Why we may not find intentions in the brain

Intentions are commonly conceived of as discrete mental states that are the direct cause of actions. In the last several decades, neuroscientists have taken up the project of finding the neural implementation of intentions, and a number of areas have been posited as implementing these states. We argue, however, that the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for. Therefore, adopting the notion of ‘intention’ in neuroscientific explanations can easily lead to misinterpretation of the data, and can negatively influence investigation into the neural correlates of intentional action.We suggest reinterpreting the mechanisms underlying intentional action, and we will discuss the elements that such a reinterpretation needs to account for

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex proble

The role of the posterior parietal cortex in cognitive-motor integration

"When interacting with an object within the environment, one must combine visual information with the felt limb position (i.e. proprioception) in order compute an appropriate coordinated muscle plan for accurate motor control. Amongst the vast reciprocally connected parieto-frontal connections responsible for guiding a limb throughout space, the posterior parietal cortex (PPC) remains a front-runner as a crucial node within this network. Our brain is primed to reach directly towards a viewed object, a situation that has been termed ""standard"". Such direct eye-hand coordination is common across species and is crucial for basic survival. Humans, however, have developed the capacity for tool-use and thus have learned to interact indirectly with an object. In such ""non-standard"" situations, the directions of gaze and arm movement are spatially decoupled and rely on both the implementation of a cognitive rule and online feedback of the decoupled limb. The studies included within this dissertation were designed to further characterize the role of the PPC in different types of visually-guided reaching which require one to think and to act simultaneously (i.e. cognitive-motor integration). To address the relative contribution of different cortical networks responsible for cognitive-motor integration, we tested three patients with optic ataxia (OA; two unilateral - first study, and one bilateral -second study) as well as healthy participants during a cognitively-demanding dual task (third study) on a series of visually-guided reaching tasks each requiring a relative weighting between explicit cognitive control and implicit online control of the spatially decoupled limb. We found that the eye and hand movement performance during decoupled reaching was the most compromised in OA during situations relying on sensorimotor recalibration, and the most compromised in healthy participants during a dual task relying on strategic control. Taken together, these data presented in this dissertation provide further evidence for the existence of alternate task-dependent neural pathways for cognitive-motor integration.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem

Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses

Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation

Attention in Psychology, Neuroscience, and Machine Learning

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored

A Role for Hippocampal Sharp-wave Ripples in Active Visual Search

Sharp-wave ripples (SWRs) in the hippocampus are thought to contribute to memory formation, though this effect has only been demonstrated in rodents. The SWR, a large deflection in the hippocampal LFP (local field potential), is known to occur primarily during slow wave sleep and during immobility and consummator behaviors. SWRs have widespread effects throughout the cortex, and are directly implicated in memory formation their occurrence correlates with correct performance, and their ablation impairs memory in spatial memory tasks. Though SWRs have been reported in primates, their role is poorly understood. Whether or not SWRs play a role in memory formation, as they do in rodents, has yet to be confirmed. This work encompasses three separate studies with the goal of determining whether there is a link between SWR occurrence and memory formation in the macaque. Chapter 2 establishes the validity of the modified Change Blindness task as a memory task which is sensitive to normal hippocampal function in monkeys. Chapter 3 establishes that SWR events occur during waking (and stationary) activity, during visual search, in the macaque. Until this work, the prevalence of SWRs in macaques during waking exploration was unknown. Chapter 4 shows that gaze during SWRs was more likely to be near the target object on repeated than on novel presentations, even after accounting for overall differences in gaze location with scene repetition. The increase in ripple likelihood near remembered visual objects suggests a link between ripples and memory in primates; specifically, SWRs may reflect part of a mechanism supporting the guidance of search based on experience. The amalgamation of this work reveals several novel findings and establishes an important step towards understanding the role that SWRs play in memory formation in predominantly-visual primate brains

Neurocognitive Investigations of Habitual Behavior Modification

Addiction is a disorder characterized by maladaptive associative learning processes in which behavior can result despite negative health outcomes. Research from human and animal models suggests that dysfunction within frontostriatal neural circuitry may contribute to a shift from goal-directed to habit-based action selection. The goal of the present dissertation was to examine the impact of acute psychosocial stress and non-invasive transcranial alternating current stimulation on increasing and reducing habitual responding, respectively. We assessed the importance of stress timing on potentiating habitual responding in healthy males in Chapter 2 and found that stress prior to execution and learning of S-R associations increased perseverative errors. The underlying biological mechanism of this shift in behavior related to sympathetic activation; we found that males that were able to mount a parasympathetic response to counteract the biological effects of stress were less likely to perseverate. Similarly, Chapter 3 was designed to examine the relationship between stress timing and menstrual cycle phase effects on habitual responding in healthy females. In contrast to our male results, we showed that regardless of menstrual cycle phase (menstrual versus luteal) and stress timing, females did not show increased perseverative responding. These results demonstrated differences in the experience and biological response to acute psychosocial stress, and suggested that differences in ovarian hormone levels may contribute to behavior under conditions of stress. In Chapter 4 we tested the use of non-invasive transcranial alternating current stimulation in healthy controls and individuals with an addiction history to diminish perseverative errors after response devaluation. Contrary to our predictions, true versus sham stimulation increased perseverative errors in healthy controls, while there were more subtle improvements in performance in the addiction history group, not specific to perseverative responding. Together, these data demonstrate conditions in which goal-directed behavior can be shifted toward habit-based actions, and suggest that concomitant shifts from top-down (prefrontal) to bottom-up (striatal) control within the brain contributes to changes in these response selection strategies. More broadly, these findings implicate frontostriatal circuitry and habitual behaviors as a highly promising research area to develop novel treatment methods for disorders characterized by intractable behaviors.Doctor of Philosoph