We explore whether benchmarks can be solved using simple n-gram patterns and whether LLMs exploit these patterns to solve benchmark tasks.

Evaluation of the physical common-sense reasoning abilities of LLMs (Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro) by embedding them in a 3D environment (Animal-AI Testbed) and comparing their performance to other agents and human children.

An analysis of the design and outcomes of the Melting Pot competition, which measures agents’ ability to cooperate with others. We developed cognitive profiles for the agents submitted to the competition.

Applying the Maximum Entropy approach to awareness growth in the Bayesian framework, i.e. incorporating new events that we previously did not consider possible.

Investigating the effects of (good) explanations and the explainer’s reliability on our beliefs in what is being explained.

We bring together two closely related, but distinct, notions: argument and explanation. We provide a review of relevant research on these notions, drawn both from the cognitive science and the artificial intelligence (AI) literatures. We identify key directions for future research, indicating areas where bringing together cognitive science and AI perspectives would be mutually beneficial.

We explore some of the undesirable effects of providing explanations of AI systems to human users and ways to mitigate such effects. We show how providing counterfactual explanations of AI systems’ predictions unjustifiably changes people’s beliefs about causal relationships in the real world. We also show how health warning style messaging can prevent such a change in beliefs.

A discussion of the consequences of directly applying the insights from the psychology of explanation (that mostly focuses on causal explanations) to explainable AI (where most AI systems are based on associations).

What do we do with our existing models when we encounter new variables to consider? Does the order in which we learn variables matter? The paper investigates two modeling strategies and experimentally tests how people reason when presented with new variables and in different orders.

Empirical testing of the effects of the propensity interpretation of probability and ‘diagnostic split’ reasoning in the context of explaining away.

An experimental exploration of whether a Bayesian network modeling tool helps lay people to find correct solutions to complex problems.

What do we do with our existing models when we encounter new variables to consider? Does the order in which we learn variables matter? The paper investigates two modeling strategies and experimentally tests how people reason when presented with new variables and in different orders.

Investigating people’s reasoning in explaining away situations by manipulating the priors of causes and the structural complexity of the causal Baeysian networks.

A justification for Inference to the Best Explanation (IBE) is provided by identifying conditions under which the best explanation of evidence can offer a confirmatory boost to the hypotheses under consideration.

Analyzing confirmation between theories in cases of intertheoretic reduction (e.g. reducing thermodynamics to statistical mechanics) using Bayesian networks.