Where Am I? The Cognitive Architecture of Spatial Reorientation

Navigation is of fundamental importance to humans, just as it is for other species. And, like most other animal species, we possess a number of distinct navigational processes. This thesis examines navigation, focusing particularly on the widely studied phenomenon of reorientation following disruption to spatial behavior. In typical reorientation experiments, subjects rely on the three-dimensional surface layout of an environment to find a desired goal following disorientation, and they do so to the exclusion of other important spatial cues. An influential explanatory framework aims to account for such findings by holding that subjects possess a modular mechanism known as the geometric module, which only operates on geometric information about three-dimensional extended surfaces. This thesis provides a sustained defense of this framework and develops a new type of geometric-module theory of reorientation. I begin by making the case that, if the general geometric-module framework is right, it has deep implications for two foundational debates in philosophy of psychology: the debate about the nature of mental representations and the debate about the structure of the mind. I then address the two most pressing challenges against the framework. The first challenge comes from what I call ‘the explanatory inflexibility objection’, which holds that the geometric-module framework simply does not have the required flexibility to deal with evidence that non-geometric cues can affect subjects’ search behavior in some experimental contexts. The second challenge arises from an alternative explanatory framework, the view-matching framework, which aims to explain subjects’ behavior in reorientation experiments by appealing to snapshots, stored representations of the subjects’ two-dimensional retinal stimulation at specific locations. In answering these two challenges, I put forward a new type of geometric-module theory which has stronger implications for debates in philosophy of psychology than standard geometric-module models

Higher-order Clustering and Pooling for Graph Neural Networks

Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks, especially due to pooling operators, which aggregate learned node embeddings hierarchically into a final graph representation. However, they are not only questioned by recent work showing on par performance with random pooling, but also ignore completely higher-order connectivity patterns. To tackle this issue, we propose HoscPool, a clustering-based graph pooling operator that captures higher-order information hierarchically, leading to richer graph representations. In fact, we learn a probabilistic cluster assignment matrix end-to-end by minimising relaxed formulations of motif spectral clustering in our objective function, and we then extend it to a pooling operator. We evaluate HoscPool on graph classification tasks and its clustering component on graphs with ground-truth community structure, achieving best performance. Lastly, we provide a deep empirical analysis of pooling operators' inner functioning.Comment: CIKM 202

In defense of language-independent flexibility, or: What rodents and humans can do without language

There are two main approaches within classical cognitive science to explaining how humans can entertain mental states that integrate contents across domains. The language-based framework states that this ability arises from higher cognitive domain-specific systems that combine their outputs through the language faculty, whereas the language-independent framework holds that it comes from non-language-involving connections between such systems. This article turns on its head the most influential empirical argument for the language-based framework, an argument that originates from research on spatial reorientation. I make the case that neuroscientific findings about spatial reorientation in rodents and humans bolster the language-independent framework instead

Hemocyte siRNA uptake is increased by 5' cholesterol-TEG addition in Biomphalaria glabrata, snail vector of schistosome.

Biomphalaria glabrata is one of the snail intermediate hosts of Schistosoma mansoni, the causative agent of intestinal schistosomiasis disease. Numerous molecular studies using comparative approaches between susceptible and resistant snails to S. mansoni infection have helped identify numerous snail key candidates supporting such susceptible/resistant status. The functional approach using RNA interference (RNAi) remains crucial to validate the function of such candidates. CRISPR-Cas systems are still under development in many laboratories, and RNA interference remains the best tool to study B. glabrata snail genetics. Herein, we describe the use of modified small interfering RNA (siRNA) molecules to enhance cell delivery, especially into hemocytes, the snail immune cells. Modification of siRNA with 5' Cholesteryl TriEthylene Glycol (Chol-TEG) promotes cellular uptake by hemocytes, nearly eightfold over that of unmodified siRNA. FACS analysis reveals that more than 50% of hemocytes have internalized Chol-TEG siRNA conjugated to Cy3 fluorophores, 2 hours only after in vivo injection into snails. Chol-TEG siRNA targeting BgTEP1 (ThioEster-containing Protein), a parasite binding protein, reduced BgTEP1 transcript expression by 70-80% compared to control. The level of BgTEP1 protein secreted in the hemolymph was also decreased. However, despite the BgTEP1 knock-down at both RNA and protein levels, snail compatibility with its sympatric parasite is not affected suggesting functional redundancy among the BgTEP genes family in snail-schistosoma interaction

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Mitigating the climate crisis requires a rapid transition towards lower carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in a great number of industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the amount of energy spent on such processes, we must quickly discover more efficient catalysts to drive the electrochemical reactions. Machine learning (ML) holds the potential to efficiently model the properties of materials from large amounts of data, and thus to accelerate electrocatalyst design. The Open Catalyst Project OC20 data set was constructed to that end. However, most existing ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. Here, we propose several task-specific innovations, applicable to most architectures, which increase both computational efficiency and accuracy. In particular, we propose improvements in (1) the graph creation step, (2) atom representations and (3) the energy prediction head. We describe these contributions and evaluate them on several architectures, showing up to 5$\times$ reduction in inference time without sacrificing accuracy.Comment: Accepted at the NeurIPS 2022 AI for Accelerated Materials Design Worksho

The Motivation Problem of Epistemic Expressivists

Many philosophers have adopted epistemic expressivism in recent years. The core commitment of epistemic expressivism is that epistemic claims express conative states. This paper assesses the plausibility of this commitment. First, we raise a new type of problem for epistemic expressivism, the epistemic motivation problem. The problem arises because epistemic expressivists must provide an account of the motivational force of epistemic judgment (the mental state expressed by an epistemic claim), yet various features of our mental economy seem to show that they can’t do so. Second, we develop what we take to be the most promising response to that problem for expressivists. We end by noting that this response faces an important challenge pertaining to the psychology of epistemic criticism and praise