Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans

• URL: http://arxiv.org/abs/2407.17622v2
• Date: Thu, 5 Sep 2024 15:52:47 GMT
• Title: Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans
• Authors: Changyu Chen, Shashank Reddy Chirra, Maria José Ferreira, Cleotilde Gonzalez, Arunesh Sinha, Pradeep Varakantham,
• Abstract summary: We build on Instance Based Learning (IBL) that posits that human decisions are based on similar situations encountered in the past. We propose two new attention-based neural network models to have open form non-linear functions to model distinct and heterogeneous human decision-making.
• Score: 16.72938921687168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time because such models can help make AI systems more intuitive, personalized, mitigate any human biases, and enhance training in simulation. Some initial work has attempted to utilize neural networks (and large language models) but often assumes one common model for all humans and aims to emulate human behavior in aggregate. However, the behavior of each human is distinct, heterogeneous, and relies on specific past experiences in certain tasks. For instance, consider two individuals responding to a phishing email: one who has previously encountered and identified similar threats may recognize it quickly, while another without such experience might fall for the scam. In this work, we build on Instance Based Learning (IBL) that posits that human decisions are based on similar situations encountered in the past. However, IBL relies on simple fixed form functions to capture the mapping from past situations to current decisions. To that end, we propose two new attention-based neural network models to have open form non-linear functions to model distinct and heterogeneous human decision-making in dynamic settings. We experiment with two distinct datasets gathered from human subject experiment data, one focusing on detection of phishing email by humans and another where humans act as attackers in a cybersecurity setting and decide on an attack option. We conducted extensive experiments with our two neural network models, IBL, and GPT3.5, and demonstrate that the neural network models outperform IBL significantly in representing human decision-making, while providing similar interpretability of human decisions as IBL. Overall, our work yields promising results for further use of neural networks in cognitive modeling of human decision making.

Related papers

• Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption [64.07607726562841]
  Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration. In this work, we tackle the task of reconstructing closely interactive humans from a monocular video. We propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information.
  arXiv  Detail & Related papers  (2024-04-17T11:55:45Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Credit Assignment: Challenges and Opportunities in Developing Human-like AI Agents [0.0]
  Temporal credit assignment is crucial for learning and skill development in natural and artificial intelligence. We use a cognitive model based on a theory of decisions from experience to test different credit assignment mechanisms in a goal-seeking navigation task. We found that an IBL model that gives equal credit assignment to all decisions is able to match human performance better than other models.
  arXiv  Detail & Related papers  (2023-07-16T23:11:26Z)
• Evaluating alignment between humans and neural network representations in image-based learning tasks [5.657101730705275]
  We tested how well the representations of $86$ pretrained neural network models mapped to human learning trajectories. We found that while training dataset size was a core determinant of alignment with human choices, contrastive training with multi-modal data (text and imagery) was a common feature of currently publicly available models that predicted human generalisation. In conclusion, pretrained neural networks can serve to extract representations for cognitive models, as they appear to capture some fundamental aspects of cognition that are transferable across tasks.
  arXiv  Detail & Related papers  (2023-06-15T08:18:29Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• On Modifying a Neural Network's Perception [3.42658286826597]
  We propose a method which allows one to modify what an artificial neural network is perceiving regarding specific human-defined concepts. We test the proposed method on different models, assessing whether the performed manipulations are well interpreted by the models, and analyzing how they react to them.
  arXiv  Detail & Related papers  (2023-03-05T12:09:37Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Investigations of Performance and Bias in Human-AI Teamwork in Hiring [30.046502708053097]
  In AI-assisted decision-making, effective hybrid teamwork (human-AI) is not solely dependent on AI performance alone. We investigate how both a model's predictive performance and bias may transfer to humans in a recommendation-aided decision task.
  arXiv  Detail & Related papers  (2022-02-21T17:58:07Z)
• Human-Understandable Decision Making for Visual Recognition [30.30163407674527]
  We propose a new framework to train a deep neural network by incorporating the prior of human perception into the model learning process. The effectiveness of our proposed model is evaluated on two classical visual recognition tasks.
  arXiv  Detail & Related papers  (2021-03-05T02:07:33Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

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.