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Found 10 papers, 1 papers with code
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game
Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions.
A Computational Model of Children's Learning and Use of Probabilities Across Different Ages
Recent empirical work has shown that human children are adept at learning and reasoning with probabilities.
A Neural Model of Number Comparison with Surprisingly Robust Generalization
We propose a relatively simple computational neural-network model of number comparison.
Cognitive Models as Simulators: The Case of Moral Decision-Making
In this work, we substantiate the idea of $\textit{cognitive models as simulators}$, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster.
Bringing Order to the Cognitive Fallacy Zoo
In the eyes of a rationalist like Descartes or Spinoza, human reasoning is flawless, marching toward uncovering ultimate truth.
Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account
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 human irrationality.
Coupled feedback loops maintain synaptic long-term potentiation: A computational model of PKMzeta synthesis and AMPA receptor trafficking
In long-term potentiation (LTP), one of the most studied types of neural plasticity, synaptic strength is persistently increased in response to stimulation.
Neurons and Cognition Molecular Networks
Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models
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) plausible instances of a desired class with ease, when prompted.
Neural Implementation of Probabilistic Models of Cognition
Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes' rule to find the best explanation for available data.
Managing Uncertainty in Rule Based Cognitive Models
An experiment replicated and extended recent findings on psychologically realistic ways of modeling propagation of uncertainty in rule based reasoning.
