Ulrik R. Beierholm

Learn More
Our nervous system typically processes signals from multiple sensory modalities at any given moment and is therefore posed with two important problems: which of the signals are caused by a common event, and how to combine those signals. We investigated human perception in the presence of auditory, visual, and tactile stimulation in a numerosity judgment(More)
The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual(More)
It has been shown that human combination of crossmodal information is highly consistent with an optimal Bayesian model performing causal inference. These findings have shed light on the computational principles governing crossmodal integration/segregation. Intuitively, in a Bayesian framework priors represent a priori information about the environment,(More)
Bayesian models of multisensory perception traditionally address the problem of estimating an underlying variable that is assumed to be the cause of the two sensory signals. The brain, however, has to solve a more general problem: it also has to establish which signals come from the same source and should be integrated, and which ones do not and should be(More)
Prefrontal cortex has long been implicated in tasks involving higher order inference in which decisions must be rendered, not only about which stimulus is currently rewarded, but also which stimulus dimensions are currently relevant. However, the precise computational mechanisms used to solve such tasks have remained unclear. We scanned human participants(More)
Two fundamental questions underlie the expression of behavior, namely what to do and how vigorously to do it. The former is the topic of an overwhelming wealth of theoretical and empirical work particularly in the fields of reinforcement learning and decision-making, with various forms of affective prediction error playing key roles. Although vigor concerns(More)
Subjects typically choose to be presented with stimuli that predict the existence of future reinforcements. This so-called 'observing behavior' is evident in many species under various experimental conditions, including if the choice is expensive, or if there is nothing that subjects can do to improve their lot with the information gained. A recent study(More)
Behavioral studies have long shown that humans solve problems in two ways, one intuitive and fast (System 1, model-free), and the other reflective and slow (System 2, model-based). The neurobiological basis of dual process problem solving remains unknown due to challenges of separating activation in concurrent systems. We present a novel neuroeconomic task(More)
Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical(More)