CogOnt Seminar: B. Bruya/J. Haas

Part of our ongoing online seminar series.  See the full list of talks here.

Register using this link: https://pitt.zoom.us/webinar/register/WN_KMNKu4fmQ9Wh5ZjvXJ3qQA

Please note, registration will be for the entire seminar series.

Brian Bruya (Eastern Michigan University), “Diverse Origins of Cognitive Ontology”

ABSTRACT:

The current method of testing inherited notions of cognitive ontology through the instruments and methods of cognitive neuroscience is vulnerable to confirmation bias and resistant to contrasting alternatives.

This talk will discuss effortless attention as an example of challenging a dominant paradigm of cognitive ontology from the perspective of non-standard sources and will discuss some of the findings from very recent attempts to study effortless attention and introduce it as a legitimate element of cognitive ontology. 

I will first introduce the notion of attention as effort, advocated by Daniel Kahneman (1973), a position that stands as the current paradigm of attention in the cognitive sciences. I will then problematize this position by introducing a notion of effortlessness from classical Daoism, then an allied notion from the psychologist Mihaly Csikszentmihalyi. Finally, I will turn my focus to the concept of postvoluntary attention, first postulated by the Russian psychologist Nikolaj Dobrynin. I will introduce this idea and discuss its relevance to current research in attention and education. 

Julia Haas (Rhodes College), “Computational Cognitive Ontology”

ABSTRACT:

Much of the recent literature on cognitive ontology proposes a kind of cognitive ontological ‘splitting,’ e.g., when the neural reuse hypothesis proposes that activities of different brain regions recombine to support performance across many different task domains (Anderson 2010, 2014). This paper explores a new way of putting things back together (i.e., ‘lumping’), namely, by taxonomizing computational solutions in the brain rather than relying on traditional psychological capacities. Specifically, I argue that the brain consistently reuses computational solutions to solve certain types of problems, e.g., as when a small suite of reward functions guides selection between competing states of affairs in processes ranging from sensation to practical deliberation. Hence, whereas neural reuse suggests that a single neural structure can support several discrete psychological functions, so computational reuse proposes that seemingly discrete psychological functions are supported by a single computational solution. I then illustrate how we can leverage this notion of computational reuse to revise our extant cognitive ontologies, for example, contrasting the traditional categories of ‘desire’ and ‘motivation’ with the more computationally-informed notion of ‘valuation.’