Danielle Navarro | Andrew Perfors

Latest

Visual and affective multimodal models of word meaning in language and mind
The exploration advantage: Children's instinct to explore allows them to find information that adults miss
Where the truth lies: How sampling implications drive deception without lying
The "Small World of Words" English word association norms for over 12,000 cue words
Why do echo chambers form? The role of trust, population heterogeneity, and objective truth
Do additional features help or hurt category learning? The curse of dimensionality in human learners
Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization
When extremists win: Cultural transmission via iterated learning when populations are heterogeneous
Quantifying sentence acceptability measures: Reliability, bias, and variability
Learning word meaning with little means: An investigation into the inferential capacity of paradigmatic information
Representational and sampling assumptions drive individual differences in single category generalisation
Stronger evidence isn't always better: A role for social inference in evidence selection and interpretation
Not every credible interval is credible: On the importance of robust methods in Bayesian data analysis
Predicting human similarity judgments with distributional models: The value of word associations
Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory
A cognitive analysis of deception without lying
When do learned transformations influence similarity and categorization?
When extremists win: On the behavior of iterated learning chains when priors are heterogeneous
Sensitivity to hypothesis size during information search
The structure of sequential effects
Predicting human similarity judgments with distributional models: The value of word associations
Leaping to conclusions: Why premise relevance affects argument strength
The helpfulness of category labels in semi-supervised learning depends on category structure
Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners
Structure at every scale: A semantic network account of the similarities between very unrelated concepts
Evidence for widespread thematic structure in the mental lexicon
How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning
Language evolution can be shaped by the structure of the world
Adaptive information source selection during hypothesis testing
People are sensitive to hypothesis sparsity during category discrimination
People ignore token frequency when deciding how widely to generalize
The relevance of labels in semi-supervised learning depends on category structure
Learning time-varying categories
The role of sampling assumptions in generalization with multiple categories
What Bayesian modelling can tell us about statistical learning: What it requires and why it works
Anticipating changes: Adaptation and extrapolation in category learning
Strong structure in weak semantic similarity: A graph based account
Epistemic trust: Modeling children's reasoning about others' knowledge and intent
Hypothesis generation, the positive test strategy, and sparse categories
Enlightenment grows from fundamentals: Comment on Jones and Love
Humans use different statistics for sequence analysis depending on the task
Language evolution is shaped by the structure of the world: An iterated learning analysis
To catch a liar: The effects of truthful and deceptive testimony on inferential learning
Why are some word orders more common than others? A uniform information density account
Similarity, feature discovery, and the size principle
How does the presence of a label affect attention to other features?
Social context effects on the impact of category labels
Confirmation bias is rational when hypotheses are sparse
Joint acquisition of word order and word reference
Learning time-varying categories
When to walk away: The effect of variability on keeping options viable