Consciousness is learning: predictive processing systems that learn by
binding may perceive themselves as conscious
- URL: http://arxiv.org/abs/2301.07016v2
- Date: Mon, 17 Apr 2023 22:23:05 GMT
- Title: Consciousness is learning: predictive processing systems that learn by
binding may perceive themselves as conscious
- Authors: V.A. Aksyuk
- Abstract summary: We argue that a predictive processing system may flexibly generalize in novel situations by forming working memories for perceptions and actions from single examples.
We describe how the brain could have evolved to use perceptual value prediction for reinforcement learning of complex action policies simultaneously implementing multiple survival and reproduction strategies.
'Conscious experience' is how such a learning system perceptually represents its own functioning, suggesting an answer to the meta problem of consciousness.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine learning algorithms have achieved superhuman performance in specific
complex domains. Yet learning online from few examples and efficiently
generalizing across domains remains elusive. In humans such learning proceeds
via declarative memory formation and is closely associated with consciousness.
Predictive processing has been advanced as a principled Bayesian inference
framework for understanding the cortex as implementing deep generative
perceptual models for both sensory data and action control. However, predictive
processing offers little direct insight into fast compositional learning or the
mystery of consciousness. Here we propose that through implementing online
learning by hierarchical binding of unpredicted inferences, a predictive
processing system may flexibly generalize in novel situations by forming
working memories for perceptions and actions from single examples, which can
become short- and long-term declarative memories retrievable by associative
recall. We argue that the contents of such working memories are unified yet
differentiated, can be maintained by selective attention and are consistent
with observations of masking, postdictive perceptual integration, and other
paradigm cases of consciousness research. We describe how the brain could have
evolved to use perceptual value prediction for reinforcement learning of
complex action policies simultaneously implementing multiple survival and
reproduction strategies. 'Conscious experience' is how such a learning system
perceptually represents its own functioning, suggesting an answer to the meta
problem of consciousness. Our proposal naturally unifies feature binding,
recurrent processing, and predictive processing with global workspace, and, to
a lesser extent, the higher order theories of consciousness.
Related papers
- Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
We propose leveraging the principle of chunking to interpret artificial neural population activities.
We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities.
We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
arXiv Detail & Related papers (2025-02-03T20:30:46Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - Incremental procedural and sensorimotor learning in cognitive humanoid
robots [52.77024349608834]
This work presents a cognitive agent that can learn procedures incrementally.
We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent.
Results show that this approach is capable of solving complex tasks incrementally.
arXiv Detail & Related papers (2023-04-30T22:51:31Z) - A Study of Biologically Plausible Neural Network: The Role and
Interactions of Brain-Inspired Mechanisms in Continual Learning [13.041607703862724]
Humans excel at continually acquiring, consolidating, and retaining information from an ever-changing environment, whereas artificial neural networks (ANNs) exhibit catastrophic forgetting.
We consider a biologically plausible framework that constitutes separate populations of exclusively excitatory and inhibitory neurons that adhere to Dale's principle.
We then conduct a comprehensive study on the role and interactions of different mechanisms inspired by the brain, including sparse non-overlapping representations, Hebbian learning, synaptic consolidation, and replay of past activations that accompanied the learning event.
arXiv Detail & Related papers (2023-04-13T16:34:12Z) - Memory-Augmented Theory of Mind Network [59.9781556714202]
Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others.
Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents.
We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others.
This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
arXiv Detail & Related papers (2023-01-17T14:48:58Z) - Anti-Retroactive Interference for Lifelong Learning [65.50683752919089]
We design a paradigm for lifelong learning based on meta-learning and associative mechanism of the brain.
It tackles the problem from two aspects: extracting knowledge and memorizing knowledge.
It is theoretically analyzed that the proposed learning paradigm can make the models of different tasks converge to the same optimum.
arXiv Detail & Related papers (2022-08-27T09:27:36Z) - Cognitively Inspired Learning of Incremental Drifting Concepts [31.3178953771424]
Inspired by the nervous system learning mechanisms, we develop a computational model that enables a deep neural network to learn new concepts.
Our model can generate pseudo-data points for experience replay and accumulate new experiences to past learned experiences without causing cross-task interference.
arXiv Detail & Related papers (2021-10-09T23:26:29Z) - Learning offline: memory replay in biological and artificial
reinforcement learning [1.0136215038345011]
We review the functional roles of replay in the fields of neuroscience and AI.
Replay is important for memory consolidation in biological neural networks.
It is also key to stabilising learning in deep neural networks.
arXiv Detail & Related papers (2021-09-21T08:57:19Z) - Backprop-Free Reinforcement Learning with Active Neural Generative
Coding [84.11376568625353]
We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments.
We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference.
The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
arXiv Detail & Related papers (2021-07-10T19:02:27Z) - Visualizing and Understanding Vision System [0.6510507449705342]
We use a vision recognition-reconstruction network (RRN) to investigate the development, recognition, learning and forgetting mechanisms.
In digit recognition study, we witness that the RRN could maintain object invariance representation under various viewing conditions.
In the learning and forgetting study, novel structure recognition is implemented by adjusting entire synapses in low magnitude while pattern specificities of original synaptic connectivity are preserved.
arXiv Detail & Related papers (2020-06-11T07:08:49Z) - Revisit Systematic Generalization via Meaningful Learning [15.90288956294373]
Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity.
We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts.
arXiv Detail & Related papers (2020-03-14T15:27:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.