“Hot” and “Cold” Executive Function

1. “Hot” and “Cold” Executive Function Matthew Winchester1 Mentor: Marilyn Welsh2, Ph.D. Sponsor: Newmont Mining Abstract Executive function (EF) is a broad term for the higher order thought processes that are used in new situations, things like problem-solving, working memory, inhibitory control, and planning. It can be separated into two divisions, “hot” EF dealing with emotionally and motivationally influenced decisions, and “cold” EF dealing with purely cognitive problem solving. This study examined the various similarities and differences between “hot” and “cold” executive function, and attempted to make “cold” tasks “hotter” through giving traditional “cold” EF tasks in an incentive condition. A total of 10 volunteer participants from the 2011 FSI program, ranging in age from 15 to 17, were tested through the use of the Iowa Gambling Task (IGT), Tower of London (TOL), and Letter-Number Sequencing (LNS). The IGT represented a typical “hot” task, while both TOL and LNS were “cold” tasks. Performance on the TOL and LNS under non-incentive conditions did not correlate, and neither did the TOL or LNS under non-incentive conditions. However, the TOL task under incentive conditions did strongly correlate with scores on the IGT, indicating that the “cold” task became “hotter”. Future studies in this area should include a larger sample size in order to gather more definitive data and gain stronger statistical power. Frontier of Science 20111, Dept. of Psychology2, UNC • Iowa Gambling Task • Measure the risky decision making of the participants in a computerized, hypothetical gambling situation. • “Hot” EF task. • Requires the participant to makes a series of selections from an electronically delivered (computerized) display of four card decks, A, B, C, or D. • When a card is chosen by a mouse click, it results in a hypothetical win or a loss of some money and the task taker’s running total of the amount of money won and the amount of money borrowed is displayed on the top of the computer screen. • 100 trials given in 5 blocks of 20. • Two “good” decks (C and D) and two “bad” decks (A and B). • “Bad” deck has a high reward but occasionally results in a very large loss. The “good” deck has minimal reward but occasionally results in only a small penalty. • The participant is unable to predict when they will lose or win money; however, they presumably can figure out over time which are good decks and which are bad decks. • Tower of London (fig. 2) • “Cold” EF task. • The point of the task is to move the balls, one at a time, to build the goal pattern that is presented to the student on a large card. The balls are set up in a “starting pattern” and the participant is told how many moves (4, 5, or 6) it will take to move the balls into the goal pattern. • The 15 odd problems were given under incentive conditions, and the 15 even under standard conditions. • Letter-Number Sequencing • “Cold” EF task, specifically working memory • The participant is read a combination of numbers and letters and is asked to recall the numbers first in ascending order and then the letters in alphabetical order. • There are seven items ranging from 2-letter/number sequences (e.g., B-7) to 8-letter/number sequences (e.g., S-2-L-8-B-1-G-7). • 7 trials were given under each condition Fig. 3 Fig. 5 Fig. 4 Discussion The primary purpose of this experiment was to study the effects of incentive and non-incentive conditions on traditionally “cold” executive function tasks (Tower of London, Letter-Number Sequence), and to see that, while given during incentive conditions, the tasks would correlate more closely to a “hot” EF task (Iowa Gambling Task). The TOL and LNS would effectively become “hotter” under the incentive conditions. The research question addressed how the incentive manipulation would influence the performance (number correct) on the TOL and LNS tasks. The paired-samples t-test performed on the data collected showed almost no differences in performance on the TOL or the LNS tasks under incentive and non-incentive conditions. Hypothesis 1 stated that the scores on the TOL and LNS tasks given under non-incentive conditions would be moderately correlated with each other because they are both considered to be “cold” EF tasks. Based on the results of the experiment, the hypothesis was not supported by the data. Correlational analysis revealed a non-significant correlation. Hypothesis 2 said that the scores on the TOL and LNS tasks given under non-incentive conditions will be correlated with the “hot” EF task, IGT, at a low magnitude. This hypothesis was also unsupported by the data, with only an insignificant correlation for both the TOL and LNS. However, an interesting trend was seen between the TOL and Block 1 of the IGT, where a strong negative correlation was seen. This finding suggests that participants who performed better on the TOL under non-incentive conditions did worse (made more risky choices) on the beginning 20 trials of the IGT. Similar to Hypothesis 2, Hypothesis 3 stated that the scores on the TOL and LNS tasks, but given under incentive conditions, will be correlated with the “hot” EF task, IGT, at a moderate to high magnitude. Correlational analysis on the LNS under incentive conditions against the individual IGT trials and total net score showed a very small, non-significant correlation that ranged from being positive to negative. However, the TOL under incentive conditions showed a significant correlation to Blocks 2-5 and the net total of the IGT. These are the most important findings of this study, and although much more research does need to be performed, it does indicate that under incentive conditions, the TOL (a “cold” task), did become “hotter” by correlating more closely with the IGT (a “hot” task). Future studies involving “hot” and “cold” executive function can improve on many of the flaws seen in this experiment. Most importantly, a much larger sample must be tested, in order to collect more data, and eliminate many of the problems seen with testing a small sample, such as outliers strongly effecting the correlations, etc. Although this study shows little practical implications, mainly due to the small sample size, it does show the need for further research in this area of study. “Hot” and “cold” executive function is still being studied, and larger experiments are critical to understanding the many similarities and differences between them. Finding a strong correlation between the TOL under incentive conditions and IGT (while there was none between the TOL under non-incentive conditions and the IGT) is a very interesting result, and further experimentation could support or hurt this finding. Figure 1. The prefrontal cortex is thought to be the location of executive function • Introduction • Executive function (EF) consists of the higher order thought processes that are essential in novel situations, such as problem-solving, working memory, inhibitory control, planning, etc. (Gilbert & Burgess, 2008), which are thought to occur in the frontal lobe region of the brain (fig. 1)(Gilbert & Burgess, 2008). The prefrontal cortex consists of the front most part of the frontal lobes, and is thought to be the part of the brain that is responsible for executive function. The prefrontal cortex can be divided into several sections: the dorsolateral (responsible for “cold” EF), the orbitofrontal (responsible for “hot” EF), and the frontopolar (theorized to be the site of cognitive branching) (Hongwanishkul et al., 2010). Executive function can be further divided into two groups, both “hot” and “cold” executive function, to describe the emotional and cognitive aspects associated with each respectively (Prencipe et al., 2011). • The “hot” EF deals with emotionally and motivationally influenced decisions, and “cold” EF concerns cognitive problem solving. “Hot” executive function involves traditional EF when there is a strong motivation to perform well, especially emotional or motivational influence (Hongwanishkul et al., 2010). “Cold” (or “cool”) executive function involves the purely cognitive aspect of EF, where there is little or no emotional or consequential influence. • The purpose of this study was to observe executive functions (EF) under incentive and non-incentive conditions, using a traditionally “hot” task (Iowa Gambling Task) and “cold” tasks (Tower of London, Letter-Number Sequencing). Through the use of incentive and non-incentive settings, it is predicted that these “cold” tasks will become “hotter”. The data collected from our study will be used to answer these three hypotheses, as well as our research question: • Research Question (no apriori hypothesis): How will the incentive manipulation influence the performance (number correct) on the TOL and LNS tasks? • Hypothesis 1: The scores on the TOL and LNS tasks given under non-incentive conditions will be moderately correlated with each other because they are both considered to be “cold” EF tasks. • Hypothesis 2: The scores on the TOL and LNS tasks given under non-incentive conditions will be correlated with the “hot” EF task, IGT, at a low magnitude. • Hypothesis 3: The scores on the TOL and LNS tasks given under incentive conditions will be correlated with the “hot” EF task, IGT, at a moderate to high magnitude. Figure 2. A typical Tower of London problem Results The descriptive statistics (means and standard deviations) for the experimental measures can be found on Table 1 below. The research question addressed how the incentive manipulation would influence the performance (number correct) on the TOL and LNS tasks. The paired-samples t-test showed no significant differences in performance on the TOL or the LNS tasks under incentive and non-incentive conditions. Hypothesis 1 stated that the scores on the TOL and LNS tasks given under non-incentive conditions would be moderately correlated with each other because they are both considered to be “cold” EF tasks. Correlational analysis found a non-significant positive correlation, r (8) = 0.268, p = 0.227 (fig. 3). The scatter-plot indicates a positive association, however many outliers caused the correlation to be non-significant. Hypothesis 2 stated that the scores on the TOL and LNS tasks given under non-incentive conditions will be correlated with the “hot” EF task, IGT, at a low magnitude. Correlational analysis once again found a non-significant positive correlation between the non-incentive TOL and IGT. This suggests that better scores on the TOL were related to better (less risky) choices on the IGT. The exception to this finding was the first block of 20 IGT trials in which there was a significant negative correlation between the TOL and IGT, r (8) = -0.744, p = 0.007 (fig. 4). This means that, for the first 20 trials of the IGT, participants who did well on the TOL task made significantly worse (more risky) choices on the IGT. In general, there was a low correlation between the LNS under non-incentive conditions and IGT, meaning that higher scores on the LNS resulted in poorer choices on the IGT. The correlation between the LNS score and the first block of 20 IGT trials showed a moderate negative correlation, r (8) = -0.536, p = 0.055. Hypothesis 3 stated that the scores on the TOL and LNS tasks given under incentive conditions will be correlated with the “hot” EF task, IGT, at a moderate to high magnitude. Correlational analysis yielded several significant positive correlations between the TOL under incentive conditions and IGT Block 2, r (8) = 0.598, p = 0.034, IGT Block 3, r (8) = 0.726, p = 0.009, IGT Block 4, r (8) = 0.725, p = 0.009, IGT Block 5, r (8) = 0.633, p = 0.025, and the IGT net total, r (8) = 0.776, p = 0.004 (fig. 5). Correlations between the LNS under incentive conditions and the IGT were all non-significant, ranging from negative to positive. Acknowledgements I’d like to thank Dr. Welsh for all her tremendous help on this project and paper, including the design of the study in the first place. She was an excellent mentor, and always answered any questions I had during the research process. I would also like to thank Nathan Kirkley and ZabedahSaad for their help on editing this paper. My best regards go out to Lori Ball and UNC, for maintaining a science program as awesome as FSI. Lastly, I’d like to thank Newmont Mining for sponsoring me to attend this program; it has been a truly life-changing experience. Methods/Measures Participants A total of 10 teenagers ranging from ages 15-17 (mean = 16.1, std. deviation = 0.57) voluntarily participated in this study. The group contained 5 boys and 5 girls. All participants are active members of the 2011 Frontiers of Science Institute. General Procedure The participants were pre-divided into 2 groups, those given tasks under incentive conditions first, and those given tasks under incentive conditions second, in order to provide a counterbalanced order of testing. The group in which incentive tasks were given first was tested on 15 Tower of London problems, 7 Letter-Number Sequencing problems, and the Iowa Gambling Task. The participants were informed that their performance on the tasks would determine the number of entries to receive a prize. After the Iowa Gambling Task (IGT) the group was given another 15 Tower of London problems and 7 more Letter-Number Sequencing problems, where they were told that the performance would not count toward entries for the prize. For the second group of participants, 15 Tower of London problems and 7 Letter-Number Sequencing problems were given first, with performance not counting towards entries. Then the Iowa Gambling task, 15 different Tower of London problems, and 7 more Letter-Number Sequencing problems were given, with participants being told that performance on these tasks would count towards the drawing. References Baddeley, A. (2010, February 23). Working memory. Current Biology, 20(4). Best, J. R., & Miller, P. H. (2010, November/‌December). A Developmental Perspective on Executive Function. Child Development, 81(6). Brock, L. L., Rimm-Kaufman, S. E., Nathanson, L., & Grimm, K. J. (2009). The contributions of ‘hot’ and ‘cool’ executive funtion to children’s academic achievement, learning-related behaviors, and engagement in kindergarten. Early Childhood Research Quarterly, (24). Carlson, S. M., & Moses, L. J. (2001, July/‌August). Individual Differences in Inhibitory Control and Children’s Theory of Mind. Child Development, 72(4). Crone, E. A. (2009). Executive functions in adolescence: inferences from brain and behavior. Developmental Science. Gilbert, S. J., & Burgess, P. W. (2008, February 12). Executive function. Current Biology, 18(3). Hongwanishkul, D., Happaney, K. R., Lee, W. S. C., & Zelazo, P. D. (2010, June 8). Assessment of Hot and Cool Executive Function in Young Children: Age-Related Changes and Individual Differences. Developmental Neuropsychology, 28(2). Kerr, A., & Zelazo, P. D. (2004, June). Development of “hot” executive function: The children’s gambling task. Brain and Cognition, 55(1). Prencipe, A., Kesek, A., Cohen, J., Lamm, C., Lewis, M. D., & Zelazo, P. D. (2011). Development of hot and cool executive function during the transition to adolescence. Journal of Experimental Child Psychology, (108). Russo, N. (2003). Executive function and autism (Doctoral dissertation, McGill University, Montreal). Seguin, J. R., Arseneault, L., & Tremblay, R. E. (2007). The contribution of “cool” and “hot” components of decision-making in adolescence: Implications for developmental psychopathology. Cognitive Development, (22).