What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions

Understanding the Link Between Brain Activation, Choice, and Attitude Change for European Americans and East Asians.

How do people make difficult choices, and how does the decision process influence subsequent attitudes towards the choice options? Moreover, does culture influence decision-making and attitude change? My dissertation addresses these questions using neuroimaging data from individuals who evaluated choice options before and after making difficult choices either for the self or a close friend. In Study 1, I measured neural activation during decision-making and found that brain regions involved in self-processing and reward processing predicted attitude change for European Americans but not East Asians. Moreover, regions involved in conflict detection and negative arousal were recruited when people made difficult (versus easy) choices for the self and a close friend, whereas mentalizing regions were recruited when people made difficult choices for a close friend (versus self choices). In Study 2, I found that post-choice connectivity between regions involved in self-processing predicted attitude change. In Study 3, I found that European Americans represented information about choice outcome (chosen versus rejected) in self-processing regions, whereas East Asians represented information about choice outcome in mentalizing regions. Both European Americans and East Asians represented information about choice target (self versus friend) in both self-processing and mentalizing brain regions. The current work provides evidence for key brain regions and networks that support decision-making and attitude change for both the self and close others. This research advances understanding of how culture shapes the way in which people evaluate choice options and make choice.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135786/1/tompson_1.pd

Decision in space

Human navigation is generally believed to rely on two types of strategy adoption, route- based and map-based strategies. Both types of navigation require making spatial decisions along the traversed way. Nevertheless, formal computational and neural links between navigational strategies and mechanisms of value based decision making have so far been underexplored in humans. Here, we employed functional magnetic resonance imaging (fMRI) while subjects located different target objects in a virtual environment. We then modelled their paths using reinforcement learning (RL) algorithms, which successfully explain decision behaviour and its neural correlates. Our results show that subjects used a mixture of route and map-based navigation, and their paths could be well explained by the model-free and model-based RL algorithms. Furthermore, the value signals of model-free choices during route-based navigation modulated the BOLD signals in the ventro-medial prefrontal cortex (vmPFC). On the contrary, the BOLD signals in parahippocampal and medial temporal lobe (MTL) regions pertained to model- based value signals during map-based navigation. Our findings suggest that the brain might share computational mechanisms and neural substrates for navigation and value- based decisions, such that model-free choice guides route-based navigation and model- based choice directs map-based navigation. These findings open new avenues for computational modelling of wayfinding by directing attention to value-based decision, differing from common direction and distances approaches. The ability to find one’s way in a complex environment is crucial to everyday functioning. This navigational ability relies on the integrity of several cognitive functions and different strategies, route and map-based navigation, that individuals may adopt while navigating in the environment. As the integrity of these cognitive functions often decline with age, navigational abilities show marked changes in both normal aging and dementia. Combining a wayfinding task in a virtual reality (VR) environment and modeling technique based on reinforcement learning (RL) algorithms, we investigated the effects of cognitive aging on the selection and adoption of navigation strategies in human. The older participants performed the wayfinding task while undergoing functional Magnetic Resonance Imaging (fMRI), and the younger participants performed the same task outside the MRI machine. Compared with younger participants, older participants traversed a longer distance. They also exhibited a higher tendency to repeat previously established routes to locate the target objects. Despite these differences, the traversed paths in both groups could be well explained by the model-free and model-based RL algorithms. Furthermore, neuroimaging results from the older participants show that BOLD signal in the ventromedial prefrontal cortex (vmPFC) pertained to model-free value signals. This result provide evidence on the utility of the RL algorithms to explain how the aging brain computationally prefer to rely more on the route-based navigation

Individual differences in self-focused attention: Relationship to inhibitory control and intrinsic architecture of large-scale networks

Self-relevant material presents an encoding advantage termed the self-reference effect (SRE) in which rich pre-existing schemas allow such material to be efficiently encoded. Self-relevant material is also prioritised during information processing, acting as a powerful distractor. Furthermore, activation in the Default Mode Network (DMN), engaged during self-referential processing, has been linked to errors during tasks, suggesting self-focussed attention as a potential source of distraction. The current work explored whether individuals with a stronger SRE, thought to reflect the level of articulation of one’s self-schema, would perform worse at inhibitory control tasks that demand sustained attention on the external world. Study 1 and Study 2 confirmed this hypothesis suggesting that poor performance in inhibitory control tasks is at least in part due to attention being diverted towards the self. Study 2 explored the neural underpinnings of such relationships using a cross-sectional resting-state analysis. Connectivity of regions involved in self-referential processing was explored in relation to inhibitory control efficiency scores revealing that individuals with stronger coupling to right inferior frontal gyrus performed better at a Go/No-Go task. Similarly, the Frontoparietal Control Network (FPCN) was more coupled to the ventral striatum, commonly associated with self-relevance assignment, when SREs were smaller. Study 1 also found stronger coupling between DMN and executive control regions for individuals with better memory in the non-self control condition (low SRE), whereas individuals with stronger within DMN coupling had high self-memory scores (high SRE) suggesting integration between DMN and FPCN reduces self-focus. Study 3 measured self-focussed attention using the private self-consciousness scale and revealed the FPCN to be more coupled to fusiform/hippocampus in individuals with higher private self-consciousness scores, potentially reflecting episodic information in the working memory space. Overall we present substantial evidence supporting a strong relationship between self-bias and executive control both at the behavioural and neural levels

Ecological adaptation in the context of an actor-critic

Biological beings are the result of an evolutionary and developmental process of adaptation to the environment they perceive and where they act. Animals and plants have successfully adapted to a large variety of environments, which supports the ideal of inspiring artificial agents after biology and ethology. This idea has been already suggested by previous studies and is extended throughout this thesis. However, the role of perception in the process of adaptation and its integration in an agent capable of acting for survival is not clear.Robotic architectures in AI proposed throughout the last decade have broadly addressed the problems of behaviour selection, namely deciding "what to do next", and of learning as the two main adaptive processes. Behaviour selection has been commonly related to theories of motivation, and learning has been bound to theories of reinforcement. However, the formulation of a general theory including both processes as particular cases of the same phenomenon is still an incomplete task. This thesis focuses again on behaviour selection and learning; however it proposes to integrate both processes by stressing the ecological relationship between the agent and its environment. If the selection of behaviour is an expression of the agent's motivations, the feedback of the environment due to behaviour execution can be viewed as part of the same process, since it also influences the agent's internal motivations and the learning processes via reinforcement. I relate this to an argument supporting the existence of a common neural substrate to compute motivation and reward, and therefore relating the elicitation of a behaviour to the perception of reward resulting from its executionAs in previous studies, behaviour selection is viewed as a competition among parallel pathways to gain control over the agent's actuators. Unlike for the previous cases, the computation of every motivation in this thesis is not anymore the result of an additive or multiplicative formula combining inner and outer stimuli. Instead, the ecological principle is proposed to constrain the combination of stimuli in a novel fashion that leads to adaptive behavioural patterns. This method aims at overcoming the intrinsic limitations of any formula, the use of which results in behavioural responses restricted to a set of specific patterns, and therefore to the set of ethological cases they can justify. External stimuli and internal physiology in the model introduced in this thesis are not combined a priori. Instead, these are viewed from the perspective of the agent as modulatory elements biasing the selection of one behaviour over another guided by the reward provided by the environment, being the selection performed by an actor-critic reinforcement learning algorithm aiming at the maximum cumulative reward.In this context, the agent's drives are the expression of the deficit or excess of internal resources and the reference of the agent to define its relationship with the environment. The schema to learn object affordances is integrated in an actor-critic reinforcement learning algorithm, which is the core of a motivation and reinforcement framework driving behaviour selection and learning. Its working principle is based on the capacity of perceiving changes in the environment via internal hormonal responses and of modifying the agent's behavioural patterns accordingly. To this end, the concept of reward is defined in the framework of the agent's internal physiology and is related to the condition of physiological stability introduced by Ashby, and supported by Dawkins and Meyer as a requirement for survival. In this light, the definition of the reward used for learning is defined in the physiological state, where the effect of interacting with the environment can be quantified in an ethologically consistent manner.The above ideas on motivation, behaviour selection, learning and perception have been made explicit in an architecture integrated in an simulated robotic platform. To demonstrate the reach of their validity, extensive simulation has been performed to address the affordance learning paradigm and the adaptation offered by the framework of the actor-critic. To this end, three different metrics have been proposed to measure the effect of external and internal perception on the learning and behaviour selection processes: the performance in terms of flexibility of adaptation, the physiological stability and the cycles of behaviour execution at every situation. In addition to this, the thesis has begun to frame the integration of behaviours of an appetitive and consummatory nature in a single schema. Finally, it also contributes to the arguments disambiguating the role of dopamine as a neurotransmitter in the Basal Ganglia

Anatomical and Functional Organization of Domain-General Brain Regions

How does complex brain activity organize thought and behaviour? Theoretical proposals have long emphasized that intelligent behaviour must be supported by a flexible control system. Numerous brain imaging studies identified a domain-general or “multiple-demand” (MD) brain system co-activated accompanying many tasks and is hypothesised to play a central role in cognitive control. However, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding its anatomy and physiology. To address these limitations, the experiments in chapters 2 and 3 capitalize on novel multi-modal magnetic resonance imaging (MRI) methods developed by the Human Connectome Project. Chapter 2 delineated nine cortical MD patches per hemisphere and subdivided them into 10 regions forming a core of most strongly activated and functionally interconnected regions, surrounded by a penumbra of 17 additional regions. MD activations were also identified in specific subcortical and cerebellar regions. Chapter 3 investigated the relation between the newly defined MD regions and previously identified sensory-biased cortical regions. Contrasting auditory and visual low working memory demands revealed the strongest sensory-biases are localized just outside of MD regions. And additional working memory demands revealed MD activations showed no sensory biases. Chapter 4 used human electrophysiological recordings from the lateral frontal cortex to functionally map cognitive control regions during awake neurosurgeries. By contrasting a hard vs easy cognitive demand, spectral analysis revealed localized power increases in the gamma range (>30 Hz) that overlap with a canonical mask of the fronto-parietal control network. These findings contrast with spatially non-specific power decreases in the beta range (12-30 Hz). Thus, using similar task difficulty manipulations, electrophysiology and MRI functional signals converged on localizing lateral frontal regions related to cognitive control and support their clinical potential for intraoperative mapping of cognitive control. All together, the distributed anatomical organization, mosaic functional preferences, and strong functional interconnectivity of MD regions, suggest a skeleton for integrating and organizing the diverse components of cognitive operations. The precise anatomical delineation of MD regions provides the groundwork for refined analyses of their functions

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) imaging of neural tissue. EM microscopy is one of the key tools used to analyze neural tissue and understand the brain, but the huge amounts of data it produces make automated analysis necessary. In addition to the challenges specific to EM data, the common problems encountered in image segmentation must also be addressed. These problems include extracting discriminative features from the data and constructing a statistical model using ground-truth data. Although complex models appear to be more attractive because they allow for more expressiveness, they also lead to a higher computational complexity. On the other hand, simple models come with a lower complexity but less faithfully express the real world. Therefore, one of the most challenging tasks in image segmentation is in constructing models that are expressive enough while remaining tractable. In this work, we propose several automated graph partitioning approaches that address these issues. These methods reduce the computational complexity by operating on supervoxels instead of voxels, incorporating features capable of describing the 3D shape of the target objects and using structured models to account for correlation in output variables. One of the non-trivial issues with such models is that their parameters must be carefully chosen for optimal performance. A popular approach to learning model parameters is a maximum-margin approach called Structured SVM (SSVM) that provides optimality guarantees but also suffers from two main drawbacks. First, SSVM-based approaches are usually limited to linear kernels, since more powerful nonlinear kernels cause the learning to become prohibitively expensive. In this thesis, we introduce an approach to “kernelize” the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring their high computational cost. Second, the optimality guarentees are violated for complex models with strong inter-relations between the output variables. We propose a new subgradient-based method that is more robust and leads to improved convergence properties and increased reliability. The different approaches presented in this thesis are applicable to both natural and medical images. They are able to segment mitochondria at a performance level close to that of a human annotator, and outperform state-of-the-art segmentation techniques while still benefiting from a low learning time