How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks.

The influence of memory for prior instances on performance in a conflict detection task

In 3 experiments, the authors examined the role of memory for prior instances for making relative judgments in conflict detection. Participants saw pairs of aircraft either repeatedly conflict with each other or pass safely before being tested on new aircraft pairs, which varied in similarity to the training pairs. Performance was influenced by the similarity between aircraft pairs. Detection time was faster when a conflict pair resembled a pair that had repeatedly conflicted. Detection time was slower, and participants missed conflicts, when a conflict pair resembled a pair that had repeatedly passed safely. The findings identify aircraft features that are used as inputs into the memory decision process and provide an indication of the processes involved in the use of memory for prior instances to make relative judgments.

Predicting driver's hypovigilance on monotonous roads: literature review.

Drivers' ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks. Particularly, highway design reduces the driving task mainly to a lane-keeping one. It contributes to hypovigilance and road crashes as drivers are often not aware that their driving behaviour is impaired. Monotony increases fatigue, however, the fatigue community has mainly focused on endogenous factors leading to fatigue such as sleep deprivation. This paper focuses on the exogenous factor monotony which contributes to hypovigilance. Objective measurements of the effects of monotonous driving conditions on the driver and the vehicle's dynamics is systematically reviewed with the aim of justifying the relevance of the need for a mathematical framework that could predict hypovigilance in real-time. Although electroencephalography (EEG) is one of the most reliable measures of vigilance, it is obtrusive. This suggests to predict from observable variables the time when the driver is hypovigilant. Outlined is a vision for future research in the modelling of driver vigilance decrement due to monotonous driving conditions. A mathematical model for predicting drivers’ hypovigilance using information like lane positioning, steering wheel movements and eye blinks is provided. Such a modelling of driver vigilance should enable the future development of an in-vehicle device that detects driver hypovigilance in advance, thus offering the potential to enhance road safety and prevent road crashes.

A model to predict hypovigilance during a monotonous task

The driving task requires sustained attention during prolonged periods, and can be performed in highly predictable or repetitive environments. Such conditions could create drowsiness or hypovigilance and impair the ability to react to critical events. Identifying vigilance decrement in monotonous conditions has been a major subject of research, but no research to date has attempted to predict this vigilance decrement. This pilot study aims to show that vigilance decrements due to monotonous tasks can be predicted through mathematical modelling. A short vigilance task sensitive to short periods of lapses of vigilance called Sustained Attention to Response Task is used to assess participants’ performance. This task models the driver’s ability to cope with unpredicted events by performing the expected action. A Hidden Markov Model (HMM) is proposed to predict participants’ hypovigilance. Driver’s vigilance evolution is modelled as a hidden state and is correlated to an observable variable: the participant’s reactions time. This experiment shows that the monotony of the task can lead to an important vigilance decline in less than five minutes. This impairment can be predicted four minutes in advance with an 86% accuracy using HMMs. This experiment showed that mathematical models such as HMM can efficiently predict hypovigilance through surrogate measures. The presented model could result in the development of an in-vehicle device that detects driver hypovigilance in advance and warn the driver accordingly, thus offering the potential to enhance road safety and prevent road crashes.

Three primary school students’ cognition about 3D rotation in a virtual reality learning environment

This paper reports on three primary school students’ explorations of 3D rotation in a virtual reality learning environment (VRLE) named VRMath. When asked to investigate if you would face the same direction when you turn right 45 degrees first then roll up 45 degrees, or when you roll up 45 degrees first then turn right 45 degrees, the students found that the different order of the two turns ended up with different directions in the VRLE. This was contrary to the students’ prior predictions based on using pen, paper and body movements. The findings of this study showed the difficulty young children have in perceiving and understanding the non-commutative nature of 3D rotation and the power of the computational VRLE in giving students experiences that they rarely have in real life with 3D manipulations and 3D mental movements.

Real-time evaluation of driver’s alertness on highways

Suburbanisation has been internationally a major phenomenon in the last decades. Suburb-to-suburb routes are nowadays the most widespread road journeys; and this resulted in an increment of distances travelled, particularly on faster suburban highways. The design of highways tends to over-simplify the driving task and this can result in decreased alertness. Driving behaviour is consequently impaired and drivers are then more likely to be involved in road crashes. This is particularly dangerous on highways where the speed limit is high. While effective countermeasures to this decrement in alertness do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behaviour in real-time. The aim of this study is to evaluate in real-time the level of alertness of the driver through surrogate measures that can be collected from in-vehicle sensors. Slow EEG activity is used as a reference to evaluate driver's alertness. Data are collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device (N=25 participants). Four different types of highways (driving scenario of 40 minutes each) are implemented through the variation of the road design (amount of curves and hills) and the roadside environment (amount of buildings and traffic). We show with Neural Networks that reduced alertness can be detected in real-time with an accuracy of 92% using lane positioning, steering wheel movement, head rotation, blink frequency, heart rate variability and skin conductance level. Such results show that it is possible to assess driver's alertness with surrogate measures. Such methodology could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring in real-time drivers' behaviour on highways, and therefore it could result in improved road safety.

Modelling Cued-Target Recall using Quantum Inspired Models of Target Activation

This article presents and evaluates Quantum Inspired models of Target Activation using Cued-Target Recall Memory Modelling over multiple sources of Free Association data. Two components were evaluated: Whether Quantum Inspired models of Target Activation would provide a better framework than their classical psychological counterparts and how robust these models are across the different sources of Free Association data. In previous work, a formal model of cued-target recall did not exist and as such Target Activation was unable to be assessed directly. Further to that, the data source used was suspected of suffering from temporal and geographical bias. As a consequence, Target Activation was measured against cued-target recall data as an approximation of performance. Since then, a formal model of cued-target recall (PIER3) has been developed [10] with alternative sources of data also becoming available. This allowed us to directly model target activation in cued-target recall with human cued-target recall pairs and use multiply sources of Free Association Data. Featural Characteristics known to be important to Target Activation were measured for each of the data sources to identify any major differences that may explain variations in performance for each of the models. Each of the activation models were used in the PIER3 memory model for each of the data sources and was benchmarked against cued-target recall pairs provided by the University of South Florida (USF). Two methods where used to evaluate performance. The first involved measuring the divergence between the sets of results using the Kullback Leibler (KL) divergence with the second utilizing a previous statistical analysis of the errors [9]. Of the three sources of data, two were sourced from human subjects being the USF Free Association Norms and the University of Leuven (UL) Free Association Networks. The third was sourced from a new method put forward by Galea and Bruza, 2015 in which pseudo Free Association Networks (Corpus Based Association Networks - CANs) are built using co-occurrence statistics on large text corpus. It was found that the Quantum Inspired Models of Target Activation not only outperformed the classical psychological model but was more robust across a variety of data sources.

When Working Memory and Attention Compete: Characterizing the Dynamic Interdependence between our Mental Workspace and External Environment

All of us are taxed with juggling our inner mental lives with immediate external task demands. For many years, the temporary maintenance of internal information was considered to be handled by a dedicated working memory (WM) system. It has recently become increasingly clear, however, that such short-term internal activation interacts with attention focused on external stimuli. It is unclear, however, exactly why these two interact, at what level of processing, and to what degree. Because our internal maintenance and external attention processes co-occur with one another, the manner of their interaction has vast implications for functioning in daily life. The work described here has employed original experimental paradigms combining WM and attention task elements, functional magnetic resonance imaging (fMRI) to illuminate the associated neural processes, and transcranial magnetic stimulation (TMS) to clarify the causal substrates of attentional brain function. These studies have examined a mechanism that might explain why (and when) the content of WM can involuntarily capture visual attention. They have, furthermore, tested whether fundamental attentional selection processes operate within WM, and whether they are reciprocal with attention. Finally, they have illuminated the neural consequences of competing attentional demands. The findings indicate that WM shares representations, operating principles, and cognitive resources with externally-oriented attention.

Analysis of slow- and fast-alpha band asymmetry during performance of a saccadic eye movement task: Dissociation between memory- and attention-driven systems

This study aimed at analyzing the relationship between slow- and fast-alpha asymmetry within frontal cortex and the planning, execution and voluntary control of saccadic eye movements (SEM), and quantitative electroencephalography (qEEG) was recorded using a 20-channel EEG system in 12 healthy participants performing a fixed (i.e., memory-driven) and a random SEM (i.e., stimulus-driven) condition. We find main effects for SEM condition in slow- and fast-alpha asymmetry at electrodes F3-F4, which are located over premotor cortex, specifically a negative asymmetry between conditions. When analyzing electrodes F7-F8, which are located over prefrontal cortex, we found a main effect for condition in slow-alpha asymmetry, particularly a positive asymmetry between conditions. In conclusion, the present approach supports the association of slow- and fast-alpha bands with the planning and preparation of SEM, and the specific role of these sub-bands for both, the attention network and the coordination and integration of sensory information with a (oculo)-motor response. (C) 2011 Elsevier B.V. All rights reserved.

Memory and attention problems in children with brain tumors at diagnosis

Purpose: Results from previous studies indicate that children with brain tumors (BT) might present with cognitive problems at diagnosis and thus before the start of any medical treatment. The question remains whether these problems are due to the underlying tumor itself or due to the high level of emotional and physical stress which is involved at diagnosis of a malignant disorder. All children with a de novo oncological diagnosis not involving the central nervous systems (CNS) are usually exposed to a comparable level of distress. However, patients with cancer not involving the CNS are not expected to show disease-related cognitive problems. Thus they serve as a well-balanced control group (CG) to help distinguish between the probable causes of the effect. Method: In a pilot study we analyzed an array of cognitive functions in 16 children with BT and 17 control patients. In both groups, tests were administered in-patient at diagnosis before any therapeutic intervention such as surgery, chemotherapy od irradiation. Results: Performance of children with BT was comparable to that of CG patients in the areas of intelligence, perceptual reasoning, verbal comprehension, working memory, and processing speed. In contrast, however, BT patients performded significantly worse in verbal memory and attention. Conclusion: Memory and attention seem to be the most vulnerable funstions affected by BT, with other functions being preserved at the time of diagnosis. It ist to be expected that this vulnerability might exacerbate the cognitive decline after chemotherapy and radiation treatment - known to impair intellectual performance. The findings highlight the need of early cognitive assessments in children with BT in order to introduce cognitive training as early as possible to minimize or even prevent cognitive long-term sequelae. This might improve long-term academic and professional outcome of these children, but especially helps their return to school after hospitalization.