Emergence of an Adaptive Command for Orienting Behavior in Premotor Brainstem Neurons of Barn Owls.

The midbrain map of auditory space commands sound-orienting responses in barn owls. Owls precisely localize sounds in frontal space but underestimate the direction of peripheral sound sources. This bias for central locations was proposed to be adaptive to the decreased reliability in the periphery of sensory cues used for sound localization by the owl. Understanding the neural pathway supporting this biased behavior provides a means to address how adaptive motor commands are implemented by neurons. Here we find that the sensory input for sound direction is weighted by its reliability in premotor neurons of the midbrain tegmentum of owls (male and female), such that the mean population firing rate approximates the head-orienting behavior. We provide evidence that this coding may emerge through convergence of upstream projections from the midbrain map of auditory space. We further show that manipulating the sensory input yields changes predicted by the convergent network in both premotor neural responses and behavior. This work demonstrates how a topographic sensory representation can be linearly read out to adjust behavioral responses by the reliability of the sensory input.SIGNIFICANCE STATEMENT This research shows how statistics of the sensory input can be integrated into a behavioral command by readout of a sensory representation. The firing rate of midbrain premotor neurons receiving sensory information from a topographic representation of auditory space is weighted by the reliability of sensory cues. We show that these premotor responses are consistent with a weighted convergence from the topographic sensory representation. This convergence was also tested behaviorally, where manipulation of stimulus properties led to bidirectional changes in sound localization errors. Thus a topographic representation of auditory space is translated into a premotor command for sound localization that is modulated by sensory reliability.

Cross-modal object recognition and dynamic weighting of sensory inputs in a fish.

Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environmental changes more rapidly. So far, spontaneous cross-modal object recognition has only been shown in a few mammalian species, raising the question as to whether such a high-level function may be associated with complex mammalian brain structures, and therefore absent in animals lacking a cerebral cortex. Here we use an object-discrimination paradigm based on operant conditioning to show, for the first time to our knowledge, that a nonmammalian vertebrate, the weakly electric fish Gnathonemus petersii, is capable of performing spontaneous cross-modal object recognition and that the sensory inputs are weighted dynamically during this task. We found that fish trained to discriminate between two objects with either vision or the active electric sense, were subsequently able to accomplish the task using only the untrained sense. Furthermore we show that cross-modal object recognition is influenced by a dynamic weighting of the sensory inputs. The fish weight object-related sensory inputs according to their reliability, to minimize uncertainty and to enable an optimal integration of the senses. Our results show that spontaneous cross-modal object recognition and dynamic weighting of sensory inputs are present in a nonmammalian vertebrate.

Reliability of Visual and Somatosensory Feedback in Skilled Movement: The Role of the Cerebellum.

The integration of vision and somatosensation is required to allow for accurate motor behavior. While both sensory systems contribute to an understanding of the state of the body through continuous updating and estimation, how the brain processes unreliable sensory information remains to be fully understood in the context of complex action. Using functional brain imaging, we sought to understand the role of the cerebellum in weighting visual and somatosensory feedback by selectively reducing the reliability of each sense individually during a tool use task. We broadly hypothesized upregulated activation of the sensorimotor and cerebellar areas during movement with reduced visual reliability, and upregulated activation of occipital brain areas during movement with reduced somatosensory reliability. As specifically compared to reduced somatosensory reliability, we expected greater activations of ipsilateral sensorimotor cerebellum for intact visual and somatosensory reliability. Further, we expected that ipsilateral posterior cognitive cerebellum would be affected with reduced visual reliability. We observed that reduced visual reliability results in a trend towards the relative consolidation of sensorimotor activation and an expansion of cerebellar activation. In contrast, reduced somatosensory reliability was characterized by the absence of cerebellar activations and a trend towards the increase of right frontal, left parietofrontal activation, and temporo-occipital areas. Our findings highlight the role of the cerebellum for specific aspects of skillful motor performance. This has relevance to understanding basic aspects of brain functions underlying sensorimotor integration, and provides a greater understanding of cerebellar function in tool use motor control.

Variance misperception explains illusions of confidence in simple perceptual decisions.

Confidence in a perceptual decision is a judgment about the quality of the sensory evidence. The quality of the evidence depends not only on its strength ('signal') but critically on its reliability ('noise'), but the separate contribution of these quantities to the formation of confidence judgments has not been investigated before in the context of perceptual decisions. We studied subjective confidence reports in a multi-element perceptual task where evidence strength and reliability could be manipulated independently. Our results reveal a confidence paradox: confidence is higher for stimuli of lower reliability that are associated with a lower accuracy. We show that the subjects' overconfidence in trials with unreliable evidence is caused by a reduced sensitivity to stimulus variability. Our results bridge between the investigation of miss-attributions of confidence in behavioral economics and the domain of simple perceptual decisions amenable to neuroscience research.

Dynamic weighting of multisensory stimuli shapes decision-making in rats and humans.

Stimuli that animals encounter in the natural world are frequently time-varying and activate multiple sensory systems together. Such stimuli pose a major challenge for the brain: Successful multisensory integration requires subjects to estimate the reliability of each modality and use these estimates to weight each signal appropriately. Here, we examined whether humans and rats can estimate the reliability of time-varying multisensory stimuli when stimulus reliability changes unpredictably from trial to trial. Using an existing multisensory decision task that features time-varying audiovisual stimuli, we independently manipulated the signal-to-noise ratios of each modality and measured subjects' decisions on single- and multi-sensory trials. We report three main findings: (a) Sensory reliability influences how subjects weight multisensory evidence even for time-varying, stochastic stimuli. (b) The ability to exploit sensory reliability extends beyond human and nonhuman primates: Rodents and humans both weight incoming sensory information in a reliability-dependent manner. (c) Regardless of sensory reliability, most subjects are disinclined to make "snap judgments" and instead base decisions on evidence presented over the majority of the trial duration. Rare departures from this trend highlight the importance of using time-varying stimuli that permit this analysis. Taken together, these results suggest that the brain's ability to use stimulus reliability to guide decision-making likely relies on computations that are conserved across species and operate over a wide range of stimulus conditions.

Natural ITD statistics predict human auditory spatial perception.

A neural code adapted to the statistical structure of sensory cues may optimize perception. We investigated whether interaural time difference (ITD) statistics inherent in natural acoustic scenes are parameters determining spatial discriminability. The natural ITD rate of change across azimuth (ITDrc) and ITD variability over time (ITDv) were combined in a Fisher information statistic to assess the amount of azimuthal information conveyed by this sensory cue. We hypothesized that natural ITD statistics underlie the neural code for ITD and thus influence spatial perception. To test this hypothesis, sounds with invariant statistics were presented to measure human spatial discriminability and spatial novelty detection. Human auditory spatial perception showed correlation with natural ITD statistics, supporting our hypothesis. Further analysis showed that these results are consistent with classic models of ITD coding and can explain the ITD tuning distribution observed in the mammalian brainstem.

When a person hears a sound, how do they work out where it is coming from? A sound coming from your right will reach your right ear a few fractions of a millisecond earlier than your left. The brain uses this difference, known as the interaural time difference or ITD, to locate the sound. But humans are also much better at localizing sounds that come from sources in front of them than from sources by their sides. This may be due in part to differences in the number of neurons available to detect sounds from these different locations. It may also reflect differences in the rates at which those neurons fire in response to sounds. But these factors alone cannot explain why humans are so much better at localizing sounds in front of them. Pavão et al. showed that the brain has evolved the ability to detect natural patterns that exist in sounds as a result of their location, and to use those patterns to optimize the spatial perception of sounds. Pavão et al. showed that the way in which the head and inner ear filter incoming sounds has two consequences for how we perceive them. Firstly, the change in ITD for sounds coming from different sources in front of a person is greater than for sounds coming from their sides. And secondly, the ITD for sounds that originate in front of a person varies more over time than the ITD for sounds coming from the periphery. By playing sounds to healthy volunteers while removing these differences, Pavão et al. found that natural ITD statistics were correlated with a person's ability to tell where a sound was coming from. By revealing the features the brain uses to determine the location of sounds, the work of Pavão et al. could ultimately lead to the development of more effective hearing aids. The results also provide clues to how other senses, including vision, may have evolved to respond optimally to the environment.