Doctoral School of Psychology (Cognitive Science) 

BME TTK, Department of Cognitive Science

Supervisor: Dr. Molnár Márk

Age-related Aspects of Sequential Risk-taking

Introducing the research area

A common element of adaptive behavioral optimization is that we take a certain degree of risk in order to use the potential benefits of it. This can be achieved by deliberate decision-making or by non-conscious, automatic processes (for example, habits or addictions). The present research focuses specifically on the age-related aspects of sequential risk-taking with an emphasis on the elderly as a potential risk group regarding socio-economic decision-making.

 

Brief introduction of the research place

The research was carried out in the former Psychophysiological Research Group (today: Experimental Psychology Research Group) of the Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences. This group focuses on the neurocognitive processes of aging, including the topics of memory, learning, decision making and control functions.

 

History and context of research

Gambling situations are present not only in casinos, but also in our everyday lives. We tend to put at risk long-term benefits for a short-term, but more tempting rewards, for example, "cheat" in our diet or have another drink even if we know that next day we’ll have an important meeting. Since the interaction between our environment and our inner state creates a continuously fluctuating risk, the ability to properly assess action-outcome contingencies is an essential part of proper decision-making [1]. Over the years many areas of the brain, especially the frontal lobe and its associated subcortical areas, undergo significant structural and functional changes (e.g. decreased density of dopaminergic receptors, changes in the functional activation pattern), which can have a key effect on decision-making processes [2,3].

Based on questionnaire data, the elderly age group is clearly risk averse, but these questionnaires typically refer to risky, dangerous situations that are clearly defined and do not affect the general everyday situations. However, in laboratory conditions, both extreme risk aversion and risk taking attitude was observed in the elderly in risk-taking tasks where the amount of information on outcome probabilities is limited. In these situations it is important to integrate the existing knowledge and the experience gained in the task, so that the participant could reach to a conclusion that he or she can use in order to maximize their incomings [4,5,6].

 

Aim of the research

In addition to examining the risk-taking behavior of the older age group, the aim of the present study was to examine the dynamics of sequential decision-making with the method of electroencephalography (EEG). We wanted to analyze the learning patterns in response to hidden outcome probability structures both in the old and young age groups. According to our assumptions, the experience gained during the task is able to improve performance in the older age group. In addition, we hypothesized age-related changes in strategic decision making, which is primarily reflected in the deviation of response times. We also expected altered EEG event-related potential (ERP) components related to specific stages of decision making.

 

Methods

The so-called Balloon Analogue Risk Task (BART) is a sequential decision making task, suitable to test the flexibility of risk taking attitudes [7,8]. In the BART, the participants are inflating virtual balloons with repeated button presses (which is the imitation of the pumping action). Their task is to inflate the balloons to a size as big as possible, but avoid their burst. The successful pumps on a balloon yield more and more points, but if the balloon bursts, the points accumulated on that balloon are lost and do not add to the existing total-score. Inflating can be stopped at any point (so-called "cash-out" event), in which case the points scored so far will be added to the total-score that have already earned. Participants are informed that the balloons will burst if they are inflated too much, but the exact burst probabilities - which follow a probabilistic distribution – are unknown to them.

Figure 1. - Distribution of outcome probabilities and rewards in one experimental trial

Figure 2. - Schematic illustration of one experimental trial in the BART, and the two possible outcome options (cash-out or balloon burst)

 

The study included 22 young people (aged 21-28) and 23 elderly (aged 62-72). In both groups the gender ratio and IQ were equalized. Each person had the opportunity to inflate 90 balloons, each balloon could have a maximum of 20 pumps (this practically means an average of about 10 pumps due to probabilistic burst probability). As a behavior index, the number of pumping, the number of burst balloons, the response times, and the so-called "exploration index” (derived from the response times) were calculated in 3 blocks (30 balls / blocks).

 

Figure 3. - The task design and the block-wise grouping of experimental trials (P1-3.)

 

During the task 62-channel EEG recording was performed (see Figure). When analyzing the EEG data, the successfully inflated and the burst balloons sequences were segmented regarding the last successful pumping action before the cash-out or the burst. The EEG segment of balloon burst was also extracted. From these, we calculated the ERPs associated with the above mentioned events involving the electrodes located in the frontocentral area (Fig.). We examined three major ERP components: the "reward positivity", the feedback-related negativity (FRN) and the P3 components [9, 10]. The former two components appear as positive and negative peaks, respectively, in the ERP 200-300 milliseconds after the stimulus presentation. The P3 is a positivity of appearing 300 - 500 milliseconds after the stimulus presentation. The reward positivity is typically associated with a rewarding stimulus and its amplitude is larger when the stimulus is unexpected. The FRN, however, is associated with punishing events, but its amplitude is also higher for unexpected, salient stimuli. The role of the P3 component is related to the evaluation and integration of the stimulus-mediated consequences.

Figure 4. - Channel locations for the EEG data acquisition, the frontocentral channels were used for the ERP analysis (orange markers)

 

Results

When evaluating the data, it is important to note that the distribution of the negative and positive "outcomes" in the BART is not random. Describing a behavioral strategy in such a situation has been interpreted by several theories (e.g. Bayesian Sequential Risk Taking Model).

Based on our results, the number of pumps and balloon bursts increased as the task progressed, meaning that participants, regardless of the age group, were more and more likely to take greater risks in the task. Based on the block-wise analysis, this quantitative increase in the young group is clearly present between the first and the second blocks, but in the elderly it is seen only in the third block. Furthermore, both the average response time and the exploration time were longer in the elderly. These results suggest that increased uncertainty and caution, as well as a delayed increase in risk taking characterized the elderly.

Figure 5. - Behavioral results of the two age-groups (means and standard deviations) with respect to the blocks (1-3.)

 

The amplitude of the reward positivity ERP component correlated with the expected rewards (before cash-outs vs. before balloon bursts) only in the young group, suggesting that they relied more on the feedback stimulus than the elderly. Regarding the amplitude changes of the P3, it reflected quite well the amplitude changes of the reward positivity, i.e. the evaluation of the above events differed only in the young, but not in the elderly. In the case of negative feedback, i.e. ERP components after the balloon bursts, no differences were found in the two age groups regarding the FRN component, but in the elderly smaller P3 amplitude was measured, meaning that in the elderly the rapid evaluation of the negative feedback was retained, but the more extensive processing was less pronounced.

 

Figure 6. - ERPs derived from the frontocentral channels

 

The study was repeated on a new sample (young group 26, average age: 23.7 years, elderly group 30, mean age: 67.7 years), this time with additional questionnaires (UPPS, ROQ-Risk Orientation Questionnaire). In this study, behavioral indicators and ERP correlates showed the same result pattern as seen in the first study. The two age groups did not differ significantly in the risk-taking indicators measured with the BART task, but the older age group achieved significantly lower scores on the UPPS sensation seeking sub-scales measuring impulsivity. Of the questionnaires only the UPPS sub-scales (the positive group urgency sub-scale in the young group, and the lack of perseverance sub-scale in the elderly group) showed significant positive correlation with the BART scores.

Based on the results, the two age groups can be distinguished along the dimension of impulsivity: in line with the previous literature, it can be assumed that aging is characterized by risk aversion, and while in the young risk-taking is mediated by the loss of control by positive emotions, in the elderly the inability of focused task performance was found to correlate with the risk-taking attitude.

 

Expected impact and further research

This research has uniquely examined the ERP correlations of sequential decision-making in old age. It was found that the elderly are characterized by cautious strategic task performance, as a consequence of which the achievement of the optimal level of risk-taking will be delayed. The ERP results also emphasize that besides the negative, punitive feedback, positive feedback also plays a significant role in uncertain decision-making situations. Future research has to focus on the behavioral and neural correlations by using computational models to give a more accurate picture of decision-making processes in old age.

 

Publications, references, links

Publications.

 

Kardos Zsófia, Tóth Brigitta, Boha Roland, File Bálint, Molnár Márk Age-dependent characteristics of feedback evaluation related to monetary gains and losses. INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY in press: (2017)

 

Kardos Zsófia, Kóbor Andrea, Takács Ádám, Tóth Brigitta, Boha Roland, File Bálint, Molnár Márk Age-related characteristics of risky decision-making and progressive expectation formation BEHAVIOURAL BRAIN RESEARCH 312: pp. 405-414. (2016)

 

Kardos, Z., Kóbor, A., Takács, Á., Tóth, B., Boha, R., File, B., Molnár, M. Age-related aspects of progressive expectation formation in the Balloon Analogue Risk Task In: European Society for Cognitive and Affective Neuroscience (ESCAN) 3rd Conference Abstract Book. Place and date of conference: Porto, Portugal, 23/06/2016-26/06/2016. pp. 89-90.

 

Molnar M, Kardos ZK, Boha R, File B, Toth B Risky and cautious choice-making - Age-dependent changes of feedback related negativity in a gambling task INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY 94:(2) p. 186. 1 p. (2014) 17th World Congress of Psychophysiology of the International-Organization-of-Psychophysiology (IOP). Hiroshima, Japan: 23/09/2014 -27/09/2014.

 

 

Links.

 

http://www.ttk.mta.hu/en/intezetek/kognitiv-idegtudomanyi-es-pszichologiai-intezet/

http://cogsci.bme.hu/?LID=1

http://www.impulsivity.org/measurement/BART

https://www.ncbi.nlm.nih.gov/pubmed/12075692

 

References.

 

[1]T. Schonberg, C.R. Fox, R. a. Poldrack, Mind the gap: Bridging economic and

naturalistic risk-taking with cognitive neuroscience, Trends Cogn. Sci. 15 (2011) 11‑19.

 

[2]S.B.R.E. Brown, K.R. Ridderinkhof, Aging and the neuroeconomics of decision

making: A review, Cogn. Affect. Behav. Neurosci. 9 (2009) 365–379.

 

[3]P.N.C. Mohr, S.C. Li, H.R. Heekeren, Neuroeconomics and aging: Neuromodulation of

economic decision making in old age, Neurosci. Biobehav. Rev. 34 (2010) 678–688.

 

[4]R. Mata, A.K. Josef, R. Hertwig, Propensity for Risk Taking Across the Life Span and

Around the Globe, Psychol. Sci. (2016) 0956797615617811.

 

[5]J.J. Rolison, Y. Hanoch, S. Wood, Risky decision making in younger and older adults:

The role of learning. Psychol. Aging. 27 (2012) 129–140.

 

[6]L. Zamarian, H. Sinz, E. Bonatti, N. Gamboz, M. Delazer, Normal aging affects

decisions under ambiguity, but not decisions under risk., Neuropsychology. 22 (2008)

645–657.

 

[7]K.S. DeMartini, R.F. Leeman, W.R. Corbin, B.A. Toll, L.M. Fucito, C.W. Lejuez, et

al., A new look at risk-taking: using a translational approach to examine risk-taking

behavior on the balloon analogue risk task., Exp. Clin. Psychopharmacol. 22 (2014)

444–452.

 

[8]C.W. Lejuez, J.P. Read, C.W. Kahler, J.B. Richards, S.E. Ramsey, G.L. Stuart, et al.,

Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task

(BART), J. Exp. Psychol. Appl. 8 (2002) 75–84.

 

[9]C.B. Holroyd, O.E. Krigolson, S. Lee, Reward positivity elicited by predictive cues,

Neuroreport. 22 (2011) 249–252.

 

[10]J. Polich, J.R. Criado, Neuropsychology and neuropharmacology of P3a and P3b, Int.

J. Psychophysiol. 60 (2006) 172–85.