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.
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.
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.
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.
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.
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.
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.
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…
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.
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.
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.
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.
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.
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.
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.
This paper uses counterfactual thinking to improve multi-agent reinforcement learning.
problem Improving decision-making in multi-agent environments.
method Proposes a deep reinforcement learning model with counterfactual thinking to generate and evaluate multiple actions.
result Counterfactual thinking enhances agents' performance in multi-agent environments, leading to higher rewards and fair competition.
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…
Proposes MLCNN for better multivariate time series forecasting.
problem Challenges in forecasting multivariate time series, especially the limitation of predicting only one future moment.
method MLCNN, a multi-task deep learning framework inspired by Construal Level Theory, fuses future visions of near and distant future predictions.
result Significant improvements in forecasting accuracy (4.59% RMSE reduction, 6.87% MAE reduction) on real-world datasets.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
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.
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.
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.
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…
Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kerne…
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…
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.
Local reservoir model explains decision consistency in learning.
problem Understanding decision consistency in learning processes.
method Proposes a local reservoir model to explain decision consistency.
result Size of local reservoir affects decision consistency.
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.