125 research outputs found
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A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing
Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used
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The Rescorla-Wagner Drift-Diffusion Model
Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this thesis we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 11 experimental phenomena and show that it can provide an adequate account for 9, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSCTD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used
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A Double Error Dynamic Asymptote Model of Associative Learning
In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed
Hardware Implementation of Classical Conditioning of Rescorla-Wagner Model with Timing Drift-Diffusion Model
A novel hardware-efficient neuronal network having conditioning functions is proposed, where its nonlinear dynamics is designed based on ergodic cellular automaton. The proposed network is implemented by an FPGA and experiments validate its conditioning functions. It is shown that the network can be implemented by fewer circuit elements and consumes lower power than a conventional conditioning model
The role of reinforcement learning in perceptual decision-making
Evidence accumulation is an important core component of perceptual
decision-making that allows organisms to mitigate the e ects of environmental
uncertainty by combining information in time. Simple theoretical
models of evidence accumulation have been successful in critical aspects of
performance in psychophysical tasks, capturing the inter-dependence between
accuracy and reaction time (RT). Yet substantial ambiguity remains
concerning key features of this class of models.(...
A learning perspective on individual differences in skilled reading: Exploring and exploiting orthographic and semantic discrimination cues
The goal of the present study is to understand the role orthographic and semantic information play in the behaviour of skilled readers. Reading latencies from a self-paced sentence reading experiment in which Russian near-synonymous verbs were manipulated appear well-predicted by a combination of bottom-up sub-lexical letter triplets (trigraphs) and top-down semantic generalizations, modelled using the Naive Discrimination Learner. The results reveal a complex interplay of bottom-up and top-down support from orthography and semantics to the target verbs, whereby activations from orthography only are modulated by individual differences. Using performance on a serial reaction time task for a novel operationalization of the mental speed hypothesis, we explain the observed individual differences in reading behaviour in terms of the exploration/exploitation hypothesis from Reinforcement Learning, where initially slower and more variable behaviour leads to better performance overall
Pavlovian control of escape and avoidance
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195168.pdf (publisher's version ) (Open Access)To survive in complex environments, animals need to have mechanisms to select effective actions quickly, with minimal computational costs. As perhaps the computationally most parsimonious of these systems, Pavlovian control accomplishes this by hardwiring specific stereotyped responses to certain classes of stimuli. It is well documented that appetitive cues initiate a Pavlovian bias toward vigorous approach; however, Pavlovian responses to aversive stimuli are less well understood. Gaining a deeper understanding of aversive Pavlovian responses, such as active avoidance, is important given the critical role these behaviors play in several psychiatric conditions. The goal of the current study was to establish a behavioral and computational framework to examine aversive Pavlovian responses (activation vs. inhibition) depending on the proximity of an aversive state (escape vs. avoidance). We introduce a novel task in which participants are exposed to primary aversive (noise) stimuli and characterized behavior using a novel generative computational model. This model combines reinforcement learning and drift-diffusion models so as to capture effects of invigoration/inhibition in both explicit choice behavior as well as changes in RT. Choice and RT results both suggest that escape is associated with a bias for vigorous action, whereas avoidance is associated with behavioral inhibition. These results lay a foundation for future work that promise to provide insights into typical and atypical aversive Pavlovian responses involved in psychiatric disorders, allowing us to quantify both implicit and explicit indices of vigorous choice behavior in the context of aversion.12 p
Evidence accumulation in a Laplace domain decision space
Evidence accumulation models of simple decision-making have long assumed that
the brain estimates a scalar decision variable corresponding to the
log-likelihood ratio of the two alternatives. Typical neural implementations of
this algorithmic cognitive model assume that large numbers of neurons are each
noisy exemplars of the scalar decision variable. Here we propose a neural
implementation of the diffusion model in which many neurons construct and
maintain the Laplace transform of the distance to each of the decision bounds.
As in classic findings from brain regions including LIP, the firing rate of
neurons coding for the Laplace transform of net accumulated evidence grows to a
bound during random dot motion tasks. However, rather than noisy exemplars of a
single mean value, this approach makes the novel prediction that firing rates
grow to the bound exponentially, across neurons there should be a distribution
of different rates. A second set of neurons records an approximate inversion of
the Laplace transform, these neurons directly estimate net accumulated
evidence. In analogy to time cells and place cells observed in the hippocampus
and other brain regions, the neurons in this second set have receptive fields
along a "decision axis." This finding is consistent with recent findings from
rodent recordings. This theoretical approach places simple evidence
accumulation models in the same mathematical language as recent proposals for
representing time and space in cognitive models for memory.Comment: Revised for CB
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