Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics

We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward. DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence representation, classical reinforcement learning, and a computational neuroscience model of working memory, called Item and Order working memory, which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both a simulated and real robot that must learn a specific rewarding sequence of elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs on the level of the discrete SARSA(\lambda), validating the feasibility of general reinforcement learning without compromising neural dynamics.Comment: Sohrob Kazerounian, Matthew Luciw are Joint first author

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

A Psychogenetic Algorithm for Behavioral Sequence Learning

This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J.Piaget. Specifically, we modeled some elements of cognitive structure learning in children from 0 to 4 months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest new, interesting approaches to problem so far not satisfactorily solved in the field of machine learning. Further, we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience. The model was implemented and tested in some simple experimental settings, with reference to the task of learning sensorimotor sequences

From byproduct to design factor: on validating the interpretation of process indicators based on log data

International large-scale assessments such as PISA or PIAAC have started to provide public or scientific use files for log data; that is, events, event-related attributes and timestamps of test-takers’ interactions with the assessment system. Log data and the process indicators derived from it can be used for many purposes. However, the intended uses and interpretations of process indicators require validation, which here means a theoretical and/or empirical justification that inferences about (latent) attributes of the test-taker’s work process are valid. This article reviews and synthesizes measurement concepts from various areas, including the standard assessment paradigm, the continuous assessment approach, the evidence-centered design (ECD) framework, and test validation. Based on this synthesis, we address the questions of how to ensure the valid interpretation of process indicators by means of an evidence-centered design of the task situation, and how to empirically challenge the intended interpretation of process indicators by developing and implementing correlational and/or experimental validation strategies. For this purpose, we explicate the process of reasoning from log data to low-level features and process indicators as the outcome of evidence identification. In this process, contextualizing information from log data is essential in order to reduce interpretative ambiguities regarding the derived process indicators. Finally, we show that empirical validation strategies can be adapted from classical approaches investigating the nomothetic span and construct representation. Two worked examples illustrate possible validation strategies for the design phase of measurements and their empirical evaluation

Automated system for integration and display of physiological response data

The system analysis approach was applied in a study of physiological systems in both 1-g and weightlessness, for short and long term experiments. A whole body, algorithm developed as the first step in the construction of a total body simulation system is described and an advanced biomedical computer system concept including interactive display/command consoles is discussed. The documentation of the design specifications, design and development studies, and user's instructions (which include program listings) for these delivered end-terms; the reports on the results of many research and feasibility studies; and many subcontract reports are cited in the bibliography

From byproduct to design factor. On validating the interpretation of process indicators based on log data

International large-scale assessments such as PISA or PIAAC have started to provide public or scientific use files for log data; that is, events, event-related attributes and timestamps of test-takers\u27 interactions with the assessment system. Log data and the process indicators derived from it can be used for many purposes. However, the intended uses and interpretations of process indicators require validation, which here means a theoretical and/or empirical justification that inferences about (latent) attributes of the test-taker\u27s work process are valid. This article reviews and synthesizes measurement concepts from various areas, including the standard assessment paradigm, the continuous assessment approach, the evidence-centered design (ECD) framework, and test validation. Based on this synthesis, we address the questions of how to ensure the valid interpretation of process indicators by means of an evidence-centered design of the task situation, and how to empirically challenge the intended interpretation of process indicators by developing and implementing correlational and/or experimental validation strategies. For this purpose, we explicate the process of reasoning from log data to low-level features and process indicators as the outcome of evidence identification. In this process, contextualizing information from log data is essential in order to reduce interpretative ambiguities regarding the derived process indicators. Finally, we show that empirical validation strategies can be adapted from classical approaches investigating the nomothetic span and construct representation. Two worked examples illustrate possible validation strategies for the design phase of measurements and their empirical evaluation. (DIPF/Orig.

Validating simulated interaction for retrieval evaluation

A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations

Real Time Validation of Online Situation Awareness Questionnaires in Simulated Approach Air Traffic Control

Measuring Situation Awareness (SAw) to evaluate an operator's ability to handle complex dynamic situations and the use of assistance systems have become a standard approach in Human Factors research. Ideally, the operators should be supported by enabling and disabling assistance systems depending on their SAw. On the one hand, if the situation's complexity increases and therefore SAw is likely to be reduced, additional systems may help to prevent overextension by taking control over the specific task or task components. On the other hand, there has been evidence that high levels of automation may reduce the ability to intervene in a timely manner if needed due to mental underload. Adjusting the usage of assistance systems based on the operator's SAw may help to overcome both limitations. However, existing measurement tools for SAw require post-simulation analysis. This way it is not possible to make decisions based on the operator's SAw in parallel with the situation at hand. Software capable of analyzing the current state of a given situation has been developed to allow real time assessment of SAw. This software was designed to measure SAw of approach air traffic controllers in the real time NLR ATM Research Simulator. SAw was measured by presenting questionnaires during three different scenarios. Before an item was presented, the simulation's log files were analyzed to provide the software with the correct answer. This way, validating responses and evaluating SAw immediately was possible. In a first study, 57 non-expert subjects were presented with online probe questionnaires in real time simulated approach air traffic control scenarios. It was found that the software was able to measure SAw in real time. In the future, such systems could be used to make decisions about the need for further assistance while the situation is still happening. This way, operators would only get the necessary amount of assistance without reducing their work to passive monitoring