Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenario

Brain Categorization: Learning, Attention, and Consciousness

How do humans and animals learn to recognize objects and events? Two classical views are that exemplars or prototypes are learned. A hybrid view is that a mixture, called rule-plus-exceptions, is learned. None of these models learn their categories. A distributed ARTMAP neural network with self-supervised learning incrementally learns categories that match human learning data on a class of thirty diagnostic experiments called the 5-4 category structure. Key predictions of ART models have received behavioral, neurophysiological, and anatomical support. The ART prediction about what goes wrong during amnesic learning has also been supported: A lesion in its orienting system causes a low vigilance parameter.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-01-1-0624), the National Geospatial Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (EIA-01-30851, IIS-97-20333, SBE-0354378); Office of Naval Research (N00014-95-1-0657, N00014-01-1-0624

Memory, learning and language in autism spectrum disorder

Background and aims: The ‘dual-systems’ model of language acquisition has been used by Ullman and colleagues to explain patterns of strength and weakness in the language of higher-functioning people with autism spectrum disorder (ASD). Specifically, intact declarative/explicit learning is argued to compensate for a deficit in non-declarative/implicit procedural learning, constituting an example of the so-called ‘see-saw’ effect. Ullman and Pullman (2015) extended their argument concerning a see-saw effect on language in ASD to cover other perceived anomalies of behaviour, including impaired acquisition of social skills. The aim of this paper is to present a critique of Ullman and colleagues’ claims, and to propose an alternative model of links between memory systems and language in ASD. Main contribution: We argue that a 4-systems model of learning, in which intact semantic and procedural memory are used to compensate for weaknesses in episodic memory and perceptual learning, can better explain patterns of language ability across the autistic spectrum. We also argue that attempts to generalise the ‘impaired implicit learning/spared declarative learning’ theory to other behaviours in ASD are unsustainable. Conclusions: Clinically significant language impairments in ASD are under-researched, despite their impact on everyday functioning and quality of life. The relative paucity of research findings in this area lays it open to speculative interpretation which may be misleading. Implications: More research is need into links between memory/learning systems and language impairments across the spectrum. Improved understanding should inform therapeutic intervention, and contribute to investigation of the causes of language impairment in ASD with potential implications for prevention

Semantic memory

The Encyclopedia of Human Behavior, Second Edition is a comprehensive three-volume reference source on human action and reaction, and the thoughts, feelings, and physiological functions behind those actions

Illusory correlation, group size and memory

Two studies were conducted to test the predictions of a multi-component model of distinctiveness-based illusory correlation (IC) regarding the use of episodic and evaluative information in the production of the phenomenon. Extending on the standard paradigm, participants were presented with 4 groups decreasing in size, but all exhibiting the same ratio of positive to negative behaviours. Study 1 (N = 75) specifically tested the role of group size and distinctiveness, by including a zero-frequency cell in the design. Consistent with predictions drawn from the proposed model, with decreasing group size, the magnitude of the IC effect showed a linear in- crease in judgments thought to be based on evaluative information. In Study 2 (N = 43), a number of changes were introduced to a group assignment task (double presentation, inclusion of decoys) that allowed a more rig- orous test of the predicted item-specific memory effects. In addition, a new multilevel, mixed logistic regression approach to signal-detection type analysis was used, providing a more flexible and reliable analysis than previ- ously. Again, with decreasing group size, IC effects showed the predicted monotonic increase on the measures (group assignment frequencies, likability ratings) thought to be dependent on evaluative information. At the same time, measures thought to be based on episodic information (free recall and group assignment accuracy) partly revealed the predicted enhanced episodic memory for smaller groups and negative items, while also supporting a distinctiveness-based approach. Additional analysis revealed that the pattern of results for judg- ments though to be based on evaluative information was independent of interpersonal variation in behavioral memory, as predicted by the multi-component model, and in contrast to predictions of the competing models. The results are discussed in terms of the implications of the findings for the proposed mechanisms of illusory correlation

Neural implementation of psychological spaces

Psychological spaces give natural framework for construction of mental representations. Neural model of psychological spaces provides a link between neuroscience and psychology. Categorization performed in high-dimensional spaces by dynamical associative memory models is approximated with low-dimensional feedforward neural models calculating probability density functions in psychological spaces. Applications to the human categorization experiments are discussed