Discrete modes of social information processing predict individual behavior of fish in a group

Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an active mode in which they are sensitive to the swimming patterns of conspecifics, and a passive mode where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors behavior. At the group level, switching between active and passive modes is uncorrelated among fish, yet correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates, as well as to other species

Comparing Segmentation by Time and by Motion in Visual Search: An fMRI Investigation

Abstract Brain activity was recorded while participants engaged in a difficult visual search task for a target defined by the spatial configuration of its component elements. The search displays were segmented by time (a preview then a search display), by motion, or were unsegmented. A preparatory network showed activity to the preview display, in the time but not in the motion segmentation condition. A region of the precuneus showed (i) higher activation when displays were segmented by time or by motion, and (ii) correlated activity with larger segmentation benefits behaviorally, regardless of the cue. Additionally, the results revealed that success in temporal segmentation was correlated with reduced activation in early visual areas, including V1. The results depict partially overlapping brain networks for segmentation in search by time and motion, with both cue-independent and cue-specific mechanisms.</jats:p

Salient sounds distort time perception and production

The auditory world is often cacophonous, with some sounds capturing attention and distracting us from our goals. Despite the universality of this experience, many questions remain about how and why sound captures attention, how rapidly behavior is disrupted, and how long this interference lasts. Here, we use a novel measure of behavioral disruption to test predictions made by models of auditory salience. Models predict that goal-directed behavior is disrupted immediately after points in time that feature a high degree of spectrotemporal change. We find that behavioral disruption is precisely time-locked to the onset of distracting sound events: Participants who tap to a metronome temporarily increase their tapping speed 750 ms after the onset of distractors. Moreover, this response is greater for more salient sounds (larger amplitude) and sound changes (greater pitch shift). We find that the time course of behavioral disruption is highly similar after acoustically disparate sound events: Both sound onsets and pitch shifts of continuous background sounds speed responses at 750 ms, with these effects dying out by 1,750 ms. These temporal distortions can be observed using only data from the first trial across participants. A potential mechanism underlying these results is that arousal increases after distracting sound events, leading to an expansion of time perception, and causing participants to misjudge when their next movement should begin

A Framework for Designing 3d Virtual Environments

The process of design and development of virtual environments can be supported by tools and frameworks, to save time in technical aspects and focusing on the content. In this paper we present an academic framework which provides several levels of abstraction to ease this work. It includes state-of-the-art components we devised or integrated adopting open-source solutions in order to face specific problems. Its architecture is modular and customizable, the code is open-source.\u

Measuring risky-driving propensity in pre-drivers: The Violation Willingness Scale

There is a growing recognition that the antecedents of risky driving attitudes can be traced to the pre-driving period. Few measures of driving-specific risk taking aimed at pre-drivers (defined here as those who are not permitted to drive independently) have been validated, however, meaning our understanding of the development of risky driving attitudes is limited. This paper reports the construction of a self-report Violation Willingness Scale (VWS) for pre-drivers, examination of the existing Attitudes to Driving Violations Scale (ADVS) in pre-drivers and some preliminary data on the development of propensity to risky driving. Study One found that the VWS and ADVS had strong psychometric properties in a sample of pre-drivers aged 16–19 years of age. Study Two found the VWS and ADVS showed moderate to strong and somewhat independent relationships with a number of existing measures of risky driving behaviour in a sample of fully licensed drivers (age range 18–65 years). This evidence supports the ADVS and VWS as valid tools to measure the propensity to risky driving in pre-drivers. We also discuss preliminary evidence on the relationship between propensity to risky driving and stage of driver training and experience, which indicates that willingness to commit most violations diminishes with driving experience while attitudes and willingness to speed become riskier