgments among line drawings of recognizable and novel objects. For object sketches, human judgments were dominated by semantic category information; DNN representations contributed little additional information. In contrast, such features explained significant unique variance perceived similarity of abstract drawings. In both cases, a vision transformer trained to blend representations of images and their natural language descriptions showed the greatest ability to explain human perceptual similarity-an observation consistent with contemporary views of semantic representation and processing in the human mind and brain. Together, the results suggest that the building blocks of visual similarity may arise within systems that learn to use visual information, not for specific classification, but in service of generating semantic representations of objects.

Why are some images more likely to be remembered than others? Previous work focused on the influence of global, low-level visual features as well as image content on memorability. To better understand the role of local, shape-based contours, we here investigate the memorability of photographs and line drawings of scenes. We find that the memorability of photographs and line drawings of the same scenes is correlated. We quantitatively measure the role of contour properties and their spatial relationships for scene memorability using a Random Forest analysis. To determine whether this relationship is merely correlational or if manipulating these contour properties causes images to be remembered better or worse, we split each line drawing into two half-images, one with high and the other with low predicted memorability according to the trained Random Forest model. In a new memorability experiment, we find that the half-images predicted to be more memorable were indeed remembered better, confirming a causal role of shape-based contour features, and, in particular, T junctions in scene memorability. We performed a categorization experiment on half-images to test for differential access to scene content. We found that half-images predicted to be more memorable were categorized more accurately. However, categorization accuracy for individual images was not correlated with their memorability. These results demonstrate that we can measure the contributions of individual contour properties to scene memorability and verify their causal involvement with targeted image manipulations, thereby bridging the gap between low-level features and scene semantics in our understanding of memorability.