In what ways is human cognition language-augmented cognition?
How important is the human capacity for language for understanding what distinguishes human cognition from that of other animals? Work in the lab has been examining how categorization, memory, and reasoning are augmented by learning and using words. A recent direction of this work is to view words as high-level hypotheses within a hierarchical predictive system.
Review and theory papers describing this work can be found here, here, here, and here. You can also read some popular-media coverage from the New York Times, New Scientist, a review of a recent paper on an NPR blog or listen to Gary Lupyan talking to Shane Mauss on the Here We Are podcast
To what extent is basic perception influenced by knowledge and expectations?
We perceive as we do because our perceptual systems have been honed by evolution to transform energy into forms useful for guiding our actions. But the form of this transformation often depends on the organisms’s current needs. Work in the lab examines how knowledge and expectations can act as priors changing how people perceive things. Many of these investigations involve testing how language may augment (visual) perception. These studies take as inspiration Edward Sapir’s remark that “even comparatively simple acts of perception [may be] very much more at the mercy of the social patterns called words than we might suppose”.
Our work on this topic can be found here. You can also popular press coverage of some of this work here and here, and read a in-depth review of how such top-down effects on perception arise when one views the brain in a predictive-coding framework.
Why are there different languages? Do languages adapt to the needs and biases of their learners and users?
One of the most remarkable aspects of human language is its diversity. Although all human languages share certain design features, such as the use of discrete words and a compositional structure, languages vary enormously in their patterns of naming and in the grammatical devices they employ. What forces are responsible for creating these differences? Do languages diversify simply due to random drift, as has been long assumed? Or might there be some selection at work that drives languages apart in a way analogous to the forces that produce diversity in the biological realm?
We have been investigating how languages are affected by social and demographic factors such as the number and diversity of language-users. We term the idea that languages adapt to biases of their users, the linguistic-niche hypothesis. This work is funded by an NSF INSPIRE award. You can read more about this work in the New Scientist, the Economist, or read a recent chapter on this topic.
Why are some explanations especially satisfying? Why do different scientists prefer different theories to account for the same data?
Some explanations seem very compelling, even if they are factually wrong. Other explanations seem completely unsatisfying, even if they are, technically speaking, correct. A new line of research is examining what makes some explanations more satisfying than others and why some people prefer some types of explanations. In a related project, we are examining whether certain cognitive biases predict whether a given scientist prefers one type of theory/explanatory framework over another. This work is part of the Templeton-funded Metaknowledge network.
Openings in the lab
The lab is accepting graduate students for Fall, 2017. If you are interested, please send us an email. We are particularly looking for students with experience in deep neural networks and/or cultural evolution and agent-based modeling. If you are an undergraduate at UW-Madison interested in getting involved as a research assistant, please send an email as well.