Joseph Austerweil

Joseph Austerweil
University of California, Berkeley | UCB · Department of Psychology

About

99
Publications
10,412
Reads
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950
Citations
Citations since 2016
19 Research Items
488 Citations
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2016201720182019202020212022020406080
2016201720182019202020212022020406080
2016201720182019202020212022020406080
Education
August 2007 - May 2012
University of California, Berkeley
Field of study
  • Psychology
August 2003 - May 2007
Brown University
Field of study
  • Applied Mathematics-Computer Science

Publications

Publications (99)
Preprint
Over the last few decades, psychologists have increasingly found that the mind stores and uses the statistics of its environment. However, less work has analyzed whether the environmental statistics have changed and what that would imply for the mind. In this chapter, we consider human memory as the solution to the computational problem of predicti...
Article
Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Baye...
Article
Theory of mind enables an observer to interpret others' behavior in terms of unobservable beliefs, desires, intentions, feelings, and expectations about the world. This also empowers the person whose behavior is being observed: By intelligently modifying her actions, she can influence the mental representations that an observer ascribes to her, and...
Preprint
Full-text available
Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectations people have about a learner when they teach them in an online manner using rewards and punishment. We focus on a common reinforcement learning method, Q-learning, an...
Article
Full-text available
Are bilinguals more creative than monolinguals? Some prior research suggests bilinguals are more creative because the knowledge representations for their second language are similarly structured to those of highly creative people. However, there is contrasting research showing that the knowledge representations of bilinguals’ second language are ac...
Article
Full-text available
The verbal fluency task—listing words from a category or words that begin with a specific letter—is a common experimental paradigm that is used to diagnose memory impairments and to understand how we store and retrieve knowledge. Data from the verbal fluency task are analyzed in many different ways, often requiring manual coding that is time intens...
Article
Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the probl...
Article
Full-text available
The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, incl...
Article
Full-text available
A defining characteristic of Alzheimer’s disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of...
Preprint
Full-text available
The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, incl...
Article
One popular and classic theory of how the mind encodes knowledge is an associative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individua...
Article
Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been p...
Preprint
One popular and classic theory of how the mind encodes knowledge is an as- sociative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individ...
Conference Paper
Full-text available
This paper proposes a Bayesian account of asymmetries found in speech perception: In many languages, listeners show greater sensitivity if a non-coronal sound (/b/, /p/, /g/, /k/) is changed to coronal sounds (/d/, /t/) than vice versa. The currently predominant explanation for these asymmetries is that they reflect innate constraints from Universa...
Conference Paper
Full-text available
This paper proposes a Bayesian account of asymmetries found in speech perception: In many languages, listeners show greater sensitivity if a non-coronal sound (/b/, /p/, /g/, /k/) is changed to coronal sounds (/d/, /t/) than vice versa. The currently predominant explanation for these asymmetries is that they reflect innate constraints from Universa...
Conference Paper
Full-text available
Human behavior is frequently guided by social and moral norms; in fact, no societies, no social groups could exist without norms. However, there are few cognitive science approaches to this central phenomenon of norms. While there has been some progress in developing formal representations of norm systems (e.g., deontological approaches), we do not...
Article
How does the visual system recognize images of a novel object after a single observation despite possible variations in the viewpoint of that object relative to the observer? One possibility is comparing the image with a prototype for invariance over a relevant transformation set (e.g., translations and dilations). However, invariance over rotation...
Article
Full-text available
The Sapir-Whorf hypothesis holds that our thoughts are shaped by our native language, and that speakers of different languages therefore think differently. This hypothesis is controversial in part because it appears to deny the possibility of a universal groundwork for human cognition, and in part because some findings taken to support it have not...
Article
Representations are a key explanatory device used by cognitive psychologists to account for human behavior. Understanding the effects of context and experience on the representations people use is essential, because if two people encode the same stimulus using different representations, their response to that stimulus may be different. We present a...
Article
Generalization–deciding whether to extend a property from one stimulus to another stimulus–is a fundamental problem faced by cognitive agents in many different settings. Shepard (1987) provided a mathematical analysis of generalization in terms of Bayesian inference over the regions of psychological space that might correspond to a given property....
Article
The Bayesian generalization framework has been successful in explaining how people generalize a property from a few observed stimuli to novel stimuli, across several different domains. To create a successful Bayesian generalization model, modelers typically specify a hypothesis space and prior probability distribution for each specific domain. How-...