Know-how, intellectualism, and memory systems

ABSTRACTA longstanding tradition in philosophy distinguishes between knowthatand know-how. This traditional “anti-intellectualist” view is soentrenched in folk psychology that it is often invoked in supportof an allegedly equivalent distinction between explicit and implicitmemory, derived from the so-called “standard model of memory.”In the last two decades, the received philosophical view has beenchallenged by an “intellectualist” view of know-how. Surprisingly, defenders of the anti-intellectualist view have turned to the cognitivescience of memory, and to the standard model in particular, todefend their view. Here, I argue that this strategy is a mistake. As it turns out, upon closer scrutiny, the evidence from cognitivepsychology and neuroscience of memory does not support theanti-intellectualist approach, mainly because the standard modelof memory is likely wrong. However, this need not be interpretedas good news for the intellectualist, for it is not clear that theempirical evidence necessarily supp..

Enhancing vocabulary learning through understanding the human memory system: Episodic memory

Vocabulary acquisition in learning languages is a crucial aspect of language learning, and effective teaching theories are essential for the retention and application of new vocabulary. However, understanding the memory system is important for educators to design and implement effective vocabulary teaching strategies that support student learning and retention. This article provides a comprehensive overview of the human memory system, including sensory, short-term, and long-term memory, with a focus on the differences between semantic and episodic memory. It explains the impact of episodic memory on vocabulary recall. In addition, it puts forward two teaching theories that enhance the utilization of episodic memories. Experiential Learning and Elaboration Theory focus on connecting vocabulary with personal experience for episodic memory and using previous knowledge to help students remember new vocabulary

Task-adaptable, Pervasive Perception for Robots Performing Everyday Manipulation

Intelligent robotic agents that help us in our day-to-day chores have been an aspiration of robotics researchers for decades. More than fifty years since the creation of the first intelligent mobile robotic agent, robots are still struggling to perform seemingly simple tasks, such as setting or cleaning a table. One of the reasons for this is that the unstructured environments these robots are expected to work in impose demanding requirements on a robota s perception system. Depending on the manipulation task the robot is required to execute, different parts of the environment need to be examined, the objects in it found and functional parts of these identified. This is a challenging task, since the visual appearance of the objects and the variety of scenes they are found in are large. This thesis proposes to treat robotic visual perception for everyday manipulation tasks as an open question-asnswering problem. To this end RoboSherlock, a framework for creating task-adaptable, pervasive perception systems is presented. Using the framework, robot perception is addressed from a systema s perspective and contributions to the state-of-the-art are proposed that introduce several enhancements which scale robot perception toward the needs of human-level manipulation. The contributions of the thesis center around task-adaptability and pervasiveness of perception systems. A perception task-language and a language interpreter that generates task-relevant perception plans is proposed. The task-language and task-interpreter leverage the power of knowledge representation and knowledge-based reasoning in order to enhance the question-answering capabilities of the system. Pervasiveness, a seamless integration of past, present and future percepts, is achieved through three main contributions: a novel way for recording, replaying and inspecting perceptual episodic memories, a new perception component that enables pervasive operation and maintains an object belief state and a novel prospection component that enables robots to relive their past experiences and anticipate possible future scenarios. The contributions are validated through several real world robotic experiments that demonstrate how the proposed system enhances robot perception

Integer Sparse Distributed Memory and Modular Composite Representation

Challenging AI applications, such as cognitive architectures, natural language understanding, and visual object recognition share some basic operations including pattern recognition, sequence learning, clustering, and association of related data. Both the representations used and the structure of a system significantly influence which tasks and problems are most readily supported. A memory model and a representation that facilitate these basic tasks would greatly improve the performance of these challenging AI applications.Sparse Distributed Memory (SDM), based on large binary vectors, has several desirable properties: auto-associativity, content addressability, distributed storage, robustness over noisy inputs that would facilitate the implementation of challenging AI applications. Here I introduce two variations on the original SDM, the Extended SDM and the Integer SDM, that significantly improve these desirable properties, as well as a new form of reduced description representation named MCR.Extended SDM, which uses word vectors of larger size than address vectors, enhances its hetero-associativity, improving the storage of sequences of vectors, as well as of other data structures. A novel sequence learning mechanism is introduced, and several experiments demonstrate the capacity and sequence learning capability of this memory.Integer SDM uses modular integer vectors rather than binary vectors, improving the representation capabilities of the memory and its noise robustness. Several experiments show its capacity and noise robustness. Theoretical analyses of its capacity and fidelity are also presented.A reduced description represents a whole hierarchy using a single high-dimensional vector, which can recover individual items and directly be used for complex calculations and procedures, such as making analogies. Furthermore, the hierarchy can be reconstructed from the single vector. Modular Composite Representation (MCR), a new reduced description model for the representation used in challenging AI applications, provides an attractive tradeoff between expressiveness and simplicity of operations. A theoretical analysis of its noise robustness, several experiments, and comparisons with similar models are presented.My implementations of these memories include an object oriented version using a RAM cache, a version for distributed and multi-threading execution, and a GPU version for fast vector processing

Item content versus contextual strengthening following retrieval

Includes bibliographical references.2015 Summer.Despite a substantial literature describing the memory benefit resulting from testing (i.e., memory retrieval), relatively few investigations have attempted to detail how retrieval acts as a memory modifier. One core issue concerns the extent to which testing and studying effect fundamentally similar or different processes or components of memories. The present paper introduces two computational models, both based in REM theory (Shiffrin & Steyvers, 1997) and designed to provide a plausible basis for describing the testing effect at a more mechanistic level than existing theories. The two models are derived from the same set of core assumptions about the functioning of the memory system, and differ only in their specifications of the components of memories that are modified as a result of retrieval. The “Item Model” (IM) assumes that retrieval serves primarily to strengthen the target item content representation of information that is retrieved. In contrast, the “Context Model” (CM) assumes that retrieval serves to embed additional contextual information into the target memory trace, facilitating the subsequent ability of the memory system to locate such items. This manuscript provides coverage of relevant areas in the literature that have bearing on the IM and CM, details the implementation of the models and their larger framework, and reports on 4 experiments designed to test contrasting predictions of the IM and CM. Experiment 1 observed a testing effect using a mixed list, but not a pure list design, implying that testing may serve to enhance the search process by strengthening context information in memory. Experiments 2-4 were designed to examine the effects of reinstating contextual information during final testing on the testing effect. Experiments 2 and 3 found that reinstating either perceptual contextual elements (Exp. 2), or semantic context cues (Exp. 3) at the time of final test did not significantly impact the magnitude of the testing effect. However, Experiment 4 found that reinstating the initial learning mental/temporal context at the time of final test mitigated the magnitude of the testing effect. Potential nuanced interactions between testing and context in memory are discussed

Cognitive Architectures for Language Agents

Recent efforts have incorporated large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning. However, these efforts have largely been piecemeal, lacking a systematic framework for constructing a fully-fledged language agent. To address this challenge, we draw on the rich history of agent design in symbolic artificial intelligence to develop a blueprint for a new wave of cognitive language agents. We first show that LLMs have many of the same properties as production systems, and recent efforts to improve their grounding or reasoning mirror the development of cognitive architectures built around production systems. We then propose Cognitive Architectures for Language Agents (CoALA), a conceptual framework to systematize diverse methods for LLM-based reasoning, grounding, learning, and decision making as instantiations of language agents in the framework. Finally, we use the CoALA framework to highlight gaps and propose actionable directions toward more capable language agents in the future.Comment: 16 pages of main content, 10 pages of references, 5 figures. Equal contribution among the first two authors, order decided by coin flip. A CoALA-based repo of recent work on language agents: https://github.com/ysymyth/awesome-language-agent

Understanding Proactive Facilitation In Cued Recall

Confusion of older information with newer information is a potent source of memory errors. For example, remembering exactly where you parked in a parking garage can be difficult, if it somewhere you frequently park. The reason for the difficulty is because the older memories for parking the garage are easily confused with the most recent one. The current project focused on understanding how memories for recent experiences interact, or interferes, with other related information. In a typical memory interference experiment participants study multiple lists of pairs of items. Items from an initial study list (e.g., A-B) reappear on a second study list paired with new, other items (e.g., A-Br). Performance for A-Br pairs is contrasted with control pairs exclusive to the second study list (e.g., A-B, C-D). In the current series of experiments we used such a paradigm to examine a phenomena called proactive facilitation (PF). This is the observation that the memory for a second presentation of a target (Br) is better when cued by its partner (A) despite being studied with a different partner during its initial presentation. This contrasts proactive interference (PI), a common finding that oftentimes memory is worse in the very same scenario. Indeed a combination of PF and PI appear to be present during recall. When Aue, Criss, and Fischetti (2012) employed such a design they observed PI evidenced by more incorrect responses for A-Br pairs, as well as PF evidenced by more correct responses for A-Br pairs relative to C-D pairs. They proposed multiple explanations for PF and a subset are evaluated in the current series of experiments. I examined three hypotheses in an attempt to understand PF. First, I examined whether it is the case that, in the aforementioned design, participants were more willing to provide a response for A-Br pairs and they simply happen to be outputting both more correct (PF) and incorrect (PI) responses. Second I examined whether participants were spending more time searching memory, resulting in the additional responses being provided. Third, I examined whether participants were encoding the items better the second time they are encountered. In general the data appear to be most consistent with the idea that a portion of items, when encountered a second time, are encoded more completely. Implications for models of memory are discussed

Memory Manipulations in Extended Reality

Human memory has notable limitations (e.g., forgetting) which have necessitated a variety of memory aids (e.g., calendars). As we grow closer to mass adoption of everyday Extended Reality (XR), which is frequently leveraging perceptual limitations (e.g., redirected walking), it becomes pertinent to consider how XR could leverage memory limitations (forgetting, distorting, persistence) to induce memory manipulations. As memories highly impact our self-perception, social interactions, and behaviors, there is a pressing need to understand XR Memory Manipulations (XRMMs). We ran three speculative design workshops (n=12), with XR and memory researchers creating 48 XRMM scenarios. Through thematic analysis, we define XRMMs, present a framework of their core components and reveal three classes (at encoding, pre-retrieval, at retrieval). Each class differs in terms of technology (AR, VR) and impact on memory (influencing quality of memories, inducing forgetting, distorting memories). We raise ethical concerns and discuss opportunities of perceptual and memory manipulations in XR