The influence of memory in a threshold model for distributed task assignment

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability

Attentional capture by meaning: A multi-level modelling study

We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models

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

Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate

Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.Defense Advanced Projects Research Agency SyNaPSE program (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC #801881-BS under HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (NSF SBE-0354378

Cognitive processes in categorical and associative priming: a diffusion model analysis

Cognitive processes and mechanisms underlying different forms of priming were investigated using a diffusion model approach. In a series of 6 experiments, effects of prime-target associations and of a semantic and affective categorical match of prime and target were analyzed for different tasks. Significant associative and categorical priming effects were found in standard analyses of response times (RTs) and error frequencies. Results of diffusion model analyses revealed that priming effects of associated primes were mapped on the drift rate parameter (v), while priming effects of a categorical match on a task-relevant dimension were mapped on the extradecisional parameters (t(0) and d). These results support a spreading activation account of associative priming and an explanation of categorical priming in terms of response competition. Implications for the interpretation of priming effects and the use of priming paradigms in cognitive psychology and social cognition are discussed

dARTMAP: A Neural Network for Fast Distributed Supervised Learning

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning, An implementation algorithm here describes one class of dARTMAP networks. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule for improved contrast control at the coding field. A dARTMAP system reduces to fuzzy ARTMAP when coding is winner-take-all. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657