Holographic Declarative Memory: Distributional Semantics as the Architecture of Memory.

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model. High-dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high-level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high-level cognition and provide human-readable, explainable models, but scale poorly to naturalistic, non-symbolic, or big data. Vector-symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector-space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector-space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition.

An fMRI Study of Risky Decision Making: The Role of Mental Preparation and Conflict.

INTRODUCTION: The current study aimed to elucidate the role of preparatory cognitive control in decision making and its neural correlates using functional Magnetic Resonance Imaging (fMRI). To this effect, by employing a series of new cognitive tasks, we assessed the role of preparatory cognitive control in monetary (risky) decision making. METHODS: The participants had to decide between a risky and a safe gamble based on their chance of winning (high or low). In the 2-phase gambling task (similar to Cambridge gambling task), the chance and the gamble were presented at the same time (i.e. in a single phase), but in a new 3-phase gambling task, the chance is presented before the gamble. The tasks ended with a feedback phase. RESULTS: In the 3-phase task, holding the chance in memory to guide their decision enabled the participants to have more control on their risk taking behaviors as shown by activation in a network of brain areas involved in the control and conflict, including dorsal Anterior Cingulate Cortex (dACC), indexed by faster reaction times and better performance in the gambling task, and the temporal lobe, which has a role in holding contextual information. DISCUSSION: Holding information in memory to guide decision presumably enables the participants to have more control on their risk taking behaviors. The conflict and uncertainty resulting from this risky decision was indexed by the activation of dACC, known to be activated in conflict and cognitive control.

Achieving across-laboratory replicability in psychophysical scaling.

It is well known that, although psychophysical scaling produces good qualitative agreement between experiments, precise quantitative agreement between experimental results, such as that routinely achieved in physics or biology, is rarely or never attained. A particularly galling example of this is the fact that power function exponents for the same psychological continuum, measured in different laboratories but ostensibly using the same scaling method, magnitude estimation, can vary by a factor of three. Constrained scaling (CS), in which observers first learn a standardized meaning for a set of numerical responses relative to a standard sensory continuum and then make magnitude judgments of other sensations using the learned response scale, has produced excellent quantitative agreement between individual observers' psychophysical functions. Theoretically it could do the same for across-laboratory comparisons, although this needs to be tested directly. We compared nine different experiments from four different laboratories as an example of the level of across experiment and across-laboratory agreement achievable using CS. In general, we found across experiment and across-laboratory agreement using CS to be significantly superior to that typically obtained with conventional magnitude estimation techniques, although some of its potential remains to be realized.

Positive and negative congruency effects in masked priming: a neuro-computational model based on representation, attention, and conflict.

Studies on masked and unmasked priming have long shown reliable positive effects of the congruent prime on target processing. Paradoxically, a negative effect has also been found, showing faster and more accurate responses in the incongruent compared to the congruent trials. Positive effects have been found with a short time between the prime and the target, while negative effects have been found with a long time between the prime and the target. This has been modeled by assuming that the prime initiates a motor self-inhibitory process that causes these effects (Bowman, H., Schlaghecken, F., Eimer, M., 2006. A neural network model of inhibitory processes and cognitive control. Vis. Cogn. 13, 401-480). We have developed an alternative explanation based on attentional neuro-modulation. In this paper we show that attentional neuro-modulation can be used to model a wide range of findings in this area.

Cyberpsychology: a human-interaction perspective based on cognitive modeling.

This paper argues for the relevance of cognitive modeling and cognitive architectures to cyberpsychology. From a human-computer interaction point of view, cognitive modeling can have benefits both for theory and model building, and for the design and evaluation of sociotechnical systems usability. Cognitive modeling research applied to human-computer interaction has two complimentary objectives: (1) to develop theories and computational models of human interactive behavior with information and collaborative technologies, and (2) to use the computational models as building blocks for the design, implementation, and evaluation of interactive technologies. From the perspective of building theories and models, cognitive modeling offers the possibility to anchor cyberpsychology theories and models into cognitive architectures. From the perspective of the design and evaluation of socio-technical systems, cognitive models can provide the basis for simulated users, which can play an important role in usability testing. As an example of application of cognitive modeling to technology design, the paper presents a simulation of interactive behavior with five different adaptive menu algorithms: random, fixed, stacked, frequency based, and activation based. Results of the simulation indicate that fixed menu positions seem to offer the best support for classification like tasks such as filing e-mails. This research is part of the Human-Computer Interaction, and the Broadband Visual Communication research programs at the National Research Council of Canada, in collaboration with the Carleton Cognitive Modeling Lab at Carleton University.

Computer modeling of airbag-induced ocular injury in pilots wearing night vision goggles.

BACKGROUND: Airbags have saved lives in automobile crashes for many years and are now planned for use in helicopters. The purpose of this study was to investigate the potential for ocular injuries to helicopter pilots wearing night vision goggles when the airbag is deployed. METHODS: A nonlinear finite element model of the human eye was created. Ocular structures such as the fatty tissue, extraocular muscles, and bony orbit were included. The model was imported into Madymo (Mathematical Dynamical Models) and used to determine the worst-case position of a helicopter pilot wearing night vision goggles. This was evaluated as the greatest Von Mises stress in the eye when the airbag was deployed. RESULTS: The worst-case position was achieved by minimizing the distance between the eyes and goggles, having the occupant look directly into the airbag, and making initial contact with the airbag halfway through its full deployment. Simulations with the goggles both remaining fastened to and breaking away from the aviator helmet were performed. Finally, placing a protective lens in front of the eyes was found to reduce the stress to the eye but increase the force experienced by the surrounding orbital bones. CONCLUSION: The finite element model of the eye proved effective for evaluating the experimental parameters.