Modelling dataset bias in machine-learned theories of economic decision-making.

Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networks on a new online large-scale dataset, choices13k. Here we systematically analyse the relationships between several models and datasets using machine-learning methods and find evidence for dataset bias. Because participants' choices in stochastically dominated gambles were consistently skewed towards equipreference in the choices13k dataset, we hypothesized that this reflected increased decision noise. Indeed, a probabilistic generative model adding structured decision noise to a neural network trained on data from a laboratory study transferred best, that is, outperformed all models apart from those trained on choices13k. We conclude that a careful combination of theory and data analysis is still required to understand the complex interactions of machine-learning models and data of human risky choices.

People learn a two-stage control for faster locomotor interception.

People can use the constant target-heading (CTH) strategy or the constant bearing (CB) strategy to guide their locomotor interception. But it is still unclear whether people can learn new interception behavior. Here, we investigated how people learn to adjust their steering to intercept targets faster. Participants steered a car to intercept a moving target in a virtual environment similar to a natural open field. Their baseline interceptions were better accounted for by the CTH strategy. After five learning sessions across multiple days, in which participants received feedback about their interception durations, they adopted a two-stage control: a quick initial burst of turning accompanied by an increase of the target-heading angle during early interception was followed by significantly less turning with small changes in target-heading angle during late interception. The target's bearing angle did not only show this two-stage pattern but also changed comparatively little during late interception, leaving it unclear which strategy participants had adopted. In a following test session, the two-stage pattern of participants' turning adjustment and the target-heading angle transferred to new target conditions and a new environment without visual information about an allocentric reference frame, which should preclude participants from using the CB strategy. Indeed, the pattern of the target's bearing angle did not transfer to all the new conditions. These results suggest that participants learned a two-stage control for faster interception: they learned to quickly increase the target-heading angle during early interception and subsequently follow the CTH strategy during late interception.

Putting perception into action with inverse optimal control for continuous psychophysics.

Psychophysical methods are a cornerstone of psychology, cognitive science, and neuroscience where they have been used to quantify behavior and its neural correlates for a vast range of mental phenomena. Their power derives from the combination of controlled experiments and rigorous analysis through signal detection theory. Unfortunately, they require many tedious trials and preferably highly trained participants. A recently developed approach, continuous psychophysics, promises to transform the field by abandoning the rigid trial structure involving binary responses and replacing it with continuous behavioral adjustments to dynamic stimuli. However, what has precluded wide adoption of this approach is that current analysis methods do not account for the additional variability introduced by the motor component of the task and therefore recover perceptual thresholds that are larger compared to equivalent traditional psychophysical experiments. Here, we introduce a computational analysis framework for continuous psychophysics based on Bayesian inverse optimal control. We show via simulations and previously published data that this not only recovers the perceptual thresholds but additionally estimates subjects' action variability, internal behavioral costs, and subjective beliefs about the experimental stimulus dynamics. Taken together, we provide further evidence for the importance of including acting uncertainties, subjective beliefs, and, crucially, the intrinsic costs of behavior, even in experiments seemingly only investigating perception.

Humans often perceive the world around them subjectively. Factors like light brightness, the speed of a moving object, or an individual's interpretation of facial expressions may influence perception. Understanding how humans perceive the world can provide valuable insights into neuroscience, psychology, and even people's spending habits, making human perception studies important. However, these so-called psychophysical studies often consist of thousands of simple yes or no questions, which are tedious for adult volunteers, and nearly impossible for children. A new approach called 'continuous psychophysics' makes perception studies shorter, easier, and more fun for participants. Instead of answering yes or no questions (like in classical psychophysics experiments), the participants follow an object on a screen with their fingers or eyes. One question about this new approach is whether it accounts for differences that affect how well participants follow the object. For example, some people may have jittery hands, while others may be unmotivated to complete the task. To overcome this issue, Straub and Rothkopf have developed a mathematical model that can correct for differences between participants in the variability of their actions, their internal costs of actions, and their subjective beliefs about how the target moves. Accounting for these factors in a model can lead to more reliable study results. Straub and Rothkopf used data from three previous continuous psychophysics studies to construct a mathematical model that could best predict the experimental results. To test their model, they then used it on data from a continuous psychophysics study conducted alongside a classical psychophysics study. The model was able to correct the results of the continuous psychophysics study so they were more consistent with the results of the classical study. This new technique may enable wider use of continuous psychophysics to study a range of human behavior. It will allow larger, more complex studies that would not have been possible with conventional approaches, as well as enable research on perception in infants and children. Brain scientists may also use this technique to understand how brain activity relates to perception.

How do people steer a car to intercept a moving target: Interceptions in different environments point to one strategy.

Which strategy people use to guide locomotor interception remains unclear despite considerable research and the importance of an answer with ramification into the heuristics and biases debate. Because the constant bearing (CB) strategy corresponds to the target-heading (CTH) strategy with an additional constraint, these two strategies can be confounded experimentally. But, the two strategies are distinct in the information they require: while the CTH strategy only requires access to the relative angle between the direction of motion and the target, the CB strategy requires access to a stable allocentric reference frame. Here, we manipulated the visual information about allocentric reference frames in three virtual environments and asked participants to steer a car to intercept a moving target. Participants' interception paths showed different degrees of curvature and their target-heading angles were approximately constant, consistent with the CTH strategy. By contrast, the target's bearing angle continuously changed in all participants except one. This particular participant produced linear interception paths with little change in the target's bearing angle, seemingly consistent with both strategies. This participant continued this pattern of steering even in the environment without any visual information about allocentric reference frames. Therefore, this pattern of steering is attributed to the CTH strategy rather than the CB strategy. The overall results add important evidence for the conclusion that locomotor interception is better accounted for by the CTH strategy and that experimentally observing a straight interception trajectory with a CB angle is not sufficient evidence for the CB strategy.

Looking for Image Statistics: Active Vision With Avatars in a Naturalistic Virtual Environment.

The efficient coding hypothesis posits that sensory systems are tuned to the regularities of their natural input. The statistics of natural image databases have been the topic of many studies, which have revealed biases in the distribution of orientations that are related to neural representations as well as behavior in psychophysical tasks. However, commonly used natural image databases contain images taken with a camera with a planar image sensor and limited field of view. Thus, these images do not incorporate the physical properties of the visual system and its active use reflecting body and eye movements. Here, we investigate quantitatively, whether the active use of the visual system influences image statistics across the visual field by simulating visual behaviors in an avatar in a naturalistic virtual environment. Images with a field of view of 120° were generated during exploration of a virtual forest environment both for a human and cat avatar. The physical properties of the visual system were taken into account by projecting the images onto idealized retinas according to models of the eyes' geometrical optics. Crucially, different active gaze behaviors were simulated to obtain image ensembles that allow investigating the consequences of active visual behaviors on the statistics of the input to the visual system. In the central visual field, the statistics of the virtual images matched photographic images regarding their power spectra and a bias in edge orientations toward cardinal directions. At larger eccentricities, the cardinal bias was superimposed with a gradually increasing radial bias. The strength of this effect depends on the active visual behavior and the physical properties of the eye. There were also significant differences between the upper and lower visual field, which became stronger depending on how the environment was actively sampled. Taken together, the results show that quantitatively relating natural image statistics to neural representations and psychophysical behavior requires not only to take the structure of the environment into account, but also the physical properties of the visual system, and its active use in behavior.

The visual control of interceptive steering: How do people steer a car to intercept a moving target?

The visually guided interception of a moving target is a fundamental visuomotor task that humans can do with ease. But how humans carry out this task is still unclear despite numerous empirical investigations. Measurements of angular variables during human interception have suggested three possible strategies: the pursuit strategy, the constant bearing angle strategy, and the constant target-heading strategy. Here, we review previous experimental paradigms and show that some of them do not allow one to distinguish among the three strategies. Based on this analysis, we devised a virtual driving task that allows investigating which of the three strategies best describes human interception. Crucially, we measured participants' steering, head, and gaze directions over time for three different target velocities. Subjects initially aligned head and gaze in the direction of the car's heading. When the target appeared, subjects centered their gaze on the target, pointed their head slightly off the heading direction toward the target, and maintained an approximately constant target-heading angle, whose magnitude varied across participants, while the target's bearing angle continuously changed. With a second condition, in which the target was partially occluded, we investigated several alternative hypotheses about participants' visual strategies. Overall, the results suggest that interceptive steering is best described by the constant target-heading strategy and that gaze and head are coordinated to continuously acquire visual information to achieve successful interception.