Memory traces--changes in the memory system that result from the perception and encoding of an event--were measured in pioneering studies by Endel Tulving and Michael J. Watkins in 1975. These and further experiments informed the maturation of Tulving's memory model, from the GAPS (General Abstract Processing System} to the SPI (Serial-Parallel Independent) model. Having current top of the line LLMs revisit the original Tulving-Watkins tests may help in assessing whether foundation models completely instantiate or not this class of psychological models.

The Tulving Test was designed to investigate memory performance in recognition and recall tasks. Its results help assess the relevance of the "Synergistic Ecphory Model" of memory and similar RK paradigms in human performance. This paper starts investigating whether the more than forty-year-old framework sheds some light on LLM's acts of remembering.

The recent practical successes [26] of Artificial Intelligence (AI) programs of the Reinforcement Learning and Deep Learning varieties in game playing, natural language processing and image classification, are now calling attention to the envisioned pitfalls of their hypothetical extension to wider domains of human behavior. Several voices from the industry and academia are now routinely raising concerns over the advances [49] of often heavily media-covered representatives of this new generation of programs such as Deep Blue, Watson, Google Translate, AlphaGo and AlphaZero. Most of these cutting-edge algorithms generally fall under the class of supervised learning, a branch of the still evolving taxonomy of Machine Learning techniques in AI research. In most cases the implementation choice is artificial neural networks software, the workhorse of the Connectionism school of thought in both AI and Cognitive Psychology. Confronting the current wave of connectionist architectures, critics usually raise issues of interpretability (Can the remarkable predictive capabilities be 1 trusted in real-life tasks? Are these capabilities transferable to unfamiliar situations or to different tasks altogether? How informative are the results about the real world; about human cognition?

Su-Doku, a popular combinatorial puzzle, provides an excellent testbench for heuristic explorations. Several interesting questions arise from its deceptively simple set of rules. How many distinct Su-Doku grids are there? How to find a solution to a Su-Doku puzzle? Is there a unique solution to a given Su-Doku puzzle? What is a good estimation of a puzzle's difficulty? What is the minimum puzzle size (the number of "givens")? This paper explores how these questions are related to the well-known alldifferent constraint which emerges in a wide variety of Constraint Satisfaction Problems (CSP) and compares various algorithmic approaches based on different formulations of Su-Doku.

In his recent article in AI Magazine, "AI prepares for 2001," Nils Nilsson put forward a paradigm of AI based Sufficiency implies finding a guide to investigate the on a declarative representation of knowledge with semantic case of human beings. I would like improve problem-solving performances succeeds only because to present some ideas and concepts stemming from current syntax mirrors semantics in the domains where the research in Genetic Epistemology (GE), initiated by Jean programs were applied. Piaget, there is then no need for any distinction between This interrogation is precisely the core of the Piagetian rules and metarules or knowledge-base and inference engines. The "epistemic program" should undergo by itself a GE is concerned with knowledge considered as a process, series of revisions of represeutations, and thus experiment [Piaget (1964)]. The obvious point of convergence different schemes of perceptions-or inference enginesas of AI and GE is precisely this concept of knowledge as a the mathematico-logical structure underlying the dynamic process.