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.
Machine learning detectors can be accurate but introduce bias.
problem The accuracy of machine learning detectors affects the reliability of measured constructs.
method Examined the impact of detector accuracy on estimated correlations between constructs and phenomena.
result The expected correlation between phenomena decreases as detector accuracy decreases.
Combines psychological ratings with machine learning to map images to psychological similarity spaces.
problem Mapping images to psychological similarity spaces with machine learning.
method Combines psychologically derived similarity ratings with machine learning techniques to constrain the learning process.
result Results support the feasibility of mapping images to psychological similarity spaces.
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.
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.
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.
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.
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.
We consider a simple stochastic differential equation for modeling bubbles in social context. A prime example is bubbles in asset pricing, but similar mechanisms may control a range of social phenomena driven by psychological factors (for example, popularity of rock groups, or a number of students pursuing a given majo…
Cognitive psychology insights reveal shape bias in DNNs.
problem Interpreting modern deep neural networks (DNNs).
method Applied developmental psychology's word learning theory to DNNs.
result DNNs exhibit a shape bias similar to human word learning.
Study uses AI to simulate stock market behavior, revealing how trader psychology affects market stability.
problem Understanding how trader psychology impacts stock market stability at the mesoscale.
method Developed a multi-agent system with reinforcement learning, incorporating trader psychology traits.
result Agents with traits of delay discounting and greed lead to more stable markets.
Detects radical content on Twitter using textual, psychological, and behavioral signals.
problem Limit the spread of extremist narratives on social media.
method Analyzed extremist material, created contextual text-based model, inferred psychological properties, evaluated on Twitter.
result Radical users exhibit distinguishable textual, psychological, and behavioral properties.
Study reveals biases and generalization patterns in deep image models.
problem Understanding the inductive bias of deep generative models in high dimensions.
method Proposed a framework to empirically investigate bias and generalization using designed training datasets.
result Identified similarities to human psychology in model behavior and patterns.
SINN combines social science and deep learning for predicting opinion dynamics.
problem Predicting opinion dynamics in social networks using traditional models requires extensive calibration with real data.
method SINN integrates theoretical models and social media data using physics-informed neural networks (PINNs) and matrix factorization.
result SINN outperforms six baseline methods in predicting opinion dynamics on real-world and synthetic datasets.
New method learns psychological similarity spaces for unseen stimuli.
problem Generalizing psychological similarity spaces to new stimuli.
method Learn mapping from raw stimuli to similarity space using ANNs.
result ANNs can successfully map raw stimuli into similarity spaces.
Transforms neural nets into generative models for better sample generation.
problem Generating plausible instances of a class with neural networks.
method Markov Chain Monte Carlo (MCMC) with Metropolis-adjusted Langevin algorithm.
result CCNNs can now generate samples from a category of interest.
Paper reviews intrinsic motivations and their role in open-ended learning.
problem Understanding intrinsic motivations and their role in open-ended learning.
method Defining intrinsic motivations, presenting psychological/neuroscientific and computational models.
result Links between psychological/neuroscientific and computational models of intrinsic motivations.
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.
Dual Variable Learning Rates improve neural network training efficiency.
problem Training neural networks efficiently and effectively.
method DVLR uses different learning rates for correct and incorrect responses, and adjusts rates based on network performance.
result DVLR consistently improves neural network accuracy across different types and domains.
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.
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
New model DWM mimics human memory, learning to retain, ignore or forget.
problem Lack of effective separation between episodic and working memory in neural networks.
method Designed Differentiable Working Memory (DWM) inspired by psychological studies.
result DWM learns psychology-inspired tasks faster and generalizes to longer sequences.
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.
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…
Bayesian Neural Networks show similar developmental stages as human learning.
problem Comparing human and machine learning stages.
method Operationalized developmental stages as data-set size, analyzed on three tasks.
result Bayesian Neural Networks exhibit similar developmental patterns to human learning.
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.
Unified cognitive system from diverse fMRI studies using neural representations.
problem Aggregate heterogeneous brain function data into a universal cognitive system.
method Multi-task learning and multi-scale dimension reduction to learn low-dimensional cognitive representations.
result Achieves best prediction performance on large reference datasets and small datasets.
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.
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…
Temporal Difference Learning explains emotions and behavior.
problem Understanding how emotions evolve and impact behavior.
method Temporal Difference Reinforcement Learning (TDRL) theory.
result Emotions are TD error assessments aiding survival.
Deep learning models need better theoretical understanding to avoid costly mistakes.
problem Deep learning models often make costly mistakes in sensitive applications.
method Theoretical analysis of deep learning models using various mathematical and psychological perspectives.
result Theoretical understanding of deep learning models is limited and requires convergence of different perspectives.
Study of sentence representations in AI shows parallels to human learning.
problem Understanding how AI systems learn and generalize from training data.
method Diagnostic tests, performance analysis, training distribution effects, representation changes with augmentations.
result AI systems can learn abstract rules and generalize under certain conditions, similar to human zero-shot reasoning.
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…
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.
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…
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…
Synthesizes AI and human creativity for more compelling art.
problem Making AI-generated art more human-like.
method Combining Deep Learning and Cognitive Psychology theories.
result Demonstrates how AI can incorporate human creativity theories.
A risk-sensitive approach to inverse reinforcement learning.
problem Risk-sensitive reinforcement learning in Markov decision processes.
method Gradient-based algorithm minimizing a loss function defined on observed behavior.
result Demonstrated performance on Grid World and ride-sharing examples.
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.
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…
Study on how technical analysis affects wealth distribution in agent-based model of investors.
problem Understanding wealth distribution and return rates in financial markets.
method Agent-based model with psychological profiles and decision-making strategies.
result Anti-imitation strategy becomes most profitable when applied to investors' hub.
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.
MAES architecture improves working memory task performance through multi-task and transfer learning.
problem Improving performance on complex working memory tasks.
method Memory-Augmented Encoder-Solver (MAES) architecture with dual recurrent neural network controllers and a shared memory module.
result MAES models achieve task-size generalization, handling inputs 50 times longer than training data.
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.
The paper argues against the inefficiency of explaining deep learning phenomena.
problem Explaining deep learning phenomena often leads to ad hoc hypotheses.
method Analyzing recent literature to assess the relevance of these phenomena.
result Many phenomena do not appear in real-world applications and may be inefficient.
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.
Study classifies stock price data into stationary and non-stationary periods for mechanical trading.
problem Classifying stock price fluctuations into stationary and non-stationary periods for trading.
method Stationarity analysis using KM2O-Langevin theory and trend-based indicators for stationary periods, oscillator-based indicators for non-stationary periods. result Back testing confirms the strategy is a safe trading strategy with small maximum drawdown.