Journal of Clinical and Experimental Neuropsychology, 2014 Vol. 36, No. 10, 1112–1123, http://dx.doi.org/10.1080/13803395.2014.983464

Discounting preferences and response consistency as markers of functional ability in community-dwelling older adults Cutter A. Lindbergh1, Antonio N. Puente1, Joshua C. Gray1, James MacKillop1, and L. Stephen Miller1,2 1 2

Department of Psychology, University of Georgia, Athens, GA, USA Bio-Imaging Research Center, Paul D. Coverdell Center, University of Georgia, Athens, GA, USA

(Received 18 June 2014; accepted 29 October 2014) Introduction: Predictors of functional independence in older adults are in need. Based on findings that delay discounting, probability discounting, and the ability to respond consistently use cognitive abilities and neural systems with central relevance to functional ability, the present study evaluated whether these behavioral economic variables account for variance in everyday functioning in older adults. It was hypothesized that greater preference for immediate/probabilistic rewards and response inconsistency would independently predict decrements in instrumental activities of daily living (IADLs). Method: Participants included 64 community-dwelling older adults (65–85 years; mean age = 76.25 years; 76.60% female). Exclusionary criteria were neurological illness, illiteracy, substance dependence within the past 5 years, score of ≤20 on the Mini-Mental State Examination, and/ or presence of dementia. Delay/probability discounting tasks consisted of a series of dichotomous selections between smaller, immediate/guaranteed and larger, delayed/probabilistic monetary values. Area under the curve (AUC) was used to index levels of discounting, while response (in)consistency was based on the percentage of contradictory responses. The Direct Assessment of Functional Status–Revised (DAFS–R) provided a performance-based assessment of IADLs. Hierarchical regression analyses were conducted to determine whether discounting preferences and response consistency accounted for variance in functional ability over and above relevant demographic characteristics. Results: Demographic characteristics accounted for significance variance in IADLs (p = .001, R2 = .237). Probability discounting AUC (p = .014, ΔR2 = .075) and response (in)consistency (p = .046, ΔR2 = .050) each accounted for significant additional variance in functional ability, as did delay discounting response (in)consistency (p = .010, ΔR2 = .081). Delay discounting AUC did not add significantly to the model (p = .861). Conclusions: Discounting preferences and choice consistency hold potential to serve as relatively fast and inexpensive markers of functional decline, likely due to neurocognitive deterioration relevant to both behavioral economic decision making and functional independence. Keywords: Aging; Cognition; Delay discounting; Functional ability; Probability discounting.

The ability to function independently tends to decline with age (e.g., Loewenstein & Mogosky, 1999), which is an increasing concern given the rapid growth of the population aged 65 and older (National Institute on Aging, 2011). Loss of functional capacity is associated with both emotional and economic burdens to society (e.g., Covinsky et al., 2003; Willis, 1991). To illustrate, it was recently found that dementia, a condition

characterized by progressive cognitive decline and associated loss of independence in everyday activities, costs the United States between $157 and 215 billion annually (Hurd, Martorell, Delavande, Mullen, & Langa, 2013). A total of 75 to 84% of this expense is attributable to institutional and home-based care resulting from limitations in the ability to carry out activities of daily living (Hurd et al., 2013).

Address correspondence to: Cutter A. Lindbergh, Department of Psychology, University of Georgia, Athens, GA 30602, USA (E‐mail: [email protected]).

© 2014 Taylor & Francis

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Functional independence has been conceptualized as being composed of both instrumental activities of daily living (IADLs) and basic activities of daily living (BADLs). The former are more cognitively complex self-care tasks, such as preparing food, managing finances, and cooking, whereas the latter include more basic activities, such as toileting, grooming, and feeding (Depp & Jeste, 2009). IADLs tend to decline prior to BADLs (Njegovan, Man-Son-Hing, Mitchell, & Molnar, 2001) and are associated with a range of outcomes, including dementia onset (e.g., Pérès et al., 2008), psychological well-being (e.g., Cummings, 2002), health care utilization (Fogel, Hyman, Rock, & Wolf-Klein, 2000), and mortality (e.g., Naeim, Keeler, & Reuben, 2007). Self-report, informant-report, or performancebased measures are typically employed to assess levels of functional independence. Although each approach provides unique information (SchmitterEdgecombe, Parsey, & Cook, 2011), self- and otherreport measures may be more susceptible to bias and are limited by the degree of insight of the reporter, resulting in either over- or underestimation of functional ability (e.g., Dassel & Schmitt, 2008; Suchy, Kraybill, & Franchow, 2011). Performancebased measures have the advantage of providing a more objective evaluation while circumventing concerns regarding validity when using a sole informant and discrepancies among ratings when using multiple informants (Marcotte & Grant, 2009). In addition, performance-based assessments may better capture variability and have demonstrated sensitivity to functional decrements in both healthy and pathologically aging older adults (Mitchell & Miller, 2008; Teng, Becker, Woo, Cummings, & Lu, 2010). Performance-based measures are not without limitations, however, such as their cumbersome nature and lengthy administration, which may render them less practical in clinical settings. Although several factors have been identified that predict functional autonomy, including social, physical, and psychological characteristics, cognitive abilities are generally the strongest predictors of IADLs (e.g., Burdick et al., 2005). Executive functioning, broadly defined as the ability to intentionally direct behavior toward achieving a goal (Banich, 2009), has consistently emerged as the most robust cognitive correlate of everyday functioning (e.g., Cahn-Weiner, Boyle, & Malloy, 2002; Gross, Rebok, Unverzagt, Willis, & Brandt, 2011; Royall et al., 2007). This relationship has held up in both cross-sectional (e.g., Tan, Hultsch, & Strauss, 2009) and longitudinal (e.g., Farias et al., 2009) designs, with changes in executive functioning occurring in tandem with changes

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in IADLs. Episodic memory functioning has also shown utility in predicting IADLs (e.g., Allaire & Marsiske, 2002; Gross et al., 2011), including in longitudinal designs that control for executive functions (Farias et al., 2009). Although considerable variability has been found in the magnitude of the relationship between cognitive functioning and everyday functioning, findings from a review of nearly 70 studies indicate that the median amount of variance in functional ability that can be attributed to cognition is less than 20% (Royall et al., 2007). It should be noted, however, that a substantial portion of the studies included in this review relied on subjective rather than performance-based assessments, suggesting that this figure may be an underestimation. Nevertheless, it is clear that a significant amount of variance remains to be accounted for in functional status. The aim of the present study was to evaluate a novel approach to improving the prediction of functional independence derived from the field of behavioral economics. This approach would be intended to supplement, rather than replace, information obtained from self- and informant-reports, potentially accounting for unique aspects of variance in functional ability not captured by traditional questionnaires. Behavioral economics represents an intersection of psychology and microeconomics with an emphasis on understanding decision making and behavior within systems of constraint (Bickel, Marsch, & Carroll, 2000). Two classic behavioral economic tasks are delay discounting and probability discounting. Delay discounting evaluates preferences between smaller, immediate rewards and larger, delayed rewards (e.g., “Would you rather have $30.00 today or $100.00 in 60 days?”), whereas probability discounting evaluates preferences between smaller, guaranteed rewards and larger, probabilistic rewards (e.g., “Would you rather have $30.00 guaranteed or a 50% chance of receiving $100.00?”; Kirby, Petry, & Bickel, 1999; Rachlin, Raineri, & Cross, 1991). Discounting has been studied extensively in conditions characterized by impairments in self-control, such as alcohol dependence, nicotine dependence, stimulant dependence, pathological gambling, attention-deficit/ hyperactivity disorder, and antisocial personality disorder (Holt, Green, & Myerson, 2003; McKerchar & Renda, 2012). Although “impulsive” and “risky” are commonly used in conjunction with delay and probability discounting, respectively, we exercise caution in using such terminology given that these tasks evaluate choice preferences rather than impulsive or risky behaviors (see Reynolds & Schiffbauer, 2005). In

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addition, it may be misleading to use descriptors like “impulsive” or “risk-prone” in comparison to populations typically assessed on these measures (e.g., pathological gamblers). For these reasons, when “impulsive,” “risky,” or variants of these terms are found in reference to the present study, they are used for efficiency’s sake to refer to the selection of immediate/probabilistic choices over delayed/guaranteed choices, relative to our sample only, and not as behavioral tendencies. There are several reasons to suggest that discounting preferences may serve to predict functional ability. Perhaps most notably, discounting choice selections involve cognitive abilities relevant to everyday functioning. Delay discounting is consistently associated with executive dysfunction (e.g., Huckans et al., 2011; Olson, Hooper, Collins, & Luciana, 2007; Shamosh et al., 2008) and actually has been conceptualized as an executive process in and of itself (Bickel & Yi, 2008). Indeed, delay discounting is composed of various executive skills, such as planning, working memory, inhibition, decision making, and reasoning (Banich, 2009). Impulsive delay discounting preferences have been associated with dysfunction in other cognitive domains as well, such as attention, delayed memory, and verbal learning (Hoffman et al., 2006; Huckans et al., 2011). Less research has investigated the relationship between cognitive functioning and probability discounting, though it was recently demonstrated that age-related changes in risk-based decision making are mediated by memory functioning and processing speed (Henninger, Madden, & Huettel, 2010), both of which are significant predictors of functional ability (e.g., Gross et al., 2011). In addition, risky selections on decision-making tasks in longitudinal designs have been shown to predict cognitive decline five years into the future (Denburg et al., 2005). In short, delay and probability discounting are worthwhile candidates to explore based on their involvement of a host of relevant cognitive functions, potentially working together in a synergistic fashion, to account for variance in functional ability. Several studies have evaluated delay discounting in older adults with mixed results. Some investigations have yielded lower discount rates in older adults than in young adults (e.g., Jimura et al., 2011; Lockenhoff, O’Donoghue, & Dunning, 2011), others stability of discounting into old age (e.g., Green, Myerson, Lichtman, Rosen, & Fry, 1996; Samanez-Larkin et al., 2011), and still others greater levels of discounting (Read & Read, 2004). While methodological differences (e.g., uncontrolled demographic and socioeconomic variables;

Green et al., 1996) have likely contributed to these discrepant findings, another important factor may be heterogeneity in cognitive and neurological aspects of aging (Halfmann, Hedgcock, & Denburg, 2013). Indeed, it was recently demonstrated that cognitively impaired older adults discount to a significantly greater extent than unimpaired older adults (Halfmann et al., 2013). This finding may be understood through a competing neural systems model, which conceptualizes discounting choices as the product of competitive exchanges between “executive” cortical systems and “impulsive” subcortical systems (Peters & Büchel, 2011). Age-related pathobiological processes in executive regions involved in cognitive control, such as lateral prefrontal cortex and posterior parietal cortex, may contribute to impulsive preferences (Peters & Büchel, 2011). Importantly, degeneration in these brain regions is also associated with decrements in ADLs (e.g., Mioshi, Hodges, & Hornberger, 2013), suggesting that greater delay discounting may serve to index functional deficits. This notion fits with the larger view that reward-based decision making is susceptible to alterations in old age due to changes in prefrontal cortex, striatum, and associated neurotransmitter systems (e.g., dopaminergic and serotonergic)— processes that are likely accelerated in pathologically aging adults who would be considered particularly at risk for functional dependence (Marschner et al., 2005). In addition to discounting preferences, delay and probability discounting tasks allow for an assessment of the more general capacity for response consistency (i.e., the ability to make a series of logically consistent choices). There have been few systematic investigations of response consistency, as it is generally used as a validity indicator to exclude participants from analyses based on the assumption that under normal circumstances respondents’ preferences should represent fairly stable and coherent intrinsic values (Brown et al., 2008). However, to the extent that inconsistent responding reflects neurocognitive dysfunction rather than a lack of effort or understanding, it becomes an informative variable to consider for purposes other than data validity. Such an approach is supported in the present study by findings that choice inconsistency on reward-based decision-making tasks correlates with cognitive deficits relevant to functional status and is observed in conditions characterized by executive dysfunction, including schizophrenia, Parkinson’s disease, and HIV (Arentoft, Thames, Panos, Patel, & Hinkin, 2013; Delazer et al., 2009; Weller et al., 2014). Of particular relevance, dementia and mild

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cognitive impairment (MCI), a predementia syndrome characterized by modest cognitive loss and subtle decrements in the ability to perform complex activities of daily living (Albert et al., 2011), are both associated with response inconsistency on risk-based decision-making tasks (Sinz, Zamarian, Benke, Wenning, & Delazer, 2008; Zamarian, Weiss, & Delazer, 2011). In addition, older adults exhibit more inconsistent choice patterns under conditions of risk than do young adults, indicating that response consistency is sensitive to cognitive aging (Tymula, Belmaker, Ruderman, Glimcher, & Levy, 2013). In summary, delay discounting, probability discounting, and the ability to make consistent selections utilize cognitive functions and underlying neural systems that have demonstrated relevance to functional outcomes, specifically with respect to IADLs. Cognitively impaired older adults exhibit greater delay discounting, and age-related changes in risk-based decision making are mediated by cognitive abilities relevant to everyday activities. Response consistency is impacted by pathological aging and other conditions featuring executive dysfunction. Based on these findings, the present study constitutes an initial investigation to determine whether discounting preferences and choice consistency, as candidate proxies of neurocognitive integrity, may serve as markers of functional decline in community-dwelling older adults. It was hypothesized that a propensity for immediate/probabilistic choice preferences, as indexed by area-under-thecurve (AUC) values, would be associated with diminished IADL capacity on a performancebased measure of functional status. In addition, it was expected that IADL performance would be worse among older adults who were more inconsistent in their discounting preferences, as indexed by the percentage of contradictory selections across different delays/odds against.

METHOD A total of 65 community-dwelling older adults from 65 to 85 years of age were recruited for the study via flyers and newspaper advertisements. Exclusionary criteria included a history of neurological illness, self-reported illiteracy, substance dependence within the past 5 years, severe cognitive impairment indexed by a score of ≤20 on the Mini-Mental State Examination (MMSE; Folstein, Folstein, McHugh, & Fanjiang, 2000), and/or presence of dementia (Albert et al., 2011). One participant was excluded from analyses due to invalid responses on discounting control trials (incorrect

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TABLE 1 Descriptive statistics Variable Age (years) Sex (% female) Race (% African American) Peak household income (USD) Predicted FSIQ Education (years) MMSE

%

Mean (SD) 76.25 (5.96)

76.60 10.90 87,181.25 106.98 15.55 27.23

(76,621.35) (11.94) (2.89) (1.72)

Note. USD = United States dollars; FSIQ = full-scale intelligence quotient, predicted from an algorithm incorporating both Wechsler Test of Adult Reading performance and demographic variables; education = total years of formal education attained; MMSE = Mini-Mental State Examination.

selections between larger and smaller rewards, both available immediately), leaving 64 cases for final analyses. Descriptive statistics are presented in Table 1. Participants were compensated with $20.00 for their time, as well as a gift bag containing University of Georgia souvenirs worth approximately $15.00. In addition, participants were provided a 1-in-6 chance via dice roll to receive the monetary amount of one of their randomly selected responses on the delay discounting task, which ranged in value from $10.00 to $100.00 (Kirby & Petry, 2004). Participants were informed (prior to starting the task) that if the monetary prize happened to be a delayed choice, then the cash value would be delivered to them in the exact number of days as that specified in the delay. The dice roll was used to increase effort, attention, and ecological validity (Kirby & Petry, 2004).

Cognitive measures Mini-Mental State Examination The Mini-Mental State Examination (MMSE) was employed to screen out individuals with severe cognitive impairment (Folstein, Folstein, & McHugh, 1975; Folstein et al., 2000). MMSE scores range from 0 to 30 points, with scores of ≤20 generally indicating dementia or a major psychological disorder (Folstein et al., 1975) and invalid self-report (Bedard et al., 2003). The reliability and validity of this measure as a screening instrument for cognitive impairment has been established (Folstein et al., 1975). The Wechsler Test of Adult Reading The Wechsler Test of Adult Reading (WTAR; Psychological Corporation, 2001) was used to

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evaluate premorbid intellectual functioning (Wechsler, 2001). The WTAR has an administration time of approximately 5 minutes, during which the examinee’s pronunciation of words from a list is assessed, as reading recognition has demonstrated considerable resistance to cognitive decline (Wechsler, 2001). The WTAR produces a full-scale intelligence quotient (FSIQ) estimate based on an algorithm incorporating both WTAR performance and demographic (i.e., age, education, race, sex, and geographic region) variables. Functional ability measure Direct Assessment of Functional Status–Revised The Direct Assessment of Functional Status– Revised (DAFS–R) was employed to provide a clinician-rated, performance-based assessment of functional ability across 10 domains, including time orientation, communication, financial skills, grocery shopping, dressing and grooming, eating, driving, meal preparation, providing demographic information, and taking a telephone message (Loewenstein et al. 1989; McDougall, Becker, Vaughan, Acee, & Delville, 2010). Criteria specified by Mitchell and Miller (2008) were applied to separate IADLs from BADLs for our analyses, such that IADLs included the more cognitively complex domains of communication (i.e., preparing a letter to mail; operating the telephone), financial skills (i.e., identifying currency and counting change; writing a check; balancing a checkbook), grocery shopping (i.e., remembering grocery items; selecting grocery items from a list; making change), driving (i.e., correctly identifying and explicating road signs), meal preparation (i.e., following cooking instructions), and taking a telephone message (i.e., identifying and documenting key information). An IADL total score ranging from 0–91 is calculated based on dichotomous ratings of whether the participant successfully completes critical aspects of each task. The DAFS has evidenced good psychometric properties in older adult samples, with subscale test–retest reliabilities ranging from .55 to .91, interrater agreement of at least 85%, and internal consistency (i.e., Cronbach’s alpha) of .68 (slightly lower than typical due to diversity of ADLs assessed; Loewenstein et al., 1989; McDougall et al., 2010). DAFS performance is significantly related to measures of cognitive functioning, such as the MMSE (r = .57, p < .01), and reliably discriminates older adults with MCI (IADL mean score = 70) from high-functioning controls (IADL mean score = 79; McDougall et al., 2010; Puente, Terry, Faraco, Brown, & Miller, 2014).

Behavioral economic measures Delay discounting Delay discounting was evaluated in an 80-item task consisting of dichotomously presented options between smaller, immediate rewards (i.e., $10.00, $20.00, $30.00, $40.00, $50.00, $60.00, $70.00, $80.00, $90.00, or $99.00) and a larger reward of $100 with a delay of 1, 7, 14, 30, 60, 90, 180, or 365 days (Amlung, Sweet, Acker, Brown, & MacKillop, 2014). For example, “Would you rather have $40.00 today or $100.00 in 60 days?” The task provides estimates of “indifference points” for the eight delay intervals, which represent the subjective value of the larger reward for each delay to receipt. An indifference point thus provides the monetary value at which an individual is “indifferent” as to whether to choose the immediate or the delayed reward. The task was computer-adapted using Inquisit software (Inquisit, 2011), and instructions were given to indicate item choices by mouse click. Ten control trials were evaluated that involved dichotomous selections between smaller and larger monetary values, all of which were presented as immediately available rewards. The reliability and validity of delay discounting across various populations and cultures have been supported, with 3-month test–retest stability ranging from .45 to .75 depending on the specific discounting parameter employed (Kirby & Petry, 2004; Kirby et al., 1999; McKerchar & Renda, 2012; Ohmura, Takahashi, Kitamura, & Wehr, 2006).

Probability discounting The probability discounting task (Rachlin et al., 1991) consisted of 66 dichotomously presented options between smaller, guaranteed monetary values (i.e., $1.00, $10.00, $20.00, $30.00, $40.00, $50.00, $60.00, $70.00, $80.00, $90.00, or $99.00) and a larger, probabilistic monetary value of $100.00 with a probability of receipt of .01, .10, .25, .50, .75, or .99. For example, “Would you rather have $30.00 guaranteed or a 50% chance of receiving $100.00?” Similar to the delay discounting task described above, indifference points can be calculated at each of the six probability intervals. Inquisit software (Inquisit, 2011) was employed to computerize the task, and instructions were given to denote personal preferences by mouse click. Probability discounting has been established as a valid and reliable measure, with test–retest correlations ranging from .54 to .86 across a 3-month period depending on the chosen parameter (e.g., Holt et al., 2003; McKerchar & Renda, 2012; Ohmura et al., 2006).

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Procedure Participants were evaluated in a single session beginning with written informed consent. Following provision of consent, the MMSE was administered to identify and screen out individuals with severe cognitive deficits. Key demographic information, including age, presence of neurological illness, and history of substance dependence, was gathered next. The DAFS–R, discounting tasks, and WTAR were counterbalanced across participants to control for the possibility of order effects. It should be noted that participants also were asked to complete a few additional tasks and self-report measures as part of a larger study, which were unrelated to the present study and were thus excluded from the present analyses. At the end of the testing session, participants were debriefed and provided with compensation for their time and effort. All procedures in the study received ethical approval from the University’s Institutional Review Board.

Data analysis Based on procedures used in previous discounting studies to prevent leverage of the data by single cases (e.g., Acker, Amlung, Stojek, Murphy, & MacKillop, 2012), an iterative process was applied using a z score threshold of ±3.29 in which outlying values were replaced with values one unit above/below the nearest nonoutlying case (Tabachnick & Fidell, 2004). Two outliers on the probability discounting task were identified from application of this procedure. Discounting was analyzed using area under the curve (AUC), which is a widely employed measure of both delay discounting and probability discounting (e.g., Green, Myerson, Oliveira, & Chang, 2013; Ohmura, Takahashi, & Kitamura, 2005; Olson et al., 2007). AUC does not rely on a particular theoretical orientation, such as whether discounting is best described by exponential or hyperbolic curves, and thereby circumvents issues stemming from model-fit error due to incorrect assumptions about the form of the data (Myerson, Green, Warusawitharana, 2001). In addition, AUC tends to approximate a normal distribution whereas other parameters (e.g., k) can be highly skewed, requiring transformations of the data or use of nonparametric statistical tests (Ohmura et al., 2006). The calculation of AUC involves plotting each of the delay/probability intervals along the horizontal axis with the indifference points (i.e., the subjective value of the larger, delayed/probabilistic reward, as described above) along the vertical axis.

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The indifference points are then connected with lines, and the resulting area underneath these lines is summed (for more details, see Myerson et al., 2001). AUC values range from 0.00 to 1.00, with greater values indicating greater preference for probabilistic choices on the probability discounting task and greater preference for delayed choices on the delay discounting task. To provide an example, the subjective values of the larger, delayed rewards would be expected to decrease rather quickly with each increasing delay in a respondent with impulsive choice preferences, resulting in a steeper “curve” and thus a smaller AUC value. Response consistency was based on the percentage of contradictory responses within each delay/ probability interval (i.e., relative to the point of indifference; Amlung et al., 2014). For example, selecting $50.00 today over $100.00 in 30 days on one trial and $100 in 30 days over $60.00 today on another trial would be considered a contradiction on the delay discounting task. An overall measure of response consistency was then calculated by averaging across consistency values at each delay/ probability interval. Hierarchical regression analyses were performed to determine whether discounting task variables accounted for variance in functional ability over and above demographic variables that have demonstrated relevance to functional ability, including age, income, predicted FSIQ, and years of formal education (e.g., Allaire & Marsiske, 2002; Berkman & Gurland, 1998). Demographic characteristics that significantly correlated with IADLs in our sample were entered into the first step of the hierarchical model, and the discounting variable of interest (i.e., delay/probability discounting AUC or response consistency) was entered into the second step. F tests were calculated to evaluate the significance of R2 and change in R2 (ΔR2), while t-values were used to determine which demographic predictors contributed uniquely to variance in functional ability.

RESULTS Descriptive statistics for discounting preferences, response consistency, and functional ability are presented in Table 2. Zero-order bivariate correlations among discounting variables, demographic characteristics, and IADLs are shown in Table 3. Of the demographic characteristics, income, predicted FSIQ, and years of formal education significantly related to functional ability in our sample and thus were controlled for in each of the hierarchical regressions.

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Probability discounting

TABLE 2 Discounting task variables and functional ability Variable

Mean (SD)

Range

Delay discounting AUC Response consistency

.49 (0.27) .97 (0.03)

.05–.95 .84–1.00

Probability discounting AUC Response consistency

.10 (0.09) .97 (0.03)

.01–.40 .84–1.00

DAFS–R IADLs total score

74.64 (7.98)

49.00–85.00

Note. AUC = area under the curve; response consistency = percentage of noncontradictory responses; DAFS–R = Direct Assessment of Functional Status–Revised; IADLs = instrumental activities of daily living.

Two independent hierarchical regression analyses were performed for prediction of functional ability by (a) probability discounting AUC and (b) probability discounting response consistency. AUC accounted for significant additional variance over and above the demographic variables, ΔF(1, 59) = 6.435, p = .014, ΔR2 = .075, as did response consistency, ΔF(1, 59) = 4.170, p = .046, ΔR2 = .050. None of the demographic variables included in these models emerged as significant unique predictors, with the exception of income in the model predicting functional ability from probability discounting AUC (p = .045).

Delay discounting

DISCUSSION

Two independent hierarchical regression analyses were conducted for prediction of functional ability by (a) delay discounting AUC and (b) delay discounting response consistency. The first step of the models revealed that, collectively, demographic characteristics were significant predictors of functional ability, F(3, 60) = 6.229, p = .001, R2 = .237). In the second step, AUC did not add significantly to the model, ΔF(1, 59) = 0.031, p = .861, ΔR2 ≤ .001, though response consistency did account for significant additional variance over and above the demographic variables, ΔF(1, 59) = 6.998, p = .010, ΔR2 = .081. None of the demographic variables (i.e., income, predicted FSIQ, or years of formal education) were significant unique predictors in either of these models (p > .05).

The present study evaluated the predictive utility of discounting preferences and choice consistency on functional ability in older adults. Consistent with our hypotheses, decrements in functional ability were independently predicted by greater preference for risky (i.e., probabilistic) choices and response inconsistency on the probability discounting task. In addition, diminished functional capacity was predicted by delay discounting response inconsistency, but contrary to expectation, not impulsive (i.e., immediate) choice preferences. Importantly, variables from the discounting tasks that significantly accounted for variance in functional ability did so above and beyond relevant demographic characteristics, including income, intellectual functioning, and years of formal education.

TABLE 3 Zero-order bivariate correlations Variable 1. 2. 3. 4. 5. 6. 7. 8. 9.

Age (years) Peak household income (USD) Predicted FSIQ Education (years) DD AUC DD response consistency PD AUC PD response consistency DAFS–R IADLs

1

2

3

4

5

6

7

8

9

–.21

–.05 .47***

–.09 .42*** .77***

–.06 .15 .33** .26*

–.07 .29* .22 .25* .31*

–.17 –.07 –.25* –.24 .33** .02

.09 .32* .28* .32** –.07 .46*** –.23

–.12 .39** .43*** .41*** .12 .42*** –.37** .39**

Notes. USD = United States dollars; FSIQ = full-scale intelligence quotient, predicted from an algorithm incorporating both Wechsler Test of Adult Reading performance and demographic variables; education = total years of formal education attained; DD = delay discounting; AUC = area under the curve; response consistency = percentage of noncontradictory responses; PD = probability discounting; DAFS–R = Direct Assessment of Functional Status–Revised; IADLs = instrumental activities of daily living. *p < .05. **p < .01. ***p < .001.

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Interestingly, risky but not impulsive response preferences predicted levels of functional independence, which may seem to contradict literature suggesting that a common psychological mechanism— such as a general “impulsivity trait”—underlies both delay and probability discounting (Hayden & Platt, 2007). However, this notion has been challenged by observations that delay and probability discount rates are not always correlated in the expected, negative direction; several studies have found a lack of correlation (e.g., Ohmura et al., 2005; Olson et al., 2007) or even a positive correlation (Holt et al., 2003; Richards, Zhang, Mitchell, & de Wit, 1999). Moreover, neuroimaging studies indicate that although delay and probability discounting utilize overlapping brain regions, they also employ unique neural systems, suggesting at least partial dissociation (Peters & Büchel, 2009; Weber & Huettel, 2008). The results from our sample support the presence of an inverse relationship between impulsive and risky choice preferences (i.e., a positive correlation between respective discounting AUC values: r = .33, p ≤ .01). A possible explanation for this observation is that increased delays to reward are associated with an increased “risk” that some misfortune will occur to prevent the reward from being enjoyed or received. Accordingly, preference for immediate rewards on the delay discounting task may reflect aversion to this risk, particularly in an older adult sample with limited future horizons in terms of remaining life expectancy (Read & Read, 2004). It is noteworthy that delay discounting response inconsistency emerged as a predictor despite our finding that immediate choice preferences did not account for significant variance in functional ability. Unfortunately, the present study does not shed light onto why this may be the case, though it is consistent with observations of response inconsistency on risk-based and intertemporal decision-making tasks in conditions characterized by cognitive decrements relevant to functional independence (e.g., executive dysfunction), such as HIV, Parkinson’s disease, schizophrenia, MCI, and dementia (Arentoft et al., 2013; Delazer et al., 2009; Sinz et al., 2008; Weller et al., 2014; Zamarian et al., 2011). Taken together, these findings, in conjunction with our own, highlight the utility of considering response consistency —that is, the ability to form and maintain an internally consistent cognitive template for choice preferences—as a variable in and of itself rather than purely as a means of screening out invalid responders, particularly in the context of predicting functional ability. This notion is consistent with complex decision-making models (e.g., expectancy-valence) that advise distillation of overall task performance

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into separate psychological components, including choice consistency, which in turn can help identify populations with distinct neuropsychological profiles and associated outcomes (Yechiam, Busemeyer, Stout, & Bechara, 2005). The observed relationship between probability discounting preferences and functional ability likely reflects pathophysiological processes in neural structures demonstrated to be critical to risk-based decision making, including prefrontal, parietal, and hippocampal regions (Huettel, Stowe, Gordon, Warner, & Platt, 2006; Kuhnen & Knutson, 2005; Rogers et al., 1999). Given the lack of relationship between delay discounting preferences and functional ability, brain areas with comparatively greater involvement in probability discounting may be implicated in particular, such as middle occipital gyrus, lateral prefrontal cortex, and posterior parietal cortex (involved in estimation, calculation, and numerosity processing more broadly; Peters & Büchel, 2009; Weber & Huettel, 2008). Indeed, dementias such as Alzheimer’s disease—a primary cause of functional decline among the aging population—have been associated with neurodegeneration in these regions (e.g., Pennanen et al., 2004; Singh et al., 2006). It is notable that these neurodegenerative processes commence decades prior to the onset of dementia (Weiner et al., 2013), suggesting that alterations in risk-based decision making may predict more severe functional deficits in the future. Potential neural mechanisms underlying the observed relationship between response consistency and functional ability remain to be identified and represent an important question to be addressed in future neuroimaging studies. The present study is limited in its generalizability given that the participants were largely female and Caucasian. It will be important for future research to evaluate whether discounting preferences and response inconsistency similarly predict functional ability in a sample characterized by greater diversity. Another limitation is that delay and probability discounting were only assessed at one magnitude of reward (i.e., $100) despite the case made by Kirby (1997) of a so-called “magnitude effect” whereby smaller rewards tend to be discounted to a greater extent than larger rewards. It will be helpful for future studies to investigate whether the predictive utility of discounting on functional ability is moderated by the size of the reward, particularly for delay discounting, as magnitude effects have been found in several studies (e.g., Amlung & MacKillop, 2011; Estle, Green, Myerson, & Holt, 2006), and it is possible that a relationship between impulsive preferences and functional ability would emerge at different levels

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of reward. In addition, given that the probability discounting task and, to some extent, the delay discounting task involve the capacity to understand and manipulate numerical information, it is possible that our findings may have been influenced by varying levels of numeracy skills in our sample (Tymula et al., 2013). An important future avenue for research is to evaluate whether discounting preferences and response consistency account for variance in functional independence when controlling for cognitive functioning and executive functions in particular, which, as noted above, currently appear to be the best predictor of IADLs. This will be necessary to determine whether discounting tasks may serve as supplements to more traditional cognitive measures in clinical settings, potentially evaluating unique aspects of functional ability and increasing predictive power due to involvement of a number of cognitive abilities (e.g., executive functions and memory) working in synergy. Relatedly, it will be informative to evaluate which cognitive processes (e.g., executive skills) mediate the relationship of discounting preferences to functional ability, and whether the same or different processes explain the association between response consistency and functional ability. The extent to which response consistency, conceptualized as the general ability to make logically coherent selections, predicts ADL capacity on other decision-making tasks is another important question. Based on findings that various factors contribute to individual differences in discount rates (e.g., Peters & Büchel, 2011; Shamosh et al., 2008), it will be fruitful to examine whether repeated assessments across time might increase predictive power. Unfortunately, these questions were beyond the scope of the present study. Clinicians are increasingly being asked to make determinations regarding a patient’s ability to operate autonomously in the community, and predictive measures that are fast, inexpensive, and easy to administer and interpret are in need (Gross et al., 2011). Although preliminary, the observation that discounting preferences and choice consistency predict everyday functional capacity above and beyond income, intellectual functioning, and educational attainment supports the potential of a novel, behavioral economic approach to help meet this need. REFERENCES Acker, J., Amlung, M., Stojek, M., Murphy, J. G., & MacKillop, J. (2012). Individual variation in behavioral economic indices of the relative value of alcohol: Incremental validity in relation to impulsivity,

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