Hasil untuk "Psychology"

H. Hartmann

R. Glaser

The contributors to this volume address reasoning and problem solving as fundamental to learning and teaching and to modern literacy. The research on expertise and the development of competence makes it clear that structures of knowledge and cognitive process should be tightly linked throughout education to attain high levels of ability. The longstanding pedagogical assumption that the attainment of useful knowledge proceeds from lower level learning based on the practice of fundamental skills that demand little thought, to higher level competence in which problem solving finally plays an increasing role, is no longer tenable. It is now clear that thinking is not an outcome of basic learning, but is part of the basic acquisition of knowledge and skill. In learning to read, for example, decoding the printed word and understanding simple texts is an act of problem solving, requiring inference and elaboration by the reader. The prevalence of reasoning with information at all levels makes the details of its involvement a fundamental influence on learning and instruction -- a recurring theme in each of the chapters. A rich variety of topics is addressed including: *an analysis of the components of teaching competence *the evolution of a learner's mathematical understanding *the use of causal models for generating scientific explanations *the facilitation of meaningful learning through text illustrations *the competence of children in argumentative interaction that results in conceptual change.

E. P. Sarafino

R. Gordon

M. Argyle

P. Wason, P. Johnson-Laird

C. Izard

R. Skemp

大山 正, B. Wilpert, 三隅 二不二

J. Berry

Donald E. Brown

E. Durán, B. Duran

Daniel C. Molden, C. Dweck

Maciej Chudek, J. Henrich

Ruud Wetzels, D. Matzke, Michael D. Lee et al.

N. Fouad, C. Grus, R. Hatcher et al.

M. Kalkan, Canani Kaygusuz

For the last fifty years, social history has witnessed a transformation that was not experienced in any period before. The facts that how people are affected and through what sort of characteristics individuals try to handle this situation has been a multi-perspective issue and was studied thoroughly. Understanding intersocietal and interpersonal relationship systems that are based on fluctuation and competition was aimed and studies were carried out to determine what characteristics that individuals had in order to survive in this period. For over thirty years, the role of entrepreneurship in dealing with competition has drawn researchers’ extensive interest. In spite of this, the concept of entrepreneurship hasn’t had an operational definition that everyone agrees on because it is multi-dimensional and it is affected by many variables.

Yutao Yang, Junsong Li, Qianjun Pan et al.

Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who continuously refine their proficiency through clinical practice and accumulated experience. To bridge this gap, we propose an Experience-Driven Lifelong Learning Agent (\texttt{PsychAgent}) for psychological counseling. First, we establish a Memory-Augmented Planning Engine tailored for longitudinal multi-session interactions, which ensures therapeutic continuity through persistent memory and strategic planning. Second, to support self-evolution, we design a Skill Evolution Engine that extracts new practice-grounded skills from historical counseling trajectories. Finally, we introduce a Reinforced Internalization Engine that integrates the evolved skills into the model via rejection fine-tuning, aiming to improve performance across diverse scenarios. Comparative analysis shows that our approach achieves higher scores than strong general LLMs (e.g., GPT-5.4, Gemini-3) and domain-specific baselines across all reported evaluation dimensions. These results suggest that lifelong learning can improve the consistency and overall quality of multi-session counseling responses.

Yu yang

In recent years, breakthroughs in artificial intelligence (AI) technology have triggered global industrial transformations, with applications permeating various fields such as finance, healthcare, education, and manufacturing. However, this rapid iteration is accompanied by irrational development, where enterprises blindly invest due to technology hype, often overlooking systematic value assessments. This paper develops a multi-dimensional evaluation model that integrates information theory's entropy reduction principle, economics' bounded rationality framework, and psychology's irrational decision theories to quantify AI product value. Key factors include positive dimensions (e.g., uncertainty elimination, efficiency gains, cost savings, decision quality improvement) and negative risks (e.g., error probability, impact, and correction costs). A non-linear formula captures factor couplings, and validation through 10 commercial cases demonstrates the model's effectiveness in distinguishing successful and failed products, supporting hypotheses on synergistic positive effects, non-linear negative impacts, and interactive regulations. Results reveal value generation logic, offering enterprises tools to avoid blind investments and promote rational AI industry development. Future directions include adaptive weights, dynamic mechanisms, and extensions to emerging AI technologies like generative models.

Kailash Gopal Darmasubramanian, Akash Pareek, Arindam Khan et al.

Optimizing the timing and frequency of ads is a central problem in digital advertising, with significant economic consequences. Existing scheduling policies rely on simple heuristics, such as uniform spacing and frequency caps, that overlook long-term user interest. However, it is well-known that users' long-term interest and engagement result from the interplay of several psychological effects (Curmei, Haupt, Recht, Hadfield-Menell, ACM CRS, 2022). In this work, we model change in user interest upon showing ads based on three key psychological principles: mere exposure, hedonic adaptation, and operant conditioning. The first two effects are modeled using a concave function of user interest with repeated exposure, while the third effect is modeled using a temporal decay function, which explains the decline in user interest due to overexposure. Under our psychological behavior model, we ask the following question: Given a continuous time interval $T$, how many ads should be shown, and at what times, to maximize the user interest towards the ads? Towards answering this question, we first show that, if the number of displayed ads is fixed, then the optimal ad-schedule only depends on the operant conditioning function. Our main result is a quasi-linear time algorithm that outputs a near-optimal ad-schedule, i.e., the difference in the performance of our schedule and the optimal schedule is exponentially small. Our algorithm leads to significant insights about optimal ad placement and shows that simple heuristics such as uniform spacing are sub-optimal under many natural settings. The optimal number of ads to display, which also depends on the mere exposure and hedonistic adaptation functions, can be found through a simple linear search given the above algorithm. We further support our findings with experimental results, demonstrating that our strategy outperforms various baselines.