Robert L. West - Academia.edu (original) (raw)

Papers by Robert L. West

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In this paper we used a holographic memory system to model Zbrodoff's (1995) findings on the prob... more In this paper we used a holographic memory system to model Zbrodoff's (1995) findings on the problem size effect, a wellknown effect in the area of Math Cognition. The data showed the effects of manipulating both frequency and interference. We successfully modeled this using DHSM (Rutledge-Taylor & West, 2007), which has previously been used to model the fan effect (Anderson, 1974; Rutledge-Taylor & West, 2008). This demonstrates that frequency and interference effects arise naturally as a function of how holographic systems work.

Cognitive modeling is a complex endeavor so it is not surprising that the goals and intentions of... more Cognitive modeling is a complex endeavor so it is not surprising that the goals and intentions of modelers are often misunderstood, even by other modelers. To try to clarify this we have attempted to map out the various philosophical and theoretical commitments that one makes when creating a cognitive model or architecture. The goal of this is to avoid misunderstandings between the adherents of different modeling systems and between cognitive modelers and the rest of the scientific community.

ABSTRACT In this paper we introduce a new cognitive modeling system called Emergic Networks. The ... more ABSTRACT In this paper we introduce a new cognitive modeling system called Emergic Networks. The Emergic Network system is designed to facilitate functional, nonlinear decomposition with the aim of understanding how different neural systems can interact to produce specific instances of cognitive functionality. The first part of the paper briefly describes the motivation for the system and the second part briefly describes the system and provides a web location for downloading. http://dpleibovitz.upwize.com/?p=203

ABSTRACT Accounting for dendritic+ processing facilitates richer neural encoding schemes that can... more ABSTRACT Accounting for dendritic+ processing facilitates richer neural encoding schemes that can ultimately lead to simpler networks while improving their neurobiological plausibility. Dendritic+ processing is an example of several modeling tradeoffs: how local complexifications can improve global simplicity, and how functional network circuitry can be traded against representational circuitry. This is demonstrated within a model of narrow slit viewing based on an emergic network architecture (Leibovitz & West, 2012).

ABSTRACT In this talk, we describe a model of narrow slit viewing that deals with both spatial an... more ABSTRACT In this talk, we describe a model of narrow slit viewing that deals with both spatial and temporal completion for borders and surfaces. The model is based on functionality derived from the dynamic interactions of a neural model. We contrast this model with FACADE, which models vision using neural models of modules corresponding to functionality.

We demonstrate that the key components of cognitive architectures—declarative and procedural memo... more We demonstrate that the key components of cognitive architectures—declarative and procedural memory—and their key capabilities—learning, memory retrieval, probability judgement, and utility estimation—can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model. High-dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high-level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high-level cognition and provide human-readable, explainable models, but scale poorly to naturalistic, non-symbolic, or big data. Vector-symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in...

WikiSilo is a tool for theorizing across interdisciplinary fields such as Cognitive Science using... more WikiSilo is a tool for theorizing across interdisciplinary fields such as Cognitive Science using a specific vocabulary and structure. It is designed to show if a particular cognitive theory is complete and coherent at multiple levels of discourse, and commensurable with and relevant to a wider domain of cognition. WikiSilo is also a minimalist theory and methodology about effectively doing science, and is therefore a form of epistemizing. WikiSilo theory provides for a disciplined exploration of explanatory space via an axiomatic hierarchy of epistemizing and ontologizing postulates. The WikiSilo tool, via a software version control system, supports the long term goal of working toward coherent and unified theories. More generally, WikiSilo facilitates self-organization leading to academic silos with well-defined conceptual frameworks that are vertically related as compared to poorly related ad-hoc academic fiefdoms.

We prove that MINERVA 2, a widely-used model of biological long-term memory, is mathematically eq... more We prove that MINERVA 2, a widely-used model of biological long-term memory, is mathematically equivalent to an auto-associative memory implemented as a fourth order tensor. We further propose an alternative implementation of MINERVA 2 as a holographic lateral inhibition network. Our work clarifies the relationship between MINERVA 2 and other memory models, and shows that MINERVA 2 and derivative models can be neurally implemented and scaled-up to longterm learning tasks.

Journal of Behavioral Decision Making, 2010

Erev, Ert, and Roth organized three choice prediction competitions focused on three related choic... more Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: One shot decisions from description (decisions under risk), one shot decisions from experience, and repeated decisions from experience. Each competition was based on two experimental datasets: An estimation dataset, and a competition dataset. The studies that generated the two datasets used the same methods and subject pool, and examined decision problems randomly selected from the same distribution. After collecting the experimental data to be used for estimation, the organizers posted them on the Web, together with their fit with several baseline models, and challenged other researchers to compete to predict the results of the second (competition) set of experimental sessions. Fourteen teams responded to the challenge: The last seven authors of this paper are members of the winning teams. The results highlight the robustness of the difference between decisions from description...

… presented at the annual meeting of …, 2008

Studies have shown a positive priming effect with a short time between the prime and the target. ... more Studies have shown a positive priming effect with a short time between the prime and the target. The prime increases the performance on the target if they are congruent and decreases the performance when they are incongruent. Paradoxically, a negative priming effect has ...

Proceedings of the …, 2010

We present data demonstrating that interference plays a role in the fan effect. We also show that... more We present data demonstrating that interference plays a role in the fan effect. We also show that this cannot be accounted for using ACT-R. An ACT-R model is fit to the data and we discuss options for altering the model to account for the data.

… of the 29th Annual Conference of the …, 2007

The computational model in present paper confirms that memory-based accounts are sufficient to ac... more The computational model in present paper confirms that memory-based accounts are sufficient to account for a high rate of success at first-pass referent retrieval for anaphoric (and non-anaphoric) nouns. Because even definite noun phrases can often be non-anaphoric (e.g., Poesio & Vieira, 1998), an adequate model must account for how a reader makes an explicit or implicit decision about the anaphoric status of a noun (herein: The Anaphoric Classification Problem). We explain why we are inclined to reject the conventional intuition that: the failure to find/retrieve a referent within the discourse then, serially, leads to treating a (possibly anaphoric) noun as a new referent. Instead, we extend the memory-based account to address this classification problem. We suggest that LTM contains both generic referent types and specific referent tokens, which simultaneously compete for retrieval via resonance. The nature of what is retrieved (token vs. type) determines whether the reader effectively treats a noun as anaphoric or not. Our model predicts whether an anaphor in a given text will be misinterpreted as a new referent during first-pass processing. The influence of anaphor word choice is explained, and encompasses metaphoric anaphors.

We examined the effect of cognitive noise on human game playing abilities. Human subjects played ... more We examined the effect of cognitive noise on human game playing abilities. Human subjects played a guessing game against an ACT-R model set at different noise levels. Counter to the normal effect for noise (i.e., to increase randomness) increasing noise over certain ranges increased the win rate in both the ACT-R model and in the humans. We then attempted to model the human results using ACT-R, Q-Learning, neural networks, and Simple Recursive Neural Networks. Overall, ACT-R produced the best match to the data. However, none of these models were able to reproduce a secondary counter intuitive human win rate effect.

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Dynamically Structured Holographic Memory (DSHM) is a cognitive model of associative memory that ... more Dynamically Structured Holographic Memory (DSHM) is a cognitive model of associative memory that can be applied to the problem of recommendation. DSHM uses holographically reduced representations to encode the associations between objects that it learns about to generate recommendations. We compare the recommendations from this holographic recommender to a user-based collaborative filtering algorithm on several dataset, including MovieLens, and two bibliographic datasets from a scientific digital ...

Biologically Inspired Cognitive Architectures, 2014

We describe the DSHM (Dynamically Structured Holographic Memory) model of human memory, which use... more We describe the DSHM (Dynamically Structured Holographic Memory) model of human memory, which uses high dimensional vectors to represent items in memory. The complexity and intelligence of human behavior can be attributed, in part, to our ability to utilize vast knowledge acquired over a lifetime of experience with our environment. Thus models of memory, particularly models that can scale up to lifetime learning, are critical to modeling human intelligence. DHSM is based on the BEAGLE model of language acquisition (Jones and Mewhort, 2007) and extends this type of model to general memory phenomena. We demonstrate that DHSM can model a wide variety of human memory effects. Specifically, we model the fan effect, the problem size effect (from math cognition), dynamic game playing (detecting sequential dependencies from memories of past moves), and time delay learning (using an instance based approach). This work suggests that DSHM is suitable as a basis for learning both over the short-term and over the lifetime of the agent, and as a basis for both procedural and declarative memory. We argue that cognition needs to be understood at both the symbolic and sub-symbolic levels, and demonstrate that DSHM intrinsically operates at both of these levels of description. In order to situate DSHM in a familiar context, we discuss the relationship between DHSM and ACT-R. Dynamically Structured Holographic Memory 3 Dynamically Structured Holographic Memory Cognitive science, as a discipline, provides explanations for why cognitive phenomena occur and how they occur. The explanation for how a phenomenon occurs often involves a description of what processes underlie it. This need for process level accounts makes modeling, which is explicit in mechanical details, a particularly useful tool in generating explanations for cognitive phenomena. To achieve a full, theoretical understanding of a cognitive process, explanations need to be provided at both symbolic (i.e., representational) and sub-symbolic levels of description. The classic symbolic approaches to modeling do not account for how the symbol manipulations described in the model could arise from neural tissue, nor do they account for how the symbols themselves come into existence. Classic connectionist approaches are more concerned with neural plausibility, but are notoriously opaque, doing little to aid our understanding of the cognitive processes modeled. By contrast, the vector-symbolic approach to modeling explicitly provides an account at both levels of description. Vector Symbolic Architectures (VSAs), a term coined by Gayler (2003), are a set of techniques for instantiating and manipulating symbolic structures in distributed representations. Research into VSAs has been motivated by limitations in the ability of traditional connectionist models (i.e., non-recurrent models with one or two layers of connections) to represent knowledge with complicated structure (Plate, 1995). Like human memory, vector symbolic architectures can store complicated and recursive relations between ideas. VSAs use vectors with hundreds of dimensions, but the number of dimensions does not grow with either the quantity or complexity of the experiences stored within the vectors. For a formal analysis of VSAs, including time complexity considerations, see Kelly, Blostein, and Mewhort, 2013 as well as Plate, 1995.

This paper outlines a proposal to add emotion to the Standard Model. Part 1 describes how emotion... more This paper outlines a proposal to add emotion to the Standard Model. Part 1 describes how emotional factors impact decision-making and Part 2 outlines a way to augment the standard model to include these factors.

Proceedings of the 34th annual meeting of the Cognitive Science Society (CogSci 2012), Aug 1, 2012

carleton.ca

Journal of Behavioral Decision Making, 2010

… presented at the annual meeting of …, 2008

Proceedings of the …, 2010

… of the 29th Annual Conference of the …, 2007

carleton.ca

Biologically Inspired Cognitive Architectures, 2014

Proceedings of the 34th annual meeting of the Cognitive Science Society (CogSci 2012), Aug 1, 2012