The neurocognitive correlates of non-substance addictive behaviors

Neurocognitive deficits have been implicated as transdiagnostic risk markers of substance use disorders. However, these have yet to be comprehensively evaluated in other, non-substance addictions. In a large, general community sample (N = 475) the present study evaluated the neurocognitive correlates of problem alcohol use and three non-substance-related addictive behaviors: addictive eating (AE), problematic pornography use (PPU), and problematic use of the internet (PUI), to identify potential shared and distinct neurocognitive correlates. A sample of Australian residents (54.4 % female M[SD] age = 32.4[11.9] years) completed a comprehensive online assessment of neurocognitive tasks tapping into eight distinct expert-endorsed domains purportedly associated with addiction. Multiple linear regressions with bootstrapping were used to examine associations among each addictive behavior of interest and neurocognition, trait impulsivity, and compulsivity, as well as key covariates. Neurocognition was differentially associated with each addictive behavior. None of the neurocognitive domains were significantly associated with problematic alcohol use or AE (p >.05), poorer performance monitoring was significantly associated with higher levels of PPU and PUI (β = −0.10, p =.049; β = −0.09, p =.028), and a preference for delayed gratification was associated with more severe PUI (β = −0.10, p =.025). Our findings have theoretical implications for how we understand non-substance addiction and suggest the need for a more nuanced approach to studying addictive behaviors that take into account the underlying neurocognitive mechanisms associated with each type of addiction

Intrinsic Valuation of Information in Decision Making under Uncertainty

<div><p>In a dynamic world, an accurate model of the environment is vital for survival, and agents ought regularly to seek out new information with which to update their world models. This aspect of behaviour is not captured well by classical theories of decision making, and the cognitive mechanisms of information seeking are poorly understood. In particular, it is not known whether information is valued only for its instrumental use, or whether humans also assign it a non-instrumental intrinsic value. To address this question, the present study assessed preference for non-instrumental information among 80 healthy participants in two experiments. Participants performed a novel information preference task in which they could choose to pay a monetary cost to receive advance information about the outcome of a monetary lottery. Importantly, acquiring information did not alter lottery outcome probabilities. We found that participants were willing to incur considerable monetary costs to acquire payoff-irrelevant information about the lottery outcome. This behaviour was well explained by a computational cognitive model in which information preference resulted from aversion to temporally prolonged uncertainty. These results strongly suggest that humans assign an intrinsic value to information in a manner inconsistent with normative accounts of decision making under uncertainty. This intrinsic value may be associated with adaptive behaviour in real-world environments by producing a bias towards exploratory and information-seeking behaviour.</p></div

Behavioral results for Experiment 1.

<p>(A) Mean proportion of informative stimulus choices (denoted <i>Pr(Info)</i>) as a function of information cost. Error bars represent the standard error of the mean (SEM). Mean proportion of information-seeking choices was monotonically decreasing in cost of information. (B) Histogram of overall proportion of informative stimulus choices across participants, demonstrating inter-individual differences in behaviour. N = 40.</p

Behavioural results for Experiment 2.

<p>Mean proportion of informative stimulus choices (denoted <i>Pr(Info)</i>) as a function of information cost and information rate. Light blue bars represent the fast speed condition, medium blue bars the moderate speed condition, and dark blue bars the slow rate conditions. Error bars represent SEM. Proportions of information-seeking choices decreased as information rate slowed, particularly for positive information cost conditions (costs of 1, 3, and 5 cents). N = 40.</p

Block-wise behavioural results for Experiment 1.

<p>Mean proportion of information-seeking choices, denoted <i>Pr(Info)</i>, as a function of information cost and block number across participants. Choice proportions for blocks one to seven are presented in ascending order left to right within each of the four cost conditions. Error bars represent SEM. Preference for information was static across the task.</p

Behavioural model fits for 4405 choices by 40 participants.

<p>Behavioural model fits for 4405 choices by 40 participants.</p

Model fit.

<p> Actual (blue) and UP-predicted (grey) group-level mean proportions of information-seeking choices as a function of information cost across participants. Error bars represent SEM. N = 40.</p

Latent mixture model of choice probability.

<p>In the present study, choice probabilities were assumed to be a latent mixture of erroneous button presses (with probability <i>ε</i>) and accurate button presses (with probability 1−<i>ε</i>). Since erroneous button presses are by definition undirected, these choices are therefore equally likely to result in the selection of the informative signal or the non-informative signal. This has the effect of placing a floor of and a ceiling of 1− on choice probabilities for each option.</p

Task schematic.

<p>(A) Choice schematic. In the informative stimulus, the colour composition of cards perfectly predicted lottery outcome. In the non-informative stimulus, cards had no predictive validity for the lottery outcome. The information cost <i>c</i> was subtracted from lottery winnings only in the case of a win outcome. As a result, a loss always resulted in the same outcome (receive 0 cents) regardless of the participant’s choice of stimulus. (B) Trial schematic. Participants first received information regarding the identity and cost of the informative stimulus (both counterbalanced across trials), and then made a choice using left and right arrow keys within 2 seconds (left/right mapping of A and B counterbalanced across trials). Participants were then presented for 2 seconds with a choice information screen, following which cards from the chosen stimulus were revealed sequentially at a constant rate of 3 seconds per card (18 seconds total delay). Participants were informed that all outcomes were predetermined, and that choice of stimulus was unrelated to win probability. If the participant failed to respond during the choice window, the non-informative stimulus was shown and no reward was subsequently delivered.</p

Individual-participant model fits.

<p>(A) Actual informative choice proportion, denoted Pr(Info) (horizontal axis) versus informative choice proportion as predicted by the UP model (vertical axis). Each circle indicates one participant. Euclidean distance from the diagonal (grey line) represents error in prediction. (B) Actual informative choice proportion (horizontal axis) versus informative choice proportion as predicted by the EVI model (vertical axis). Each circle indicates one participant. Euclidean distance from the diagonal (grey line) represents error in prediction. Across all participants, the EVI systematically under-predicted informative choice proportions (all participants fell below the diagonal).</p