Rats exhibit reference-dependent choice behavior

Rational regulation of water-seeking effort in rodents

We confirm that rats can act as rational economic agents, making choices about how much work to do to obtain a reward in a way that optimally trades off the value of the reward against the cost of the effort. Contrary to the notion that bigger rewards are more motivating, rats worked harder in economies where rewards were small, ensuring a sufficient minimum income of water. But they chose to earn and consume more water per day when water was “cheap” (available for little work). We present a mathematical model explaining why rats work when they do (surprisingly, not just when they are thirsty) and suggesting where in the brain animals might compute the current value of working for water.

In the laboratory, animals’ motivation to work tends to be positively correlated with reward magnitude. But in nature, rewards earned by work are essential to survival (e.g., working to find water), and the payoff of that work can vary on long timescales (e.g., seasonally). Under these constraints, the strategy of working less when rewards are small could be fatal. We found that instead, rats in a closed economy did more work for water rewards when the rewards were stably smaller, a phenomenon also observed in human labor supply curves. Like human consumers, rats showed elasticity of demand, consuming far more water per day when its price in effort was lower. The neural mechanisms underlying such “rational” market behaviors remain largely unexplored. We propose a dynamic utility maximization model that can account for the dependence of rat labor supply (trials/day) on the wage rate (milliliter/trial) and also predict the temporal dynamics of when rats work. Based on data from mice, we hypothesize that glutamatergic neurons in the subfornical organ in lamina terminalis continuously compute the instantaneous marginal utility of voluntary work for water reward and causally determine the amount and timing of work.

Risky choice framing by experience: A methodological note

In classic research on judgment and decision making under risk, risk is described by providing participants with the respective outcomes and probabilities in a summary format. Recent research has introduced a different paradigm – decisions-by-experience – where participants learn about risk by sampling from the outcomes, rather than by summary descriptions. This latter research reports a description-experience gap, indicating that some of the classic patterns of risk attitude reverse when people experience the risk. Recent research has attempted to investigate risky choice framing in the decisions-by-experience paradigm. I discuss how this research runs into problems in properly manipulating framing in decisions by experience. Drawing from framing research with animals, I argue that framing effects also exist in experience tasks. The classic Asian Disease task, however, awaits proper translation into an experience paradigm.

Decomposing loss aversion from gaze allocation and pupil dilation

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals.

Gains v. losses, or context dependence generated by confusion?

Tversky and Kahneman introduced the term framing for the finding that people give different answers to the same question depending on the way it is posed. One form of framing involves presenting the same outcome as either a gain or a loss. An experiment on starlings by Marsh and Kacelnik suggests that this form of framing occurs in non-humans. We argue that the experimental result demonstrates framing in the general sense of context dependence but does not provide compelling evidence of framing in terms of gains and losses. A version of scalar utility theory which is extended to include the possibility of memory errors accounts for the data and suggests future lines of research.

Review: brain neurobiology of gambling disorder based on rodent models

Different literature reviews of gambling disorder (GD) neurobiology have been focused on human studies, others have focused on rodents, and others combined human and rodent studies. The main question of this review was: which are the main neurotransmitters systems and brain structures relevant for GD based on recent rodent studies? This work aims to review the experimental findings regarding the rodent´s neurobiology of GD. A search in the Pub Med database was set (October 2012-October 2017) and 162 references were obtained. After screening, 121 references were excluded, and only 41 references remained from the initial output. More, other 25 references were added to complement (introduction section, neuroanatomical descriptions) the principal part of the work. At the end, a total of 66 references remained for the review. The main conclusions are: 1) according to studies that used noninvasive methods for drug administration, some of the neurotransmitters and receptors involved in behaviors related to GD are: muscarinic, N-methyl-D-aspartate (NMDA), cannabinoid receptor 1 (CB₁), cannabinoid receptor 2 (CB₂), dopamine 2 receptor (D₂), dopamine 3 receptor (D₃), and dopamine 4 receptor (D₄); 2) moreover, there are other neurotransmitters and receptors involved in GD based on studies that use invasive methods of drug administration (eg, brain microinjection); example of these are: serotonin 1A receptor (5-HT_1A), noradrenaline receptors, gamma-aminobutyric acid receptor A (GABA_A), and gamma-aminobutyric acid receptor B (GABA_B); 3) different brain structures are relevant to behaviors linked to GD, like: amygdala (including basolateral amygdala (BLA)), anterior cingulate cortex (ACC), hippocampus, infralimbic area, insular cortex (anterior and rostral agranular), nucleus accumbens (NAc), olfactory tubercle (island of Calleja), orbitofrontal cortex (OFC), medial prefrontal cortex (mPFC), prefrontal cortex (PFC) - subcortical network, striatum (ventral) and the subthalamic nucleus (STN); and 4) the search for GD treatments should consider this diversity of receptor/neurotransmitter systems and brain areas.

Fractionating choice: A study on reward discrimination, preference, and relative valuation in the rat (Rattus norvegicus)

Choice behavior combines discrimination between distinctive outcomes, preference for specific outcomes and relative valuation of comparable outcomes. Previous work has focused on 1 component (i.e., preference) disregarding other influential processes that might provide a more complete understanding. Animal models of choice have been explored primarily utilizing extensive training, limited freedom for multiple decisions and sparse behavioral measures constrained to a single phase of motivated action. The present study used a paradigm that combines different elements of previous methods with the goal to distinguish among components of choice and explore how well components match predictions based on risk-sensitive foraging strategies. In order to analyze discrimination and relative valuation, it was necessary to have an option that shifted and an option that remained constant. Shifting outcomes among weeks included a change in single-option outcome (0 to 1 to 2 pellets) or a change in mixed-option outcome (0 or 5 to 0 or 3 to 0 or 1 pellets). Constant outcomes among weeks were also mixed-option (0 or 3 pellets) or single-option (1 pellet). Shifting single-option outcomes among weeks led to better discrimination, more robust preference and significant incentive contrast effects for the alternative outcome. Shifting multioptions altered choice components and led to dissociations among discrimination, preference, and reduced contrast effects. During extinction, all components were impacted with the greatest deficits during the shifting mixed-option outcome sessions. Results suggest choice behavior can be optimized for 1 component but suboptimal for others depending upon the complexity of alterations in outcome value between options. (PsycINFO Database Record

Relative gains, losses, and reference points in probabilistic choice in rats

Theoretical reference points have been proposed to differentiate probabilistic gains from probabilistic losses in humans, but such a phenomenon in non-human animals has yet to be thoroughly elucidated. Three experiments evaluated the effect of reward magnitude on probabilistic choice in rats, seeking to determine reference point use by examining the effect of previous outcome magnitude(s) on subsequent choice behavior. Rats were trained to choose between an outcome that always delivered reward (low-uncertainty choice) and one that probabilistically delivered reward (high-uncertainty). The probability of high-uncertainty outcome receipt and the magnitudes of low-uncertainty and high-uncertainty outcomes were manipulated within and between experiments. Both the low- and high-uncertainty outcomes involved variable reward magnitudes, so that either a smaller or larger magnitude was probabilistically delivered, as well as reward omission following high-uncertainty choices. In Experiments 1 and 2, the between groups factor was the magnitude of the high-uncertainty-smaller (H-S) and high-uncertainty-larger (H-L) outcome, respectively. The H-S magnitude manipulation differentiated the groups, while the H-L magnitude manipulation did not. Experiment 3 showed that manipulating the probability of differential losses as well as the expected value of the low-uncertainty choice produced systematic effects on choice behavior. The results suggest that the reference point for probabilistic gains and losses was the expected value of the low-uncertainty choice. Current theories of probabilistic choice behavior have difficulty accounting for the present results, so an integrated theoretical framework is proposed. Overall, the present results have implications for understanding individual differences and corresponding underlying mechanisms of probabilistic choice behavior.

Mechanisms of Individual Differences in Impulsive and Risky Choice in Rats

Individual differences in impulsive and risky choice are key risk factors for a variety of maladaptive behaviors such as drug abuse, gambling, and obesity. In our rat model, ordered individual differences are stable across choice parameters, months of testing, and span a broad spectrum, suggesting that rats, like humans, exhibit trait-level impulsive and risky choice behaviors. In addition, impulsive and risky choices are highly correlated, suggesting a degree of correlation between these two traits. An examination of the underlying cognitive mechanisms has suggested an important role for timing processes in impulsive choice. In addition, in an examination of genetic factors in impulsive choice, the Lewis rat strain emerged as a possible animal model for studying disordered impulsive choice, with this strain demonstrating deficient delay processing. Early rearing environment also affected impulsive behaviors, with rearing in an enriched environment promoting adaptable and more self-controlled choices. The combined results with impulsive choice suggest an important role for timing and reward sensitivity in moderating impulsive behaviors. Relative reward valuation also affects risky choice, with manipulation of objective reward value (relative to an alternative reference point) resulting in loss chasing behaviors that predicted overall risky choice behaviors. The combined results are discussed in relation to domain-specific versus domain-general subjective reward valuation processes and the potential neural substrates of impulsive and risky choice.