Recurrent, Robust and Scalable Patterns Underlie Human Approach and Avoidance

Abstract

BackgroundApproach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory.Methodology/Principal FindingsThree sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both.Conclusions/SignificanceThese law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).

Behavioral characteristics of pair bonding in the black tufted-ear marmoset (Callithrix penicillata)

“…It can be assumed that the conditioned incentive properties of the partner, hence the response of approach, had reached an asymptotic level by Day 6. Any further increase in response intensity would be offset because of habituation (McSweeney & Murphy, 2009; Carr & Epstein, 2011) or because of negative alliesthesia (Cabanac, 1971; Brondel et al, 2009; Kim et al, 2010). Thus, performance of conditioned responses stabilizes or gets reduced, as is the case with some sociosexual behaviors in long term marmoset pairs (see Introduction) and in human long term relationships (Klusmann, 2002).…”

Effects of extended-release naltrexone on the brain response to drug-related stimuli in patients with opioid use disorder

“…Third, our stimuli were not matched on pleasantness (see Appendix 1, "Stimuli" sec tion, for more details) and were not assessed on other dimensions, including arousal, dominance, 58 or effort ex penditure to view. 59 Finally, the limited number of brain regions of interest (i.e., NAcc and mOFC), the focus on the session × stimulus interaction instead of pairwise compari son between sessions across stimulus categories, and a rel atively small sample size may have limited our ability to fully unravel the effects of XRNTX on brain function (e.g., neural responses to sexual and aversive stimuli). It is our hope that our study sets the stage for filling these gaps in future research.…”

Keypress-Based Musical Preference Is Both Individual and Lawful

“…Our prior work has shown that both frameworks appear to work well within the range of interpretable human keypressing (Breiter and Kim, 2008; Kim et al, 2010; Lee et al, 2015). In this manuscript, we addressed this question by comparing the R 2 values for the approach and avoidance curves between the logarithmic and power-law fits, in order to see whether one of the functions had significantly higher goodness of fit values.…”