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About 10,000 papers for “reverse correlation”

Improving Reverse Correlation

Koopmann-Holm, Birgit, Kevane, Michael · Figshare · 2020

Research using reverse correlation paradigms has become frequent over the last decade. In this paradigm, participants choose between a large number of image pairs, according to a prompt (e.g., “please choose the face that most resembles a trustworthy face”). The image pairs are generated by imposing random noise on the pixels that make up the image of a base face. The patterns of choices by participants are then used to compute an “average” matrix of noise pixels that, when imposed onto the base face, reflect an underlying shared mental construct of the “face” of the attribute. However, examinations of the reliability and validity of this approach remain rare. In the present paper, we examine the effects of filtering out data from individual participants or trials to increase the signal-to-noise ratio of composite images. We focus on order effects of trials, compliance and reliability effects, as well as the diagnostic quality of image pairs. We present different diagnostic methods to examine these three aspects using data from six reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, Germany, and the U.S.) using two different base faces (i.e., Black and White). For each of the six studies, we compare the composite images of the complete sample to composite images that exclude non-complier respondents or non-diagnostic image pairs. Our R scripts are publicly available for easy implementation of our suggestions in related research.

Cited by 0Open access

Improving Reverse Correlation

Birgit Koopmann‐Holm, Michael Kevane · Figshare · 2020

Research using reverse correlation paradigms has become frequent over the last decade. In this paradigm, participants choose between a large number of image pairs, according to a prompt (e.g., “please choose the face that most resembles a trustworthy face”). The image pairs are generated by imposing random noise on the pixels that make up the image of a base face. The patterns of choices by participants are then used to compute an “average” matrix of noise pixels that, when imposed onto the base face, reflect an underlying shared mental construct of the “face” of the attribute. However, examinations of the reliability and validity of this approach remain rare. In the present paper, we examine the effects of filtering out data from individual participants or trials to increase the signal-to-noise ratio of composite images. We focus on order effects of trials, compliance and reliability effects, as well as the diagnostic quality of image pairs. We present different diagnostic methods to examine these three aspects using data from six reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, Germany, and the U.S.) using two different base faces (i.e., Black and White). For each of the six studies, we compare the composite images of the complete sample to composite images that exclude non-complier respondents or non-diagnostic image pairs. Our R scripts are publicly available for easy implementation of our suggestions in related research.

Cited by 0Open access

Reverse correlation results.

Jeroen J. A. van Boxtel (37899), Hongjing Lu (162654) · Figshare · 2015

<p>The frequency of annotated signature movements relative to the time of a button press for intact and scrambled conditions. Punching motions (<b>a</b>) show significant correlations before the button press. Leg crossings (<b>b</b>), and leg extensions (<b>c</b>) did not correlate with button presses at any point in time before the button press. The proportions have been normalized by dividing them by the standard deviation over all proportions over time of the respective condition. Because the mean is zero, these normalized proportions are in effect Z-values. Significant correlations, corrected for false discovery rate, are indicated with filled circles (red for scrambled, blue for intact).</p>

Cited by 0Open access

Improving Reverse Correlation

Birgit Koopmann‐Holm, Michael Kevane · Figshare · 2020

Research using reverse correlation paradigms has become frequent over the last decade. In this paradigm, participants choose between a large number of image pairs, according to a prompt (e.g., “please choose the face that most resembles a trustworthy face”). The image pairs are generated by imposing random noise on the pixels that make up the image of a base face. The patterns of choices by participants are then used to compute an “average” matrix of noise pixels that, when imposed onto the base face, reflect an underlying shared mental construct of the “face” of the attribute. However, examinations of the reliability and validity of this approach remain rare. In the present paper, we examine the effects of filtering out data from individual participants or trials to increase the signal-to-noise ratio of composite images. We focus on order effects of trials, compliance and reliability effects, as well as the diagnostic quality of image pairs. We present different diagnostic methods to examine these three aspects using data from six reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, Germany, and the U.S.) using two different base faces (i.e., Black and White). For each of the six studies, we compare the composite images of the complete sample to composite images that exclude non-complier respondents or non-diagnostic image pairs. Our R scripts are publicly available for easy implementation of our suggestions in related research.

Cited by 0Open access

Improving Reverse Correlation

Koopmann-Holm, Birgit, Kevane, Michael · Figshare · 2020

Research using reverse correlation paradigms has become frequent over the last decade. In this paradigm, participants choose between a large number of image pairs, according to a prompt (e.g., “please choose the face that most resembles a trustworthy face”). The image pairs are generated by imposing random noise on the pixels that make up the image of a base face. The patterns of choices by participants are then used to compute an “average” matrix of noise pixels that, when imposed onto the base face, reflect an underlying shared mental construct of the “face” of the attribute. However, examinations of the reliability and validity of this approach remain rare. In the present paper, we examine the effects of filtering out data from individual participants or trials to increase the signal-to-noise ratio of composite images. We focus on order effects of trials, compliance and reliability effects, as well as the diagnostic quality of image pairs. We present different diagnostic methods to examine these three aspects using data from six reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, Germany, and the U.S.) using two different base faces (i.e., Black and White). For each of the six studies, we compare the composite images of the complete sample to composite images that exclude non-complier respondents or non-diagnostic image pairs. Our R scripts are publicly available for easy implementation of our suggestions in related research.

Cited by 0Open access

Reverse correlation methods.

Tsvetomira Dumbalska (13955935), Katarzyna Rudzka (2269645), Hannah E. Smithson (3956378) et al. · Figshare · 2022

<p><b>A</b>: We filtered each noisy stimulus through a pool of oriented Gabor patches to calculate energy across orientation space. The higher the stimulus energy at a given orientation, the more the noisy stimulus resembles a Gabor patch of that orientation. The collection of energies of a given stimulus across orientation space constitutes the energy profile of that stimulus. <b>B</b>: We regressed choices (p(CW)) on z-scored stimulus energy via separate binomial regressions in each orientation bin. The resulting beta coefficients make up the decision kernel. Thus, the decision kernel indexes the relationship between energy at a given orientation and choice. Positive kernel values suggest that the more a stimulus resembles a Gabor patch of that orientation, the more likely is a participant to make a clockwise choice.</p>

Cited by 0Open access

Reverse correlation results.

Lorenz Weise (6167147), Barbara Drüke (8559540), Siegfried Gauggel (189181) et al. · Figshare · 2024

<p>(A) Decision weights, quantifying how fluctuations in the chosen stimulus dimension (red) and non-chosen dimension (blue) affect trait judgments. (B) Confidence weights, quantifying how stimulus fluctuations affect metacognitive judgments. Shaded areas indicate 95% confidence intervals.</p>

Cited by 0Open access

Improving Reverse Correlation

Koopmann-Holm, Birgit, Kevane, Michael · Figshare · 2020

Research using reverse correlation paradigms has become frequent over the last decade. In this paradigm, participants choose between a large number of image pairs, according to a prompt (e.g., “please choose the face that most resembles a trustworthy face”). The image pairs are generated by imposing random noise on the pixels that make up the image of a base face. The patterns of choices by participants are then used to compute an “average” matrix of noise pixels that, when imposed onto the base face, reflect an underlying shared mental construct of the “face” of the attribute. However, examinations of the reliability and validity of this approach remain rare. In the present paper, we examine the effects of filtering out data from individual participants or trials to increase the signal-to-noise ratio of composite images. We focus on order effects of trials, compliance and reliability effects, as well as the diagnostic quality of image pairs. We present different diagnostic methods to examine these three aspects using data from six reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, Germany, and the U.S.) using two different base faces (i.e., Black and White). For each of the six studies, we compare the composite images of the complete sample to composite images that exclude non-complier respondents or non-diagnostic image pairs. Our R scripts are publicly available for easy implementation of our suggestions in related research.

Cited by 0Open access

Improving Reverse Correlation

Birgit Koopmann‐Holm, Michael Kevane · Figshare · 2020

Examinations of the reliability and validity of classification images of faces using the reverse correlation approach remain rare. In the present paper, we focus on order effects of trials, compliance and reliability effects, as well as the degree of contextual contrast of image pairs. We present different diagnostic methods to examine these three aspects using data from 12 reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, China, the Netherlands, the U.S., and an international sample) using five different base faces (i.e., female Black, female Asian, female and gender-neutral White, and Black/White/female/male morphed composite). For each of the 12 studies, we compare the individual CIs of subgroups of likely non-complier respondents and trials with non-contrastful image pairs to individual CIs of likely compliers and contrastful image pairs. In an appendix, we also examine the effects of filtering out data from individual participants and trials on the signal-to-noise ratio of group CIs. R scripts are publicly available for easy implementation of our suggestions in related research.<br>

Cited by 0Open access

Improving Reverse Correlation

Koopmann-Holm, Birgit, Kevane, Michael · Figshare · 2020

Research using reverse correlation paradigms has become frequent over the last decade. In this paradigm, participants choose between a large number of image pairs, according to a prompt (e.g., “please choose the face that most resembles a trustworthy face”). The image pairs are generated by imposing random noise on the pixels that make up the image of a base face. The patterns of choices by participants are then used to compute an “average” matrix of noise pixels that, when imposed onto the base face, reflect an underlying shared mental construct of the “face” of the attribute. However, examinations of the reliability and validity of this approach remain rare. In the present paper, we examine the effects of filtering out data from individual participants or trials to increase the signal-to-noise ratio of composite images. We focus on order effects of trials, compliance and reliability effects, as well as the diagnostic quality of image pairs. We present different diagnostic methods to examine these three aspects using data from six reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, Germany, and the U.S.) using two different base faces (i.e., Black and White). For each of the six studies, we compare the composite images of the complete sample to composite images that exclude non-complier respondents or non-diagnostic image pairs. Our R scripts are publicly available for easy implementation of our suggestions in related research.

Cited by 0Open access

Improving Reverse Correlation

Koopmann-Holm, Birgit, Kevane, Michael · Figshare · 2020

Examinations of the reliability and validity of classification images of faces using the reverse correlation approach remain rare. In the present paper, we focus on order effects of trials, compliance and reliability effects, as well as the degree of contextual contrast of image pairs. We present different diagnostic methods to examine these three aspects using data from 12 reverse correlation studies conducted both in-lab and online with diverse samples (i.e., from Burkina Faso, China, the Netherlands, the U.S., and an international sample) using five different base faces (i.e., female Black, female Asian, female and gender-neutral White, and Black/White/female/male morphed composite). For each of the 12 studies, we compare the individual CIs of subgroups of likely non-complier respondents and trials with non-contrastful image pairs to individual CIs of likely compliers and contrastful image pairs. In an appendix, we also examine the effects of filtering out data from individual participants and trials on the signal-to-noise ratio of group CIs. R scripts are publicly available for easy implementation of our suggestions in related research.<br>

Cited by 0Open access

Psychophysical reverse correlation results.

William Turner (2220598), Daniel Feuerriegel (11404019), Robert Hester (402636) et al. · Figshare · 2022

&lt;p&gt;Panels A) and D) show the residual stimulus fluctuations time-locked to stimulus onset. Panel A) shows correct responses (green), error responses (red), corrected errors (blue) and spoilt correct responses (orange). Panel D) shows fast (light blue) and slow (dark blue) corrected errors, and fast (light orange) and slow (dark orange) spoilt responses. Panels B and E show the same data, time-locked to the initial response (the time of the initial response is indicated by the grey dashed lines). Panels C and F show the data from trials containing a change of mind, time-locked to the change-of-mind response (the time of the change-of-mind response is indicated by the grey dashed lines). In all panels positive values on the y-axis indicate more evidence supporting the correct response. For display purposes a moving average smoothing function with a span of 3 frames was applied and a median split for change-of-mind response time was used in panels D-F. However, statistical analyses were conducted on the unsmoothed, continuous data. For illustrative purposes, t-tests (alpha level of .01) were also conducted to compare levels of residual evidence between specific trial types, at each frame. Residual evidence values were pooled across participants for these tests. Orange dots denote timepoints at which there was a significant difference between correct and spoilt correct trials (Panels A-B), or where there was a significant difference between fast and slow spoilt trials (Panels D-F). Blue dots denote timepoints at which there was a significant difference between error and corrected error trials (Panels A-B), or where there was a significant difference between fast and slow corrected error trials (Panels D-F). Finally, black dots (Panel C) denote significant differences between corrected errors and spoilt correct responses, and grey dots (all panels) denote time points at which there was a significant difference for an alpha level of .05.&lt;/p&gt;

Cited by 0Open access

Reverse Correlation Preregistration Agendum

Simon, Jeremy C., Goodall, Grace, Wunmi Awelewa · Open Science Framework · 2021

Between Phase I and Phase II we determined that we would not be able to collect liking data, so this preregistration exists to show that that decision was made a priori.

Cited by 0Open access

Reverse correlation in neurophysiology

Dario L. Ringach, R. Shapley · Cognitive Science · 2004

This article presents a review of reverse correlation in neurophysiology. We discuss the basis of reverse correlation in linear transducers and in spiking neurons. The application of reverse correlation to measure the receptive fields of visual neurons using white noise and m-sequences, and classical findings about spatial and color processing in the cortex resulting from such measurements, are emphasized. Finally, we describe new developments in reverse correlation, including “sub-space” and categorical reverse-correlation. Recent results obtained by applying such methods in the orientation, spatial-frequency and Fourier domains have revealed the importance of cortical inhibition in the establishment of sharp tuning selectivity in single neurons.

Cited by 148Open access