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AbstractThis experiment examined single‐process and dual‐process accounts of the development of visual recognition memory. The participants, 6–7‐year‐olds, 9–10‐year‐olds and adults, were presented with a list of pictures which they encoded under shallow or deep conditions. They then made recognition and confidence judgments about a list containing old and new items. We replicated the main trends reported by Ghetti and Angelini () in that recognition hit rates increased from 6 to 9 years of age, with larger age changes following deep than shallow encoding. Formal versions of the dual‐process high threshold signal detection model and several single‐process models (equal variance signal detection, unequal variance signal detection, mixture signal detection) were fit to the developmental data. The unequal variance and mixture signal detection models gave a better account of the data than either of the other models. A state‐trace analysis found evidence for only one underlying memory process across the age range tested. These results suggest that single‐process memory models based on memory strength are a viable alternative to dual‐process models for explaining memory development.

A model for visual recall tasks was presented in terms of visual information storage (VIS), scanning, rehearsal, and auditory information storage (AIS). It was shown first that brief visual stimuli are stored in VIS in a form similar to the sensory input. These visual "images" contain considerably more information than is transmitted later. They can be sampled by scanning for items at high rates of about 10 msec per letter. Recall is based on a verbal receding of the stimulus (rehearsal), which is remembered in AIS. The items retained in AIS are usually rehearsed again to prevent them from decaying. The human limits in immediate-memory (reproduction) tasks are inherent in the AIS-Rehearsal loop. The main implication of the model for human factors is the importance of the auditory coding in visual tasks.

AbstractThis paper deals with a class of optimal control problems which arises in advertising models with Volterra Ornstein-Uhlenbeck process representing the product goodwill. Such choice of the model can be regarded as a stochastic modification of the classical Nerlove-Arrow model that allows to incorporate both presence of uncertainty and empirically observed memory effects such as carryover or distributed forgetting. We present an approach to solve such optimal control problems based on an infinite dimensional lift which allows us to recover Markov properties by formulating an optimization problem equivalent to the original one in a Hilbert space. Such technique, however, requires the Volterra kernel from the forward equation to have a representation of a particular form that may be challenging to obtain in practice. We overcome this issue for Hölder continuous kernels by approximating them with Bernstein polynomials, which turn out to enjoy a simple representation of the required type. Then we solve the optimal control problem for the forward process with approximated kernel instead of the original one and study convergence. The approach is illustrated with simulations.

Abstract The nonword repetition (NWR) test has been shown to be a good predictor of children's vocabulary size. NWR performance has been explained using phonological working memory, which is seen as a critical component in the learning of new words. However, no detailed specification of the link between phonological working memory and long‐term memory (LTM) has been proposed. In this paper, we present a computational model of children's vocabulary acquisition (EPAM‐VOC) that specifies how phonological working memory and LTM interact. The model learns phoneme sequences, which are stored in LTM and mediate how much information can be held in working memory. The model's behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. EPAM‐VOC shows a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAM‐VOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM‐VOC provides a simple and elegant computational account of some of the key processes involved in the learning of new words: it specifies how phonological working memory and LTM interact; makes testable predictions; and suggests that developmental changes in NWR performance may reflect differences in the amount of information that has been encoded in LTM rather than developmental changes in working memory capacity.