Andrew Zaldivar

Who are the crowdworkers?: shifting demographics in mechanical turk

Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.

Simulation of how neuromodulation influences cooperative behavior

Neuromodulators can have a strong effect on how organisms cooperate and compete for resources. To better understand the effect of neuromodulation on cooperative behavior, a computational model of the dopaminergic and serotonergic systems was constructed and tested in games of conflict and cooperation. This neural model was based on the assumptions that dopaminergic activity increases as expected reward increases, and serotonergic activity increases as the expected cost of an action increases. The neural model guided the behavior of an agent that played a series of Hawk-Dove games against an opponent. The agent adapted its behavior appropriately to changes in environmental conditions and to changes in its opponents strategy. The neural agent tended to engage in Hawk-like behavior in low-risk situations and Dove-like behavior in high-risk situations. When the simulated dopaminergic activity was greater than the serotonergic activity, the agent tended to escalate a fight. These results suggest how the neuromodulatory systems shape decision-making and adaptive behavior in competitive and cooperative situations.

Effect of neuromodulation on performance in game playing: A modeling study

Neuromodulators can have a strong effect on how organisms learn and compete for resources. Neuromodulators, such as dopamine (DA) and serotonin (5-HT), are known to be important in predicting rewards, costs, and punishments. To better understand the effect of neuromodulation on decision-making, a computational model of the dopaminergic and serotonergic systems was constructed and tested in games of conflict. This neural model was based on the assumptions that dopaminergic activity increases as expected reward increases, and serotonergic activity increases as the expected cost of an action increases. Specifically, the neural model guided the learning of an agent that played a series of Hawk-Dove games against an opponent. The model responded appropriately to changes in environmental conditions or to changes in its opponents strategy. The neural agent became Dove-like in its behavior when its dopaminergic system was compromised, and became Hawk-like in its behavior when its serotonergic system was compromised. Our model suggests how neuromodulatory systems can shape decision-making and adaptive learning in competitive situations.

Model of the interactions between neuromodulators and prefrontal cortex during a resource allocation task

Neuromodulators such as dopamine (DA), serotonin (5-HT), and acetylcholine (ACh) are crucial to the representations of reward, cost, and attention respectively. Recent experiments suggest that the reward and cost of actions are also partially represented in orbitofrontal and anterior cingulate cortices in that order. Previous models of action selection with neuromodulatory systems have not extensively considered prefrontal contributions to action selection. Here, we extend these models of action selection to include prefrontal structures in a resource allocation task. The model adapts to its environment, modulating its aggressiveness based on its successes. Selective lesions demonstrate how neuromodulatory and prefrontal areas drive learning and performance of strategy selection.

Interactions between the neuromodulatory systems and the amygdala: exploratory survey using the Allen Mouse Brain Atlas

Neuromodulatory systems originate in nuclei localized in the subcortical region of the brain and control fundamental behaviors by interacting with many areas of the central nervous system. An exploratory survey of the cholinergic, dopaminergic, noradrenergic, and serotonergic receptor expression energy in the amygdala, and in the neuromodulatory areas themselves was undertaken using the Allen Mouse Brain Atlas. The amygdala was chosen because of its importance in cognitive behavior and its bidirectional interaction with the neuromodulatory systems. The gene expression data of 38 neuromodulatory receptor subtypes were examined across 13 brain regions. The substantia innominata of the basal forebrain and regions of the amygdala had the highest amount of receptor expression energy for all four neuromodulatory systems examined. The ventral tegmental area also displayed high receptor expression of all four neuromodulators. In contrast, the locus coeruleus displayed low receptor expression energy overall. In general, cholinergic receptor expression was an order of magnitude greater than other neuromodulatory receptors. Since the nuclei of these neuromodulatory systems are thought to be the source of specific neurotransmitters, the projections from these nuclei to target regions may be inferred by receptor expression energy. The comprehensive analysis revealed many connectivity relations and receptor localization that had not been previously reported. The methodology presented here may be applied to other neural systems with similar characteristics, and to other animal models as these brain atlases become available.

A dynamic, embodied paradigm to investigate the role of serotonin in decision-making

Serotonin (5-HT) is a neuromodulator that has been attributed to cost assessment and harm aversion. In this review, we look at the role 5-HT plays in making decisions when subjects are faced with potential harmful or costly outcomes. We review approaches for examining the serotonergic system in decision-making. We introduce our group’s paradigm used to investigate how 5-HT affects decision-making. In particular, our paradigm combines techniques from computational neuroscience, socioeconomic game theory, human–robot interaction, and Bayesian statistics. We will highlight key findings from our previous studies utilizing this paradigm, which helped expand our understanding of 5-HT’s effect on decision-making in relation to cost assessment. Lastly, we propose a cyclic multidisciplinary approach that may aid in addressing the complexity of exploring 5-HT and decision-making by iteratively updating our assumptions and models of the serotonergic system through exhaustive experimentation.

Responsible research with crowds: pay crowdworkers at least minimum wage

High-level guidelines for the treatment of crowdworkers.

Allen Brain Atlas-Driven Visualizations: a web-based gene expression energy visualization tool.

The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources.

Investigation of Biases and Compensatory Strategies Using a Probabilistic Variant of the Wisconsin Card Sorting Test

The Wisconsin Card Sorting Test (WCST) evaluates a subject’s ability to shift to a new pattern of behavior in response to the presentation of unexpected negative feedback. The present study introduces a novel version of the traditional WCST by integrating a probabilistic component into its traditional rule shifting to add uncertainty to the task, as well as the option to forage for information during any particular trial. These changes transformed a task that is trivial for neurotypical individuals into a challenging environment useful for evaluating biases and compensatory strategizing. Sixty subjects performed the probabilistic WCST at four uncertainty levels to determine the effect of uncertainty on subject performance and strategy. Results revealed that increasing the level of uncertainty during a run of trials correlated with a reduction in rational strategizing in favor of both random choice and information foraging, evoking biases and suboptimal strategies such as satisfaction of search, negativity bias, and probability matching.