17 research outputs found
ELABORASI MODEL PEMBELAJARAN THINKING PROBLEM SOLVING DENGAN TWO STAY TWO STRAY DALAM MEMBANGUN KEMAMPUAN GENERALISASI SISWA
ELABORASI MODEL PEMBELAJARAN THINKING PROBLEM SOLVING DENGANTWO STAY TWO STRAY DALAM MEMBANGUN KEMAMPUAN GENERALISASISISW
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks
A key property of linguistic conventions is that they hold over an entire
community of speakers, allowing us to communicate efficiently even with people
we have never met before. At the same time, much of our language use is
partner-specific: we know that words may be understood differently by different
people based on our shared history. This poses a challenge for accounts of
convention formation. Exactly how do agents make the inferential leap to
community-wide expectations while maintaining partner-specific knowledge? We
propose a hierarchical Bayesian model to explain how speakers and listeners
solve this inductive problem. To evaluate our model's predictions, we conducted
an experiment where participants played an extended natural-language
communication game with different partners in a small community. We examine
several measures of generalization and find key signatures of both
partner-specificity and community convergence that distinguish our model from
alternatives. These results suggest that partner-specificity is not only
compatible with the formation of community-wide conventions, but may facilitate
it when coupled with a powerful inductive mechanism.Comment: CogSci 202
The growth and form of knowledge networks by kinesthetic curiosity
Throughout life, we might seek a calling, companions, skills, entertainment,
truth, self-knowledge, beauty, and edification. The practice of curiosity can
be viewed as an extended and open-ended search for valuable information with
hidden identity and location in a complex space of interconnected information.
Despite its importance, curiosity has been challenging to computationally model
because the practice of curiosity often flourishes without specific goals,
external reward, or immediate feedback. Here, we show how network science,
statistical physics, and philosophy can be integrated into an approach that
coheres with and expands the psychological taxonomies of specific-diversive and
perceptual-epistemic curiosity. Using this interdisciplinary approach, we
distill functional modes of curious information seeking as searching movements
in information space. The kinesthetic model of curiosity offers a vibrant
counterpart to the deliberative predictions of model-based reinforcement
learning. In doing so, this model unearths new computational opportunities for
identifying what makes curiosity curious
Towards a computational psychiatry of juvenile obsessive-compulsive disorder
Obsessive-Compulsive Disorder (OCD) most often emerges during adolescence, but we know little about the aberrant neural and cognitive developmental mechanisms that underlie its emergence during this critical developmental period. To move towards a computational psychiatry of juvenile OCD, we review studies on the computational, neuropsychological and neural alterations in juvenile OCD and link these findings to the adult OCD and cognitive neuroscience literature. We find consistent difficulties in tasks entailing complex decision making and set shifting, but limited evidence in other areas that are altered in adult OCD, such as habit and confidence formation. Based on these findings, we establish a neurocomputational framework that illustrates how cognition can go awry and lead to symptoms of juvenile OCD. We link these possible aberrant neural processes to neuroimaging findings in juvenile OCD and show that juvenile OCD is mainly characterised by disruptions of complex reasoning systems
Exploration in the wild
Making good decisions requires people to appropriately explore their available options and generalize what they have learned.
While computational models have successfully explained exploratory behavior in constrained laboratory tasks, it is unclear to
what extent these models generalize to complex real world choice problems. We investigate the factors guiding exploratory
behavior in a data set consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We
find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. We find
evidence for several theoretical predictions: (1) customers engage in uncertainty-directed exploration, (2) they adjust their level
of exploration to the average restaurant quality in a city, and (3) they use feature-based generalization to guide exploration
towards promising restaurants. Our results provide new evidence that people use sophisticated strategies to explore complex,
real-world environments
Human complex exploration strategies are enriched by noradrenaline-modulated heuristics
An exploration-exploitation trade-off, the arbitration between sampling a lesser-known against a known rich option, is thought to be solved using computationally demanding exploration algorithms. Given known limitations in human cognitive resources, we hypothesised the presence of additional cheaper strategies. We examined for such heuristics in choice behaviour where we show this involves a value-free random exploration, that ignores all prior knowledge, and a novelty exploration that targets novel options alone. In a double-blind, placebo-controlled drug study, assessing contributions of dopamine (400mg amisulpride) and noradrenaline (40mg propranolol), we show that value-free random exploration is attenuated under the influence of propranolol, but not under amisulpride. Our findings demonstrate that humans deploy distinct computationally cheap exploration strategies and where value-free random exploration is under noradrenergic control