Troping the Enemy: Metaphor, Culture, and the Big Data Black Boxes of National Security

This article considers how cultural understanding is being brought into the work of the Intelligence Advanced Research Projects Activity (IARPA), through an analysis of its Metaphor program. It examines the type of social science underwriting this program, unpacks implications of the agency’s conception of metaphor for understanding so-called cultures of interest, and compares IARPA’s to competing accounts of how metaphor works to create cultural meaning. The article highlights some risks posed by key deficits in the Intelligence Community\u27s (IC) approach to culture, which relies on the cognitive linguistic theories of George Lakoff and colleagues. It also explores the problem of the opacity of these risks for analysts, even as such predictive cultural analytics are becoming a part of intelligence forecasting. This article examines the problem of information secrecy in two ways, by unpacking the opacity of “black box,” algorithm-based social science of culture for end users with little appreciation of their potential biases, and by evaluating the IC\u27s nontransparent approach to foreign cultures, as it underwrites national security assessments

Herbert Simon's decision-making approach: Investigation of cognitive processes in experts

This is a post print version of the article. The official published can be obtained from the links below - PsycINFO Database Record (c) 2010 APA, all rights reserved.Herbert Simon's research endeavor aimed to understand the processes that participate in human decision making. However, despite his effort to investigate this question, his work did not have the impact in the “decision making” community that it had in other fields. His rejection of the assumption of perfect rationality, made in mainstream economics, led him to develop the concept of bounded rationality. Simon's approach also emphasized the limitations of the cognitive system, the change of processes due to expertise, and the direct empirical study of cognitive processes involved in decision making. In this article, we argue that his subsequent research program in problem solving and expertise offered critical tools for studying decision-making processes that took into account his original notion of bounded rationality. Unfortunately, these tools were ignored by the main research paradigms in decision making, such as Tversky and Kahneman's biased rationality approach (also known as the heuristics and biases approach) and the ecological approach advanced by Gigerenzer and others. We make a proposal of how to integrate Simon's approach with the main current approaches to decision making. We argue that this would lead to better models of decision making that are more generalizable, have higher ecological validity, include specification of cognitive processes, and provide a better understanding of the interaction between the characteristics of the cognitive system and the contingencies of the environment

Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing

The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning

This is the peer reviewed version of the following article: Frank Broz et al, “The ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learning”, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe

ANALISIS PEMAHAMAN MATEMATIS MAHASISWA DALAM MENYELESAIKAN SOAL ANALISIS REAL 1 DITINJAU DARI COGNITIVE STYLE FIELD DEPENDENT DAN FIELD INDEPENDENT

Analysis of Students' Mathematical Understanding in Solving Real 1 Analysis Question Viewed from the Cognitive Style Field Dependent and Field Independent of Mathematic Education Study Program IAI Muhammadiyah Sinjai. Thesis. Sinjai: Mathematic Education Study Program. Faculty of Tarbiyah and Teacher Training IAI Muhammadiyah Sinjai, 2021. This study aims to describe: (1) students' mathematical understanding in solving real 1 analysis questions in terms of cognitive style field dependent; (2) students' mathematical understanding in solving real 1 analysis questions in terms of cognitive style field independent. This research is a case study research using a qualitative approach. The subjects of this study were students of fourth semester of the Mathematic Education Study Program. The method of data collection is by interview and solve question test. While the data analysis is done through data reduction, data presentation and drawing conclusions. The results of this study indicate that students' mathematical understanding in solving real 1 analysis questions in terms of cognitive style field dependent, namely students understand the problem by reading the problem, making an understood plan of completion and then completing the solution plan based on the understanding of previous learning, then checking the answers by rereading the question. Thus, students do not understand mathematically the test of solving real 1 analysis question given. Meanwhile, students' mathematical understanding in solving real 1 analysis questions in terms of cognitive style field independent, namely students understand the problem by reading the questions, making a solution plan that is understood and considered appropriate and then completes the plan based on the steps that have been made and understanding in previous learning then checks the answers by testing the solution steps. Thus, students do not understand mathematically the test of solving real 1 analysis question given even though students have completed the test and can explain the steps for solving it

Implementation of CONCEIVER++ : an object-oriented program understanding system.

Problem statement: Understanding on computer program is a complex cognitive activity. It is ability and also a difficult task especially for novice programmer. The object-oriented languages has widely used in education and industry recently. In programming it is important to have such software which can aid programmers or students to code the program. But, available program understanding systems using the plan based approach usually are developed for non-object-oriented programming languages. Reviewed from the available system also showed that none of the plan formalisms used is for an object-oriented language. Specifically, problem arises when the existing system is not usable for teaching programming purposes. Program understanding system with plan for object-oriented does not exist was the main reason why this research is being carried out. Approach: Method used on developed the program understanding system named CONCEIVER++ is Unified Approach (UA). The process involved from UA for developing and testing the system is iterative development and continuous testing. The process must be iterate and reiterate until satisfied with the system. In order to test the quality assurance of the system is by choosing the black box testing strategies. Results: The object-oriented program understanding system has been successfully implemented. The implementation is tested with an example of Java programming code. The binary search tree for control flow graph and linked list for plan has been generated. Results of understanding the meaning or semantic of the program codes also has been produced. The black box testing had shows that all statements of line of code of the example program have been recognized and the correctness output has been checked. Conclusion: The understanding module of CONCEIVER++, which are code/CFG processor, plan processor and recognition engine has been tested. All line of codes (or nodes) has been recognized and got correct meaning using the developed module

How Do Teachers Teach Memory Skills?

Research on teachers\u27 efforts to influence the ways in which children approach memory tasks and understand and regulate their own memory processes has been limited, possibly because of the restrictive views of memory held by cognitive theories that have previously guided research efforts. A more complex perspective on the memory skills that develop over the elementary school years has been elaborated by developmental psychologists and information-processing theorists, but their work has had limited influence on either teacher-training practices or research in teaching. In order to begin to apply this newer perspective to an understanding of classroom teaching processes, research needs to consider teacher practices and expectations for children\u27s learning and memory. A program of research that has been concerned with how teachers teach memory and metacognitive skills and with teachers\u27 views of memory processes is summarized in this article, and implications for teacher training are discussed

Grounding cognitive-level processes in behavior: the view from dynamic systems theory

Marr's seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because dynamic systems theory (DST) focuses on time and interdependence of components, DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: dynamic field theory (DFT). We review research applying DFT to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding of behavior and cognition that results from a DST perspective. These point to a central challenge for cognitive science research as defined by Marr-emergence. We argue that appreciating emergence raises questions about the utility of computational-level analyses and opens the door to insights concerning the origin of novel forms of behavior and thought (e.g., a new chess strategy). We contend this is one of the most fundamental questions about cognition and behavior

Local Attentional Bias Increases Approach Motivation: Evidence from Event-Related Potentials and Frequency Analyses

Over twenty years of research have examined the cognitive consequences of positive affect states, and suggested that positive affect leads to a broadening of cognition. However, this research has primarily examined positive affect that is low in approach motivational intensity (e.g. contentment). In my program of research, I have systematically examined positive affect that varies in approach motivational intensity, and found that positive affect high in approach motivation (e.g. desire) narrow cognition, whereas positive affect low in approach motivation broaden cognition. In this dissertation, I will review past models and present a motivational dimension model of affect that expands understanding of how affective states influence attentional and cognitive breadth. I then review a body of research that has varied the motivational intensity of positive and negative affect and found that affect of low motivational intensity broadens cognitive processes, whereas affect of high motivational intensity narrows cognitive processes. Furthermore, a bi-directional link exists between attentional narrowing and approach motivation, such that a narrowed attentional focus to appetitive stimuli causes greater approach motivation than a broadened attentional focus