Relation learning and reasoning on computational models of high level cognition

Relational reasoning is central to many cognitive processes, ranging from “lower” processes like object recognition to “higher” processes such as analogy-making and sequential decision-making. The first chapter of this thesis gives an overview of relational reasoning and the computational demands that it imposes on a system that performs relational reasoning. These demands are characterized in terms of the binding problem in neural networks. There has been a longstanding debate in the literature regarding whether neural network models of cognition are, in principle, capable of relation-base processing. In the second chapter I investigated the relational reasoning capabilities of the Story Gestalt model (St. John, 1992), a classic connectionist model of text comprehension, and a Seq-to-Seq model, a deep neural network of text processing (Bahdanau, Cho, & Bengio, 2015). In both cases I found that the purportedly relational behavior of the models was explainable by the statistics of their training datasets. We propose that both models fail at relational processing because of the binding problem in neural networks. In the third chapter of this thesis, I present an updated version of the DORA architecture (Doumas, Hummel, & Sandhofer, 2008), a symbolic-connectionist model of relation learning and inference that uses temporal synchrony to solve the binding problem. We use this model to perform relational policy transfer between two Atari games. Finally, in the fourth chapter I present a model of relational reinforcement that is able to select relevant relations, from a potentially large pool of applicable relations, to characterize a problem and learn simple rules from the reward signal, helping to bridge the gap between reinforcement learning and relational reasoning

The relational processing limits of classic and contemporary neural network models of language processing

Whether neural networks can capture relational knowledge is a matter of long-standing controversy. Recently, some researchers have argued that (1) classic connectionist models can handle relational structure and (2) the success of deep learning approaches to natural language processing suggests that structured representations are unnecessary to model human language. We tested the Story Gestalt model, a classic connectionist model of text comprehension, and a Sequence-to-Sequence with Attention model, a modern deep learning architecture for natural language processing. Both models were trained to answer questions about stories based on abstract thematic roles. Two simulations varied the statistical structure of new stories while keeping their relational structure intact. The performance of each model fell below chance at least under one manipulation. We argue that both models fail our tests because they can't perform dynamic binding. These results cast doubts on the suitability of traditional neural networks for explaining relational reasoning and language processing phenomena

Relation learning in a neurocomputational architecture supports cross-domain transfer

Humans readily generalize, applying prior knowledge to novelsituations and stimuli. Advances in machine learning have be-gun to approximate and even surpass human performance, butthese systems struggle to generalize what they have learnedto untrained situations. We present a model based on well-established neurocomputational principles that demonstrateshuman-level generalisation. This model is trained to play onevideo game (Breakout) and performs one-shot generalisationto a new game (Pong) with different characteristics. The modelgeneralizes because it learns structured representations that arefunctionally symbolic (viz., a role-filler binding calculus) fromunstructured training data. It does so without feedback, andwithout requiring that structured representations are specifieda priori. Specifically, the model uses neural co-activation todiscover which characteristics of the input are invariant and tolearn relational predicates, and oscillatory regularities in net-work firing to bind predicates to arguments. To our knowledge,this is the first demonstration of human-like generalisation ina machine system that does not assume structured representa-tions to begin with

A theory of relation learning and cross-domain generalization

People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the LISA and DORA models of relational inference and learning. The resulting model learns both the content and format (i.e., structure) of relational representations from non-relational inputs without supervision, when augmented with the capacity for reinforcement learning, leverages these representations to learn individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference. We demonstrate the capacity of the model to learn structured relational representations from a variety of simple visual stimuli, and to perform cross-domain generalization between video games (Breakout and Pong) and between several psychological tasks. We demonstrate that the model's trajectory closely mirrors the trajectory of children as they learn about relations, accounting for phenomena from the literature on the development of children's reasoning and analogy making. The model's ability to generalize between domains demonstrates the flexibility afforded by representing domains in terms of their underlying relational structure, rather than simply in terms of the statistical relations between their inputs and outputs.Comment: Includes supplemental materia

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

SOD2 Gene Variants (rs4880 and rs5746136) and Their Association with Breast Cancer Risk

The superoxide dismutase (SOD) is the principal antioxidant defense system in the body that is activated by a reactive oxygen species. Some variants of the SOD2 gene have been associated with cancer. The rs4880 variant was determined by PCR real-time and the rs5746136 variant by PCR-RFLP in healthy subjects and in breast cancer (BC) patients. The rs4880 and rs5746136 variants were associated with BC susceptibility when BC patients and the control group were compared for the CT, TT, CTCC, and the T alleles (p < 0.05). The CT genotype of the rs4880 variant showed significant statistical differences in patients and controls aged ≤ 45 years old, and with hormonal consumption (p < 0.05). The rs4880 variant was associated with BC patients with CTTT genotype and obesity, the presence of DM2-SAH, and a non-chemotherapy response (p < 0.05). Additionally, the rs5746136 variant was associated with susceptibility to BC with Ki-67 (≥20%), luminal A type BC, and a chemotherapy partial response (p < 0.05) in BC patients who carry TT, TC, and CTTT genotypes, respectively. The haplotype T/T (OR 1.98; 95% CI 1.20–3.26, p = 0.005) was observed to be a risk factor for BC. The rs4880 and rs5746136 variants in the SOD2 gene were associated with BC susceptibility

The Seventeenth Data Release of the Sloan Digital Sky Surveys:complete release of MaNGA, MaStar and APOGEE-2 data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys