The cognitive framework of conceptual spaces bridges the gap between symbolic and subsymbolic AI by proposing an intermediate conceptual layer where knowledge is represented geometrically. There are two main approaches for obtaining the dimensions of this conceptual similarity space: using similarity ratings from psych…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
Trend · papers per month
48 results for “Learning Psychology”
The author suggests using non-Euclidean geometry for psychometric models.
problem Current psychometric models lack geometric insights.
method Illustrates how non-Euclidean geometry can be applied to psychometrics.
result Geometric concepts may improve psychometric model understanding.
A new model of learning corrects for chance to improve learning outcomes.
problem The importance of chance-corrected measures in learning.
method Developed two models: Informatron and AdaBook, based on empirical psychological results.
result Chance correction facilitates learning, as shown by computational results.
AI models assess psychological risks in currency trading.
problem Identifying psychological risks in currency traders.
method Developed a decision tree model to identify patterns in historical data.
result Enhanced decision-making through real-time alerts.
PsychFM predicts individual gambling choices using psychological and machine learning models.
problem Predicting individual gambling choices with high precision.
method PsychFM combines machine learning and psychological theories.
result PsychFM outperforms existing models like random forest and factorization machines.
The book explores essential stats and psychology for quantitative trading.
problem Developing a quantitative trading system.
method Logical progression through articles on statistics, quantitative trading, and psychology.
result Essential elements for quantitative trading systems.
Deep neural networks (DNNs) have achieved unprecedented performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions. This has caused a recent surge of interest in methods for rendering modern neural systems more interpretable. In this work, we propose to address th…
There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuroscience on the other. This paper aims to review some relevant contributions from the two literature threads and to draw links between them. …
New model explains price dynamics of Bitcoin with psychological factors.
problem Understanding price variations in cryptocurrency markets with psychological factors.
method Extended agent-based model with heterogeneous psychological parameters.
result Model shows diverse dynamics based on psychological correlation.
The paper interprets financial markets as crowds during booms and busts.
problem Understanding market irrationality during booms and busts.
method Integrates crowd psychology into behavioural finance.
result Markets behave like psychological crowds during booms and busts.
This paper introduces and evaluates a novel training method for neural networks: Dual Variable Learning Rates (DVLR). Building on insights from behavioral psychology, the dual learning rates are used to emphasize correct and incorrect responses differently, thereby making the feedback to the network more specific. Furt…
This paper focuses on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks. Since psychological attribute datasets typically suffer from small sample sizes, we apply transfer learning with two ba…
New framework for forecasting psychological processes from ILD.
problem Forecasting psychological processes at the individual level from ILD.
method A novel modeling framework addressing challenges in ILD.
result Improved forecasting of psychological processes at the individual level.
Recent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives in practical inference of behavioural economy in financial markets and microstructure study. We here present the latest results from a recently published stock market simulator built around a multi-agent s…
New pipeline for causal research in psychology and social sciences.
problem Underuse of causal approaches in psychology and social science.
method Formal specification of theories, reduction of complexity, estimation of causal effects.
result Facilitates scientific inquiry compatible with testing causal theories.
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models is not well understood. In this paper we propose a framework to systematically investigate bias and generalization in deep generative models …
Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors. Understanding the relation between an individual's behavioral patterns and psychological states can help identify strategies to improve quality of life. One challenge in analyzing physiological d…
Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans. Applying knowledge gained from psychological studies, we designed a new model called Differentiable Working Memory (DWM) in order to specifically emulate human workin…
IPGP framework improves psychological assessment by integrating shared and unique traits.
problem Tackles the debate on shared vs unique personality traits across individuals.
method Uses Gaussian process coregionalization model for non-Gaussian ordinal data, with stochastic variational inference for scalability.
result Improves prediction and estimation of individualized factor structures compared to existing methods.
Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While deep neural networks (DNNs) originated as engineering methods and models of biological networks in neuroscience and psychology, they have quickly become a centerpiece of the machin…
Robots learn actions and language through curiosity-driven self-exploration.
problem Efficient development of actions and language in infants and robots.
method Curiosity-driven self-exploration using Q-learning to amortize active inference.
result Curiosity-driven exploration enables faster learning and compositional generalization.
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain func…
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.
method Causal inference, graph analysis, temporal complexity measures, machine learning.
result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.
The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open on…
We compare the acquisition of knowledge in humans and machines. Research from the field of developmental psychology indicates, that human-employed hypothesis are initially guided by simple rules, before evolving into more complex theories. This observation is shared across many tasks and domains. We investigate whether…
Model predicts increased social unrest during COVID-19 using social media data.
problem Detecting rising conflict potential in societies during pandemics.
method Neural implicit motive pattern recognition from social media texts.
result Significant increase in conflict indicators during the pandemic.
The simplest field theory description of the multivariate statistics of forward rate variations over time and maturities, involves a quadratic action containing a gradient squared rigidity term. However, this choice leads to a spurious kink (infinite curvature) of the normalized correlation function for coinciding matu…
The cognitive framework of conceptual spaces proposes to represent concepts as regions in psychological similarity spaces. These similarity spaces are typically obtained through multidimensional scaling (MDS), which converts human dissimilarity ratings for a fixed set of stimuli into a spatial representation. One can d…
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
problem Traditional trading strategies fail during high volatility and regime shifts.
method Combines trend-following, mean-reversion, sentiment analysis, machine learning, and market regime filtering.
result Hybrid model achieved 135.49% return on investment over 24 months.
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual th…
Defines Learning Analytics' foundational structure and scope.
problem Lack of theoretical foundation in Learning Analytics.
method Proposes an axiomatic theory based on psychological learning and LA methodology.
result Clarifies the epistemological stance of Learning Analytics and its limitations.
Study shows trust and trustworthiness emerge through reinforcement learning.
problem Trust and trustworthiness are universal but not predicted by traditional economic models.
method Used Q-learning algorithm to simulate trust and trustworthiness dynamics in a trust game.
result High levels of trust and trustworthiness emerge when individuals consider both past and future experiences.
Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles like random forests (Strobl, Malley, and Tutz, 2009) are a useful tool for finding st…
Auction theory and demographyq-fin.GN
The paper analyzes auction theory and its impact on buyer demographics.
problem Understanding the dynamics of auctions and buyer demographics.
method Analyzing various auction scenarios and their psychological aspects.
result Identified key auction criteria and their influence on buyer groups.
Integrating economic and psychological insights in binary choice models with social interactionsphysics.soc-ph
We investigate a class of binary choice models with social interactions. We propose a unifying perspective that integrates economic models using a utility function and psychological models using an impact function. A general approach for analyzing the equilibrium structure of these models within mean-field approximatio…
Behavioral Finance has become a challenge to the scientific community. Based on the assumption that behavioral aspects of investors may explain some features of the Stock Market, we propose an agent based model to study quantitatively this relationship. In order to approximate the simulated market to the complexity of …
Mathew Effect in Artificial Stock Marketcond-mat.stat-mech
In this article, we established a stock market model based on agents' investing mentality. The agents decide whether to purchase the shares at the probability, according to their anticipation of the market's behaviors. The expectation of the amount of shares they want to buy is directly proportional to the value of ass…
Human categorization is one of the most important and successful targets of cognitive modeling in psychology, yet decades of development and assessment of competing models have been contingent on small sets of simple, artificial experimental stimuli. Here we extend this modeling paradigm to the domain of natural images…
This work suggests modifications to a previously introduced class of heterogeneous agent models that allow for the inclusion of different types of agent motivations and behaviours in a unified way. The agents operate within a highly simplified environment where they are only able to be long or short one unit of the ass…
An algorithm detects anomalies based on human perception principles.
problem Anomaly detection in data.
method Inspired by Gestalt psychology and Helmholtz principle, the algorithm models anomalies as unexpected elements in random distributions.
result The algorithm efficiently detects anomalies with minimal user intervention and promising results on multivariate data.
We describe an exercise of using Big Data to predict the Michigan Consumer Sentiment Index, a widely used indicator of the state of confidence in the US economy. We carry out the exercise from a pure ex ante perspective. We use the methodology of algorithmic text analysis of an archive of brokers' reports over the peri…
This volume is a collection of contributions from the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information Processing Systems (NIPS 2015) conference. Modern multivariate statistical methods developed in the rapidly growing field of machine learning are being increasingly…
An efficient algorithm selects the correct number of latent dimensions in multidimensional probit models.
problem Determining the correct number of latent dimensions in multidimensional probit graded response models.
method Adaptive Bayesian dimension selection framework using cumulative ordered spike-and-slab (COSS) prior and Albert--Chib latent response augmentation.
result The proposed method accurately recovers latent structures and avoids repeated model fitting.
This summarizes the study of the financial and economic crisis in Europe. The starting questions were: 1) Why do we have a crisis? Unde venis? 2) What will be the outcome? Quo vadis? Here is the reasoning which touches many areas, ranging from financial to politics and from psychology and economy.
Various psychological factors affect how individuals express emotions. Yet, when we collect data intended for use in building emotion recognition systems, we often try to do so by creating paradigms that are designed just with a focus on eliciting emotional behavior. Algorithms trained with these types of data are unli…
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the laten…
Paper uses inverse optimization to measure risk preference from investment portfolios.
problem Measuring subjective risk preference in investment portfolios.
method Inverse optimization on mean-variance framework.
result Quantified risk preference parameters validated with existing measures.
We calibrate and test various variants of field theory models of the interest rate with data from eurodollars futures. A model based on a simple psychological factor are seen to provide the best fit to the market. We make a model independent determination of the volatility function of the forward rates from market data…