Automated scientific minimization of regret

• URL: http://arxiv.org/abs/2505.17661v1
• Date: Fri, 23 May 2025 09:26:43 GMT
• Title: Automated scientific minimization of regret
• Authors: Marcel Binz, Akshay K. Jagadish, Milena Rmus, Eric Schulz,
• Abstract summary: We introduce automated scientific minimization of regret (ASMR)<n>ASMR is a framework for automated computational cognitive science.<n>We show that ASMR discovers cognitive models that predict human behavior at noise ceiling while retaining interpretability.
• Score: 7.633645883658148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimization, ASMR leverages Centaur -- a recently proposed foundation model of human cognition -- to identify gaps in an interpretable cognitive model. These gaps are then addressed through automated revisions generated by a language-based reasoning model. We demonstrate the utility of this approach in a multi-attribute decision-making task, showing that ASMR discovers cognitive models that predict human behavior at noise ceiling while retaining interpretability. Taken together, our results highlight the potential of ASMR to automate core components of the cognitive modeling pipeline.

Related papers

• APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech [20.311467577180274]
  Speech quality assessment aims to quantify subjective human perception of speech.<n>We propose an auditory perception guided-MOS prediction model (APG-MOS)<n>APG-MOS integrates auditory modeling with semantic analysis to enhance consistency with human judgments.
  arXiv  Detail & Related papers  (2025-04-29T05:45:09Z)
• Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems [0.0]
  We introduce a novel mathematical framework that unifies neural population dynamics, hippocampal sharp wave-ripple (SpWR) generation, and cognitive consistency constraints inspired by Heider's theory.<n>Our model leverages low-dimensional manifold representations to capture structured neural drift and incorporates a balance energy function to enforce coherent synaptic interactions.<n>This work paves the way for scalable neuromorphic architectures that bridge neuroscience and artificial intelligence, offering more robust and adaptive learning mechanisms for future intelligent systems.
  arXiv  Detail & Related papers  (2025-02-25T18:28:25Z)
• Shifting Attention to You: Personalized Brain-Inspired AI Models [3.0128071072792366]
  We show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model over doubles behavioral performance compared to the unmodified CLIP baseline.<n>Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human-computer interaction.
  arXiv  Detail & Related papers  (2025-02-07T04:55:31Z)
• A Dynamic Systems Approach to Modelling Human-Machine Rhythm Interaction [4.33608942673382]
  This study introduces a computational model inspired by the physical and biological processes underlying rhythm processing. Our findings demonstrate the model's ability to accurately perceive and adapt to rhythmic patterns within the human perceptible range.
  arXiv  Detail & Related papers  (2024-06-26T10:07:20Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach [55.613461060997004]
  Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks. We propose an innovative textitmetacognitive approach, dubbed textbfCLEAR, to equip LLMs with capabilities for self-aware error identification and correction.
  arXiv  Detail & Related papers  (2024-03-08T19:18:53Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural Networks [2.0646127669654826]
  We present NeuroCERIL, a brain-inspired neurocognitive architecture that uses a novel hypothetico-deductive reasoning procedure. We show that NeuroCERIL can learn various procedural skills in a simulated robotic imitation learning domain. We conclude that NeuroCERIL is a viable neural model of human-like imitation learning.
  arXiv  Detail & Related papers  (2022-11-11T19:56:11Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.