Current Research in Cognitive Psychology

Current Research in Cognitive Psychology

Problem-Solving

The article by van Gog et al. (2020) explores the significance of mental effort in promoting self-regulated learning in the context of problem-solving tasks. The authors examine the relationship between mental effort and self-regulated learning strategies to provide insights into optimizing learning outcomes. The study draws on existing research and theoretical frameworks to establish a foundation for understanding the role of mental effort. It highlights that engaging in challenging tasks requiring higher mental effort can enhance self-regulated learning. Furthermore, the authors discuss how the allocation of mental effort influences learners’ motivation, engagement, and performance.

The article stresses the importance of metacognitive awareness in self-regulated learning. It suggests that learners need to be aware of their mental effort and strategies and proposes that teachers play a crucial role in supporting learners’ development of metacognitive skills. The authors also explore the impact of instructional support. The study’s findings suggest that deliberate and purposeful allocation of mental effort positively influences self-regulated learning. Learners who engage in a higher mental effort demonstrate improved metacognitive monitoring, planning, and evaluation skills. Moreover, the article discusses the potential limitations and challenges associated with accurately measuring and assessing mental effort.

In summary, the article highlights the significant role of mental effort in fostering self-regulated learning with problem-solving tasks. It emphasizes the importance of metacognitive awareness, instructional support, and deliberate allocation of mental effort in optimizing learning outcomes. The findings have implications for educational practices, suggesting that educators should provide guidance, feedback, and appropriate challenges to facilitate learning. The article contributes to the existing literature and sheds light on the complex interplay between mental effort and self-regulated learning, providing a basis for future research and educational interventions.

Decision-Making

The article by Burton et al. (2020) conducts a comprehensive analysis of algorithm aversion in the context of augmented decision-making. The authors aim to explore the phenomenon of algorithm aversion. This occurs when individuals exhibit reluctance or skepticism toward using algorithmic recommendations. The study adopts a systematic review methodology. It examines a range of empirical studies and theoretical frameworks related to algorithm aversion. Also, it identifies factors contributing to algorithm aversion, such as the preference for human judgment, distrust in algorithmic accuracy, and concerns regarding accountability and transparency. The authors also discuss the impact of decision context, individual characteristics, and task complexity on algorithm aversion.

The article highlights the potential consequences of algorithm aversion. Accordingly, it underscores the need to understand the underlying reasons for algorithm aversion. It also suggests strategies for mitigating its negative effects; in this regard, the authors propose enhancing algorithm transparency, providing explanations for recommendations, and emphasizing the complementary nature of human-algorithm collaboration. The findings from the systematic review indicate that algorithm aversion is a complex phenomenon influenced by various psychological, sociocultural, and circumstantial factors. The article acknowledges the limitations of existing research. There is a lack of standardized measures for assessing algorithm aversion.

In summary, the article provides a comprehensive overview of algorithm aversion in augmented decision-making. It highlights the factors contributing to this phenomenon and emphasizes the importance of understanding and addressing algorithm aversion for effective decision-making processes. Further research in this area is warranted to enhance individuals’ understanding of algorithm aversion and its implications for decision-making practices.

Intelligence

The article by Kovacs and Conway (2019) presents a comprehensive framework that integrates cognitive and differential perspectives on human intelligence. The authors aim to offer a unified understanding of intelligence and its measurement. The study emphasizes the need to consider both cognitive and differential aspects of intelligence and proposes that cognitive abilities, such as working memory, play a crucial role in intelligence. Additionally, the authors argue that individual differences in these cognitive abilities contribute to the variations observed in intelligence test scores. The article also suggests that traditional IQ tests often neglect important cognitive processes. Moreover, they rely heavily on crystallized knowledge. The authors propose incorporating measures of fluid intelligence. These assess cognitive abilities that are less influenced by prior knowledge; hence, they are more indicative of individual cognitive processing efficiency.

The framework presented in the article has implications for improving IQ testing and understanding intelligence more comprehensively. A multidimensional approach should be used. Combining cognitive and differential perspectives can provide a more accurate assessment of intelligence. Furthermore, the authors advocate for the integration of cognitive measures into IQ testing. Subsequently, this will capture a broader range of cognitive abilities and enhance the predictive validity of intelligence assessments.

In summary, the article presents a unified cognitive/differential approach to human intelligence. It highlights the limitations of traditional IQ tests and proposes a more comprehensive framework that incorporates cognitive abilities and fluid intelligence. The findings have implications for the field of intelligence testing, suggesting ways to enhance the accuracy and validity of IQ assessments. Further research is needed to validate and refine this integrated approach to intelligence assessment.

References

Burton, J. W., Stein, M. K., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220-239. https://doi.org/10.1002/bdm.2155

Kovacs, K., & Conway, A. R. (2019). A unified cognitive/differential approach to human intelligence: Implications for IQ testing. Journal of Applied Research in Memory and Cognition, 8(3), 255-272. https://doi.org/10.1016/j.jarmac.2019.05.003

Van Gog, T., Hoogerheide, V., & Van Harsel, M. (2020). The role of mental effort in fostering self-regulated learning with problem-solving tasks. Educational Psychology Review, 32, 1055-1072. https://doi.org/10.1007/s10648-020-09544-y

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Current Research in Cognitive Psychology

Instructions
Current Research in Cognitive Psychology II
We covered much over the past several weeks. As we continue to expand our understanding of cognitive psychology it is important to evaluate current research.

For this assignment, you will provide a detailed description of each topic.

Topics

Memory models and processes including knowledge representation and organization
Language acquisition and contextual influences
Intelligence
Problem-solving
Creativity
Decision-making
Reasoning
Article review:

Choose 3 from the list above and find a scholarly journal article for each.
You can find current (not more than 5 years old) scholarly research articles from the South University Library databases only.
Remember the articles have to be related to psychology.
Provide an in-depth analysis for each article, integrating information from your course and text readings.