Order recall in verbal short-term memory: The role of semantic networks

In their recent article, Acheson, MacDonald, and Postle (Journal of Experimental Psychology: Learning, Memory, and Cognition 37:44-59, 2011) made an important but controversial suggestion: They hypothesized that (a) semantic information has an effect on order information in short-term memory (STM) and (b) order recall in STM is based on the level of activation of items within the relevant lexico-semantic long-term memory (LTM) network. However, verbal STM research has typically led to the conclusion that factors such as semantic category have a large effect on the number of correctly recalled items, but little or no impact on order recall (Poirier & Saint-Aubin, Quarterly Journal of Experimental Psychology 48A:384-404, 1995; Saint-Aubin, Ouellette, & Poirier, Psychonomic Bulletin & Review 12:171-177, 2005; Tse, Memory 17:874-891, 2009). Moreover, most formal models of short-term order memory currently suggest a separate mechanism for order coding-that is, one that is separate from item representation and not associated with LTM lexico-semantic networks. Both of the experiments reported here tested the predictions that we derived from Acheson et al. The findings show that, as predicted, manipulations aiming to affect the activation of item representations significantly impacted order memory

Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis

Background: The NLST reported a significant 20% reduction in lung cancer mortality with three annual low-dose CT (LDCT) screens and the Dutch-Belgian NELSON trial indicates a similar reduction. We present the results of the UKLS trial. Methods: From October 2011 to February 2013, we randomly allocated 4 055 participants to either a single invitation to screening with LDCT or to no screening (usual care). Eligible participants (aged 50-75) had a risk score (LLPv2) ≥ 4.5% of developing lung cancer over five years. Data were collected on lung cancer cases to 31 December 2019 and deaths to 29 February 2020 through linkage to national registries. The primary outcome was mortality due to lung cancer. We included our results in a random-effects meta-analysis to provide a synthesis of the latest randomised trial evidence. Findings: 1 987 participants in the intervention and 1 981 in the usual care arms were followed for a median of 7.3 years (IQR 7.1-7.6), 86 cancers were diagnosed in the LDCT arm and 75 in the control arm. 30 lung cancer deaths were reported in the screening arm, 46 in the control arm, (relative rate 0.65 [95% CI 0.41-1.02]; p=0.062). The meta-analysis indicated a significant reduction in lung cancer mortality with a pooled overall relative rate of 0.84 (95% CI 0.76-0.92) from nine eligible trials. Interpretation: The UKLS trial of single LDCT indicates a reduction of lung cancer death of similar magnitude to the NELSON and NLST trials and was included in a meta-analysis of nine randomised trials which provides unequivocal support for lung cancer screening in identified risk groups. Funding: NIHR Health Technology Assessment programme; NIHR Policy Research programme; Roy Castle Lung Cancer Foundation

Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis.

Background: The NLST reported a significant 20% reduction in lung cancer mortality with three annual low-dose CT (LDCT) screens and the Dutch-Belgian NELSON trial indicates a similar reduction. We present the results of the UKLS trial. Methods: From October 2011 to February 2013, we randomly allocated 4 055 participants to either a single invitation to screening with LDCT or to no screening (usual care). Eligible participants (aged 50-75) had a risk score (LLPv2) ≥ 4.5% of developing lung cancer over five years. Data were collected on lung cancer cases to 31 December 2019 and deaths to 29 February 2020 through linkage to national registries. The primary outcome was mortality due to lung cancer. We included our results in a random-effects meta-analysis to provide a synthesis of the latest randomised trial evidence. Findings: 1 987 participants in the intervention and 1 981 in the usual care arms were followed for a median of 7.3 years (IQR 7.1-7.6), 86 cancers were diagnosed in the LDCT arm and 75 in the control arm. 30 lung cancer deaths were reported in the screening arm, 46 in the control arm, (relative rate 0.65 [95% CI 0.41-1.02]; p=0.062). The meta-analysis indicated a significant reduction in lung cancer mortality with a pooled overall relative rate of 0.84 (95% CI 0.76-0.92) from nine eligible trials. Interpretation: The UKLS trial of single LDCT indicates a reduction of lung cancer death of similar magnitude to the NELSON and NLST trials and was included in a meta-analysis of nine randomised trials which provides unequivocal support for lung cancer screening in identified risk groups. Funding: NIHR Health Technology Assessment programme; NIHR Policy Research programme; Roy Castle Lung Cancer Foundation

Long-term associative learning predicts verbal short-term memory performance

Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information that are often argued to enable long-term learning. However, there is considerable evidence for an influence of long-term linguistic learning on performance in short-term memory tasks that brings into question the role of a specialized short-term memory system separate from long-term knowledge. Using natural language corpora, we show experimentally and computationally that performance on three widely used measures of short-term memory (digit span, nonword repetition, and sentence recall) can be predicted from simple associative learning operating on the linguistic environment to which a typical child may have been exposed. The findings support the broad view that short-term verbal memory performance reflects the application of long-term language knowledge to the experimental setting

Serial reconstruction of order and serial recall in verbal short-term memory

A series of experiments was carried out on verbal short-term memory for lists of words. In the first experiment, participants were tested via immediate serial recall and word frequency and list set size were manipulated. With closed lists the same set of items was repeatedly sampled, with open lists no item was presented more than once. In serial recall, effects of word frequency and set size were found. When a serial reconstruction of order task was used, in a second experiment, robust effects of word frequency emerged but set size failed to show an effect. The effect of word frequency in order reconstruction were further examined in two final experiments. The data from these experiments revealed that the effects of word frequency are robust and are apparently not exclusively indicative of output processes. A multiple mechanisms account is adopted in which word frequency can influence both retrieval and pre-retrieval processes

Time Scale Hierarchies in the Functional Organization of Complex Behaviors

Traditional approaches to cognitive modelling generally portray cognitive events in terms of ‘discrete’ states (point attractor dynamics) rather than in terms of processes, thereby neglecting the time structure of cognition. In contrast, more recent approaches explicitly address this temporal dimension, but typically provide no entry points into cognitive categorization of events and experiences. With the aim to incorporate both these aspects, we propose a framework for functional architectures. Our approach is grounded in the notion that arbitrary complex (human) behaviour is decomposable into functional modes (elementary units), which we conceptualize as low-dimensional dynamical objects (structured flows on manifolds). The ensemble of modes at an agent’s disposal constitutes his/her functional repertoire. The modes may be subjected to additional dynamics (termed operational signals), in particular, instantaneous inputs, and a mechanism that sequentially selects a mode so that it temporarily dominates the functional dynamics. The inputs and selection mechanisms act on faster and slower time scales then that inherent to the modes, respectively. The dynamics across the three time scales are coupled via feedback, rendering the entire architecture autonomous. We illustrate the functional architecture in the context of serial behaviour, namely cursive handwriting. Subsequently, we investigate the possibility of recovering the contributions of functional modes and operational signals from the output, which appears to be possible only when examining the output phase flow (i.e., not from trajectories in phase space or time)

From short-term store to multicomponent working memory: The role of the modal model

The term “modal model” reflects the importance of Atkinson and Shiffrin’s paper in capturing the major developments in the cognitive psychology of memory that were achieved over the previous decade, providing an integrated framework that has formed the basis for many future developments. The fact that it is still the most cited model from that period some 50 years later has, we suggest, implications for the model itself and for theorising in psychology more generally. We review the essential foundations of the model before going on to discuss briefly the way in which one of its components, the short-term store, had influenced our own concept of a multicomponent working memory. This is followed by a discussion of recent claims that the concept of a short-term store be replaced by an interpretation in terms of activated long-term memory. We present several reasons to question these proposals. We conclude with a brief discussion of the implications of the longevity of the modal model for styles of theorising in cognitive psychology

Modeling working memory: An interference model of complex span

This article introduces a new computational model for the complex-span task, the most popular task for studying working memory. SOB-CS is a two-layer neural network that associates distributed item representations with distributed, overlapping position markers. Memory capacity limits are explained by interference from a superposition of associations. Concurrent processing interferes with memory through involuntary encoding of distractors. Free time in-between distractors is used to remove irrelevant representations, thereby reducing interference. The model accounts for benchmark findings in four areas: (1) effects of processing pace, processing difficulty, and number of processing steps; (2) effects of serial position and error patterns; (3) effects of different kinds of item-distractor similarity; and (4) correlations between span tasks. The model makes several new predictions in these areas, which were confirmed experimentally