By exploiting students' response logs, SCM aims to predict their exercise

By exploiting students' response logs, SCM aims to predict their exercise

Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language - Endowed Intelligent Agents (LEIAs) . S hapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree situations permit . Atypical outcomes are treated using shapes - based recovery method s, such as learning on the fly, asking a human partner for help, or seeking an actionable, even if imperfect, situational understanding . Although shapes is an umbrella term, it is not vague: shapes - based modeling involves particular objectives, hypotheses, modeling strategies, knowledge bases, and actual models of wide - ranging phenomena, all implemented within a particular cognitive architecture . Such s pecificity is needed both to vet the our hypotheses and to achieve our practical aims of building useful agent systems that are explainable, extensible, and worthy of our trust, even in critical domains . However, a lthough the LEIA example of shapes - based modeling is specific, the principles can be applied more broadly, giving new life to knowledge - based and hybrid AI .

Computational cognitive models, which formalize theories of cognition, enable researchers to quantify cognitive processes and arbitrate between competing theories by fitting models to behavioral data. Traditionally, these models are handcrafted, which requires significant domain knowledge, coding expertise, and time investment. Previous work has demonstrated that Large Language Models (LLMs) are adept at pattern recognition in-context, solving complex problems, and generating executable code. In this work, we leverage these abilities to explore the potential of LLMs in automating the generation of cognitive models based on behavioral data. We evaluated the LLM in two different tasks: model identification (relating data to a source model), and model generation (generating the underlying cognitive model). We performed these tasks across two cognitive domains - decision making and learning. In the case of data simulated from canonical cognitive models, we found that the LLM successfully identified and generated the ground truth model. In the case of human data, where behavioral noise and lack of knowledge of the true underlying process pose significant challenges, the LLM generated models that are identical or close to the winning model from cognitive science literature. Our findings suggest that LLMs can have a transformative impact on cognitive modeling. With this project, we aim to contribute to an ongoing effort of automating scientific discovery in cognitive science.

Following the general theoretical framework of VSA (Vector Symbolic Architecture), a cognitive model with the use of sparse binary hypervectors is proposed. In addition, learning algorithms are introduced to bootstrap the model from incoming data stream, with much improved transparency and efficiency. Mimicking human cognitive process, the training can be performed online while inference is in session. Word-level embedding is re-visited with such hypervectors, and further applications in the field of NLP (Natural Language Processing) are explored.

How might we use cognitive modeling to consider the ways in which antiblackness, and racism more broadly, impact the design and development of AI systems? We provide a discussion and an example towards an answer to this question. We use the ACT-R/{\Phi} cognitive architecture and an existing knowledge graph system, ConceptNet, to consider this question not only from a cognitive and sociocultural perspective, but also from a physiological perspective. In addition to using a cognitive modeling as a means to explore how antiblackness may manifest in the design and development of AI systems (particularly from a software engineering perspective), we also introduce connections between antiblackness, the Human, and computational cognitive modeling. We argue that the typical eschewing of sociocultural processes and knowledge structures in cognitive architectures and cognitive modeling implicitly furthers a colorblind approach to cognitive modeling and hides sociocultural context that is always present in human behavior and affects cognitive processes.

How should we decide among competing explanations of a cognitive process given limited observations? The problem of model selection is at the heart of progress in cognitive science. In this paper, Minimum Description Length (MDL) is introduced as a method for selecting among computational models of cognition. We also show that differential geometry provides an intuitive understanding of what drives model selection in MDL. Finally, adequacy of MDL is demonstrated in two areas of cognitive modeling.

Two of the most important ideas underpinning contemporary cognitive science-and the closely related AI subfield of computational cognitive modeling-are the suppositions that the human mind uses cognitive structures and that progress in understanding the mind can come from modeling those structures and the algorithms which operate on them. The semantic fluency task (SFT), sometimes called the verbal fluency task Welsh et al. [1991], is commonly employed in service of those goals. In SFT, participants name as many items belonging to a particular semantic category (animals, fruits, etc.) as they can in a fixed amount of time (typically 40-180 seconds). Despite this task's simplicity, the lists generated by participants (which we call semantic fluency lists or SFLs) offer insights into the structure of human knowledge and the heuristics used for memory retrieval. For example, words sharing semantic features tend to group in clusters, and there is often a temporal delay before a participant switches from one cluster to another. Multiple approaches to computationally modeling behaviors in SFT have been proposed Hills et al. [2012], Abbott et al. [2015], Zemla et al. [2016], Zemla and Austerweil [2017], Avery and Jones [2018], most relying on graph-based representations in which words are represented as nodes, and edges correspond to some meaningful semantic relationship between the nodes. However, to date, no work has explored whether transformer-based language models (TLMs) can be any better at modeling the generation of SFLs. And there are multiple reasons, at least from an exploratory perspective, to suspect TLMs might do well in this regard, e.g.: (1) a large body of literature demonstrates why semantic memory can not be sufficiently represented purely by fixed associative links between lexical nodes--at minimum, representations must allow for dynamic role binding, hierarchical (or otherwise unidirectional) activations, and enough richness to carry out structure-sensitive similarity assessments Holyoak and Hummel [2000], Sun [2002]; (2) TLMs perform unexpectedly well on human-oriented linguistic benchmarks Wang et al. [2019], and they are typically pre-trained using a lengthy process designed to embed deep semantic knowledge, resulting in a dense encoding of semantic relationships Cui et al. [2020]; (3) The pre-training process often proceeds by optimizing LMs to perform well on the MLM (masked language modeling) task, which shares more than a passing resemblance to the kind of word prediction that some

The Spring Symposium on Knowledge-based Environments for Teaching and Learning focused on the use of technology to facilitate learning, training, teaching, counseling, coaxing and coaching. Sixty participants from academia and industry assessed progress made to date and speculated on new tools for building second generation systems. Selection of topics and participants was motivated by a desire for ideological breadth and depth. Panel leaders included William J. Clancey and Alan Lesgold (researchers of realworld systems); Kurt VanLehn (champion of cognitive models); Beverly Park Woolf (defender of discourse systems); Elliot Soloway (advocate for alternative environments); and Sarah Douglas (spokesperson for supportive systems). Researchers have moved away from building omniscient tutors capable of detecting all possible errors and misconceptions.

The Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches was held on 19 to 20 August 1995 in Montreal, Canada, in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence. The focus of the workshop was on learning and architectures that feature hybrid representations and support hybrid learning. The general consensus was that hybrid connectionist-symbolic models constitute a promising avenue to the development of more robust, more powerful, and more versatile architectures for both cognitive modeling and intelligent systems. The workshop was cochaired by myself and Frederic Alexandre. It featured 23 presentations, including 2 invited talks and 2 panel discussions.