Combination and competition between path integration and landmark navigation in the estimation of heading direction.

Successful navigation requires the ability to compute one's location and heading from incoming multisensory information. Previous work has shown that this multisensory input comes in two forms: body-based idiothetic cues, from one's own rotations and translations, and visual allothetic cues, from the environment (usually visual landmarks). However, exactly how these two streams of information are integrated is unclear, with some models suggesting the body-based idiothetic and visual allothetic cues are combined, while others suggest they compete. In this paper we investigated the integration of body-based idiothetic and visual allothetic cues in the computation of heading using virtual reality. In our experiment, participants performed a series of body turns of up to 360 degrees in the dark with only a brief flash (300ms) of visual feedback en route. Because the environment was virtual, we had full control over the visual feedback and were able to vary the offset between this feedback and the true heading angle. By measuring the effect of the feedback offset on the angle participants turned, we were able to determine the extent to which they incorporated visual feedback as a function of the offset error. By further modeling this behavior we were able to quantify the computations people used. While there were considerable individual differences in performance on our task, with some participants mostly ignoring the visual feedback and others relying on it almost entirely, our modeling results suggest that almost all participants used the same strategy in which idiothetic and allothetic cues are combined when the mismatch between them is small, but compete when the mismatch is large. These findings suggest that participants update their estimate of heading using a hybrid strategy that mixes the combination and competition of cues.

Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models.

Path integration is thought to rely on vestibular and proprioceptive cues yet most studies in humans involve primarily visual input, providing limited insight into their respective contributions. We developed a paradigm involving walking in an omnidirectional treadmill in which participants were guided on two sides of a triangle and then found their back way to origin. In Experiment 1, we tested a range of different triangle types while keeping the distance of the unguided side constant to determine the influence of spatial geometry. Participants overshot the angle they needed to turn and undershot the distance they needed to walk, with no consistent effect of triangle type. In Experiment 2, we manipulated distance while keeping angle constant to determine how path integration operated over both shorter and longer distances. Participants underestimated the distance they needed to walk to the origin, with error increasing as a function of the walked distance. To attempt to account for our findings, we developed configural-based computational models involving vector addition, the second of which included terms for the influence of past trials on the current one. We compared against a previously developed configural model of human path integration, the Encoding-Error model. We found that the vector addition models captured the tendency of participants to under-encode guided sides of the triangles and an influence of past trials on current trials. Together, our findings expand our understanding of body-based contributions to human path integration, further suggesting the value of vector addition models in understanding these important components of human navigation.

Grid coding, spatial representation, and navigation: Should we assume an isomorphism?

Grid cells provide a compelling example of a link between cellular activity and an abstract and difficult to define concept like space. Accordingly, a representational perspective on grid coding argues that neural grid coding underlies a fundamentally spatial metric. Recently, some theoretical proposals have suggested extending such a framework to nonspatial cognition as well, such as category learning. Here, we provide a critique of the frequently employed assumption of an isomorphism between patterns of neural activity (e.g., grid cells), mental representation, and behavior (e.g., navigation). Specifically, we question the strict isomorphism between these three levels and suggest that human spatial navigation is perhaps best characterized by a wide variety of both metric and nonmetric strategies. We offer an alternative perspective on how grid coding might relate to human spatial navigation, arguing that grid coding is part of a much larger conglomeration of neural activity patterns that dynamically tune to accomplish specific behavioral outputs.

Dopaminergic prediction errors in the ventral tegmental area reflect a multithreaded predictive model.

Dopamine neuron activity is tied to the prediction error in temporal difference reinforcement learning models. These models make significant simplifying assumptions, particularly with regard to the structure of the predictions fed into the dopamine neurons, which consist of a single chain of timepoint states. Although this predictive structure can explain error signals observed in many studies, it cannot cope with settings where subjects might infer multiple independent events and outcomes. In the present study, we recorded dopamine neurons in the ventral tegmental area in such a setting to test the validity of the single-stream assumption. Rats were trained in an odor-based choice task, in which the timing and identity of one of several rewards delivered in each trial changed across trial blocks. This design revealed an error signaling pattern that requires the dopamine neurons to access and update multiple independent predictive streams reflecting the subject's belief about timing and potentially unique identities of expected rewards.

Differential Altered Auditory Event-Related Potential Responses in Young Boys on the Autism Spectrum With and Without Disproportionate Megalencephaly.

Autism spectrum disorder (ASD), characterized by impairments in social communication and repetitive behaviors, often includes altered responses to sensory inputs as part of its phenotype. The neurobiological basis for altered sensory processing is not well understood. The UC Davis Medical Investigation of Neurodevelopmental Disorders Institute Autism Phenome Project is a longitudinal, multidisciplinary study of young children with ASD and age-matched typically developing (TD) controls. Previous analyses of the magnetic resonance imaging data from this cohort have shown that ∼15% of boys with ASD have disproportionate megalencephaly (DM) or brain size to height ratio, that is 1.5 standard deviations above the TD mean. Here, we investigated electrophysiological responses to auditory stimuli of increasing intensity (50-80 dB) in young toddlers (27-48 months old). Analyses included data from 36 age-matched boys, of which 24 were diagnosed with ASD (12 with and 12 without DM; ASD-DM and ASD-N) and 12 TD controls. We found that the two ASD subgroups differed in their electrophysiological response patterns to sounds of increasing intensity. At early latencies (55-115 ms), ASD-N does not show a loudness-dependent response like TD and ASD-DM, but tends to group intensities by soft vs. loud sounds, suggesting differences in sensory sensitivity in this group. At later latencies (145-195 ms), only the ASD-DM group shows significantly higher amplitudes for loud sounds. Because no similar effects were found in ASD-N and TD groups, this may be related to their altered neuroanatomy. These results contribute to the effort to delineate ASD subgroups and further characterize physiological responses associated with observable phenotypes. Autism Res 2019, 12: 1236-1250. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately 15% of boys with ASD have much bigger brains when compared to individuals with typical development. By recording brain waves (electroencephalography) we compared how autistic children, with or without big brains, react to sounds compared to typically developing controls. We found that brain responses in the big-brained group are different from the two other groups, suggesting that they represent a specific autism subgroup.