Response to Tucker & Vuchinich (2015): Behavioral economics in the broader context of addiction science

Utility of a brief measure of cannabis demand: Day-level associations with cannabis use

BACKGROUND

Cannabis demand (i.e., relative value) is usually assessed as a trait-level risk-factor for cannabis use and consequences. This study examined within-person variability in day-level intensity (i.e., amount consumed at zero cost) and Omax (i.e., maximum cannabis expenditure) and tested hypotheses that demand would be positively associated with day-level cannabis use.

METHODS

Young adults (n=85) reporting past-month simultaneous alcohol and cannabis use completed two daily surveys for 30 days. Morning surveys assessed prior-day cannabis use and evening surveys assessed day-level demand (i.e., intensity, Omax). Multilevel models tested day-level effects of intensity and Omax on any cannabis use and flower use frequency and quantity (i.e., grams).

RESULTS

Approximately 52 % and 46 % of variability in intensity and Omax, respectively, was due to within-person change. At the day-level, higher intensity and Omax were associated with higher likelihood of any cannabis use, regardless of formulation; Omax was associated with use of flower in particular; and intensity was associated with the highest quantity of use. At the person-level, only Omax was associated with flower use likelihood, and only intensity was associated with flower quantity across days.

CONCLUSIONS

Cannabis demand demonstrated day-to-day variability, conceivably in response to various internal states and external factors. Intensity and Omax were related to elevated likelihood of using any cannabis, particularly flower, at the day-level. Overall, these data illustrate the validity and utility of brief cannabis demand measures, which might be used to further understand cannabis' reinforcing value at a fine-grained level.

Value-based decision-making in regular alcohol consumers following experimental manipulation of alcohol value

BACKGROUND

Devaluation of alcohol leads to reductions in alcohol choice and consumption; however, the cognitive mechanisms that underpin this relationship are not well-understood. In this study we applied a computational model of value-based decision-making (VBDM) to decisions made about alcohol and alcohol-unrelated cues following experimental manipulation of alcohol value.

METHOD

Using a pre-registered within-subject design, thirty-six regular alcohol consumers (≥14 UK units per week) completed a two-alternative forced choice task where they chose between two alcohol images (in one block) or two soft drink images (in a different block) after watching videos that emphasised the positive (alcohol value), and separately, the negative (alcohol devalue) consequences of alcohol. On each block, participants pressed a key to select the image depicting the drink they would rather consume. A drift-diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition.

FINDINGS

In the alcohol devalue condition, soft drink EA rates were significantly higher compared to alcohol EA rates (p = 0.04, d = 0.31), and compared to soft drink EA rates in the alcohol value condition (p = 0.01, d = 0.38). However, EA rates for alcoholic drinks and response thresholds (for either drink type) were unaffected by the experimental manipulation.

CONCLUSIONS

In line with behavioural economic models of addiction that emphasise the important role of alternative reinforcement, experimentally manipulating alcohol value is associated with changes in the internal cognitive processes that precede soft drink choice.

Alcohol demand assessed daily: Validity, variability, and the influence of drinking-related consequences

BACKGROUND

Alcohol demand, typically assessed at the trait-level, via single administration, reflects individualized alcohol value. We examined correspondence between baseline trait-level and daily brief measures of alcohol demand, and whether demand changes day-to-day in response to recent drinking-related consequences. Understanding whether consequences influence demand fluctuations may provide insight into when demand can be reduced in the context of intervention.

METHODS

Heavy drinking college students (n = 95, age 18-20, 52% female) completed a baseline 14-item alcohol purchase task (APT). Observed demand indices were: intensity (consumption at zero cost), O_max (maximum expenditure), and breakpoint (cost whereby consumption is suppressed to zero). Participants subsequently completed 28 daily reports including a 3-item APT (one item corresponding to each baseline index) and prior day drinking and consequences.

RESULTS

Intraclass correlations revealed within-person variability (i.e., day-to-day change) across daily demand indices. In hierarchical linear models (HLM), each daily demand index was significantly predicted by its corresponding baseline full APT index, when all three baseline indices were entered, suggesting convergent validity of the daily measure. Lower day-level intensity was predicted by more prior day negative consequences, controlling for several day- and person-level variables in HLM. Recent positive consequences did not impact intensity, and daily O_max and breakpoint were not predicted by any tested day- or person-level variables.

CONCLUSIONS

APT indices collected daily map on well to traditional single-administration APT metrics and change in response to recent consequences. Intensity demonstrated the greatest within-person variability, the strongest association with its corresponding full APT index, and theoretically-consistent prediction by negative consequences of drinking.