Distinct Visual Working Memory Systems for View-Dependent and View-Invariant Representation

Background

How do people sustain a visual representation of the environment? Currently, many researchers argue that a single visual working memory system sustains non-spatial object information such as colors and shapes. However, previous studies tested visual working memory for two-dimensional objects only. In consequence, the nature of visual working memory for three-dimensional (3D) object representation remains unknown.

Methodology/Principal Findings

Here, I show that when sustaining information about 3D objects, visual working memory clearly divides into two separate, specialized memory systems, rather than one system, as was previously thought. One memory system gradually accumulates sensory information, forming an increasingly precise view-dependent representation of the scene over the course of several seconds. A second memory system sustains view-invariant representations of 3D objects. The view-dependent memory system has a storage capacity of 3–4 representations and the view-invariant memory system has a storage capacity of 1–2 representations. These systems can operate independently from one another and do not compete for working memory storage resources.

Conclusions/Significance

These results provide evidence that visual working memory sustains object information in two separate, specialized memory systems. One memory system sustains view-dependent representations of the scene, akin to the view-specific representations that guide place recognition during navigation in humans, rodents and insects. The second memory system sustains view-invariant representations of 3D objects, akin to the object-based representations that underlie object cognition.     

Attention to surfaces modulates motion processing in extrastriate area MT

In the visual system, early atomized representations are grouped into higher-level entities through processes of perceptual organization. Here we present neurophysiological evidence that a representation of a simple object, a surface defined by color and motion, can be the unit of attentional selection at an early stage of visual processing. Monkeys were cued by the color of a fixation spot to attend to one of two transparent random-dot surfaces, one red and one green, which occupied the same region of space. Motion of the attended surface drove neurons in the middle temporal (MT) visual area more strongly than physically identical motion of the non-attended surface, even though both occurred within the spotlight of attention. Surface-based effects of attention persisted even without differential surface coloring, but attentional modulation was stronger with color. These results show that attention can select surface representations to modulate visual processing as early as cortical area MT.     

From shared to distinct self–other representations in empathy: evidence from neurotypical function and socio-cognitive disorders

Neuroscientific research has identified two fundamental components of empathy: shared emotional representations between self and other, and self–other distinction. The concept of shared representations suggests that during empathy, we co-represent another person''s affect by engaging brain and bodily functions underpinning the first-hand experience of the emotion we are empathizing with. This possible grounding of empathy in our own emotional experiences explains the necessity for self–other distinction, which is the capacity to correctly distinguish between our own affective representations and those related to the other. In spite of the importance of these two components in empathy, several aspects still remain controversial. This paper addresses some of them and focuses on (i) the distinction between shared activations versus representations, raising the question what shared representations entail in terms of the underlying neural mechanisms, (ii) the possible mechanisms behind self–other distinction in the cognitive and the affective domains, and whether they have distinct neural underpinnings and (iii) the consequences associated with a selective impairment of one of the two components, thereby addressing their importance in mental disorders such as autism spectrum disorders, psychopathy and alexithymia.     

On multipole representation of current generators

In Yeh, Martinek, and de Beaumont (Bull. Math. Biophysics,20, 203–216, 1958), multipole representations of current generators in a volume conductor are used, based upon the Taylor series expansion of the potential function. In Yeh, (Bull. Math. Biophysics,23, 263–276, 1961) multipole representations of current generators in a spherical volume conductor are used, based upon the spherical harmonic expansion. This paper correlates these two systems of multipole representations so that formulations in terms of one system of the representations may be readily transformed into formulations in terms of the other system. Since the Taylor series representation is more graphic, whereas the spherical harmonic representation is more compact, such a transformation between these two systems of formulations can serve useful purposes in the application of the theory of electrocardiography. This investigation was supported by the National Heart Institute under Research Grant H-2263 (c-4).     

On some representations of Boolean functions. Application to the theory of switching elements nets

We consider three representations of a Boolean function, two of them in the real field and one in a Galois field modulo-2. By using these representations, an arbitrary Boolean function of n variables can always be expanded as a power series on the Boolean variables cut off to the n-th degree (n-form). Such representations are particularly useful for a unified treatment of the dynamics of a net of switching elements.     

Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks

In order to process data of proteins, a numerical representation for an amino acid is often necessary. Many suitable parameters can be derived from experiments or statistical analysis of databases. To ensure a fast and efficient use of these sources of information, a reduction and extraction of relevant information out of these parameters is a basic need. In this approach established methods like principal component analysis (PCA) are supplemented by a method based on symmetric neural networks. Two different parameter representations of amino acids are reduced from five and seven dimensions, respectively, to one, two, three, or four dimensions by using a symmetric neural network approach alternatively with one or three hidden layers. It is possible to create general reduced parameter representations for amino acids. To demonstrate the ability of this approach, these reduced sets of parameters are applied for the ab initio prediction of protein secondary structure from primary structure only. Artificial neural networks are implemented and trained with a diverse representation of 430 proteins out of the PDB. An essentially faster training and also prediction without a decrease in accuracy is obtained for the reduced parameter representations in comparison with the complete set of parameters. The method is transferable to other amino acids or even other molecular building blocks, like nucleic acids, and therefore represents a general approach.Electronic Supplementary Material available.     

Representing the UK's cattle herd as static and dynamic networks

Network models are increasingly being used to understand the spread of diseases through sparsely connected populations, with particular interest in the impact of animal movements upon the dynamics of infectious diseases. Detailed data collected by the UK government on the movement of cattle may be represented as a network, where animal holdings are nodes, and an edge is drawn between nodes where a movement of animals has occurred. These network representations may vary from a simple static representation, to a more complex, fully dynamic one where daily movements are explicitly captured. Using stochastic disease simulations, a wide range of network representations of the UK cattle herd are compared. We find that the simpler static network representations are often deficient when compared with a fully dynamic representation, and should therefore be used only with caution in epidemiological modelling. In particular, due to temporal structures within the dynamic network, static networks consistently fail to capture the predicted epidemic behaviour associated with dynamic networks even when parameterized to match early growth rates.     

Can lemurs (<Emphasis Type="Italic">Eulemur fulvus</Emphasis> and <Emphasis Type="Italic">E. macaco</Emphasis>) use abstract representations of quantities to master the reverse-reward contingency task?

In previous studies we demonstrated that brown and black lemurs (Eulemur fulvus and E. macaco) showed self-control abilities under a reverse-reward contingency. They were able to significantly select the smaller quantity of food to be rewarded with the larger one and to generalize this ability when presented with two rewards that differed in quality. In the present study, previously trained subjects had to choose between graphic representations of two different quantities of food under the reverse-reward contingency. Three out of four subjects learned to associate a graphic representation of the reward with the corresponding quantity. Only one subject consistently selected the representation of the smaller quantity to be rewarded with the larger quantity of food and therefore showed abstraction as well as relative numerousness skills. Indeed, she was able to discriminate between representations of different quantities and to ordinate them. We discuss how primates mentally represent food quantities and how self-control is involved in foraging strategies.     

Transforming bottom-up topographic representations with top-down signals in the brain

There has been considerable success in allocating function to the different parts of the brain. We also know much about brain organisation in different regions of the brain and how different brain regions connect to one another. One of the most important next steps for modern neuroscience is to work out how different areas of the brain interact with one another. In particular we need to know how sensory regions communicate with association areas and vice versa. This article explores how top-down signals originating from association areas may be used to process and transform bottom-up representations originating from sensory areas of the brain. Simple models of networks containing topographically organised ensembles of neurons used to integrate and process information are described. The different models can be used to process information in a variety of different ways that could be used as the starting point for a variety of cognitive operations, in particular the extraction of abstract information from sensory representations.     

Transforming bottom-up topographic representations with top-down signals in the brain

There has been considerable success in allocating function to the different parts of the brain. We also know much about brain organisation in different regions of the brain and how different brain regions connect to one another. One of the most important next steps for modern neuroscience is to work out how different areas of the brain interact with one another. In particular we need to know how sensory regions communicate with association areas and vice versa. This article explores how top-down signals originating from association areas may be used to process and transform bottom-up representations originating from sensory areas of the brain. Simple models of networks containing topographically organised ensembles of neurons used to integrate and process information are described. The different models can be used to process information in a variety of different ways that could be used as the starting point for a variety of cognitive operations, in particular the extraction of abstract information from sensory representations.     

Reverse Correlating Love: Highly Passionate Women Idealize Their Partner’s Facial Appearance

A defining feature of passionate love is idealization—evaluating romantic partners in an overly favorable light. Although passionate love can be expected to color how favorably individuals represent their partner in their mind, little is known about how passionate love is linked with visual representations of the partner. Using reverse correlation techniques for the first time to study partner representations, the present study investigated whether women who are passionately in love represent their partner’s facial appearance more favorably than individuals who are less passionately in love. In a within-participants design, heterosexual women completed two forced-choice classification tasks, one for their romantic partner and one for a male acquaintance, and a measure of passionate love. In each classification task, participants saw two faces superimposed with noise and selected the face that most resembled their partner (or an acquaintance). Classification images for each of high passion and low passion groups were calculated by averaging across noise patterns selected as resembling the partner or the acquaintance and superimposing the averaged noise on an average male face. A separate group of women evaluated the classification images on attractiveness, trustworthiness, and competence. Results showed that women who feel high (vs. low) passionate love toward their partner tend to represent his face as more attractive and trustworthy, even when controlling for familiarity effects using the acquaintance representation. Using an innovative method to study partner representations, these findings extend our understanding of cognitive processes in romantic relationships.     

Repetitive Microstimulation Alters the Cortical Representation of Movements in Adult Rats

In order to examine the effects of repetitive stimulation on functional cortical organization, standard intracortical microstimulation (ICMS) techniques were used to generate maps of movement representations in motor cortex of rat. After identification of caudal and rostral forelimb fields and adjacent vibrissae and neck fields, one or more representational borders were defined in greater detail. Then a microelectrode was introduced into one of these representational fields, and ICMS current pulses were delivered at a rate of 1/sec for 1 to 3 hr. Following repetitive ICMS, significant changes in movement representations were observed using current levels that were either suprathreshold or subthreshold for evoking the site-specific movement. Electromyographic activity could be evoked at suprathreshold and near-threshold current levels, but not at the subthreshold current levels used here. Significant border shifts ranged from 210 to 670 μm. In each case in which shifts occurred, there appeared to be expansion of the movement represented at the repetitively stimulated site. The effects were progressive and reversible. These results suggest that at least under these unusual experimental circumstances, large representational changes can be generated very rapidly within motor cortex in the absence of any evident peripheral feedback.     

Repetitive microstimulation alters the cortical representation of movements in adult rats

In order to examine the effects of repetitive stimulation on functional cortical organization, standard intracortical microstimulation (ICMS) techniques were used to generate maps of movement representations in motor cortex of rat. After identification of caudal and rostral forelimb fields and adjacent vibrissae and neck fields, one or more representational borders were defined in greater detail. Then a microelectrode was introduced into one of these representational fields, and ICMS current pulses were delivered at a rate of 1/sec for 1 to 3 hr. Following repetitive ICMS, significant changes in movement representations were observed using current levels that were either suprathreshold or subthreshold for evoking the site-specific movement. Electromyographic activity could be evoked at suprathreshold and near-threshold current levels, but not at the subthreshold current levels used here. Significant border shifts ranged from 210 to 670 microns. In each case in which shifts occurred, there appeared to be expansion of the movement represented at the repetitively stimulated site. The effects were progressive and reversible. These results suggest that at least under these unusual experimental circumstances, large representational changes can be generated very rapidly within motor cortex in the absence of any evident peripheral feedback.     

Attention Enhances the Retrieval and Stability of Visuospatial and Olfactory Representations in the Dorsal Hippocampus

A key question in the analysis of hippocampal memory relates to how attention modulates the encoding and long-term retrieval of spatial and nonspatial representations in this region. To address this question, we recorded from single cells over a period of 5 days in the CA1 region of the dorsal hippocampus while mice acquired one of two goal-oriented tasks. These tasks required the animals to find a hidden food reward by attending to either the visuospatial environment or a particular odor presented in shifting spatial locations. Attention to the visuospatial environment increased the stability of visuospatial representations and phase locking to gamma oscillations—a form of neuronal synchronization thought to underlie the attentional mechanism necessary for processing task-relevant information. Attention to a spatially shifting olfactory cue compromised the stability of place fields and increased the stability of reward-associated odor representations, which were most consistently retrieved during periods of sniffing and digging when animals were restricted to the cup locations. Together, these results suggest that attention selectively modulates the encoding and retrieval of hippocampal representations by enhancing physiological responses to task-relevant information.     

From theory to data: Representing neurons in the 1940s

Recent literature on the role of pictorial representation in the life sciences has focused on the relationship between detailed representations of empirical data and more abstract, formal representations of theory. The standard argument is that in both a historical and epistemic sense, this relationship is a directional one: beginning with raw, unmediated images and moving towards diagrams that are more interpreted and more theoretically rich. Using the neural network diagrams of Warren McCulloch and Walter Pitts as a case study, I argue that while in the empirical sciences, pictorial representation tends to move from data to theory, in areas of the life sciences that are predominantly theoretical, when abstraction occurs at the outset, the relationship between detail and abstraction in pictorial representations can be of a different character.     

Stereotypes of autism

In their landmark papers, both Kanner and Asperger employed a series of case histories to shape clinical insight into autistic disorders. This way of introducing, assessing and representing disorders has disappeared from today''s psychiatric practice, yet it offers a convincing model of the way stereotypes may build up as a result of representations of autism. Considering that much of what society at large learns on disorders on the autism spectrum is produced by representations of autism in novels, TV-series, movies or autobiographies, it will be of vital importance to scrutinize these representations and to check whether or not they are, in fact, misrepresenting autism. In quite a few cases, media representations of talent and special abilities can be said to have contributed to a harmful divergence between the general image of autism and the clinical reality of the autistic condition.     

An Exploration of the Relations between External Representations and Working Memory

It is commonly hypothesized that external representations serve as memory aids and improve task performance by means of expanding the limited capacity of working memory. However, very few studies have directly examined this memory aid hypothesis. By systematically manipulating how information is available externally versus internally in a sequential number comparison task, three experiments were designed to investigate the relation between external representations and working memory. The experimental results show that when the task requires information from both external representations and working memory, it is the interaction of information from the two sources that determines task performance. In particular, when information from the two sources does not match well, external representations hinder instead of enhance task performance. The study highlights the important role the coordination among different representations plays in distributed cognition. The general relations between external representations and working memory are discussed.