Name: a two-interval forced-choice task for multisensory comparisons
Uploaded: 2018-11-09T14:00:00
Description: Watch this Scientific Journal Video about A Two-interval Forced-choice Task for Multisensory Comparisons at JoVE.com

This work was supported by the Consejo Nacional de Ciencia y Tecnolog&#237;a (CONACyT), CB-256767. The authors thank Isaac Mor&#225;n for his technical assistance and Ana Escalante from the Computer Unit of the Instituto de Fisiolog&#237;a Celular (IFC) for her valuable assistance.

The experiments were approved by the Bioethical Committee of the Institute of Cellular Physiology of UNAM (No. CECB_08) and carried out under the guidelines of The Code of Ethics of the World Medical Association.
1. Experimental Set-up
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	<li>Material and stimuli set-up for performing an aperiodic interval discrimination (AID) task
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		<li>Perform this experiment on a computer with a minimum of 8 GB RAM, 2.5 GHz processor, and a 60 Hz refreshing rate monitor to create...

<ol>
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A., Alcal&#225;-Quintana, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sampling+plans+for+fitting+the+psychometric+function.">Sampling plans for fitting the psychometric function.</a> Spanish Journal of Psychology. 8 (2), 256-289 (2005).</li><li>Ulrich, R., Miller, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Threshold+estimation+in+two-alternative+forced-choice+(2AFC)+tasks:+The+Spearman-K&#228;rber+method.">Threshold estimation in two-alternative forced-choice (2AFC) tasks: The Spearman-K&#228;rber method.</a> Perception and Psychophysics. 66 (3), 517-533 (2004).</li><li>Garc&#237;a-P&#233;rez, M. A., Alcal&#225;-Quintana, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+the+estimation+of+psychometric+functions+in+2AFC+discrimination+tasks.">Improving the estimation of psychometric functions in 2AFC discrimination tasks.</a> Frontiers in Psychology. 2, 96 (2011).</li><li>Ulrich, R., Vorberg, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+difference+limen+in+2AFC+tasks:+Pitfalls+and+improved+estimators.">Estimating the difference limen in 2AFC tasks: Pitfalls and improved estimators.</a> Attention, Perception, and Psychophysics. 71 (6), 1219-1227 (2009).</li><li>Green, D. M., Swets, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Signal detection theory and psychophysics. , (1966).</li><li>Brainard, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Psychophysics+Toolbox.">The Psychophysics Toolbox.</a> Spatial Vision. 10 (4), 443-446 (1997).</li><li>Peirce, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PsychoPy-Psychophysics+software+in+Python.">PsychoPy-Psychophysics software in Python.</a> Journal of Neuroscience Methods. 162 (1-2), 8-13 (2007).</li><li>Ivry, R. B., Hazeltine, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perception+and+production+of+temporal+intervals+across+a+range+of+durations:+Evidence+for+a+common+timing+mechanism.">Perception and production of temporal intervals across a range of durations: Evidence for a common timing mechanism.</a> Journal of Experimental Psychology: Human Perception and Performance. 21 (1), 3-18 (1995).</li><li>Karmarkar, U. R., Buonomano, D. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Timing+in+the+Absence+of+Clocks:+Encoding+Time+in+Neural+Network+States.">Timing in the Absence of Clocks: Encoding Time in Neural Network States.</a> Neuron. 53 (3), 427-438 (2007).</li><li>Merchant, H., Harrington, D. L., Meck, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+Basis+of+the+Perception+and+Estimation+of+Time.">Neural Basis of the Perception and Estimation of Time.</a> Annual Review of Neuroscience. 36 (1), 313-336 (2013).</li><li>Levinson, J. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Flicker fusion phenomena. 160 (3823), 21-28 (1968).</li><li>Grahn, J. A., Henry, M. J., McAuley, J. 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Fabiola Duarte GUI Fabiola Duarte Available from: <a target="_blank" href="https://www.ifc.unam.mx">https://www.ifc.unam.mx</a> (2018)</li><li>Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A., Ghazanfar, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Natural+Statistics+of+Audiovisual+Speech.">The Natural Statistics of Audiovisual Speech.</a> PLoS Computational Biology. 5 (7), e1000436 (2009).</li><li>Linares, D., L&#243;pez-Moliner, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=quickpsy:+An+R+Package+to+Fit+Psychometric+Functions+for+Multiple+Groups.">quickpsy: An R Package to Fit Psychometric Functions for Multiple Groups.</a> The R Journal. 8 (1), 122-131 (2016).</li><li>Garc&#237;a-P&#233;rez, M. 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In psychophysics, the selection of a task depends on particular interests in perceptual phenomena5,6. For instance, this protocol consisted of recreating a previously reported paradigm on the time-interval perception of visual, auditory, and audiovisual stimuli of aperiodically arrayed pulses, which implemented the 2IFC method13. Here, as in most of the psychophysics tasks, adequate hardware and software are essential to create, reproduce,...

The purpose of this protocol is to convey a procedure for a standard experiment on psychophysics. Psychophysics is the study of perception phenomena through the measure of behavioral responses, elicited by sensory inputs1,2,3. Usually, human psychophysics is an inexpensive and essential tool to implement in imaging or neurophysiological experiments4. However, it is never easy to select the most appropriate psychophysical method out of many that exist, and the selection somewhat depends on experience and preference. Nevertheless, we encourage beginners to revise available methodologies thoroughly in order to learn about selection criteria5,6,7. Here, we provide a procedure for performing a 2IFC task, which many researchers frequently use for studying perceptual processes such as working memory8, decision making9,10, or time perception11,12,13.
To guide the readers along the method, we recreate a report on the perceptual duration of visual (V), auditory (A), and audiovisual (AV) intervals of aperiodic sequences of pulses. We will refer to this task as an aperiodic interval discrimination (AID) task13. When attempting to describe this paradigm in psychophysics jargon, it would be a class-A, type-1, performance-based, criterion-dependent discrimination task that uses a non-adaptive method of the constants and a hyperbolic tangent (tanh) model to calculate a differential threshold. Even when such a characterization sounds somewhat entangled, we will use it to introduce the reader to some general aspects of psychophysics, hoping to provide decision criteria for new experiments and maybe even the possibility of tailoring the current protocol to other needs.
Any psychophysical experiment, such as a 2IFC task, requires implementing stimuli, a task, a method, an analysis, and a measurement6. The goal is to obtain the psychometric function that better accounts for the measured performance14. A 2IFC task consists of presenting to participants, who are na&#239;ve to the purpose of the experiment, trials of two sequential stimuli. After comparing the stimuli, they report the outcome by selecting one, and only one, out of two possible responses that better suits their perception.
With stimuli, we refer to technical considerations about the sensory modality under study. A class-A experiment consists of the comparison of stimuli of the same modality within a trial, whereas class-B experiments include cross-modal comparisons. Other essential considerations about stimuli include their implementation, such as the technical ways of modulating stimuli within a required range. For example, if we want to find the just noticeable difference (JND) between two flutter frequencies vibrating on the skin15, we need a precision stimulator to generate frequencies within the confines of flutter (i.e., 4 - 40 Hz). In other words, the dynamic operating range of the technical elements depend on the dynamic spectrum of each sensory modality.
Selecting a task is about the perceptual phenomenon under study. For instance, finding whether two stimuli are the same, or equivalent, may rely on different brain mechanisms than those resolving if a stimulus is longer or shorter than a reference16 (as in the AID paradigm). Intrinsically, stimuli selection defines the type of obtained responses. Type-1 experiments, sometimes closely related to the so-called performance experiments, include correct or incorrect responses. In contrast, a type-2 experiment (or appearance experiment) produces mostly qualitative answers that depend on the participant&#39;s criteria and not on any explicitly imposed criterion; in other words, criterion-independent experiments. It is noteworthy that 2IFC task responses are criterion dependent because, in every trial, the standard stimulus (sometimes called base or reference stimulus) constitutes the criterion on which the comparison&#39;s perception depends.
The method may refer to three things; first, it may refer to the mechanism for selecting the range of stimuli to test or, in other words, to an already known range of stimulus variability, as opposed to adaptive methods aimed to establish the adequate range17. These adaptive matters are recommended for quickly finding detection and discrimination thresholds and for minimum trial repetitions18. Also, adaptive methods are optimal for pilot experiments. The second definition of a method is the scale of stimuli modulations (e.g., the method of the constants) or a logarithmic scale. The selected scale may or may not be a direct consequence of the outcome of an adaptive method, but primarily, it regards the dynamics of the studied sensory modality. Lastly, the method also refers to the number of trials and their presentation order.
As for analysis, it relates to the statistics of experimental measurements. Regardless of selecting appropriate analytical methods for comparisons between test and control groups, psychophysics is mostly about measuring absolute or differential thresholds between two conditions (e.g., presence vs. absence of a stimulus, or the JND between two stimuli), particularly in 2IFC19. Such measurements derive from psychometric functions (i.e., continuous models of behavior as a function of the probability of detecting or discerning one of the conditions at stake). Selecting the model function depends on the scale or, in other words, on the spacing of the values of the independent variable. Functions such as cumulative normal, logistic, Quick, and Weibull are appropriate for values spaced linearly, whereas Gumbel and log-Quick are better suited for logarithmic spacing. Alternative models also exist, such as the tanh employed in the AID task. Importantly, selecting a correct model depends on the parameters of interest, as considered in the design of the experiment20. After fitting the data to a model, it should be possible to derive two parameters: &#945; and &#946; parameters. In the case of a logistic function typically employed in a 2IFC paradigm, &#945; refers to the abscissas value projecting to the point of subjective equality (i.e., at half the logistic). The &#946; parameter refers to the slope at &#945; value (i.e., the steepness of the transition between conditions). Finally, a parameter commonly obtained out of a psychometric curve is the differential limen21 (DL). In a 2IFC experiment, the DL relates to &#946;, but strictly, corresponds to the minimum perceived difference between two intervals. The formula to determine the DL is the following equation (1).
<img alt="Equation 1" src="/files/ftp_upload/58408/58408eq1.jpg" />(1)
Here, x stands for independent variable values projecting at a 0.75 and 0.25 performance measured directly at the sigmoidal curve. Up until this point, we have covered only some generalities about psychometric functions. We recommend further study of estimating and interpreting psychometric functions, with these and other parameters22.
Other technical aspects to consider when implementing a psychophysical experiment are related to equipment and software. Memory and speed capacities of commercial computers nowadays are usually optimal for processing in high-fidelity visual and auditory tasks. Moreover, the dynamic resolution of complementary material, such as noise-blocking headphones, speakers, and monitors, must fulfill the sampling rate at which the sensory modalities operate (e.g., frequency, amplitude, contrast, and refreshing rate). Also, software programs such as PsychToolbox23 and PsychoPy24 are easy to implement and highly efficient at synchronizing tasks&#39; events and equipment.
The previously described AID task assembles many of the topics described above for a 2IFC paradigm. Interestingly, it explores the perception of V, A, and AV intervals in the range of milliseconds, where most of the brain&#39;s processes occur25,26,27. Paradoxically, it is also a challenging lapse for studying vision, which, compared to audition, begets a somewhat constrained sampling rate28. In this sense, multimodal comparisons require additional theoretical scopes12,29,30. Sometimes, they need further tailoring to encompass a common modulation spectrum or to achieve congruent interpretations.
This protocol focuses on a discrimination task (i.e., a 2IFC where a base stimulus, also called reference or standard, is contrasted against a set of comparison or test stimuli to find a JND or, in other words, a discrimination threshold). Here, the task is set to study the capacity of humans to discriminate time intervals of V, A, or AV aperiodic patterns of pulses13. We provide information on creating and parameterizing stimuli, as well as on analyses of accuracy and reaction times. Importantly, we discuss how to interpret subjects&#39; time perception from the psychometrics statistical outcome parameters, and some experimental and analytical alternatives within topics of a 2IFC psychophysical method.

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	<tbody>
		<tr>
			<td>Lapt top Dell Precision</td>
			<td>Dell</td>
			<td>M6800 CTO</td>
			<td>Procesador Intel Core i7-4710MQ, 2.5GHz RAM 16 GB, 64-bit OS; 17.3&#34; screen 1920 x 1080; 60 Hz refreshing rate</td>
		</tr>
		<tr>
			<td>Noise-blocking headphones</td>
			<td>Bose</td>
			<td>QC25</td>
			<td>Headphones QuietComfort 25, noise-blocking</td>
		</tr>
		<tr>
			<td>Decibel meter</td>
			<td>Extech Instruments</td>
			<td>SL 130G</td>
			<td>Sound Level meter (dB), range 30 to 130 dB, this meter meets ANSI and IEC Type 2 sound level meter standards</td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
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		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
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			<td>Labview</td>
			<td>National Instruments</td>
			<td>Labview 2014</td>
			<td>Labview SP1 130, 64-bits, version 14</td>
		</tr>
		<tr>
			<td>Matlab</td>
			<td>Mathworks Inc</td>
			<td>Matlab 2016a</td>
			<td>The Mathworks Inc., Natick, MA, USA</td>
		</tr>
		<tr>
			<td>GUI To create Visual and Acoustic stimuli. Created by Fabiola Duarte</td>
			<td>Mathworks Inc</td>
			<td>Matlab 2016a</td>
			<td>The Mathworks Inc., Natick, MA, USA</td>
		</tr>
	</tbody>
</table>

a two-interval forced-choice task for multisensory comparisons

We provide a procedure for a psychophysics experiment in humans based on a previously described paradigm aimed to characterize the perceptual duration of intervals within the range of milliseconds of visual, acoustic, and audiovisual aperiodic trains of six pulses. In this task, each of the trials consists of two consecutive intramodal intervals where the participants press the upward arrow key to report that the second stimulus lasted longer than the reference, or the downward arrow key to indicate otherwise. The analysis of the behavior results in psychometric functions of the probability of estimating the comparison stimulus to be longer than the reference, as a function of the comparison intervals. In conclusion, we advance a way of implementing standard programming software to create visual, acoustic, and audiovisual stimuli, and to generate a two-interval forced-choice (2IFC) task by delivering stimuli through noise-blocking headphones and a computer&#39;s monitor.

This protocol presented a method to perform a psychophysics experiment in humans. The technique replicated previous research on the discrimination of intervals of AP trains of V, A, and AV pulses, which was performed using a 2IFC method. The stimuli resulted from P and AP distributions of trains of six 50-ms pulses in different intervals within the range of milliseconds (i.e., from 500 ms to 1,100 ms in steps of 100 ms). Figure 2A shows some interval...

Watch this Scientific Journal Video about A Two-interval Forced-choice Task for Multisensory Comparisons at JoVE.com

A Two-interval Forced-choice Task for Multisensory Comparisons

Psychophysics is essential for studying perception phenomena through sensory information. Here we present a protocol to perform a two-interval forced-choice task as implemented in a previous report on human psychophysics where participants estimated the duration of visual, auditory, or audiovisual intervals of aperiodic trains of pulses.

We provide a procedure for a psychophysics experiment in humans based on a previously described paradigm aimed to characterize the perceptual duration of ...

This method can help answer critical questions in the field of perception neuroscience. Such as how time intervals are estimated by different sensory modalities. The main advantage of this technique is that it provides measurements of accuracy and reaction times which are perceptual outcomes of behavioral processes.Though this method can provide insight into human perception, it can also be applied to other animal models, such as trained or human primates. Begin by opening the experimental software. Click on the Set Path option in the Menu tab.Then select the Add Folder button, select the stimuli GUI folder, and press the Save button. Then click on the Close button. Open the stimuli GUI M file using the open option under the Main Menu tab, then press F5 on the keyboard to display the GUI.Next, click on the condition pop-up menu to select the preferred distribution of pulses. Select the desired number of pulses in the number of pulses pop-up menu. Next, click on the generate IPI button to display the IPI's values at the IPI values box, and to see a plot of the resultant distribution of the pulses.Then, click on the generate video button and wait for the pop-up window displaying gray, four degree circles to close. Click on the Play button to see the created V stimulus. Finally, click on the Generate Audio button to observe a plot of the created audio and click on the Play button to listen to the new audio.Begin by creating sets of P and AP VA and AV trials by listing the names of the created stimuli in a spreadsheet. Use different columns to include all the information required during the task, such as the modality of the reference and comparison stimuli, the number of repetitions per trial, the stimuli durations, and the expected response. Next, open the tasks file and load the created stimuli by selecting the stimulus folder from the control panel.Use the Up and Down buttons on the dialog box to display a zero. Then, press the folder icon to select the stimulus folder. Next, press the white noise button located on the control panel to activate the background noise.Then, place the decibel meter as near as possible to the headphones and set the volume control to 65 dB SPL. Adjust the background volume control located on the control panel to 55 dB SPL. Test the task by clicking on the run, right arrow icon under the Tools tab and perform some test trials.Lastly, hold down the Spacebar to begin each trial after the appearance of the visual queue at the center of the screen. Release the Spacebar after the delivery of a pair of stimuli and press the Upward or Downward arrow key to finalize the trial. Begin by asking the participant to fill out a questionnaire regarding their age, gender, handedness, and physical or psychological conditions.Tell the participant about the aim, procedures, and duration of the experiment. Be careful no bias is induced, then ask the participants to sign an informed consent form to participate in the experiment. Next, escort the participant to the testing room and have them sit comfortably in front of the monitor.Situated at a distance of 60 centimeters away. Place the keyboard at a reachable distance and adjust the headphones to the participant's head. Calibrate the decibel meter as previously described.Then, run the software to begin the training trials. Instruct the participant to start a trial after the appearance of the visual queue by pressing and holding down the Spacebar for the entire trial. Signal to the participant to release the Spacebar after the presentation of two sequential stimuli and to press the Upward arrow key if the second stimulus lasted longer than the first or to press the Downward arrow key if it lasted for a shorter period of time.Signal to the participant to take the headphones off and tell them to use only the right index finger to complete the task and comment on the possibility of taking a five minute break in case the participant feels tired or distracted during the experiment. Turn on the noise blocking feature of the headphones and let the participant practice 10 to 15 trials. Finally, have the participant complete the task.Non overlapping confidence intervals of the parameters obtained from the tanh function fit indicated statistical differences. This result is apparent at the visual aperiodic sigmoid shifting downward. Suggesting that the participants perceived the intervals longer than the reference as shorter.The acoustic and audio visual performances were similar during periodic and aperiodic conditions, indicating an A dominance over V in AV discriminations. Further, in the visual condition, the transition from shorter to longer occurred faster in the aperiodic condition. This result suggests that the participants were confident of their decisions as evidenced by the reaction times.In contrast, reaction times of periodic and aperiodic audio visual resemble those of the acoustic conditions, also suggesting an acoustic dominance. Simultaneously with this procedure, methods like EEG, FMRI, or neurophysiological recordings in trained animals can be implemented in order to answer additional questions like where and how different brain areas produce the perception of time intervals.

Research

JoVE Journal

Behavior

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