The Neurodynamic Decision Variable in Human Multi-Alternative Perceptual Choice

The neural dynamics underpinning binary perceptual decisions and their transformation into actions are well studied, but real-world decisions typically offer more than two response alternatives. How does decision-related evidence accumulation dynamically influence multiple action representations in humans? The heightened conservatism required in multiple compared to binary choice scenarios suggests a mechanism which compensates for increased uncertainty when multiple choices are present by supressing baseline activity. Here, we tracked action representations using corticospinal excitability during four and two-choice perceptual decisions, and modelled them using a sequential sampling framework. We found that the predictions made by leaky competing accumulator models in order to accommodate multiple choices (i.e. reduced baseline activity to compensate increased uncertainty) were borne out by dynamic changes in human action representations. This suggests a direct and continuous influence of interacting evidence accumulators, each favouring a different decision alternative, on downstream corticospinal excitability during complex choice

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations

Top-down and bottom-up neurodynamic evidence in patients with tinnitus

AbstractAlthough a peripheral auditory (bottom-up) deficit is an essential prerequisite for the generation of tinnitus, central cognitive (top-down) impairment has also been shown to be an inherent neuropathological mechanism. Using an auditory oddball paradigm (for top-down analyses) and a passive listening paradigm (for bottom-up analyses) while recording electroencephalograms (EEGs), we investigated whether top-down or bottom-up components were more critical in the neuropathology of tinnitus, independent of peripheral hearing loss. We observed significantly reduced P300 amplitudes (reflecting fundamental cognitive processes such as attention) and evoked theta power (reflecting top-down regulation in memory systems) for target stimuli at the tinnitus frequency of patients with tinnitus but without hearing loss. The contingent negative variation (reflecting top-down expectation of a subsequent event prior to stimulation) and N100 (reflecting auditory bottom-up selective attention) were different between the healthy and patient groups. Interestingly, when tinnitus patients were divided into two subgroups based on their P300 amplitudes, their P170 and N200 components, and annoyance and distress indices to their tinnitus sound were different. EEG theta-band power and its Granger causal neurodynamic results consistently support a double dissociation of these two groups in both top-down and bottom-up tasks. Directed cortical connectivity corroborates that the tinnitus network involves the anterior cingulate and the parahippocampal areas, where higher-order top-down control is generated. Together, our observations provide neurophysiological and neurodynamic evidence revealing a differential engagement of top-down impairment along with deficits in bottom-up processing in patients with tinnitus but without hearing loss

Rules from Words: A Dynamic Neural Basis for a Lawful Linguistic Process

Listeners show a reliable bias towards interpreting speech sounds in a way that conforms to linguistic restrictions (phonotactic constraints) on the permissible patterning of speech sounds in a language. This perceptual bias may enforce and strengthen the systematicity that is the hallmark of phonological representation. Using Granger causality analysis of magnetic resonance imaging (MRI)- constrained magnetoencephalography (MEG) and electroencephalography (EEG) data, we tested the differential predictions of rule-based, frequency–based, and top-down lexical influence-driven explanations of processes that produce phonotactic biases in phoneme categorization. Consistent with the top-down lexical influence account, brain regions associated with the representation of words had a stronger influence on acoustic-phonetic regions in trials that led to the identification of phonotactically legal (versus illegal) word-initial consonant clusters. Regions associated with the application of linguistic rules had no such effect. Similarly, high frequency phoneme clusters failed to produce stronger feedforward influences by acoustic-phonetic regions on areas associated with higher linguistic representation. These results suggest that top-down lexical influences contribute to the systematicity of phonological representation

Applying Classification and Decision Rule to Predict Outcome of Whiplash in an Ambulatory Patient Recovering from Incomplete Spinal Cord Trauma

It is not known whether a clinical prediction rule or a whiplash associated disorder (WAD) classification may be applied effectively in a second traumatic event to predict the outcome in a patient recovering from cord trauma. Therefore, the purpose of this case was to apply a decision rule and a whiplash classification to predict the outcome of a second traumatic event in a recovering incomplete spinal cord injured (SCI) patient who was ambulatory

Embodied prediction

Versions of the “predictive brain” hypothesis rank among the most promising and the most conceptually challenging visions ever to emerge from computational and cognitive neuroscience. In this paper, I briefly introduce (section 1) the most radical and comprehensive of these visions —the account of “active inference”, or “action-oriented predictive processing” (Clark 2013a), developed by Karl Friston and colleagues. In section 2, I isolate and discuss four of the framework’s most provocative claims: (i) that the core flow of information is top-down, not bottom-up, with the forward flow of sensory information replaced by the forward flow of prediction error; (ii) that motor control is just more top-down sensory prediction; (iii) that efference copies, and distinct “controllers”, can be replaced by top-down predictions; and (iv) that cost functions can fruitfully be replaced by predictions. Working together, these four claims offer a tantalizing glimpse of a new, integrated framework for understanding perception, action, embodiment, and the nature of human experience. I end (section 3) by sketching what may be the most important aspect of the emerging view: its ability to embed the use of fast and frugal solutions (as highlighted by much work in robotics and embodied cognition) within an over-arching scheme that includes more structured, knowledge-intensive strategies, combining these fluently and continuously as task and context dictate

Polar and Cartesian Structure in the Data of Fitts's (1954) Classic Experiments-With a Criterion for Distinguishing a Strong and a Weak Version of Fitts' Law

International audienc

The brain’s router : a cortical network model of serial processing in the primate brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a ‘‘router’’ network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates.Fil: Zylberberg, Ariel. Laboratory of Integrative Neuroscience, Physics Department, University of Buenos Aires, Buenos Aires, Argentina. Institute of Biomedical Engineering, Faculty of Engineering, University of Buenos Aires, Buenos Aires, Argentina