Cognitive task in natural language for the first 10 levels of the Sokoban game in the metacognitive architecture CARINA

The purpose of this article is to describe the natural language made for the creation a Cognitive Model for the first 10 levels of the sokoban game in the metacognitive architecture CARINA. A Cognitive Model is the theoretical foundation and empirical specification of mental representations and processes that intervene in cognitive functions or processes. SOKOBAN is a game, which its main objective is simple, the player must transport one or more boxes to sites called storage spaces. The methodology used for this work is a cognitive modeling, which consists of seven steps

Is my Aha! bigger than yours? Investigating individual differences in the experience of insight

A Neural Network Theory (NNT) account of insight processes and individual differences in cognitive ability predicts that, compared to routine problem solving, insight experiences will be associated with less involvement of control functions and will occur less frequently among people with greater fluid ability. The present study investigated the role of fluid intelligence and metacognitive control in the elicitation of Aha experiences. Seventy-six participants, predominantly university students (84% female), attempted a set of problems, including classic insight, non-insight and riddles. Subjective experiences of insight, certainty and suddenness of the solution process were measured, using a purpose-built concurrent rating apparatus and retrospective report. Participants completed Raven’s Advanced Progressive Matrices (fluid intelligence) and an attention-switching task (metacognitive control). Hierarchical Generalised Linear Modelling was used to model Aha experiences as a function of item-level predictors (Level 1) and person-characteristics (Level 2). The overall odds of reporting an Aha experience were 0.42. Higher fluid intelligence, but not metacognitive control, was associated with reduced odds of reporting Aha on a problem (OR=0.88, 95% CI: 0.82,0.95), controlling for accuracy, solution suddenness, and verbal skills. Aha experiences were significantly associated with multiple theoretically meaningful retrospective and concurrent problem-solving experience ratings, with fluid intelligence moderating some associations. These findings support the NNT account of insight as a special process and fluid intelligence as a factor limiting the complexity, and accessible solution states from the initial problem representation, leading to the requirement for an alternative representation. The study demonstrates some methodological solutions to difficulties inherent in measuring insight. A chimpanzee named Sultan, two sticks, and a hard-to-reach banana; this is the scenario from which almost a century of research investigating “Aha!” experiences emerged (Ohlsson, 1992). Early Gestalt psychologist Wolfgang Kohler observed that after repeated attempts to reach the banana, Sultan entered a failure-induced sulk. However, he became suddenly re-energised, and purposefully joined two sticks together to retrieve the banana (Kohler, 1921 cited in Ohlsson 1992). How, after numerous attempts and apparent acceptance of failure, did the solution to this intractable problem suddenly appear in Sultan's consciousness? What processes simmering beyond conscious awareness conjured a fully formed solution and planted it so abruptly into Sultan's dormant and tortured mind? "Aha!" Aha experiences are thought to be indicative of a moment of insight and have historically been associated with exceptional creativity, scientific discovery and genius (Hill & Kemp, 2018; Metcalfe & Wiebe, 1987; Shen et al., 2016; Sternberg & Davidson, 1995). These experiences have been shown to be distinct neurophysiological phenomena (Bowden & Jung-Beeman, 2003; Kounios et al., 2006; Sandkühler & Bhattacharya, 2008; Tik et al., 2018) that facilitate memory (Danek et al., 2013), improve learning (Dominowski & Buyer, 2000), and provide motivation during difficult learning (Liljedahl, 2005). Current definitions of insight moments describe them as the occurrence of a solution or path to a solution suddenly and unexpectedly coming to mind following a pause in active thinking when a problem-solver feels unable to make further progress (Bowden et al., 2005; Sternberg & Davidson, 1995). Insight is asserted to be a special process that is distinct from analytical problem-solving (Knoblich et al., 1999; Ohlsson, 1992; Sternberg & Davidson, 1995). Analytical problem-solving is continuous and incremental and does not engender a salient Aha experience (Schooler et al., 1993). Despite a substantial body of research seeking to demystify these processes, the specialness of insight is still the subject of much debate. Recent research suggests that the lack of clarity is a result of the way insight is operationalised in many studies as “solving an insight problem” (Danek et al., 2016; Webb et al., 2016) without verifying that the problem-solver has experienced an Aha moment. These studies indicate that insight is not reliably evoked by these problems, suggesting the processes engaged in solving insight problems may be idiosyncratic (Danek et al., 2016; Webb et al., 2016). That is, it is possible that rather than task requirements, individual differences of the problem-solver influence whether or not insight processes are used to solve a problem and the subsequent occurence of an Aha experience. Two individual characteristics that may be relevant to the Aha experience are fluid intelligence and metacognitive control. Fluid intelligence is defined as the ability to use controlled and deliberate mental operations to solve problems, deduce patterns, identify relations and draw inferences (McGrew, 2009). Metacognitive control is a facet of metacognition that refers explicitly to the control processes involved in regulating and directing information processing resources (Nelson & Narens, 1990). Differing levels of these abilities may influence the cognitive processes engaged during problem-solving (Dix et al., 2016). Thus, the central aim of this study is to investigate whether individual differences in fluid intelligence or metacognitive control influence whether an Aha experience occurs upon problem-solving. The present study agrees with several others that an Aha reported by the problem-solver is verification that insight has occurred (Bowden et al., 2005; Danek et al., 2016; Webb et al., 2016). However, some researchers argue that Aha experiences occur randomly (Chuderski, 2014) or are related to post-solution affect of evaluations of the solution (Topolinski & Reber, 2010). The second aim of the present study is to determine if Aha moments are associated with problem-solving experiences that are indicative of a special process. Due to current limitations in methodology, a new device was developed to accomplish this aim. This is described in section 2.5

The Role of Metacognition in Visual Art Education

Metacognition is a conscious activity that occurs in the brain when an individual monitors or controls his or her thinking. Research in multiple fields has found that metacognition plays a significant role in a person’s learning. It is currently a popular trend in general education to teach students metacognitive strategies, and research has shown that it is a practical tool to boost student success. Moreover, metacognition is most effective when it is taught explicitly and regularly practiced by the students. There is a need for more research into the effectiveness of explicitly teaching metacognition in middle and high school visual arts classrooms. Art education has undergone architectural changes over the past few decades; as of late, it is moving towards a more open-ended approach which is demonstrated in current art standards. Based on the author’s student teaching experiences and the literature review of this thesis, she proposes what art curriculum could look like when metacognition and cognitive development are the focus of the classroom. She concludes that embedding metacognitive strategies in the visual art curriculum will help students develop critical thinking and self-reflective skills in addition to artistic skills

Towards Evaluating AI Systems for Moral Status Using Self-Reports

As AI systems become more advanced and widely deployed, there will likely be increasing debate over whether AI systems could have conscious experiences, desires, or other states of potential moral significance. It is important to inform these discussions with empirical evidence to the extent possible. We argue that under the right circumstances, self-reports, or an AI system's statements about its own internal states, could provide an avenue for investigating whether AI systems have states of moral significance. Self-reports are the main way such states are assessed in humans ("Are you in pain?"), but self-reports from current systems like large language models are spurious for many reasons (e.g. often just reflecting what humans would say). To make self-reports more appropriate for this purpose, we propose to train models to answer many kinds of questions about themselves with known answers, while avoiding or limiting training incentives that bias self-reports. The hope of this approach is that models will develop introspection-like capabilities, and that these capabilities will generalize to questions about states of moral significance. We then propose methods for assessing the extent to which these techniques have succeeded: evaluating self-report consistency across contexts and between similar models, measuring the confidence and resilience of models' self-reports, and using interpretability to corroborate self-reports. We also discuss challenges for our approach, from philosophical difficulties in interpreting self-reports to technical reasons why our proposal might fail. We hope our discussion inspires philosophers and AI researchers to criticize and improve our proposed methodology, as well as to run experiments to test whether self-reports can be made reliable enough to provide information about states of moral significance

NATURAL LANGUAGE FOR FACTOID-WH IN ENGLISH AS A FOREIGN LANGUAGE

The purpose of this article is to describe the natural language made for the creation a Cognitive Model for Factoid-WH questions. A cognitive Model is a Representation of the cognitive processes that occur in the human mind. A Factoid-WH question is a question, which starts with WH-interrogated word and requires a fact as an answer. The methodology of this work is a cognitive modeling which consists of seven steps, three of them will be shown as the result of the analysis of the cognitive task in natural language for this model

Teaching Hardware Reverse Engineering: Educational Guidelines and Practical Insights

Since underlying hardware components form the basis of trust in virtually any computing system, security failures in hardware pose a devastating threat to our daily lives. Hardware reverse engineering is commonly employed by security engineers in order to identify security vulnerabilities, to detect IP violations, or to conduct very-large-scale integration (VLSI) failure analysis. Even though industry and the scientific community demand experts with expertise in hardware reverse engineering, there is a lack of educational offerings, and existing training is almost entirely unstructured and on the job. To the best of our knowledge, we have developed the first course to systematically teach students hardware reverse engineering based on insights from the fields of educational research, cognitive science, and hardware security. The contribution of our work is threefold: (1) we propose underlying educational guidelines for practice-oriented courses which teach hardware reverse engineering; (2) we develop such a lab course with a special focus on gate-level netlist reverse engineering and provide the required tools to support it; (3) we conduct an educational evaluation of our pilot course. Based on our results, we provide valuable insights on the structure and content necessary to design and teach future courses on hardware reverse engineering

2015 Annual Research Symposium Abstract Book

2015 annual volume of abstracts for science research projects conducted by students at Trinity Colleg