Computationally determining the salience of decision points for real-time wayfinding support

This study introduces the concept of computational salience to explain the discriminatory efficacy of decision points which in turn may have applications to providing real-time assistance to users of navigational aids. This research compared algorithms for calculating the computational salience of decision points and validated the results via three methods: high-salience decision points were used to classify wayfinders; salience scores were used to weight a conditional probabilistic scoring function for real-time wayfinder performance classification; and salience scores were correlated with wayfinding-performance metrics. As an exploratory step to linking computational and cognitive salience a photograph-recognition experiment was conducted. Results reveal a distinction between algorithms useful for determining computational and cognitive saliences. For computational salience information about the structural integration of decision points is effective while information about the probability of decision-point traversal shows promise for determining cognitive salience. Limitations from only using structural information and motivations for future work that include non-structural information are elicited

A Design of Global Workspace Model with Attention: Simulations of Attentional Blink and Lag-1 Sparing

There are many developed theories and implemented arti ̄cial systems in the area of machine consciousness, while none has achieved that. For a possible approach, we are interested in implementing a system by integrating di®erent theories. Along this way, this paper proposes a model based on the global workspace theory and attention mechanism, and providing a fundamental framework for our future work. To examine this model, two experiments are conducted. The ̄rst one demonstrates the agent's ability to shift attention over multiple stimuli, which accounts for the dynamics of conscious content. Another experiment of simulations of attentional blink and lag-1 sparing, which are two well-studied e®ects in psychology and neuroscience of attention and consciousness, aims to justify the agent's compatibility with human brains. In summary, the main contributions of this paper are (1) Adaptation of the global workspace framework by separated workspace nodes, reducing unnecessary computation but retaining the potential of global availability; (2) Embedding attention mechanism into the global workspace framework as the competition mechanism for the consciousness access; (3) Proposing a synchronization mechanism in the global workspace for supporting lag-1 sparing effect, retaining the attentional blink effect

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research

Army-NASA aircrew/aircraft integration program (A3I) software detailed design document, phase 3

The capabilities and design approach of the MIDAS (Man-machine Integration Design and Analysis System) computer-aided engineering (CAE) workstation under development by the Army-NASA Aircrew/Aircraft Integration Program is detailed. This workstation uses graphic, symbolic, and numeric prototyping tools and human performance models as part of an integrated design/analysis environment for crewstation human engineering. Developed incrementally, the requirements and design for Phase 3 (Dec. 1987 to Jun. 1989) are described. Software tools/models developed or significantly modified during this phase included: an interactive 3-D graphic cockpit design editor; multiple-perspective graphic views to observe simulation scenarios; symbolic methods to model the mission decomposition, equipment functions, pilot tasking and loading, as well as control the simulation; a 3-D dynamic anthropometric model; an intermachine communications package; and a training assessment component. These components were successfully used during Phase 3 to demonstrate the complex interactions and human engineering findings involved with a proposed cockpit communications design change in a simulated AH-64A Apache helicopter/mission that maps to empirical data from a similar study and AH-1 Cobra flight test

Systems Social Seience: A Design Inquiry Approach for Stabilization and Reconstruction of Social Systems

This paper explores novel approaches under the design inquiry paradigm that promise to help organizations better understand and solve socio-technical dilemmas. Design inquiry is contrasted with scientific inquiry (Section 1). Section 2 presents a meso-scale model of models methodology for design inquiry that synthesizes systems science, agent modeling and simulation, knowledge management architectures, and domain theories and knowledge. The goal is to focus computational science on exploring underlying mechanisms (white box modeling) and to support reflective theorizing and discourse to explain social dilemmas and potential resolutions. Section 3 then describes an evolving agent modeling and simulation testbed while Section 4 offers two gameworld applications that implement this approach and that serve as an example of the new types of instruments useful for systems social science. The conclusions wrapup by reviewing lessons learned about 10 criteria that have guided this research

An Examination of Hippocampal and Prefrontal Contributions to Spatial Learning and Memory using Immediate Early Gene Imaging

The hippocampus and medial prefrontal cortex are two brain regions which have repeatedly been linked to spatial learning and memory processing; however, the precise roles of individual sub-regions within these areas continue to be debated. The Morris water maze is a well-known behavioural task used to measure spatial memory. Despite its popularity, the type of spatial information animals encode and ultimately rely on for accurate navigation in this task remains unclear. Therefore, the primary objectives of this thesis were to conduct an in-depth investigation into the use of navigation strategies during memory encoding and retrieval in the water maze, and to characterise the specific contributions of the hippocampus and medial prefrontal cortex to these processes using Immediate Early Genes (IEG) imaging. In addition, we investigated the mechanisms underlying neuronal activation by inhibiting ionotropic glutamate receptors (NMDA and AMPA) during or after spatial learning. We found novel evidence that the salience (or noticeability) of environmental cues significantly impacted the type of learning strategy used (i.e. simple or complex), and that increased training led to more flexible responding (i.e. strategy switching). We also discovered that NMDA receptor-mediated activation in area CA1 (indexed by Zif268) was tightly linked to learning-related plasticity, and activation in CA3, prelimbic and anterior cingulate cortices was strongly associated with flexible spatial memory recall (i.e. pattern completion). Finally, we revealed that spatial memory deficits induced by NMDA receptor blockade could be partially prevented by extended environmental experience

The ventral basal ganglia, a selection mechanism at the crossroads of space, strategy, and reward

The basal ganglia are often conceptualised as three parallel domains that include all the constituent nuclei. The ‘ventral domain’ appears to be critical for learning flexible behaviours for exploration and foraging, as it is the recipient of converging inputs from amygdala, hippocampal formation and prefrontal cortex, putatively centres for stimulus evaluation, spatial navigation, and planning/contingency, respectively. However, compared to work on the dorsal domains, the rich potential for quantitative theories and models of the ventral domain remains largely untapped, and the purpose of this review is to provide the stimulus for this work. We systematically review the ventral domain’s structures and internal organisation, and propose a functional architecture as the basis for computational models. Using a full schematic of the structure of inputs to the ventral striatum (nucleus accumbens core and shell), we argue for the existence of many identifiable processing channels on the basis of unique combinations of afferent inputs. We then identify the potential information represented in these channels by reconciling a broad range of studies from the hippocampal, amygdala and prefrontal cortex literatures with known properties of the ventral striatum from lesion, pharmacological, and electrophysiological studies. Dopamine’s key role in learning is reviewed within the three current major computational frameworks; we also show that the shell-based basal ganglia sub-circuits are well placed to generate the phasic burst and dip responses of dopaminergic neurons. We detail dopamine’s modulation of ventral basal ganglia’s inputs by its actions on pre-synaptic terminals and post-synaptic membranes in the striatum, arguing that the complexity of these effects hint at computational roles for dopamine beyond current ideas. The ventral basal ganglia are revealed as a constellation of multiple functional systems for the learning and selection of flexible behaviours and of behavioural strategies, sharing the common operations of selection-by-disinhibition and of dopaminergic modulation

Neuromodulation of Spatial Associations: Evidence from Choice Reaction Tasks During Transcranial Direct Current Stimulation

Various portions of human behavior and cognition are influenced by covert implicit processes without being necessarily available to intentional planning. Implicit cognitive biases can be measured in behavioral tasks yielding SNARC effects for spatial associations of numerical and non-numerical sequences, or yielding the implicit association test effect for associations between insect-flower and negative-positive categories. By using concurrent neuromodulation with transcranial direct current stimulation (tDCS), subthreshold activity patterns in prefrontal cortical regions can be experimentally manipulated to reduce implicit processing. Thus, the application of tDCS can test neurocognitive hypotheses on a unique neurocognitive origin of implicit cognitive biases in different spatial-numerical and non-numerical domains. However, the effects of tDCS are not only determined by superimposed electric fields, but also by task characteristics. To outline the possibilities of task-specific targeting of tDCS, task characteristics and instructions can be varied systematically when combined with neuromodulation. In the present thesis, implicit cognitive processes are assessed in different paradigms concurrent to left-hemispheric prefrontal tDCS to investigate a verbal processing hypothesis for implicit associations in general. In psychological experiments, simple choice reaction tasks measure implicit SNARC and SNARC-like effects as relative left-hand vs. right-hand latency advantages for responding to smaller number or ordinal sequence targets. However, different combinations of polarity-dependent tDCS with stimuli and task procedures also reveal domain-specific involvements and dissociations. Discounting previous unified theories on the SNARC effect, polarity-specific neuromodulation effects dissociate numbers and weekday or month ordinal sequences. By considering also previous results and patient studies, I present a hybrid and augmented working memory account and elaborate the linguistic markedness correspondence principle as one critical verbal mechanism among competing covert coding mechanisms. Finally, a general stimulation rationale based on verbal working memory is tested in separate experiments extending also to non-spatial implicit association test effects. Regarding cognitive tDCS effects, the present studies show polarity asymmetry and task-induced activity dependence of state-dependent neuromodulation. At large, distinct combinations of the identical tDCS electrode configuration with different tasks influences behavioral outcomes tremendously, which will allow for improved task- and domain-specific targeting

Coherence in typeface design: visual similarity of characters in Cyrillic, Devanagari, and Latin

This thesis explores the visual similarity that underlies the coherence in the design of individual typefaces. Typeface designers aim to achieve a unifying coherence in their typefaces, so that characters can be identified individually as well as belonging together giving rise to an overall style. The objective is to determine whether the coherence perceived by readers differs from the coherence intended by designers. The research is cross-disciplinary, combining empirical studies of readers’ perceptions with a computational model that is based on relevant typeface design knowledge. Character similarity is studied in multiple different typefaces (fonts) intended for continuous reading in Cyrillic, Devanagari, and Latin scripts. The studies were conducted online to collect a large number of responses. The participants were presented with a sequence of character triplets. They were asked to identify the odd one out in each of these triplets judging by their visual similarity, thus making a statement about the similarity of the two complementary characters. This method studies the similarity in context, which provides more refined details about participants’ similarity judgements. The model interprets characters using two kinds of features: more specific parts and more general roles. The model learns the relative saliences of these features from a subset of the data collected in the studies. This allows the model to predict participants’ responses to the triplets from the studies and for other, unseen triplets. Additionally, the model can provide explanations of the criteria participants used in their similarity judgements and can generate similarity matrices. The model achieved high scores when predicting response probabilities and identifying the overall odd ones out. A view of coherence that is supported by readers’ perception can be used to assist designers in their creative process, help with fonts’ quality assessments, and contribute to readability research and multi-script typography