(Un)crowding modulates recurrent connectivity in the visual cortex

Introduction: In crowding, neighboring elements impede the perception of a target. Surprisingly, increasing the number of neighboring elements can decrease crowding, i.e., lead to uncrowding (Manassi et al., 2015). Few neuroimaging studies have explored the neural correlates of crowding (Bi et al., 2009; Anderson et al., 2012; Millin et al., 2013; Chen et al., 2014; Chicherov et al., 2014; Ronconi et al., 2016). The resulting evidence about the neural locus of crowding has been equivocal, but crowding is consistently found to attenuate the BOLD response. Here, we used fMRI to investigate the effects of uncrowding on the BOLD response and effective connectivity in the visual cortex. We expected that the percent BOLD signal change (PSC) would best reflect behavioral differences related to (un)crowding in the brain areas driving the crowding effect. We used dynamic causal modelling (DCM; Friston, 2003) to investigate whether (un)crowding is rather a top-down, bottom-up or recurrent processing phenomenon. In accordance with the notion that crowding depends on grouping, we hypothesized that PSC in higher visual areas would better reflect (un)crowding and that effective connectivity would reflect the involvement of top-down processing in uncrowding. Methods: The fMRI experiment consisted of seven conditions: (1) target only (eight circular oblique target gratings surrounding a central fixation dot, tilted either clockwise (CW) or counterclockwise (CCW), (2) 2-flanker (each of the 8 targets was flanked by an inside and outside vertical grating), (3) 4-flanker (one inside and three outside flanker gratings), (4) annulus-flanker (inside and outside flankers connected into annuli), and (5-7) control conditions corresponding to conditions (2-4) with targets removed. Ten participants were asked to indicate whether target gratings were tilted CW or CCW by pushing one of two buttons or to push a button randomly if targets were not present. We calculated the PSC for each participant, each target region of interest (tg-ROI; target-activated areas in V1–V4 and LOC) and each of the conditions of interest and then correlated PSC with response accuracy. We estimated the effective connectivity between tg-ROIs using bilinear DCM and constructed a group-level parametric empirical Bayesian (PEB) model (Friston et al., 2016) over all the participants. After conducting a search over nested models and computing a Bayesian model average (BMA), we compared input-modulatory connectivity (DCM.B matrix) between crowding conditions in terms of excitatory and inhibitory bottom-up and top-down connectivity. Results: Target discrimination (accuracy) was highest in the single target condition, followed by annulus-flanker, 4-flanker and 2-flanker, respectively. A linear mixed model of PSC revealed significant main effects of condition (F(3,171) = 100.06, p

Bayesian modeling of brain function and behavior

Maya Anna Jastrzebowska

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed generative problems, in which case the incorporation of prior knowledge is optimal and practical. If one wants to take full advantage of joint imaging and behavioral data, the need for comprehensive generative models is evident. Here, I exploited the versatility afforded by Bayesian modeling approaches to investigate a series of questions from clinical, systems, and cognitive neuroscience. First, I investigated the lateralization of the cortical motor network in Parkinson's disease patients on and off dopaminergic medication with fMRI and dynamic causal modeling (DCM). Group-level model comparison revealed that disease and medication effects differentially involve homotopic cortical motor connections, but medication does not appear to have a restorative effect at the systems level. Our findings suggest the presence of maladaptive mechanisms, resulting from disease progression and long-term dopamine replacement. In a second project, I considered the way in which humans handle uncertainty in the parameters of the generative model of a task at hand. Using a novel psychophysics paradigm, participants' responses to fitting a parabola to a set of noisy points were compared to the predictions of a set of regression models, including Bayesian regression and several sub-optimal models. Only Bayesian regression could explain participants' response distributions across various levels of sensory uncertainty, suggesting that humans process parameter uncertainty in accordance with Bayesian regression. This finding deepens our understanding of the way in which humans learn and generalize from sparse, ambiguous data. The topic investigated in the greatest depth in this thesis was visual crowding - the deterioration of target discrimination due to the presence of flanking objects. Traditionally, vision is modeled as a feedforward, hierarchical network. Thus, crowding is explained as a pooling of neighboring elements' features, leading to a "bottleneck" at the earliest stages of vision. However, additional flankers can paradoxically improve performance (uncrowding). First, I used DCM and fMRI to show that recurrent processing is crucial for crowding and un-crowding alike. Higher visual areas modulate the lower areas, suggesting that explicit object representation plays a key role. I then used population receptive field (pRF) mapping to investigate the effects of context on pRF size. In accordance with pooling model predictions, the pRF size in crowding is larger than in the case of no crowding. However, in uncrowding, the pRF size is smaller than in crowding and no crowding, providing further evidence against purely feedforward models. Overall, my findings provide strong empirical evidence of context-dependent feedback modulation in vision. I propose a possible implementation which integrates the observed findings into a generative model of crowding: context-agnostic feedforward mechanisms transmit the information about the target and flankers retinotopically to higher visual areas; a subsequent context-dependent feedback pRF determines whether the target will be enhanced - by decreasing the pRF size - or suppressed - by enlarging the pRF size to encompass flankers.

EPFL2020

Enactive robot vision

Mototaka Suzuki

The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels or legs. The methods developed so far in Artificial Intelligence, however, are not yet ready to cope with the complexity of the information gathered through the robot sensors and the need for rapid action in partially unknown and dynamic environments. In this thesis, I will argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same time scale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. This line of work is grounded on philosophical and psychological research showing that perception is an active process mediated by behavior. However, computational models of active vision are very rare and often rely on architectures that are preprogrammed to detect certain characteristics of the environment. Previous work have shown that complex visual tasks, such as position and size invariant shape recognition as well as navigation, can be tackled with remarkably simple neural architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons were evolved while freely interacting with their environments. I proceed further on this line of investigation and describe the application of this methodology in three situations, namely car driving with an omnidirectional camera, goal-oriented navigation of a humanoid robot, and cooperative tasks by two agents. I will show that these systems develop sensitivity to a number of oriented, retinotopic, visual features – oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system that allow them to accomplish their goals. In a second set of experiments, I will show that active vision can be exploited by the robot to perform anticipatory exploration of the environment in a task that requires landmark-based navigation. Evolved robots exploit an internal expectation system that makes use of active exploration to check for expected events in the environment. I will describe a third set of experiments where, in addition to an evolutionary process, the visual system of the robot can develop receptive fields by means of unsupervised Hebbian learning and show that these receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, I will show that these robots replicate the performance deficiencies observed in experiments of motor deprivation with kitten when they are exposed to the same type of motor deprivations. Furthermore, the analyses of our robot brains suggest an explanation for the deficiencies observed in kitten that have not yet been fully understood.

EPFL2007

Bayesian modeling of brain function and behavior

Maya Anna Jastrzebowska

EPFL2020

Enactive robot vision

Mototaka Suzuki

EPFL2007