Introduction

1. Preliminary Modeling Application to Decision Making Introduction Conclusions References Participants Results Reward Structure Scaffolding Across the Lifespan: Effects of Age, Task Complexity, and Pressure on Decision-Making Jessica A. Cooper1, Darrell A. Worthy2, Marissa A. Gorlick1 & W. Todd Maddox1 1The University of Texas at Austin Psychology Department; 2Texas A&M University Psychology Department • Data from Experiment 1 were fit with a baseline model, Softmax reinforcement learning model, and Extended Exploration Win-Stay Lose-Shift model. • The baseline model captures random responding5. • The Softmax RL model updates Expected Values of each option on each trial to develop probabilities for selecting each option6 • Basic WSLS has two free parameters, P(stay | win) and P(shift | loss). The Extended Exploration WSLS model has 4 free parameters: a win-stay and a lose-shift for initial and extended exploration trails. Rewards based on ten most recent responses using a sliding window: 0,1,1,[0,1,0,1,0,1,0,1,0,1]1,1,0 1: increasing option; 0: decreasing option • The scaffolding hypothesis of aging and cognition (STAC) suggests that the recruit of additional frontal regions occurs: • Across the lifespan in response to challenge • In older adults to compensate to age-related neural declines1 • We found an age-related performance advantage in history-dependent decision making2that we attributed to compensatory scaffolding and increased monitoring of the reward environment by older adults. • The presence of an age-based “crunch” point when additional resources cannot be recruited3 suggests that age advantage may be fragile. • The Double Whammy hypothesis of pressure suggests that pressure increases distraction and monitoring, decreasing performance when monitoring cannot be increased beyond WM requirements4. • WSLS model was the best fit for all groups. • The relative fit of WSLS to Softmax was increased in optimally performing groups (OA no pressure YA pressure) Younger adults age 18-25 from the University of Texas and Texas A&M University and older adults age 60-80 from the Austin and College Station communities. Older adults are less than 2 SD from the norm on tests of cognitive impairment. • Significant interaction between age and pressure for the extended lose-shift parameter (p<.001). • Extended lose shift parameter values positively correlated with performance r(88)=.432, (p<.001). Experiment 1: Pressure and Age This research examines the effects of pressure and task difficulty across the lifespan in a history dependent decision-making task, the Mars Farming task. We propose that additional task demands (pressure, increased task complexity) will cause older adults to reach their crunch point and performance will suffer. However, younger adults may perform better with increased task demands due to scaffolding and a shift toward heuristic-based decision making. We hypothesize that age pressure, and task difficulty all increase demands of the task, affecting neural activation. Experiment 1: 2 (pressure) x 2 (age) No pressure: 2 options (1 inc, 1 dec) Pressure: 2 options, performance bonus and fictitious partner Experiment 2: 2 (difficulty) x 2 (age-group) Medium: 4 options (2 inc, 2 dec) Hard: 4 options (1 inc. 3 dec) • Significant Age x Pressure interaction • (p<.001). • OA performed worse under pressure than no pressure (p<.05); YA performed better under pressure than no pressure (p<.05) • Older adults outperform younger adults in a history-dependent task, but the advantage depends on situational factors and task difficulty. • Younger adults under pressure and with increased task difficulty perform better on this task than older adults. • Further increasing task difficulty may also cause younger adult performance to decline. • Optimally performing groups show more sensitivity to losses after extended exploration. Experiment 2: Task Difficulty and Age • Significant Age x Task difficulty interaction (p<.01) • Older adults perform better with one decreasing option than two (p<.05) and better with two than three (p<.05) • Younger adults perform better with two decreasing options than one (p<.01) and better with two than three (p<.001) • Park, D. & Reuter-Lorenz, P.A. (2009).  The Adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173-96. • Worthy, D.A., Gorlick, M.A., Pacheco, J.L., Schnyer, D.M., & Maddox, W.T. (2011).  With Age Comes Wisdom: Decision Making in Younger and Older Adults.  Psychological Science,22(11), 1375-1380. • Reuter-Lorenz, P.A. & Cappell, K. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science. 17(3),177-182. 6. Sutton, R. S. and Barto A. G. (1998). Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MA. • Beilock, S. L., & Gray, R. (2007). Why do athletes “choke” under pressure? In G. Tenenbaum and R.C. Eklund (Eds.), Handbook of sport psychology, 3rd Ed. (pp. 425-444). Hoboken, NJ: John Wiley & Sons. • Worthy, D.A., Otto, A.R., & Maddox, W.T. (in press). Working-Memory Load and Temporal Myopia in Dynamic Decision-Making. Journal of Experimental Psychology: Learning, Memory, & Cognition. • Sutton, R. S. and Barto A. G. (1998). Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MA. Special thanks to Brittany Nix and Taylor Denny for their help with this project. For more information, please visit: http://homepage.psy.utexas.edu/homepage/Group/MaddoxLAB/index.htm This research was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program to JAC and NIDA grant DA032457 to W. Todd Maddox