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MonkeyLogic is a MATLAB toolbox for the design and execution of psychophysical tasks with high temporal precision. It is structured to allow for the flexible construction of sensory, motor, or cognitive tasks that are based upon the interaction of a subject with visual stimuli through the use of eye-position, joystick, button, lever, and / or keyboard input.
An experiment is constructed using two main programming elements: A conditions file and a timing script. The conditions file is a tab-delimited text file which enumerates each possible set of stimuli that can occur within any given trial of the experiment. The timing script is a a MATLAB program that determines when and under what conditions each of those stimuli are presented. The timing script can take full advantage of the resources of the MATLAB programming language to create straightforward tasks or to study complex, highly-contingent behaviors. Each stimulus is referred to as a “TaskObject” and can take the form of a visual object, a sound, or an analog or digital output (e.g., to drive a stimulator or injector).
This structure is derived from the CORTEX (“Computerized Real-Time EXperiments”) software developed originally by Thomas White in the lab of Robert Desimone (formerly at the NIH, now at MIT) and in wide use by many other labs as well. This organization has two main benefits: 1) The timing file(s) can be written efficiently, so as to operate independently of the actual stimuli being presented on any particular trial, and 2) The explicit enumeration of all conditions and block structures greatly facilitates data analysis.
The independence of timing files from particular stimuli allows a timing file to present, for example, a sequence of pictures whose identity and position may vary from trial to trial. Timing files call up TaskObjects without regard to these specifics. For example, suppose one is constructing a delayed-match-to-sample task. TaskObjects 1-3 each might then refer to a picture object. The timing file can first present TaskObject1, then later present TaskObject2 and TaskObject3, and wait for target acquisition of TaskObject2 (for example, by saccade or joystick maneuver), regardless of which pictures the TaskObjects actually contain and where they are placed on the screen. The specific pictures and locations are specified by the conditions file. The conditions text file would specify which picture appears first by listing that picture under the TaskObject1 column. In the delayed-match-to-sample rule, for instance, the matching target picture can be re-specified as TaskObject2, and the timing script would simply assume that TaskObject2 is always the target of the correct response. A separate line determines the TaskObjects for each individual condition (i.e., each possible permutation of TaskObjects).
Because conditions are explicitly defined and each is associated with a unique condition number, an analysis can simply pull out those conditions which conform to any specific combination of stimuli or stimulus features (e.g., picture identity, picture location, distractor position, etc).
Despite running in a non-real-time operating environment (Windows), high performance can nevertheless be achieved in an appropriately configured system, and is possible on modern, multi-core machines and by the use of DirectX extensions (SVI Toolbox) to control a video display with sub-millisecond resolution.
MonkeyLogic will store a comprehensive record of behavioral data; However, it is assumed that a separate acquisition system (e.g., Plexon) is available to acquire neural data (LFPs, EEG, spike waveforms, etc). The two data files can then by synchronized for analysis by aligning event markers stored locally with those sent digitally by MonkeyLogic to the acquisition system.
Example DMS Task
If you use MonkeyLogic in your published work, citing it with one or more of these references is greatly appreciated, as this will enable us to more easily continue funding to support and enhance it. The studies below cite at least one MonkeyLogic paper. Thank you.
Cortical Information Flow during Flexible Sensorimotor Decisions
Microcircuitry of agranular frontal cortex: contrasting laminar connectivity between occipital and frontal areas
Neuronal Prediction of Opponent’s Behavior during Cooperative Social Interchange in Primates
Mapping of Functionally Characterized Cell Classes onto Canonical Circuit Operations in Primate Prefrontal Cortex
Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition
Reversal Learning and Dopamine: A Bayesian Perspective
Functional Mapping of Face-Selective Regions in the Extrastriate Visual Cortex of the Marmoset
A MATLAB-based eye tracking control system using non-invasive helmet head restraint in the macaque
Functional MRI of visual responses in the awake, behaving marmoset
Functional Organization of the Orbitofrontal Cortex
A cortical–spinal prosthesis for targeted limb movement in paralysed primate avatars
Frontoparietal Correlation Dynamics Reveal Interplay between Integration and Segregation during Visual Working Memory
An electrocorticographic electrode array for simultaneous recording from medial, lateral, and intrasulcal surface of the cortex in macaque monkeys
Dynamic Integration of Task-Relevant Visual Features in Posterior Parietal Cortex
Encoding of Rules by Neurons of the Human Dorsolateral Prefrontal Cortex
Single-Neuron Mechanisms Underlying Cost-Benefit Analysis in the Frontal Cortex
A Comparison of Lateral and Medial Intraparietal Areas during a Visual Categorization Task
Studying task-related activity of individual neurons in the human brain
Independent Category and Spatial Encoding in Parietal Cortex
Human Dorsal Anterior Cingulate Cortex Neurons Mediate Ongoing Behavioural Adaptation
Single-Neuron Responses in the Human Nucleus Accumbens during a Financial Decision-Making Task
Sruthi Swaminathan & David J. Freedman
Nature Neuroscience, 2012, Vol. 15, No. 2, pp. 315-320
Specific Contributions of Ventromedial, Anterior Cingulate, and Lateral Prefrontal Cortex for Attentional Selection and Stimulus Valuation
Daniel Kaping, Martin Vinck, R. Matthew Hutchison, Stefan Everling & Thilo Womelsdorf
PLoS Biology, 2011, Vol. 9, No. 12, e1001224
Encoding of both Positive and Negative Reward Prediction Errors by Neurons of the Primate Lateral Prefrontal Cortex and Caudate Nucleus
Wael F. Asaad & Emad N. Eskandar
Journal of Neuroscience, 2011, Vol. 31, No. 49, pp. 17772-17787
Neural Substrates of Cognitive Capacity Limitations
Timothy J. Buschman, Markus Siegel, Jefferson E. Roy & Earl K. Miller
Proc. Natl. Acad. Sci., U.S.A., 2011, Vol. 108, No. 27, pp. 11252-11255
Range-Adapting Representation of Economic Value in the Orbitofrontal Cortex
Journal of Neuroscience, 2009, Vol. 29, No. 44, pp. 14004-14014
|This site last updated: May, 2014||