The testing effect for visual materials depends on pre-existing knowledge

Remembering facilitates future remembering. This benefit of practicing by active retrieval, as compared to more passive re-learning, is known as the testing effect, and is one of the most robust findings in the memory literature. However, it has typically been assessed using verbal materials such as word-pairs, sentences, or educational texts. We here investigate if memory for visual materials equally benefits from retrieval-mediated learning. Based on cognitive and neuroscientific theories, we hypothesise that testing effects will be limited to meaningful visual images that can be related to pre-existing knowledge. In a series of four experiments, we systematically varied the type of material (meaningful object images vs non-meaningful “squiggle” shapes), the format of the test used to probe memory (a more visually driven alternative forced-choice test vs a remember/know recognition test), and the delay of the final test (immediate vs 1 week delay). We found that abstract shapes never showed a significant testing benefit, irrespective of test format, and even benefitted more from restudy than retrieval at longer delays, where testing effects are typically most prominent. Meaningful object images did benefit from testing, particularly at long delays, and with a test format probing the recollective component of recognition memory. Together, our results indicate that retrieval enhances memory for visual materials only when they have a unique, distinct meaning. This pattern of results is predicted by cognitive and neurobiologically motivated theories proposing that retrieval’s benefits emerge through spreading activation in pre-existing semantic networks, producing better integrated and more easily accessible memory traces.

Revealing the multidimensional mental representations of natural objects underlying human similarity judgments

Objects can be characterized according to a vast number of possible criteria (e.g. animacy, shape, color, function), but some dimensions are more useful than others for making sense of the objects around us. To identify these "core dimensions" of object representations, we developed a data-driven computational model of similarity judgments for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgments and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behavior and reflected typicality judgments of those categories. Further, humans can accurately rate objects along these dimensions, highlighting their interpretability and opening up a way to generate similarity estimates from object dimensions alone. Collectively, these results demonstrate that human similarity judgments can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behavior.

Where Do the Multi-Religious Origins of Islam Lie? A Topological Approach to a Wicked Problem

The revelation of Islam in Arabic, its emergence in the Western Arabian Peninsula, and its acquaintance with Biblical literature seem to be clear indications for Islam’s birthplace and its religious foundations. While the majority of academic scholarship accepts the historicity of the revelation in Mecca and Medina, revisionist scholars have started questioning the location of early Islam with increasing fervour in recent years. Drawing on the isolation of Mecca and the lack of clear references to Mecca in ancient and non-Muslim literature before the mid-eighth century, these scholars have cast doubt on the claim that Mecca was already a trading outpost and a pilgrimage site prior to Islam, questioning the traditional Islamic and Orientalist view. Space, thus, plays a prominent role in the debate on the origins of Islam, although space is almost never conceptually discussed. In the following paper, I challenge the limited understanding of space in revisionist as well as mainstream scholarship. For the most part, this scholarship is not really interested in the multi-religious landscape sui generis, but understands early Islam either as a stable or an unstable entity that either reworked or digested the impact of Judaism and Christianity. In contrast, my contention is based on the view that Islam emerged neither “in” Mecca nor anywhere else, but that Muslims’ practical and symbolic actions produced such places as Mecca, Medina, and the Ḥijāz as the central places of Islam. My argument is threefold: Firstly, the production of the Meccan space and its central meaning for Islam were mutually dependant, gradual processes. Secondly, the creation of an exclusively Muslim space in the Ḥijāz conversely inscribed multi-religiosity into the general topology of early Islam. Thirdly, the early history of Islam hints at practices of un/doing differences, exemplified by instances of sharing, the creation of ambivalence, and processes ofpurification. Moreover, my contribution questions the way in which research on the origins of Islam has become a meaningful object of knowledge about the “true” nature of Islam against the background of populist discourses on Islam.

Reconstructing meaning from bits of information

We can easily identify a dog merely by the sound of barking or an orange by its citrus scent. In this work, we study the neural underpinnings of how the brain combines bits of information into meaningful object representations. Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of individual semantic features (e.g., “barks”, “has citrus scent”). Here, participants received clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We used machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. We discovered that the recorded brain patterns were best decoded using a combination of not only the three semantic features that were presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to observe how fragmented information is combined into a complete semantic representation of an object and suggest neuroanatomical underpinnings for this process.

A different approach to anisotropic spherical collapse with shear and heat radiation

In order to study the type of collapse mentioned in the title, we introduce a physically meaningful object, called the horizon function. It directly enters the expressions for many of the stellar characteristics. The main junction equation, which governs the collapse, transforms into a Riccati equation with simple coefficients for the horizon function. We integrate this equation in the geodesic case. The same is done in the general case when one or another of the coefficients vanish. It is shown how to build classes of star models in this formulation of the problem and simple solutions are given.

Surrogate data: A novel approach to object detection

Surrogate data: A novel approach to object detectionIn the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.