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monkeylogic ml1  (MathWorks Inc)


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    MathWorks Inc monkeylogic ml1
    An example task written with the scene framework. This is a working example that is executable in ML2. A video clip of this task and the complete task files are available at NIMH <t>MonkeyLogic</t> website. A. The fixation timer task. The total duration of fixation is indicated by the annulus-shaped counters that continuously change over time. The counter stops when the fixation is broken but resumes if the fixation is re-acquired within 4 s. B. The script for the fixation timer task. Three adapters are used and combined into a chain. Each adapter can be manipulated by changing its properties. The chain is executed via create_scene() and run_scene(). C. The cycle during run_scene(). Each adapter has two member functions: analyze() and draw(). run_scene() calls analyze() and draw() of the top-most adapter, the adapter given as an input argument to create_scene(), which triggers the iteration of all linked adapters. run_scene() repeats this cycle until analyze() of the top-most adapter returns false.
    Monkeylogic Ml1, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/monkeylogic ml1/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    monkeylogic ml1 - by Bioz Stars, 2026-03
    90/100 stars

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    1) Product Images from "NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB"

    Article Title: NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB

    Journal: Journal of neuroscience methods

    doi: 10.1016/j.jneumeth.2019.05.002

    An example task written with the scene framework. This is a working example that is executable in ML2. A video clip of this task and the complete task files are available at NIMH MonkeyLogic website. A. The fixation timer task. The total duration of fixation is indicated by the annulus-shaped counters that continuously change over time. The counter stops when the fixation is broken but resumes if the fixation is re-acquired within 4 s. B. The script for the fixation timer task. Three adapters are used and combined into a chain. Each adapter can be manipulated by changing its properties. The chain is executed via create_scene() and run_scene(). C. The cycle during run_scene(). Each adapter has two member functions: analyze() and draw(). run_scene() calls analyze() and draw() of the top-most adapter, the adapter given as an input argument to create_scene(), which triggers the iteration of all linked adapters. run_scene() repeats this cycle until analyze() of the top-most adapter returns false.
    Figure Legend Snippet: An example task written with the scene framework. This is a working example that is executable in ML2. A video clip of this task and the complete task files are available at NIMH MonkeyLogic website. A. The fixation timer task. The total duration of fixation is indicated by the annulus-shaped counters that continuously change over time. The counter stops when the fixation is broken but resumes if the fixation is re-acquired within 4 s. B. The script for the fixation timer task. Three adapters are used and combined into a chain. Each adapter can be manipulated by changing its properties. The chain is executed via create_scene() and run_scene(). C. The cycle during run_scene(). Each adapter has two member functions: analyze() and draw(). run_scene() calls analyze() and draw() of the top-most adapter, the adapter given as an input argument to create_scene(), which triggers the iteration of all linked adapters. run_scene() repeats this cycle until analyze() of the top-most adapter returns false.

    Techniques Used:

    Screenshots of NIMH MonkeyLogic (ML2). A. The subject’s screen. B. The experimenter’s screen. MonkeyLogic Graphics Library (MGL) displays visual stimuli on both screens in parallel but overlays task-related information (fixation window, eye trace, button input, user text, etc.) on the experimenter’s screen only. It supports transparent colors so that the visual stimuli can be overlapped.
    Figure Legend Snippet: Screenshots of NIMH MonkeyLogic (ML2). A. The subject’s screen. B. The experimenter’s screen. MonkeyLogic Graphics Library (MGL) displays visual stimuli on both screens in parallel but overlays task-related information (fixation window, eye trace, button input, user text, etc.) on the experimenter’s screen only. It supports transparent colors so that the visual stimuli can be overlapped.

    Techniques Used:

    Resolution of timestamps and latencies of event markers and stimuli. The timestamp resolution is the minimum interval that the tic and toc commands of MATLAB can measure. The latencies of analog stimulations, sounds and photodiode output were measured from the event markers sent out immediately after the stimuli. A negative number indicates that the signal change due to the stimulus was faster than the event marker. All measures were repeated 100 times. The numbers in parentheses are the results with MATLAB R2018a. Because of the just-in-time compilation of the MATLAB execution engine, the first few measures can be slower than the rest. For example, the first six timestamp intervals measured in ML2 with R2018a were 93.81, 15.78, 13.74, 152.55, 176.22 and 1.17 μs, but the rest of the intervals were all shorter than 1 μs.
    Figure Legend Snippet: Resolution of timestamps and latencies of event markers and stimuli. The timestamp resolution is the minimum interval that the tic and toc commands of MATLAB can measure. The latencies of analog stimulations, sounds and photodiode output were measured from the event markers sent out immediately after the stimuli. A negative number indicates that the signal change due to the stimulus was faster than the event marker. All measures were repeated 100 times. The numbers in parentheses are the results with MATLAB R2018a. Because of the just-in-time compilation of the MATLAB execution engine, the first few measures can be slower than the rest. For example, the first six timestamp intervals measured in ML2 with R2018a were 93.81, 15.78, 13.74, 152.55, 176.22 and 1.17 μs, but the rest of the intervals were all shorter than 1 μs.

    Techniques Used: Marker

    Execution time of MonkeyLogic functions. The measured times (mean ± SD) are in milliseconds and based on 100 trials of the performance test (see Methods ), including the first trial. The numbers in parentheses are the results with MATLAB R2018a. The “Entry” time refers to the amount of time required for initialization of each function, before the execution of the core activity. The “Core” time is the amount of time required to execute the essential activity of the function. The “Exit” time reflects the amount of time required to clean up and leave the function, after the core activity has completed. For the “Trial” row, the entry time corresponds to the time required to initialize all the sub-functions and analog data acquisition and the exit time is the time required to store collected data to the disk. The intertrial interval includes the time required to update behavior performance measures on the experimenter’s screen but most of it is the time required to load stimuli. The eventmarker function of ML2 is a one-line function, so there is no entry or exit time. The adapters of ML2 are executed with run_scene(), instead of toggleobject() and eyejoytrack().
    Figure Legend Snippet: Execution time of MonkeyLogic functions. The measured times (mean ± SD) are in milliseconds and based on 100 trials of the performance test (see Methods ), including the first trial. The numbers in parentheses are the results with MATLAB R2018a. The “Entry” time refers to the amount of time required for initialization of each function, before the execution of the core activity. The “Core” time is the amount of time required to execute the essential activity of the function. The “Exit” time reflects the amount of time required to clean up and leave the function, after the core activity has completed. For the “Trial” row, the entry time corresponds to the time required to initialize all the sub-functions and analog data acquisition and the exit time is the time required to store collected data to the disk. The intertrial interval includes the time required to update behavior performance measures on the experimenter’s screen but most of it is the time required to load stimuli. The eventmarker function of ML2 is a one-line function, so there is no entry or exit time. The adapters of ML2 are executed with run_scene(), instead of toggleobject() and eyejoytrack().

    Techniques Used: Activity Assay



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    monkeylogic ml1  (MathWorks Inc)
    90
    MathWorks Inc monkeylogic ml1
    An example task written with the scene framework. This is a working example that is executable in ML2. A video clip of this task and the complete task files are available at NIMH <t>MonkeyLogic</t> website. A. The fixation timer task. The total duration of fixation is indicated by the annulus-shaped counters that continuously change over time. The counter stops when the fixation is broken but resumes if the fixation is re-acquired within 4 s. B. The script for the fixation timer task. Three adapters are used and combined into a chain. Each adapter can be manipulated by changing its properties. The chain is executed via create_scene() and run_scene(). C. The cycle during run_scene(). Each adapter has two member functions: analyze() and draw(). run_scene() calls analyze() and draw() of the top-most adapter, the adapter given as an input argument to create_scene(), which triggers the iteration of all linked adapters. run_scene() repeats this cycle until analyze() of the top-most adapter returns false.
    Monkeylogic Ml1, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/monkeylogic ml1/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    monkeylogic ml1 - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

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    An example task written with the scene framework. This is a working example that is executable in ML2. A video clip of this task and the complete task files are available at NIMH MonkeyLogic website. A. The fixation timer task. The total duration of fixation is indicated by the annulus-shaped counters that continuously change over time. The counter stops when the fixation is broken but resumes if the fixation is re-acquired within 4 s. B. The script for the fixation timer task. Three adapters are used and combined into a chain. Each adapter can be manipulated by changing its properties. The chain is executed via create_scene() and run_scene(). C. The cycle during run_scene(). Each adapter has two member functions: analyze() and draw(). run_scene() calls analyze() and draw() of the top-most adapter, the adapter given as an input argument to create_scene(), which triggers the iteration of all linked adapters. run_scene() repeats this cycle until analyze() of the top-most adapter returns false.

    Journal: Journal of neuroscience methods

    Article Title: NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB

    doi: 10.1016/j.jneumeth.2019.05.002

    Figure Lengend Snippet: An example task written with the scene framework. This is a working example that is executable in ML2. A video clip of this task and the complete task files are available at NIMH MonkeyLogic website. A. The fixation timer task. The total duration of fixation is indicated by the annulus-shaped counters that continuously change over time. The counter stops when the fixation is broken but resumes if the fixation is re-acquired within 4 s. B. The script for the fixation timer task. Three adapters are used and combined into a chain. Each adapter can be manipulated by changing its properties. The chain is executed via create_scene() and run_scene(). C. The cycle during run_scene(). Each adapter has two member functions: analyze() and draw(). run_scene() calls analyze() and draw() of the top-most adapter, the adapter given as an input argument to create_scene(), which triggers the iteration of all linked adapters. run_scene() repeats this cycle until analyze() of the top-most adapter returns false.

    Article Snippet: MonkeyLogic (ML1) is a MATLAB-based toolbox for designing and executing psychophysical tasks.

    Techniques:

    Screenshots of NIMH MonkeyLogic (ML2). A. The subject’s screen. B. The experimenter’s screen. MonkeyLogic Graphics Library (MGL) displays visual stimuli on both screens in parallel but overlays task-related information (fixation window, eye trace, button input, user text, etc.) on the experimenter’s screen only. It supports transparent colors so that the visual stimuli can be overlapped.

    Journal: Journal of neuroscience methods

    Article Title: NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB

    doi: 10.1016/j.jneumeth.2019.05.002

    Figure Lengend Snippet: Screenshots of NIMH MonkeyLogic (ML2). A. The subject’s screen. B. The experimenter’s screen. MonkeyLogic Graphics Library (MGL) displays visual stimuli on both screens in parallel but overlays task-related information (fixation window, eye trace, button input, user text, etc.) on the experimenter’s screen only. It supports transparent colors so that the visual stimuli can be overlapped.

    Article Snippet: MonkeyLogic (ML1) is a MATLAB-based toolbox for designing and executing psychophysical tasks.

    Techniques:

    Resolution of timestamps and latencies of event markers and stimuli. The timestamp resolution is the minimum interval that the tic and toc commands of MATLAB can measure. The latencies of analog stimulations, sounds and photodiode output were measured from the event markers sent out immediately after the stimuli. A negative number indicates that the signal change due to the stimulus was faster than the event marker. All measures were repeated 100 times. The numbers in parentheses are the results with MATLAB R2018a. Because of the just-in-time compilation of the MATLAB execution engine, the first few measures can be slower than the rest. For example, the first six timestamp intervals measured in ML2 with R2018a were 93.81, 15.78, 13.74, 152.55, 176.22 and 1.17 μs, but the rest of the intervals were all shorter than 1 μs.

    Journal: Journal of neuroscience methods

    Article Title: NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB

    doi: 10.1016/j.jneumeth.2019.05.002

    Figure Lengend Snippet: Resolution of timestamps and latencies of event markers and stimuli. The timestamp resolution is the minimum interval that the tic and toc commands of MATLAB can measure. The latencies of analog stimulations, sounds and photodiode output were measured from the event markers sent out immediately after the stimuli. A negative number indicates that the signal change due to the stimulus was faster than the event marker. All measures were repeated 100 times. The numbers in parentheses are the results with MATLAB R2018a. Because of the just-in-time compilation of the MATLAB execution engine, the first few measures can be slower than the rest. For example, the first six timestamp intervals measured in ML2 with R2018a were 93.81, 15.78, 13.74, 152.55, 176.22 and 1.17 μs, but the rest of the intervals were all shorter than 1 μs.

    Article Snippet: MonkeyLogic (ML1) is a MATLAB-based toolbox for designing and executing psychophysical tasks.

    Techniques: Marker

    Execution time of MonkeyLogic functions. The measured times (mean ± SD) are in milliseconds and based on 100 trials of the performance test (see Methods ), including the first trial. The numbers in parentheses are the results with MATLAB R2018a. The “Entry” time refers to the amount of time required for initialization of each function, before the execution of the core activity. The “Core” time is the amount of time required to execute the essential activity of the function. The “Exit” time reflects the amount of time required to clean up and leave the function, after the core activity has completed. For the “Trial” row, the entry time corresponds to the time required to initialize all the sub-functions and analog data acquisition and the exit time is the time required to store collected data to the disk. The intertrial interval includes the time required to update behavior performance measures on the experimenter’s screen but most of it is the time required to load stimuli. The eventmarker function of ML2 is a one-line function, so there is no entry or exit time. The adapters of ML2 are executed with run_scene(), instead of toggleobject() and eyejoytrack().

    Journal: Journal of neuroscience methods

    Article Title: NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB

    doi: 10.1016/j.jneumeth.2019.05.002

    Figure Lengend Snippet: Execution time of MonkeyLogic functions. The measured times (mean ± SD) are in milliseconds and based on 100 trials of the performance test (see Methods ), including the first trial. The numbers in parentheses are the results with MATLAB R2018a. The “Entry” time refers to the amount of time required for initialization of each function, before the execution of the core activity. The “Core” time is the amount of time required to execute the essential activity of the function. The “Exit” time reflects the amount of time required to clean up and leave the function, after the core activity has completed. For the “Trial” row, the entry time corresponds to the time required to initialize all the sub-functions and analog data acquisition and the exit time is the time required to store collected data to the disk. The intertrial interval includes the time required to update behavior performance measures on the experimenter’s screen but most of it is the time required to load stimuli. The eventmarker function of ML2 is a one-line function, so there is no entry or exit time. The adapters of ML2 are executed with run_scene(), instead of toggleobject() and eyejoytrack().

    Article Snippet: MonkeyLogic (ML1) is a MATLAB-based toolbox for designing and executing psychophysical tasks.

    Techniques: Activity Assay