78,511 research outputs found
Symbols are not uniquely human
Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprentice-generated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5% of “lying” tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax
Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has recently motivated the "sensorimotor" approach to the challenging task
of automatic object recognition, where both information sources are fused to
improve robustness. In this work, the aforementioned paradigm is adopted,
surpassing current limitations of sensorimotor object recognition research.
Specifically, the deep learning paradigm is introduced to the problem for the
first time, developing a number of novel neuro-biologically and
neuro-physiologically inspired architectures that utilize state-of-the-art
neural networks for fusing the available information sources in multiple ways.
The proposed methods are evaluated using a large RGB-D corpus, which is
specifically collected for the task of sensorimotor object recognition and is
made publicly available. Experimental results demonstrate the utility of
affordance information to object recognition, achieving an up to 29% relative
error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201
Neural blackboard architectures of combinatorial structures in cognition
Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception
Visual pathways from the perspective of cost functions and multi-task deep neural networks
Vision research has been shaped by the seminal insight that we can understand
the higher-tier visual cortex from the perspective of multiple functional
pathways with different goals. In this paper, we try to give a computational
account of the functional organization of this system by reasoning from the
perspective of multi-task deep neural networks. Machine learning has shown that
tasks become easier to solve when they are decomposed into subtasks with their
own cost function. We hypothesize that the visual system optimizes multiple
cost functions of unrelated tasks and this causes the emergence of a ventral
pathway dedicated to vision for perception, and a dorsal pathway dedicated to
vision for action. To evaluate the functional organization in multi-task deep
neural networks, we propose a method that measures the contribution of a unit
towards each task, applying it to two networks that have been trained on either
two related or two unrelated tasks, using an identical stimulus set. Results
show that the network trained on the unrelated tasks shows a decreasing degree
of feature representation sharing towards higher-tier layers while the network
trained on related tasks uniformly shows high degree of sharing. We conjecture
that the method we propose can be used to analyze the anatomical and functional
organization of the visual system and beyond. We predict that the degree to
which tasks are related is a good descriptor of the degree to which they share
downstream cortical-units.Comment: 16 pages, 5 figure
Subitizing with Variational Autoencoders
Numerosity, the number of objects in a set, is a basic property of a given
visual scene. Many animals develop the perceptual ability to subitize: the
near-instantaneous identification of the numerosity in small sets of visual
items. In computer vision, it has been shown that numerosity emerges as a
statistical property in neural networks during unsupervised learning from
simple synthetic images. In this work, we focus on more complex natural images
using unsupervised hierarchical neural networks. Specifically, we show that
variational autoencoders are able to spontaneously perform subitizing after
training without supervision on a large amount images from the Salient Object
Subitizing dataset. While our method is unable to outperform supervised
convolutional networks for subitizing, we observe that the networks learn to
encode numerosity as basic visual property. Moreover, we find that the learned
representations are likely invariant to object area; an observation in
alignment with studies on biological neural networks in cognitive neuroscience
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence
Recognition of objects from partial information presents a significant
challenge for theories of vision because it requires spatial integration and
extrapolation from prior knowledge. We combined neurophysiological recordings
in human cortex with psychophysical measurements and computational modeling to
investigate the mechanisms involved in object completion. We recorded
intracranial field potentials from 1,699 electrodes in 18 epilepsy patients to
measure the timing and selectivity of responses along human visual cortex to
whole and partial objects. Responses along the ventral visual stream remained
selective despite showing only 9-25% of the object. However, these visually
selective signals emerged ~100 ms later for partial versus whole objects. The
processing delays were particularly pronounced in higher visual areas within
the ventral stream, suggesting the involvement of additional recurrent
processing. In separate psychophysics experiments, disrupting this recurrent
computation with a backward mask at ~75ms significantly impaired recognition of
partial, but not whole, objects. Additionally, computational modeling shows
that the performance of a purely bottom-up architecture is impaired by heavy
occlusion and that this effect can be partially rescued via the incorporation
of top-down connections. These results provide spatiotemporal constraints on
theories of object recognition that involve recurrent processing to recognize
objects from partial information
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