Modular Representations of Cognitive Phenomena in AI, Psychology and Neuroscience

Bryson, Joanna J.

doi:10.4018/978-1-59140-482-8.ch004

Cited by 12 publications

(6 citation statements)

References 54 publications

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“…laid the initial theoretical groundwork, suggesting the compartmentalization of cognitive processes into discrete, functional modules within the brain. expanded upon this, advocating for the parallel in artificial intelligence constructs, presenting a compelling case for modular representations within AI systems [8,9]. Against this conceptual backdrop, the current study advances the dialogue by proposing an emotionally agnostic framework for sentiment analysis.…”

Section: Introductionmentioning

confidence: 84%

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

Hutson,

Ratican

2024

J Biosen and Bioelec Res

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The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth required for accurate emotion detection. A novel suite of metrics tailored to this approach is developed, facilitating a nuanced interpretation of sentiment within text data, which is paramount for enhancing user interaction across a spectrum of text mining applications, including recommendation systems and interactive AI characters. The research undertakes a critical comparative analysis, juxtaposing the newly proposed model with traditional sentiment analysis techniques, to evaluate efficacy enhancements and to substantiate its application potential. Further, the investigation delves into the short-term memory capabilities of LLMs, exploring the implications for character AI roleplaying interfaces and their ability to recall and respond to user input within a text-driven emotional framework. Findings indicate that this emotionally-agnostic sentiment analysis approach not only simplifies the sentiment assessment process within LLMs but also opens avenues for a more precise and contextually appropriate emotional response generation. This abstracted model, devoid of human physiological constraints, represents a significant advancement in text mining, fostering improved interactions and contributing to the evolution of LLMs as adept analytical tools in various domains where emotional intelligence is crucial.

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Section: Introductionmentioning

confidence: 84%

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

Hutson,

Ratican

2024

J Biosen and Bioelec Res

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“…The conceptual bedrock of the present study builds upon extant literature that delineates the human mind as a modular entity a concept espoused by theorists like Fodor (1983) and empirical researchers alike (Bryson, 2005). Specifically, Bryson's seminal paper serves as a lynchpin, situating this study at the crossroads of neuroscience, psychology, and AI.…”

Section: The Proposed Model: Autonomous Agents As Proxies For Mental ...mentioning

confidence: 99%

Synthesizing Sentience: Integrating Large Language Models and Autonomous Agents for Emulating Human Cognitive Complexity

Ratican,

Hutson,

Plate

2023

JAIMLD

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The paper aims to present a novel methodology for emulating the intricacies of human cognitive complexity by ingeniously integrating large language models with autonomous agents. Grounded in the theoretical framework of the modular mind theory-originally espoused by Fodor and later refined by scholars such as Joanna Bryson-the study seeks to venture into the untapped potential of large language models and autonomous agents in mirroring human cognition. Recent advancements in artificial intelligence, exemplified by the inception of autonomous agents like Age in GPT, auto GPT, and baby AGI, underscore the transformative capacities of these technologies in diverse applications. Moreover, empirical studies have substantiated that persona-driven autonomous agents manifest enhanced efficacy and nuanced performance, mimicking the intricate dynamics of human interactions. The paper postulates a theoretical framework incorporating persona-driven modules that emulate psychological functions integral to general cognitive processes. This framework advocates for the deployment of a plurality of autonomous agents, each informed by specific large language models, to act as surrogates for different cognitive functionalities. Neurological evidence is invoked to bolster the theoretical architecture, delineating how autonomous agents can serve as efficacious proxies for modular cognitive centers within the human brain. Given this foundation, a theory of mind predicated upon modular constructs offers a fertile landscape for further empirical investigations and technological innovations.

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“…Horizontal decomposition identifies all the cognition processes and vertical decomposition identifies particular skills or faculties such as mathematics, language or metaphysics [4].…”

Section: How Brainreinforcesai?mentioning

confidence: 99%

Analytical Review on the Correlation between Ai and Neuroscience

Sille¹

2013

IOSR-JCE

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Modular Representations of Cognitive Phenomena in AI, Psychology and Neuroscience

Cited by 12 publications

References 54 publications

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

Synthesizing Sentience: Integrating Large Language Models and Autonomous Agents for Emulating Human Cognitive Complexity

Analytical Review on the Correlation between Ai and Neuroscience

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