Computational Inference in Cognitive Science: Operational, Societal and
Ethical Considerations
- URL: http://arxiv.org/abs/2210.13526v1
- Date: Mon, 24 Oct 2022 18:27:27 GMT
- Title: Computational Inference in Cognitive Science: Operational, Societal and
Ethical Considerations
- Authors: Baihan Lin
- Abstract summary: computational advances have transformed cognitive science into a data-driven field.
There is a proliferation of cognitive theories investigated and interpreted from different academic lens.
We identify the operational challenges, societal impacts and ethical guidelines in conducting research.
- Score: 13.173307471333619
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Emerging research frontiers and computational advances have gradually
transformed cognitive science into a multidisciplinary and data-driven field.
As a result, there is a proliferation of cognitive theories investigated and
interpreted from different academic lens and in different levels of
abstraction. We formulate this applied aspect of this challenge as the
computational cognitive inference, and describe the major routes of
computational approaches. To balance the potential optimism alongside the speed
and scale of the data-driven era of cognitive science, we propose to inspect
this trend in more empirical terms by identifying the operational challenges,
societal impacts and ethical guidelines in conducting research and interpreting
results from the computational inference in cognitive science.
Related papers
- A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and
Why? [84.46288849132634]
We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
We define three variables to encompass diverse facets of the evolution of research topics within NLP.
We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
arXiv Detail & Related papers (2023-05-22T11:08:00Z) - Expanding the Role of Affective Phenomena in Multimodal Interaction
Research [57.069159905961214]
We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing.
We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers.
We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
arXiv Detail & Related papers (2023-05-18T09:08:39Z) - Machine Psychology [54.287802134327485]
We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology.
We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table.
It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
arXiv Detail & Related papers (2023-03-24T13:24:41Z) - Model Positionality and Computational Reflexivity: Promoting Reflexivity
in Data Science [10.794642538442107]
We describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding data science work.
We describe the challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions.
arXiv Detail & Related papers (2022-03-08T16:02:03Z) - Active Inference in Robotics and Artificial Agents: Survey and
Challenges [51.29077770446286]
We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning.
We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
arXiv Detail & Related papers (2021-12-03T12:10:26Z) - Computational Argumentation and Cognition [0.3867363075280543]
This paper stems from the 1st Workshop on Computational Argumentation and Cognition (COGNITAR)
It argues that within the context of Human-Centric AI the use of theory and methods from Computational Argumentation for the study of Cognition can be a promising avenue to pursue.
The paper presents the main problems and challenges in the area that would need to be addressed, both at the scientific level but also at the level of synthesis of ideas and approaches from the various disciplines involved.
arXiv Detail & Related papers (2021-11-12T21:44:30Z) - Paradigm Shift Through the Integration of Physical Methodology and Data
Science [0.0]
Methods that integrate traditional physical and data science methodologies are new methods of mathematical analysis.
This paper highlights the significance and importance of such integrated methods from the viewpoint of scientific theory.
arXiv Detail & Related papers (2021-09-30T18:00:09Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z) - No computation without representation: Avoiding data and algorithm
biases through diversity [11.12971845021808]
We draw connections between the lack of diversity within academic and professional computing fields and the type and breadth of the biases encountered in datasets.
We use these lessons to develop recommendations that provide concrete steps for the computing community to increase diversity.
arXiv Detail & Related papers (2020-02-26T23:07:39Z) - A Survey on Causal Inference [64.45536158710014]
Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics.
Various causal effect estimation methods for observational data have sprung up.
arXiv Detail & Related papers (2020-02-05T21:35:29Z)
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.