Summary
Motivation is central to learning and development, yet existing theories are fragmented across disciplines.
Developmental theories (Deci & Ryan, 1985; Gweon, 2021; Sobel & Sommerville, 2009) emphasize basic psychological needs and social learning while educational frameworks highlight children’s beliefs about their identity, intelligence, and task value (Silverman et al., 2023; Yeager & Dweck, 2012; Wigfield & Eccles, 2000).
Computational approaches formalize motivation as cost-benefit decisions (Shenhav et al., 2017) and intrinsic rewards as drivers of exploration (Haber, 2023; Nussenbaum et al., 2017).
Can these theories be integrated into a unified account of motivation across the lifespan? This preconference synthesizes frameworks bridging developmental psychology, education, and computational modeling to advance the cognitive science of motivation.
Invited Speakers
Discussants
Tentative Schedule
| Time | Session |
|---|---|
| 12:30 – 12:40 | Opening Remarks |
| 12:40 – 1:05 | Talk 1 by Jessica Sommerville |
| 1:05 – 1:20 | Student Flashtalk I by Bella Fascendini |
| 1:20 – 1:45 | Talk 2 by Kate Nussenbaum |
| 1:45 – 1:55 | Break |
| 1:55 – 2:20 | Talk 3 by Nicholas Haber |
| 2:20 – 2:35 | Student Flashtalk II by Elaine Wang |
| 2:35 – 3:00 | Talk 4 by David Silverman |
| 3:00 – 3:30 | Closing Discussion facilitated by Alison Gopnik, Hyowon Gweon, & Julia Leonard |