scholarly journals Humans monitor learning progress in curiosity-driven exploration

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexandr Ten ◽  
Pramod Kaushik ◽  
Pierre-Yves Oudeyer ◽  
Jacqueline Gottlieb
Keyword(s):  
Empirical Evidence ◽  
Free Choice ◽  
Learning Activities ◽  
Human Cognition ◽  
Experimental Paradigm ◽  
Percent Correct ◽  
Intrinsic Goals ◽  
Computational Research ◽  
Learning Progress ◽  
Best Fit

AbstractCuriosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g., percent correct) and learning progress, that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on unlearnable activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a free-choice experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include a learning-progress component provide the best fit to task selection data. These results bridge the research in artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and introduce tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

scholarly journals Humans monitor learning progress in curiosity-driven exploration

2020 ◽  
Author(s):  
Alexandr Ten ◽  
Pramod Kaushik ◽  
Pierre-Yves Oudeyer ◽  
Jacqueline Gottlieb
Keyword(s):  
Computational Modeling ◽  
Empirical Evidence ◽  
Learning Activities ◽  
Human Cognition ◽  
Experimental Paradigm ◽  
Percent Correct ◽  
Intrinsic Goals ◽  
Computational Research ◽  
Learning Progress ◽  
Best Fit

Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g. percent correct, or PC) and learning progress (LP), that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on random activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a novel experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include an LP component provide the best fit to task selection data. These results provide a new bridge between research on artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and provide new tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

scholarly journals Humans monitor learning progress in curiosity-driven exploration

2020 ◽  
Author(s):  
Alexandr Ten ◽  
Pramod Kaushik ◽  
Pierre-Yves Oudeyer ◽  
Jacqueline Gottlieb
Keyword(s):  
Computational Modeling ◽  
Empirical Evidence ◽  
Time Scales ◽  
Human Cognition ◽  
Experimental Paradigm ◽  
Percent Correct ◽  
Intrinsic Goals ◽  
Computational Research ◽  
Learning Progress ◽  
Best Fit

Curiosity-driven learning is foundational to human cognition. Byenabling humans to autonomously decide when and what to learn,curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms drivingpeople to set intrinsic goals, when they are free to explore multiplelearning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures(e.g. percent correct, or PC) and learning progress (LP), that could beused as intrinsic utility functions to efficiently organize exploration.Such intrinsic utilities constitute computationally cheap but smartheuristics to prevent people from laboring in vain on random activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideasby means of a novel experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include an LP component provide the best fit to task selection data. These results provide a new bridge between research on artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and provide new tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales/

Neural Correspondence to Human Cognition from Analysis to Intuition – Implications of Display Design for Cognition

2017 ◽  
Vol 61 (1) ◽  
pp. 51-55 ◽  
Author(s):  
Joseph K. Nuamah ◽  
Younho Seong
Keyword(s):  
Objective Measure ◽  
Mental Effort ◽  
Human Cognition ◽  
Behavioral Measures ◽  
Display Design ◽  
Percent Correct ◽  
Task Load ◽  
Frontal Midline Theta ◽  
General Difference ◽  
Nasa Task Load Index

Psychophysiological measures can be used to determine whether a particular display produces a general difference in brain function. Such information might be valuable in efforts to improve usability in display design. In this preliminary study, we aimed to use the electroencephalography (EEG) task load index (TLI), given by the ratio of mean frontal midline theta energy to mean parietal alpha energy, to provide insight into the mental effort required by participants performing intuition-inducing and analysis-inducing tasks. We employed behavioral measures (reaction time and percent correct), and a subjective measure (NASA-Task Load Index) to validate the objective measure (TLI). The results we obtained were consistent with our hypothesis that mental effort required for analysis-inducing tasks would be different from that required for intuition-inducing tasks. Although our sample size was small, we were able to obtain a significant positive correlation between NASA-Task Load Index and TLI.

Multimodal constructs – multimodal constructions? The role of constructions in the working memory

2017 ◽  
Vol 3 (s1) ◽  
Author(s):  
Thomas Hoffmann
Keyword(s):  
Working Memory ◽  
Empirical Evidence ◽  
Human Cognition ◽  
Long Term Memory ◽  
Symbolic System ◽  
Term Memory ◽  
The Status ◽  
Precise Role

AbstractLanguage is a symbolic system, whose basic units are arbitrary and conventionalized pairings of form and meaning. In fact, in light of substantive empirical evidence, Construction Grammar approaches advocate the view that not only words but all levels of grammatical description – from morphemes, words, and idioms to abstract phrasal patterns as well as larger discourse patterns – comprise form-meaning pairings, which are collectively referred to as constructions. In this paper, I will discuss the status of multimodal usage-events (multimodal constructs) for the potential entrenchment of multimodal constructions and their implications for human cognition in general. As I will argue, constructionist approaches need to pay more attention to the role of the working memory in assembling and interpreting constructions. Drawing on verbal as well as gesture constructions, I will show that it is essential to distinguish entrenched constructions that are stored in the long-term memory from form-meaning pairings that are assembled in the working memory (online constructions). Once this distinction is made, the precise role of multimodal constructs and the nature of multimodal constructions can finally be disentangled.

scholarly journals How Far we Can Go Without Looking Under the Skin: The Bounds of Cognitive Science

2015 ◽  
Vol 40 (1) ◽  
pp. 91-109
Author(s):  
Łukasz Afeltowicz ◽  
Witold Wachowski
Keyword(s):  
Cognitive Science ◽  
Empirical Evidence ◽  
Distributed Cognition ◽  
Extended Mind ◽  
Cognitive System ◽  
Human Cognition ◽  
External Representations

Abstract The aim of this paper is to discuss the concept of distributed cognition (DCog) in the context of classic questions posed by mainstream cognitive science. We support our remarks by appealing to empirical evidence from the fields of cognitive science and ethnography. Particular attention is paid to the structure and functioning of a cognitive system, as well as its external representations. We analyze the problem of how far we can push the study of human cognition without taking into account what is underneath an individual’s skin. In light of our discussion, a distinction between DCog and the extended mind becomes important.

scholarly journals Accounting Assistance for Treasurers and Teachers for Improvement and Strengthening of Mekarwangi Elementary School, Cisauk, Tangerang [Peningkatan dan Penguatan SDN Mekarwangi, Cisauk, Tangerang Dalam Bentuk Pendampingan Akuntansi Untuk Bendahara dan Pengajar]

2019 ◽  
Vol 2 ◽  
pp. 225
Author(s):  
Bahtiar Effendi
Keyword(s):  
Elementary School ◽  
Empirical Evidence ◽  
Community Service ◽  
Quantitative Methods ◽  
Qualitative Data ◽  
School Community ◽  
Learning Activities ◽  
Research Activities ◽  
Service Activities ◽  
Accounting Reports

This community service review provides empirical evidence of the improvement and strengthening efforts at Mekarwangi Elementary School, Cisauk, Tangerang through accounting assistance activities for treasurers and teachers. This review is a continuation of previous research conducted on August 6 to November 27, 2017, through learning activities of accounting reports in the Mekarwangi Elementary School, that succeeded to proof the activities able to improve the understanding level of treasurers and teachers of the Mekarwangi Elementary School. Community service activities, participants, and the contribution that are given to resolve problems of the institution partner were the novelty from the previous research activities. This study uses descriptive quantitative methods where the data is described as qualitative data that cannot be generalized. The results of this study showed the assistance accounting activities is quite effective in the improvement and strengthening effort of SDN Mekarwangi, Cisauk, Tangerang.

Investigating human prediction: sequence termination provides insights into predictive statistics

2021 ◽  
Author(s):  
Isaac Treves
Keyword(s):  
Computational Models ◽  
Frequency Estimation ◽  
Reaction Time Task ◽  
Prior Exposure ◽  
Human Cognition ◽  
Passive Exposure ◽  
Future Work ◽  
Best Fit ◽  
Prediction Sequence ◽  
Insight Into

Prediction is a fundamental process in human cognition. Prediction means extracting one or more statistics from the distribution of past inputs and using that information to make a decision. What are the statistics underlying human predictions, and how do they change with training? To investigate these questions, we designed a sequence termination task, where participants watch temporally unfolding sequences and terminate them when they can predict the next item. We then test how well the participants’ termination points are predicted by computational models. We contrast frequency estimation models (How often did this symbol appear in the sequence?), transition models (How often did symbol A follow symbol B?), and a chunking model (What are the patterns of symbols?). In an online experiment with 65 adults, we find that participants are best fit by a transition-counting model. To assess the effect of training, we manipulated passive exposure to the sequences prior to the sequence termination task. Contrary to our expectations, prior exposure to sequences had no effect on termination performance– whether tested statistically or computationally, and despite good power. Lastly, training specifically on the termination task may shift responses towards chunking. These results provide insight into the representations, or information in mind, behind prediction. However, the lack of an effect of prior exposure makes it clear that sequence termination measures explicit, or conscious, prediction. Future work could examine whether representations in explicit prediction tasks like sequence termination are different from implicit, or unconscious, tasks like the serial reaction time task.

scholarly journals What do RL Models Measure? Interpreting Model Parameters in Cognition and Neuroscience

2021 ◽  
Author(s):  
Maria Eckstein ◽  
Linda Wilbrecht ◽  
Anne Collins
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Cognitive Science ◽  
Empirical Evidence ◽  
Cognitive Processes ◽  
Future Research ◽  
Model Parameters ◽  
Research Needs ◽  
Research Fields ◽  
Computational Research

Reinforcement learning (RL) is a concept that has been invaluable to research fields including machine learning, neuroscience, and cognitive science. However, what RL entails partly differs between fields, leading to difficulties when interpreting and translating findings.This paper lays out these differences and zooms in on cognitive (neuro)science, revealing that we often overinterpret RL modeling results, with severe consequences for future research. Specifically, researchers often assume---implicitly---that model parameters \textit{generalize} between tasks, models, and participant populations, despite overwhelming negative empirical evidence for this assumption. We also often assume that parameters measure specific, unique, and meaningful (neuro)cognitive processes, a concept we call \textit{interpretability}, for which empirical evidence is also lacking. We conclude that future computational research needs to pay increased attention to these implicit assumptions when using RL models, and suggest an alternative framework that resolves these issues and allows us to unleash the potential of RL in cognitive (neuro)science.

Our metaphorical minds

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lily I-wen Su
Keyword(s):  
Cultural Identity ◽  
Empirical Evidence ◽  
Chinese Culture ◽  
Human Cognition ◽  
Conceptual Metaphors ◽  
Common Bond ◽  
The World ◽  
Group Member

Abstract Aside from metaphor being an important language device reflecting human cognition, it also provides a window into the understanding of culture (Kövecsec 2019). Language is a function of culture because it is a form of the verbal and nonverbal systems by which a group member can communicate with another member. Language bonds together people of the same cultural identity because it functions as a common bond between people who have the same linguistic heritage. As argued in Verhagen (2008), values of one’s understanding of the world he lives in may in turn be influenced by the conceptual metaphors he unconsciously holds to visualize his world. Verhagen has provided a valuable standpoint, yet it is western-centered and European-oriented. By investigating different Chinese metaphors in proverbs, food, marriage, and time expressions, this paper intends to address the following questions: Does conceptualization via metaphors reflect any specific Chinese mode of thinking? Does such conceptualization give a taste of Chinese culture? What kind(s) of theoretical and pragmatic implications can be derived from our line of investigation? It is thus hoped that this paper may provide empirical evidence with reference to mappings between thought and language, which in turn, may serve as a way to explore culture.

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