Knowing in Aristotle part 1: Epistēmē, Nous, and non‐rational cognitive states

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

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Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽

10.1609/aimag.v40i3.4810 ◽

2019 ◽

Vol 40 (3) ◽

pp. 41-57

Author(s):

Manisha Mishra ◽

Pujitha Mannaru ◽

David Sidoti ◽

Adam Bienkowski ◽

Lingyi Zhang ◽

...

Keyword(s):

Decision Support ◽

Situational Awareness ◽

Feature Space ◽

Human Interaction ◽

Superior Performance ◽

Uncertain Environments ◽

Cognitive States ◽

Contextual Elements ◽

Human Operators ◽

Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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TRANSITIONS ACROSS COGNITIVE STATES AMONG OLDER ADULTS AND EDUCATION: A MULTI-STATE SURVIVAL MODEL

Innovation in Aging ◽

10.1093/geroni/igx004.2028 ◽

2017 ◽

Vol 1 (suppl_1) ◽

pp. 577-577

Author(s):

A. Robitaille ◽

A. Van den Hout ◽

R.J. Machado ◽

I. Cukic ◽

A. Koval ◽

...

Keyword(s):

Older Adults ◽

Survival Model ◽

Cognitive States ◽

State Survival

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The idiodynamic method: willingness to communicate and anxiety processes interacting in real time

IRAL - International Review of Applied Linguistics in Language Teaching ◽

10.1515/iral-2021-0024 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Peter D. MacIntyre ◽

Tammy Gregersen

Keyword(s):

Real Time ◽

Complex Dynamics ◽

Video Recording ◽

Research Participant ◽

Willingness To Communicate ◽

Human Communication ◽

Cognitive States ◽

Communication Anxiety ◽

Observer Ratings ◽

Teacher Empathy

Abstract The idiodynamic method is a relatively new mixed-method approach to studying in real time the complex dynamics of integrated affective and cognitive states that interact continuously with human communication. The method requires video recording a sample of communication from a research participant and then using specialized software to play the video back while collecting contemporaneous self-reported ratings (approximately one per second) on one or more focal variables of interest to the researcher, such as willingness to communicate (WTC) or communication anxiety (CA). After the participant rates the communication sample, a continuous graph of changes in the focal variable is printed. The final step is to interview the speaker to gather an explanation for changes in the ratings, for example at peaks or valleys in the graph. The method can also collect observer ratings that can then be compared with the speaker’s self-ratings. To date, studies have been conducted examining WTC, CA, motivation, perceived competence, teacher self-efficacy, teacher empathy, and strategy use, among other topics. The strengths and limitations of the method will be discussed and a specific example of its use in measuring WTC and CA will be provided.

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Transfer and unlearning of topic prominence by Chinese learners of English

IRAL - International Review of Applied Linguistics in Language Teaching ◽

10.1515/iral-2018-0205 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Hui Chang ◽

Lilong Xu

Keyword(s):

Second Language ◽

Universal Grammar ◽

L2 Acquisition ◽

L1 Transfer ◽

Chinese Speakers ◽

English Only ◽

Cognitive States ◽

Chinese Learners ◽

Topic Constructions ◽

Chinese Learners Of English

Abstract Chinese allows both gapped and gapless topic constructions without their usage being restricted to specific contexts, while English only allows gapped topic constructions which are used in certain contexts. In other words, Chinese uses ‘topic prominence’, whereas English does not. The contrast between English and Chinese topic constructions poses a learnability problem for Chinese learners of English. This paper uses an empirical study investigating first language (L1) transfer in the case of Chinese learners of English and the extent to which they are able to unlearn topic prominence as they progress in second language (L2) English. Results of an acceptability judgment test indicate that Chinese learners of English initially transfer Chinese topic prominence into their English, then gradually unlearn Chinese topic prominence as their English proficiency improves, and finally unlearn Chinese topic prominence successfully. The results support the Full Transfer Theory (Schwartz, Bonnie & Rex Sprouse. 1996. L2 cognitive states and the Full Transfer/Full Access model. Second Language Research 12. 40–72) and the Variational Learning Model (Yang, Charles. 2004. Universal Grammar, statistics or both? Trends in Cognitive Sciences 8. 451–456), but contradict the proposal that the topic prominence can never be transferred but may be unlearned from the beginning in Chinese speakers’ acquisition of English (Zheng, Chao. 2001. Nominal Constructions Beyond IP and Their Initial Restructuring in L2 Acquisition. Guangzhou: Guangdong University of Foreign Studies Ph.D. dissertation). In addition, the type of topic constructions that is used and whether or not a comma is added after the topic have an effect on learners’ transfer and unlearning of topic prominence. It is proposed that the specification of Agr(eement) and T(ense) as well as the presence of expletive subjects in English input can trigger the unlearning of topic prominence for Chinese learners of English.

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Context-specific abnormalities of the central executive network in first-episode psychosis: relationship with cognition

Psychological Medicine ◽

10.1017/s0033291720004201 ◽

2020 ◽

pp. 1-10

Author(s):

Deepak K. Sarpal ◽

Goda Tarcijonas ◽

Finnegan J. Calabro ◽

William Foran ◽

Gretchen L. Haas ◽

...

Keyword(s):

Cognitive Control ◽

Psychotic Disorders ◽

First Episode Psychosis ◽

First Episode ◽

Cognitive State ◽

Cognitive States ◽

Episode Psychosis ◽

Dorsolateral Prefrontal ◽

Parietal Lobule ◽

Context Specific

Abstract Background Cognitive impairments, which contribute to the profound functional deficits observed in psychotic disorders, have found to be associated with abnormalities in trial-level cognitive control. However, neural tasks operate within the context of sustained cognitive states, which can be assessed with ‘background connectivity’ following the removal of task effects. To date, little is known about the integrity of brain processes supporting the maintenance of a cognitive state in individuals with psychotic disorders. Thus, here we examine background connectivity during executive processing in a cohort of participants with first-episode psychosis (FEP). Methods The following fMRI study examined background connectivity of the dorsolateral prefrontal cortex (DLPFC), during working memory engagement in a group of 43 patients with FEP, relative to 35 healthy controls (HC). Findings were also examined in relation to measures of executive function. Results The FEP group relative to HC showed significantly lower background DLPFC connectivity with bilateral superior parietal lobule (SPL) and left inferior parietal lobule. Background connectivity between DLPFC and SPL was also positively associated with overall cognition across all subjects and in our FEP group. In comparison, resting-state frontoparietal connectivity did not differ between groups and was not significantly associated with overall cognition, suggesting that psychosis-related alterations in executive networks only emerged during states of goal-oriented behavior. Conclusions These results provide novel evidence indicating while frontoparietal connectivity at rest appears intact in psychosis, when engaged during a cognitive state, it is impaired possibly undermining cognitive control capacities in FEP.

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Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

Robotics ◽

10.3390/robotics10010012 ◽

2021 ◽

Vol 10 (1) ◽

pp. 12

Author(s):

Yixiang Lim ◽

Nichakorn Pongsarkornsathien ◽

Alessandro Gardi ◽

Roberto Sabatini ◽

Trevor Kistan ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Real Time ◽

Simulation Environment ◽

Adaptive Automation ◽

Cognitive States ◽

Aerial Vehicles ◽

Machine Interface ◽

Multiple Unmanned Aerial Vehicles ◽

Online Adaptation ◽

Adaptation Phase

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

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Quantifying the Predictability of Visual Scanpaths Using Active Information Storage

Entropy ◽

10.3390/e23020167 ◽

2021 ◽

Vol 23 (2) ◽

pp. 167

Author(s):

Patricia Wollstadt ◽

Martina Hasenjäger ◽

Christiane B. Wiebel-Herboth

Keyword(s):

Information Storage ◽

Gaze Behavior ◽

Behavioral Experiments ◽

Human Machine Interaction ◽

Cognitive States ◽

Visual Scanpath ◽

Active Information Storage ◽

Temporal Horizon ◽

Machine Interaction ◽

Transition Entropy

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction.

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