Learning under uncertainty in the young and older human brain: Common and distinct mechanisms of different attentional and intentional systems

The human brain is able to infer the probability of future events by combining information of past observations with current sensory input. Naturally, we are surrounded by more stimuli than we can pay attention to, so selection of relevant input is crucial. The present thesis aimed at identifying common and distinct neural correlates engaged in predictive processing in spatial attention (selection of attended locations) and motor intention (selection of prepared motor responses). Secondly, age-related influences on probabilistic inference in spatial-attention, feature-based attention (selection of attended color) and motor intention, and the impact of task difficulty were considered. Orienting attention during goal-directed behavior can be supported by visual cues, whereas reorienting to unexpected events following misguiding information is linked to behavioral costs and updating of predictions. These processes can be investigated with a cueing paradigm in which differences in reaction time (RT) between valid and invalidly cued trials increase with higher cue validity (%CV) (Posner, 1980). Bayesian models can describe the experience-dependent learning effects of inferring %CV, following novel events (Vossel et al., 2014c; Vossel, Mathys, Stephan & Friston, 2015). The principle aim of the first experiment was to identify and compare the neural correlates involved in inferring probabilities in the spatial attentional and motor intentional domain. Cues indicated either the possible location or prepared the motor response associated with the target. Instead of a fixed probability context, participants were exposed to a volatile environment, in which the validity of the cue information changed unpredictably over time. Combining functional magnetic resonance imaging (fMRI) data with behavioral estimates derived from a Bayesian learning model (Mathys, Daunizeau, Friston & Stephan, 2011) unveiled domain-specific predictability-dependent responses within the right temporoparietal junction (TPJ) for spatial attention and the left angular gyrus (ANG) and anterior cingulate (ACC) in the motor intention task. The blood oxygen level dependent (BOLD) amplitude particularly increased in accord with violations of cue predictability in high cue validity contexts (i.e. when invalid trials were least expected). Valid trials however, induced no (TPJ and ANG) or decreased modulation (ACC). A further aim was to examine possible commonalities in the neural signatures of predictability-dependent processing. Connectivity analysis uncovered common coupling of all three seed regions involved in predictability-dependent processing with the right anterior hippocampus. Since cognitive functions undergo substantial changes in healthy ageing, a second behavioral study was conducted to test whether age differentially influences probabilistic inference in different attentional subsystems, and how task difficulty impacts on learning performance. Thus, following up on the first experiment, similar tasks and the same computational model was used to assess updating behavior in healthy aging. Older and younger adults performed two separate experiments with different difficulty levels. Each experiment included three versions of a cueing task, entailing predictive spatial- (i.e. location), feature- (i.e. color of target) and motor intention cues (i.e. prepare response). Results of the easier version demonstrated a preserved ability of older adults to generate predictions and profit from all cue types. Interestingly, increased task demand uncovered a reduced ability to use motor intention cues to update predictions in older compared to younger adults. In conclusion, the results provide evidence for a segregated functional anatomy of probabilistic inference in spatial attention and motor intention. Nonetheless a common connectivity profile with the hippocampus also points at commonalities. Finally age seems to differentially impact the efficiency of learning behavior in the motor intention system, supporting the notion of independence of the attentional- and intentional subsystems

Modulation of behavior and brain activity by probabilistic inference

Expectancies and beliefs about upcoming sensory events encoded by the brain play a crucial role in shaping our perception. Therefore, stimulus detection and processing can be facilitated by prior beliefs about the stimulus’ location or its features. These beliefs are rapidly generated by former observations/experience of the individual. Bayesian principles can evidently be used to describe this probabilistic inference. The present thesis aimed to characterize the mechanisms underlying probabilistic inference in the healthy and the lesioned human brain. In healthy participants, probabilistic inference in the context of attentional deployment has already been described with the help of computational models, and the underlying neural mechanisms have been explored with functional neuroimaging (Dombert, Kuhns, et al., 2016; Kuhns et al., 2017; Vossel et al., 2015). However, it is not known how the resting-state network architecture of the brain relates to this process and how the lesioned brain performs probabilistic inference. To investigate these questions, two experiments have been conducted using modified versions of a Posner-cueing paradigm. In this context, probabilistic inference describes the ability to infer changing probabilities about the validity of a cue and the updating process of the belief about them. By manipulating the percentage of cue validity (%CV) (i.e., the proportion of valid and invalid trials) over the time course of an experiment, the participants had to infer the actual cue validity level (i.e., the probability that the cue will be valid in a given trial), so that probabilistic inference could be assessed. In Experiment 1, a modified location-cueing paradigm with block-wise changes of the %CV and true and false prior information about the %CV before each block was employed in healthy young participants. A Rescorla-Wagner model was used to characterize probabilistic inference. Moreover, resting-state fMRI was recorded before and after the task and a seedbased correlation analysis was used to define the resting-state functional connectivity (rsFC) of the right temporo-parietal junction (rTPJ). Correlations of each behavioral parameter with the rsFC before the task, as well as with changes in rsFC after the task, were assessed in a ROI-based approach. It was observed that higher intrahemispheric rsFC between rTPJ and IPS before the task was associated with slower probabilistic inference after false priors. Furthermore, increased interhemispheric rsFC between rTPJ and lTPJ after the task was related to relatively faster probabilistic inference in false blocks. Both findings support previous research and highlight that not only resting-state connectivity per se is relevant for cognitive functions but also that cognitive processing during a task can change connectivity patterns afterwards in a performance-dependent manner. In Experiment 2, probabilistic inference in stroke patients was investigated to assess a hypothesized relationship with the spatial neglect syndrome (Experiment 2a) as well as commonalities and distinctions between probabilistic inference in different cognitive subsystems (Experiment 2b). Three modified versions of the Posner-cueing task with different cue types were used to investigate spatial attention (location cues), feature-based attention (color cues) and motor-intention (motor-response cues). In contrast to Experiment 1, no prior information about the %CV was provided and probabilistic inference was operationalized by assessing the impact of the %CV manipulation on RTs by means of regression analyses as well as by asking participants to explicitly estimate the %CV. Furthermore, patients were screened for the neglect syndrome using a diverse neuropsychological test battery. Lesion-symptom mapping (VLSM) as well as lesion-network mapping was performed on the relevant behavioral parameters. The results indicated that patients’ probabilistic inference abilities across domains were not per se impaired. However, by trend it was found that some right hemisphere damaged patients exhibited difficulties using their knowledge to adapt their behavior in contralesional space as indicated by a reduced modulation of RTs by %CV in invalid contralesional trials in the spatial attention domain. However, there was no strong evidence for impairments of probabilistic inference being related to the neglect syndrome. Moreover, the correlation of the two probabilistic inference parameters (invalid contralesional %CV regression weight & averaged explicit %CV estimate) within domains revealed no significant relationship between the both, stating them as independent components of probabilistic inference, which was further supported by the VLSM results. However, the correlations across domains revealed some commonalities, which were also in line with the VLSM results. Thus, our data suggests that the neural implementations for probabilistic inference seem to be dedicated to domain-specific subsystems, which share some common nodes. Consequently, the present thesis provides novel insights into the computational mechanisms of probabilistic inference in the healthy and lesioned brain. The work thereby enables future studies to transfer the gained knowledge from basic research of healthy participants and patients to clinical applications

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

The Problem of Mental Action

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects

Attention in a Bayesian Framework

The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of perception, and use this observation to frame a new computational account of the need for, and action of, attention – unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental settings, where cues shape expectations about a small number of upcoming stimuli and thus convey “prior” information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena

Cognitive Penetration and Attention

Zenon Pylyshyn argues that cognitively driven attentional effects do not amount to cognitive penetration of early vision because such effects occur either before or after early vision. Critics object that in fact such effects occur at all levels of perceptual processing. We argue that Pylyshyn’s claim is correct—but not for the reason he emphasizes. Even if his critics are correct that attentional effects are not external to early vision, these effects do not satisfy Pylyshyn’s requirements that the effects be direct and exhibit semantic coherence. In addition, we distinguish our defense from those found in recent work by Raftopoulos and by Firestone and Scholl, argue that attention should not be assimilated to expectation, and discuss alternative characterizations of cognitive penetrability, advocating a kind of pluralism

Perceptual Consciousness and Cognitive Access from the Perspective of Capacity-Unlimited Working Memory

Theories of consciousness divide over whether perceptual consciousness is rich or sparse in specific representational content and whether it requires cognitive access. These two issues are often treated in tandem because of a shared assumption that the representational capacity of cognitive access is fairly limited. Recent research on working memory challenges this shared assumption. This paper argues that abandoning the assumption undermines post-cue-based “overflow” arguments, according to which perceptual conscious is rich and does not require cognitive access. Abandoning it also dissociates the rich/sparse debate from the access question. The paper then explores attempts to reformulate overflow theses in ways that don’t require the assumption of limited capacity. Finally, it discusses the problem of relating seemingly non-probabilistic perceptual consciousness to the probabilistic representations posited by the models that challenge conceptions of cognitive access as capacity-limited