Pupil Correlates of Decision Variables in Mice Playing a Competitive Mixed-Strategy Game

Proactive Versus Reactive Control Strategies Differentially Mediate Alcohol Drinking in Male Wistars and P Rats

Problematic alcohol consumption is associated with deficits in decision-making and alterations in prefrontal cortex neural activity likely contribute. We hypothesized that the differences in cognitive control would be evident between male Wistars and a model of genetic risk: alcohol-preferring P rats. Cognitive control is split into proactive and reactive components. Proactive control maintains goal-directed behavior independent of a stimulus, whereas reactive control elicits goal-directed behavior at the time of a stimulus. We hypothesized that Wistars would show proactive control over alcohol seeking whereas P rats would show reactive control over alcohol seeking. Neural activity was recorded from the prefrontal cortex during an alcohol seeking task with two session types. On congruent sessions, the conditioned stimulus (CS+) was on the same side as alcohol access. Incongruent sessions presented alcohol opposite the CS+. Wistars, but not P rats, made more incorrect approaches during incongruent sessions, suggesting that Wistars utilized the previously learned rule. This motivated the hypothesis that neural activity reflecting proactive control would be observable in Wistars but not P rats. While P rats showed differences in neural activity at times of alcohol access, Wistars showed differences prior to approaching the sipper. These results support our hypothesis that Wistars are more likely to engage in proactive cognitive control strategies whereas P rats are more likely to engage in reactive cognitive control strategies. Although P rats were bred to prefer alcohol, the differences in cognitive control may reflect a sequela of behaviors that mirror those in humans at risk for an AUD.

Boundary updating as a source of history effect on decision uncertainty

When sorting a sequence of stimuli into binary classes, current choices are often negatively correlated with recent stimulus history. This phenomenon-dubbed the repulsive bias-can be explained by boundary updating, a process of shifting the class boundary to previous stimuli. This explanation implies that recent stimulus history can also influence "decision uncertainty," the probability of making incorrect decisions, because it depends on the location of the boundary. However, there have been no previous efforts to elucidate the impact of previous stimulus history on decision uncertainty. Here, from the boundary-updating process that accounts for the repulsive bias, we derived a prediction that decision uncertainty increases as current choices become more congruent with previous stimuli. We confirmed this prediction in behavioral, physiological, and neural correlates of decision uncertainty. Our work demonstrates that boundary updating offers a principled account of how previous stimulus history concurrently relates to choice bias and decision uncertainty.

Pupillary dynamics of mice performing a Pavlovian delay conditioning task reflect reward-predictive signals

Pupils can signify various internal processes and states, such as attention, arousal, and working memory. Changes in pupil size have been associated with learning speed, prediction of future events, and deviations from the prediction in human studies. However, the detailed relationships between pupil size changes and prediction are unclear. We explored pupil size dynamics in mice performing a Pavlovian delay conditioning task. A head-fixed experimental setup combined with deep-learning-based image analysis enabled us to reduce spontaneous locomotor activity and to track the precise dynamics of pupil size of behaving mice. By setting up two experimental groups, one for which mice were able to predict reward in the Pavlovian delay conditioning task and the other for which mice were not, we demonstrated that the pupil size of mice is modulated by reward prediction and consumption, as well as body movements, but not by unpredicted reward delivery. Furthermore, we clarified that pupil size is still modulated by reward prediction even after the disruption of body movements by intraperitoneal injection of haloperidol, a dopamine D2 receptor antagonist. These results suggest that changes in pupil size reflect reward prediction signals. Thus, we provide important evidence to reconsider the neuronal circuit involved in computing reward prediction error. This integrative approach of behavioral analysis, image analysis, pupillometry, and pharmacological manipulation will pave the way for understanding the psychological and neurobiological mechanisms of reward prediction and the prediction errors essential to learning and behavior.

Choice perseverance underlies pursuing a hard-to-get target in an avatar choice task

People sometimes persistently pursue hard-to-get targets. Why people pursue such targets is unclear. Here, we hypothesized that choice perseverance, which is the tendency to repeat the same choice independent of the obtained outcomes, leads individuals to repeatedly choose a hard-to-get target, which consequently increases their preference for the target. To investigate this hypothesis, we conducted an online experiment involving an avatar choice task in which the participants repeatedly selected one avatar, and the selected avatar expressed their valence reactions through facial expressions and voice. We defined “hard-to-get” and “easy-to-get” avatars by manipulating the outcome probability such that the hard-to-get avatars rarely provided a positive reaction when selected, while the easy-to-get avatars frequently did. We found that some participants repeatedly selected hard-to-get avatars (Pursuit group). Based on a simulation, we found that higher choice perseverance accounted for the pursuit of hard-to-get avatars and that the Pursuit group had significantly higher choice perseverance than the No-pursuit group. Model fitting to the choice data also supported that choice perseverance can account for the pursuit of hard-to-get avatars in the Pursuit group. Moreover, we found that although baseline attractiveness was comparable among all avatars used in the choice task, the attractiveness of the hard-to-get avatars was significantly increased only in the Pursuit group. Taken together, we conclude that people with high choice perseverance pursue hard-to-get targets, rendering such targets more attractive. The tolerance for negative outcomes might be an important factor for succeeding in our lives but sometimes triggers problematic behavior, such as stalking. The present findings may contribute to understanding the psychological mechanisms of passion and perseverance for one’s long-term goals, which are more general than the romantic context imitated in avatar choice.

Monkey plays Pac-Man with compositional strategies and hierarchical decision-making

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision-making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys’ decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals’ advanced cognition.