Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to be leveraged alongside each other. The only existing non-linear spherical CNN layer that is strictly equivariant has complexity OpC 2 L 5 q, where C is a measure of representational capacity and L the spherical harmonic bandlimit. Such a high computational cost often prohibits the use of strictly equivariant spherical CNNs. We develop two new strictly equivariant layers with reduced complexity OpCL 4 q and OpCL 3 log Lq, making larger, more expressive models computationally feasible. Moreover, we adopt efficient sampling theory to achieve further computational savings. We show that these developments allow the construction of more expressive hybrid models that achieve state-of-the-art accuracy and parameter efficiency on spherical benchmark problems.

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Exploration in environments with sparse rewards is difficult for artificial agents. Curiosity driven learning -using feed-forward prediction errors as intrinsic rewards -has achieved some success in these scenarios, but fails when faced with action-dependent noise sources. We present aleatoric mapping agents (AMAs), a neuroscience inspired solution modeled on the cholinergic system of the mammalian brain. AMAs aim to explicitly ascertain which dynamics of the environment are unpredictable, regardless of whether those dynamics are induced by the actions of the agent. This is achieved by generating separate forward predictions for the mean and variance of future states and reducing intrinsic rewards for those transitions with high aleatoric variance. We show AMAs are able to effectively circumvent action-dependent stochastic traps that immobilise conventional curiosity driven agents. The code for all experiments presented in this paper is open-sourced.

Two studies tested the hypothesis that a Large Language Model (LLM) can be used to model psychological change following exposure to influential input. The first study tested a generic mode of influence -the Illusory Truth Effect (ITE) -where earlier exposure to a statement boosts a later truthfulness test rating. Analysis of newly collected data from human and LLMsimulated subjects (1000 of each) showed the same pattern of effects in both populations; although with greater per statement variability for the LLM. The second study concerns a specific mode of influence -populist framing of news to increase its persuasion and political mobilization. Newly collected data from simulated subjects was compared to previously published data from a 15-country experiment on 7286 human participants. Several effects from the human study were replicated by the simulated study, including ones that surprised the authors of the human study by contradicting their theoretical expectations; but some significant relationships found in human data were not present in the LLM data. Together the two studies support the view that LLMs have potential to act as models of the effect of influence. Previous ResearchPersonality: Miotto et al. ( 2022) used promptcompletion to administer a personality questionnaire to GPT-3, measuring the BIG-5 and other dimensions. GPT -3's personality profile was