7 citations · 15 across the 12 of their papers we have counts for
12 papers
Text Understanding in GPT-4 vs Humans
Thomas R. Shultz, Jamie M. Wise, Ardavan Salehi Nobandegani
We examine whether a leading AI system GPT4 understands text as well as humans do, first using a well-established standardized test of discourse comprehension. On this test, GPT4 p…
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game
Ardavan S. Nobandegani, Irina Rish, Thomas R. Shultz
Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theoretical analysis of the $\textit{trust gam…
A Neural Model of Number Comparison with Surprisingly Robust Generalization
Thomas R. Shultz, Ardavan S. Nobandegani, Zilong Wang
We propose a relatively simple computational neural-network model of number comparison. Training on comparisons of the integers 1-9 enable the model to efficiently and accurately s…
Cognitive Models as Simulators: The Case of Moral Decision-Making
Ardavan S. Nobandegani, Thomas R. Shultz, Irina Rish
To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive…
A Computational Model of Infant Learning and Reasoning with Probabilities
Thomas R Shultz, Ardavan S Nobandegani
Recent experiments reveal that 6- to 12-month-old infants can learn probabilities and reason with them. In this work, we present a novel computational system called Neural Probabil…
Bringing Order to the Cognitive Fallacy Zoo
Ardavan S. Nobandegani, William Campoli, Thomas R. Shultz
In the eyes of a rationalist like Descartes or Spinoza, human reasoning is flawless, marching toward uncovering ultimate truth. A few centuries later, however, culminating in the w…
A Rational Distributed Process-level Account of Independence Judgment
Ardavan S. Nobandegani, Ioannis N. Psaromiligkos
It is inconceivable how chaotic the world would look to humans, faced with innumerable decisions a day to be made under uncertainty, had they been lacking the capacity to distingui…
Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account
Ardavan S. Nobandegani, Kevin da Silva Castanheira, A. Ross Otto +1
The Availability bias, manifested in the over-representation of extreme eventualities in decision-making, is a well-known cognitive bias, and is generally taken as evidence of huma…
The Causal Frame Problem: An Algorithmic Perspective
Ardavan Salehi Nobandegani, Ioannis N. Psaromiligkos
The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology, articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we account for our apparent ab…
Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models
Ardavan Salehi Nobandegani, Thomas R. Shultz
Humans are not only adept in recognizing what class an input instance belongs to (i.e., classification task), but perhaps more remarkably, they can imagine (i.e., generate) plausib…
Probabilistic Structural Controllability in Causal Bayesian Networks
Ardavan Salehi Nobandegani, Ioannis N. Psaromiligkos
Humans routinely confront the following key question which could be viewed as a probabilistic variant of the controllability problem: While faced with an uncertain environment gove…
Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar
Ardavan Salehi Nobandegani, Ioannis N. Psaromiligkos
Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an i…