Effects of Higher-Order Cognitive Strategy Training on Gist-Reasoning and Fact-Learning in Adolescents

Improving the reasoning skills of adolescents across the United States has become a major concern for educators and scientists who are dedicated to identifying evidence-based protocols to improve student outcome. This small sample randomized, control pilot study sought to determine the efficacy of higher-order cognitive training on gist-reasoning and fact-learning in an inner-city public middle school. The study compared gist-reasoning and fact-learning performances after training in a smaller sample when tested in Spanish, many of the students’ native language, versus English. The 54 eighth grade students who participated in this pilot study were enroled in an urban middle school, predominantly from lower socio-economic status families, and were primarily of minority descent. The students were randomized into one of three groups, one that learned cognitive strategies promoting abstraction of meaning, a group that learned rote memory strategies, or a control group to ascertain the impact of each program on gist-reasoning and fact-learning from text-based information. We found that the students who had cognitive strategy instruction that entailed abstraction of meaning significantly improved their gist-reasoning and fact-learning ability. The students who learned rote memory strategies significantly improved their fact-learning scores from a text but not gist-reasoning ability. The control group showed no significant change in either gist-reasoning or fact-learning ability. A trend toward significant improvement in overall reading scores for the group that learned to abstract meaning as well as a significant correlation between gist-reasoning ability and the critical thinking on a state-mandated standardized reading test was also found. There were no significant differences between English and Spanish performance of gist-reasoning and fact-learning. Our findings suggest that teaching higher-order cognitive strategies facilitates gist-reasoning ability and student learning

Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence, 202

Matching bias in syllogistic reasoning: Evidence for a dual-process account from response times and confidence ratings

We examined matching bias in syllogistic reasoning by analysing response times, confidence ratings, and individual differences. Roberts’ (2005) “negations paradigm” was used to generate conflict between the surface features of problems and the logical status of conclusions. The experiment replicated matching bias effects in conclusion evaluation (Stupple & Waterhouse, 2009), revealing increased processing times for matching/logic “conflict problems”. Results paralleled chronometric evidence from the belief bias paradigm indicating that logic/belief conflict problems take longer to process than non-conflict problems (Stupple, Ball, Evans, & Kamal-Smith, 2011). Individuals’ response times for conflict problems also showed patterns of association with the degree of overall normative responding. Acceptance rates, response times, metacognitive confidence judgements, and individual differences all converged in supporting dual-process theory. This is noteworthy because dual-process predictions about heuristic/analytic conflict in syllogistic reasoning generalised from the belief bias paradigm to a situation where matching features of conclusions, rather than beliefs, were set in opposition to logic

Citizen science as a new tool in dog cognition research

The work of Á.M. was supported by the Hungarian Academy of Sciences (MTA 01 031).Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition. com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.Publisher PDFPeer reviewe

Neo-Thomism and the Problem of Animal Suffering

Proponents of the problem of animal suffering claim that the millions of years of apparent nonhuman animal pain and suffering provides evidence against the existence of God. Neo-Cartesianism attempts to avoid this problem mainly by denying the existence of phenomenal consciousness in nonhuman animals. However, neo-Cartesian options regarding animal minds have failed to compel many. In this essay, I explore an answer to the problem of animal suffering inspired by the medieval theologian Thomas Aquinas. Instead of focusing on phenomenal consciousness, the neo-Thomistic view of animal minds focuses on self-awareness. After proposing and providing evidence for this view, I conclude that nonhuman animal suffering is not morally significant

Development of intuitive rules: Evaluating the application of the dual-system framework to understanding children's intuitive reasoning

This is an author-created version of this article. The original source of publication is Psychon Bull Rev. 2006 Dec;13(6):935-53 The final publication is available at www.springerlink.com Published version: http://dx.doi.org/10.3758/BF0321390

External Reasoning: Towards Multi-Large-Language-Models Interchangeable Assistance with Human Feedback

Memory is identified as a crucial human faculty that allows for the retention of visual and linguistic information within the hippocampus and neurons in the brain, which can subsequently be retrieved to address real-world challenges that arise through a lifetime of learning. The resolution of complex AI tasks through the application of acquired knowledge represents a stride toward the realization of artificial general intelligence. However, despite the prevalence of Large Language Models (LLMs) like GPT-3.5 and GPT-4 , which have displayed remarkable capabilities in language comprehension, generation, interaction, and reasoning, they are inhibited by constraints on context length that preclude the processing of extensive, continually evolving knowledge bases. This paper proposes that LLMs could be augmented through the selective integration of knowledge from external repositories, and in doing so, introduces a novel methodology for External Reasoning, exemplified by ChatPDF. Central to this approach is the establishment of a tiered policy for \textbf{External Reasoning based on Multiple LLM Interchange Assistance}, where the level of support rendered is modulated across entry, intermediate, and advanced tiers based on the complexity of the query, with adjustments made in response to human feedback. A comprehensive evaluation of this methodology is conducted using multiple LLMs and the results indicate state-of-the-art performance, surpassing existing solutions including ChatPDF.com. Moreover, the paper emphasizes that this approach is more efficient compared to the direct processing of full text by LLMs.Comment: technical repor