Spatial relation categorization in infants and deep neural networks

Spatial relations, such as above, below, between, and containment, are important mediators in children's understanding of the world (Piaget, 1954). The development of these relational categories in infancy has been extensively studied (Quinn, 2003) yet little is known about their computational underpinnings. Using developmental tests, we examine the extent to which deep neural networks, pretrained on a standard vision benchmark or egocentric video captured from one baby's perspective, form categorical representations for visual stimuli depicting relations. Notably, the networks did not receive any explicit training on relations. We then analyze whether these networks recover similar patterns to ones identified in development, such as reproducing the relative difficulty of categorizing different spatial relations and different stimulus abstractions. We find that the networks we evaluate tend to recover many of the patterns observed with the simpler relations of "above versus below" or "between versus outside", but struggle to match developmental findings related to "containment". We identify factors in the choice of model architecture, pretraining data, and experimental design that contribute to the extent the networks match developmental patterns, and highlight experimental predictions made by our modeling results. Our results open the door to modeling infants' earliest categorization abilities with modern machine learning tools and demonstrate the utility and productivity of this approach.

Grounded language acquisition through the eyes and ears of a single child

Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child's everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child's input.

Association of apelin, endoglin and endocan with diabetic peripheral neuropathy in type 2 diabetic patients

OBJECTIVE

Diabetic peripheral neuropathy is a common complication of type-2 diabetes mellitus. Endocan, apelin and endoglin are thought to be associated with endothelial dysfunction, angiogenesis and inflammation. In this study, we planned to evaluate these markers in diabetic peripheral neuropathy patients.

PATIENTS AND METHODS

This single-blind, controlled clinical study was conducted on 99 type 2 diabetic patients with or without diabetic peripheral neuropathy and 53 healthy volunteer controls. Physical and laboratory examinations were done in all groups. In these groups, Endoglin, apelin and endocan levels were measured with ELISA method.

RESULTS

Endoglin, apelin and endocan concentrations in diabetic peripheral neuropathy patients were higher than other diabetes mellitus patients and healthy controls. Similarly, diabetes mellitus patient's endoglin, apelin and endocan levels were higher than healthy controls. The differences were statistically significant. We detected a significant positive correlation between endoglin, apelin and endocan levels in all groups.

CONCLUSIONS

Endoglin, apelin and endocan may reflect angiogenesis and endothelial dysfunction in diabetic peripheral neuropathy and they may be used as a marker in the future.

The Adaptive Nature of Visual Working Memory

A growing body of scientific evidence suggests that visual working memory and statistical learning are intrinsically linked. Although visual working memory is severely resource limited, in many cases, it makes efficient use of its available resources by adapting to statistical regularities in the visual environment. However, experimental evidence also suggests that there are clear limits and biases in statistical learning. This raises the intriguing possibility that performance limitations observed in visual working memory tasks can to some degree be explained in terms of limits and biases in statistical-learning ability, rather than limits in memory capacity.

A probabilistic clustering theory of the organization of visual short-term memory

Experimental evidence suggests that the content of a memory for even a simple display encoded in visual short-term memory (VSTM) can be very complex. VSTM uses organizational processes that make the representation of an item dependent on the feature values of all displayed items as well as on these items' representations. Here, we develop a probabilistic clustering theory (PCT) for modeling the organization of VSTM for simple displays. PCT states that VSTM represents a set of items in terms of a probability distribution over all possible clusterings or partitions of those items. Because PCT considers multiple possible partitions, it can represent an item at multiple granularities or scales simultaneously. Moreover, using standard probabilistic inference, it automatically determines the appropriate partitions for the particular set of items at hand and the probabilities or weights that should be allocated to each partition. A consequence of these properties is that PCT accounts for experimental data that have previously motivated hierarchical models of VSTM, thereby providing an appealing alternative to hierarchical models with prespecified, fixed structures. We explore both an exact implementation of PCT based on Dirichlet process mixture models and approximate implementations based on Bayesian finite mixture models. We show that a previously proposed 2-level hierarchical model can be seen as a special case of PCT with a single cluster. We show how a wide range of previously reported results on the organization of VSTM can be understood in terms of PCT. In particular, we find that, consistent with empirical evidence, PCT predicts biases in estimates of the feature values of individual items and also predicts a novel form of dependence between estimates of the feature values of different items. We qualitatively confirm this last prediction in 3 novel experiments designed to directly measure biases and dependencies in subjects' estimates.

Visual learning with reliable and unreliable features

Existing studies of sensory integration demonstrate how the reliabilities of perceptual cues or features influence perceptual decisions. However, these studies tell us little about the influence of feature reliability on visual learning. In this article, we study the implications of feature reliability for perceptual learning in the context of binary classification tasks. We find that finite sets of training data (i.e., the stimuli and corresponding class labels used on training trials) contain different information about a learner's parameters associated with reliable versus unreliable features. In particular, the statistical information provided by a finite number of training trials strongly constrains the set of possible parameter values associated with unreliable features, but only weakly constrains the parameter values associated with reliable features. Analyses of human subjects' performances reveal that subjects were sensitive to this statistical information. Additional analyses examine why subjects were sub-optimal visual learners.