scholarly journals Individualised dynamic internal representations from response times

2020 ◽  
Author(s):  
Balázs Török ◽  
Dávid G. Nagy ◽  
Mariann M. Kiss ◽  
Karolina Janacsek ◽  
Dezső Németh ◽  
...  
Keyword(s):  
Internal Model ◽  
Dynamic Models ◽  
Response Times ◽  
Learning Task ◽  
Internal Models ◽  
Learning Curves ◽  
Task Demands ◽  
Environmental Data ◽  
Trial Basis ◽  
The Right

AbstractInternal models are central to understand how human behaviour is adapted to the statistics of, potentially limited, environmental data. Such internal models contribute to rich and flexible inferences and thus adapt to varying task demands. However, the right internal model is not available for observers, instead approximate and transient internal models are recruited. To understand learning and momentary inferences, we need tools to characterise these approximate, yet rich and potentially dynamic models through behaviour. We used a combination of non-parametric Bayesian methods and probabilistic programming to infer individualised internal models from human response times in an implicit visuomotor learning task. Using this Cognitive Tomography approach we predict response times on a trial-by-trial basis and validate the internal model by showing its invariance across tasks and sensitivity to stimulus statistics. By tracking the performance of participants for multiple days, individual learning curves revealed transient subjective internal models and pronounced inductive biases.

CONTRIBUTIONS OF THE RIGHT AND LEFT MESIAL TEMPORAL LOBES TO MUSIC MEMORY: EVIDENCE FROM MELODIC LEARNING DIFFICULTIES

2008 ◽  
Vol 25 (4) ◽  
pp. 303-314 ◽  
Author(s):  
SARAH J. WILSON ◽  
MICHAEL M. SALING
Keyword(s):  
Learning Difficulties ◽  
Learning Task ◽  
Learning Curves ◽  
Memory Impairments ◽  
Temporal Lobes ◽  
Patient Groups ◽  
Left And Right ◽  
Mesial Temporal Lobe ◽  
The Right ◽  
Musical Context

THE AIM OF THIS STUDY WAS TO ASSESS the effects of left- and right-sided MTL damage on melodic memory using a newly developed arbitrary relational learning task. Participants included patients with MTL damage, patient controls,musicians, and musician controls. The learning curves of these groups showed striking differences, with right MTL patients failing to learn tonal (easy) melody pairs. Both patient groups had difficulty learning nontonal (hard) pairs. Performance was greatest for the musicians, particularly for the nontonal melody pairs. These differences were not primarily attributable to pitch discrimination or pitch working memory impairments. The findings point to differential contributions of the left and right mesial temporal lobes to melodic memory, with specificity of the right mesial temporal lobe emerging for melodic learning within a tonal musical context.

scholarly journals Multimetric behavioral profile and psychological traits

2021 ◽  
Author(s):  
Mario Treviño Villegas ◽  
Santiago Castiello ◽  
Braniff De la Torre-Valdovinos ◽  
Paulina Osuna Carrasco ◽  
Ricardo Medina-Coss y León
Keyword(s):  
Response Times ◽  
Learning Task ◽  
Learning Curves ◽  
Internal Variables ◽  
Sources Of Information ◽  
Psychological Traits ◽  
Behavioral Profile ◽  
Wide Range ◽  
Certainty Level ◽  
Multiple Metrics

External sources of information determine human actions. However, psychological traits (PTs), considered internal variables, also play a crucial role in decision-making. PTs are stable across time and contexts, and they define the set of behavioral repertoires that individuals express. Here, we explored how multiple metrics of adaptive behavior under uncertainty related to several PTs. Participants solved a multiple baseline reversal-learning task with volatile contingencies, from where we characterized a detailed behavioral profile based on a wide variety of measurements from their response sequences. We then tested the relationship between the multimetric behavioral profile and a wide range of self-reported psychological questionnaires. The PT measurements were based on the Hierarchical Taxonomy Of Psychopathology (HiTOP) model. We found that the learning curves alone predicted important differences in the PTs, and task response times. Similarly, the behavioral profile configurations predicted the PTs, and could be used as a ‘fingerprint’ to identify participants with a high certainty level. We discuss how this characterization could contribute to constructing a better nosological classification.

scholarly journals Learned Overweight Internal Model Can Be Activated to Maintain Equilibrium When Tactile Cues Are Uncertain: Evidence From Cortical and Behavioral Approaches

2021 ◽  
Vol 15 ◽  
Author(s):  
Olivia Lhomond ◽  
Benjamin Juan ◽  
Theo Fornerone ◽  
Marion Cossin ◽  
Dany Paleressompoulle ◽  
...  
Keyword(s):  
Internal Model ◽  
Tactile Stimulation ◽  
Balance Control ◽  
Load Condition ◽  
Internal Models ◽  
Support Surface ◽  
Extra Weight ◽  
The Right ◽  
The Brain ◽  
Load Conditions

Human adaptive behavior in sensorimotor control is aimed to increase the confidence in feedforward mechanisms when sensory afferents are uncertain. It is thought that these feedforward mechanisms rely on predictions from internal models. We investigate whether the brain uses an internal model of physical laws (gravitational and inertial forces) to help estimate body equilibrium when tactile inputs from the foot sole are depressed by carrying extra weight. As direct experimental evidence for such a model is limited, we used Judoka athletes thought to have built up internal models of external loads (i.e., opponent weight management) as compared with Non-Athlete participants and Dancers (highly skilled in balance control). Using electroencephalography, we first (experiment 1) tested the hypothesis that the influence of tactile inputs was amplified by descending cortical efferent signals. We compared the amplitude of P1N1 somatosensory cortical potential evoked by electrical stimulation of the foot sole in participants standing still with their eyes closed. We showed smaller P1N1 amplitudes in the Load compared to No Load conditions in both Non-Athletes and Dancers. This decrease neural response to tactile stimulation was associated with greater postural oscillations. By contrast in the Judoka’s group, the neural early response to tactile stimulation was unregulated in the Load condition. This suggests that the brain can selectively increase the functional gain of sensory inputs, during challenging equilibrium tasks when tactile inputs were mechanically depressed by wearing a weighted vest. In Judokas, the activation of regions such as the right posterior inferior parietal cortex (PPC) as early as the P1N1 is likely the source of the neural responses being maintained similar in both Load and No Load conditions. An overweight internal model stored in the right PPC known to be involved in maintaining a coherent representation of one’s body in space can optimize predictive mechanisms in situations with high balance constraints (Experiment 2). This hypothesis has been confirmed by showing that postural reaction evoked by a translation of the support surface on which participants were standing wearing extra-weight was improved in Judokas.

Individual Differences in Mental Rotation

2013 ◽  
Vol 60 (3) ◽  
pp. 164-171 ◽  
Author(s):  
Peter Khooshabeh ◽  
Mary Hegarty ◽  
Thomas F. Shipley
Keyword(s):  
Individual Differences ◽  
Mental Rotation ◽  
Response Times ◽  
Task Demands ◽  
Mental Rotation Task ◽  
Imagery Ability ◽  
Alternative Strategies

Two experiments tested the hypothesis that imagery ability and figural complexity interact to affect the choice of mental rotation strategies. Participants performed the Shepard and Metzler (1971) mental rotation task. On half of the trials, the 3-D figures were manipulated to create “fragmented” figures, with some cubes missing. Good imagers were less accurate and had longer response times on fragmented figures than on complete figures. Poor imagers performed similarly on fragmented and complete figures. These results suggest that good imagers use holistic mental rotation strategies by default, but switch to alternative strategies depending on task demands, whereas poor imagers are less flexible and use piecemeal strategies regardless of the task demands.

scholarly journals Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abdulkadir Canatar ◽  
Blake Bordelon ◽  
Cengiz Pehlevan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Kernel Regression ◽  
Learning Task ◽  
Learning Curves ◽  
Generalization Error ◽  
Theoretical Understanding ◽  
Classical Statistics ◽  
Deep Networks ◽  
Model Alignment

AbstractA theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here, we investigate generalization error for kernel regression, which, besides being a popular machine learning method, also describes certain infinitely overparameterized neural networks. We use techniques from statistical mechanics to derive an analytical expression for generalization error applicable to any kernel and data distribution. We present applications of our theory to real and synthetic datasets, and for many kernels including those that arise from training deep networks in the infinite-width limit. We elucidate an inductive bias of kernel regression to explain data with simple functions, characterize whether a kernel is compatible with a learning task, and show that more data may impair generalization when noisy or not expressible by the kernel, leading to non-monotonic learning curves with possibly many peaks.

scholarly journals Learning Curves: A Novel Approach for Robustness Improvement of Load Forecasting

2021 ◽  
Vol 5 (1) ◽  
pp. 38
Author(s):  
Chiara Giola ◽  
Piero Danti ◽  
Sandro Magnani
Keyword(s):  
Load Forecasting ◽  
Learning Curves ◽  
Machine Learning Techniques ◽  
Essential Information ◽  
Set Size ◽  
Novel Approach ◽  
Learning Techniques ◽  
Energy Plant ◽  
Definition Of ◽  
The Right

In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when dealing with energy management, one of the most thriving application of AI is the consumption’s optimization of energy plant generators. When designing a strategy to improve the generators’ schedule, a piece of essential information is the future energy load requested by the plant. This topic, in the literature it is referred to as load forecasting, has lately gained great popularity; in this paper authors underline the problem of estimating the correct size of data to train prediction algorithms and propose a suitable methodology. The main characters of this methodology are the Learning Curves, a powerful tool to track algorithms performance whilst data training-set size varies. At first, a brief review of the state of the art and a shallow analysis of eligible machine learning techniques are offered. Furthermore, the hypothesis and constraints of the work are explained, presenting the dataset and the goal of the analysis. Finally, the methodology is elucidated and the results are discussed.

Postural adjustments for online corrections of arm movements in standing humans

2011 ◽  
Vol 105 (5) ◽  
pp. 2375-2388 ◽  
Author(s):  
Julia A. Leonard ◽  
Valeriya Gritsenko ◽  
Ryan Ouckama ◽  
Paul J. Stapley
Keyword(s):  
Muscle Activity ◽  
Arm Movements ◽  
Task Demands ◽  
Target Displacement ◽  
Postural Adjustments ◽  
Central Target ◽  
Postural Muscle ◽  
Online Corrections ◽  
Standing Humans ◽  
The Right

The aim of this study was to investigate how humans correct ongoing arm movements while standing. Specifically, we sought to understand whether the postural adjustments in the legs required for online corrections of arm movements are predictive or rely on feedback from the moving limb. To answer this question we measured online corrections in arm and leg muscles during pointing movements while standing. Nine healthy right-handed subjects reached with their dominant arm to a visual target in front of them and aligned with their midline. In some trials, the position of the target would switch from the central target to one of the other targets located 15°, 30°, or 45° to the right of the central (midline) target. For each target correction, we measured the time at which arm kinematics, ground reaction forces, and arm and leg muscle electromyogram significantly changed in response to the target displacement. Results show that postural adjustments in the left leg preceded kinematic corrections in the limb. The corrective postural muscle activity in the left leg consistently preceded the corrective reaching muscle activity in the right arm. Our results demonstrate that corrections of arm movements in response to target displacement during stance are preceded by postural adjustments in the leg contralateral to the direction of target shift. Furthermore, postural adjustments preceded both the hand trajectory correction and the arm-muscle activity responsible for it, which suggests that the central nervous system does not depend on feedback from the moving arm to modify body posture during voluntary movement. Instead, postural adjustments lead the online correction in the arm the same way they lead the initiation of voluntary arm movements. This suggests that forward models for voluntary movements executed during stance incorporate commands for posture that are produced on the basis of the required task demands.

Hands-Free versus Hand-Held Cell Phone Conversation on a Braking Response by Young Drivers

2007 ◽  
Vol 105 (2) ◽  
pp. 514-522 ◽  
Author(s):  
Joy L. Hendrick ◽  
Jamie R. Switzer
Keyword(s):  
Cell Phone ◽  
Response Times ◽  
Cell Phones ◽  
Reaction Times ◽  
Free Cell ◽  
Visual Signal ◽  
Young Drivers ◽  
Speed Of Information Processing ◽  
Additional Support ◽  
The Right

As some states allow motorists to use hands-free cell phones only while driving, this study was done to examine some braking responses to see if conversing on these two types of cell phones affects quick responding. College-age drivers ( n = 25) completed reaction time trials in go/no-go situations under three conditions: control (no cell phone or conversation), and conversing on hands-free and hand-held cell phones. Their task involved moving the right foot from one pedal to another as quickly as possible in response to a visual signal in a lab setting. Significantly slower reaction times, movement times, and total response times were found for both cell phone conditions than for the control but no differences between hands-free and hand-held phone conditions. These findings provide additional support that talking on cell phones, regardless if it is hands-free or hand-held, reduces speed of information processing.

scholarly journals Combining Visual Contrast Information with Sound Can Produce Faster Decisions

2019 ◽  
Author(s):  
Birgitta Dresp-Langley ◽  
Marie Monfouga
Keyword(s):  
Response Times ◽  
Luminance Contrast ◽  
Forced Choice ◽  
Sound Frequency ◽  
Choice Response ◽  
Relative Depth ◽  
Probability Summation ◽  
Visual Contrast ◽  
Contrast Information ◽  
The Right

Pieron's and Chocholle’s seminal psychophysical work predicts that human response time to information relative to visual contrast and/or sound frequency decreases when contrast intensity or sound frequency increases. The goal of this study is to bring to the fore the ability of individuals to use visual contrast intensity and sound frequency in combination for faster perceptual decisions of relative depth (“nearer”) in planar (2D) object configurations on the basis of physical variations in luminance contrast. Computer controlled images with two abstract patterns of varying contrast intensity, one on the left and one on the right, preceded or not by a pure tone of varying frequency, were shown to healthy young humans in controlled experimental sequences. Their task (two-alternative forced-choice) was to decide as quickly as possible which of two patterns, the left or the right one, in a given image appeared to “stand out as if it were nearer” in terms of apparent (subjective) visual depth. The results show that the combinations of varying relative visual contrast with sounds of varying frequency exploited here produced an additive effect on choice response times in terms of facilitation, where a stronger visual contrast combined with a higher sound frequency produced shorter forced-choice response times. This new effect is predicted by cross-modal audio-visual probability summation.

scholarly journals An internal model approach for motor behavior

2021 ◽  
Vol 15 (5) ◽  
pp. 356-371
Author(s):  
Cláudio M. F. Leite ◽  
Carlos E. Campos ◽  
Crislaine R. Couto ◽  
Herbert Ugrinowitsch
Keyword(s):  
Motor Skills ◽  
Internal Model ◽  
Motor Behavior ◽  
External World ◽  
Internal Models ◽  
Sensory Inputs ◽  
Theoretical Approaches ◽  
Internal Coherence ◽  
Motor Actions ◽  
Model Approach

Interacting with the environment requires a remarkable ability to control, learn, and adapt motor skills to ever-changing conditions. The intriguing complexity involved in the process of controlling, learning, and adapting motor skills has led to the development of many theoretical approaches to explain and investigate motor behavior. This paper will present a theoretical approach built upon the top-down mode of motor control that shows substantial internal coherence and has a large and growing body of empirical evidence: The Internal Models. The Internal Models are representations of the external world within the CNS, which learn to predict this external world, simulate behaviors based on sensory inputs, and transform these predictions into motor actions. We present the Internal Models’ background based on two main structures, Inverse and Forward models, explain how they work, and present some applicability.

Sign in / Sign up

Export Citation Format

Share Document