December 2024
Volume 24, Issue 13
Open Access
Article  |   December 2024
Continuous temporal integration in the human visual system
Author Affiliations
  • Michele Deodato
    Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE
    [email protected]
  • David Melcher
    Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE
    Center for Brain and Health, NYUAD Research Institute, New York University Abu Dhabi, Abu Dhabi, UAE
    [email protected]
Journal of Vision December 2024, Vol.24, 5. doi:https://doi.org/10.1167/jov.24.13.5
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Michele Deodato, David Melcher; Continuous temporal integration in the human visual system. Journal of Vision 2024;24(13):5. https://doi.org/10.1167/jov.24.13.5.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

The human visual system is continuously processing visual information to maintain a coherent perception of the environment. Temporal integration, a critical aspect of this process, allows for the combination of visual inputs over time, enhancing the signal-to-noise ratio and supporting high-level cognitive functions. Traditional methods for measuring temporal integration often require a large number of trials made up of a fixation period, stimuli separated by a blank interval, a single forced choice, and then a pause before the next trial. This trial structure potentially introduces fatigue and biases. Here, we introduce a novel continuous temporal integration (CTI) task designed to overcome these limitations by allowing free visual exploration and continuous mouse responses to dynamic stimuli. Fifty participants performed the CTI, which involved adjusting a red bar to indicate the point where a flickering sine wave grating became indistinguishable from noise. Our results, modeled by an exponential function, indicate a reliable temporal integration window of ∼100 ms. The CTI's design facilitates rapid and reliable measurement of temporal integration, demonstrating potential for broader applications across different populations and experimental settings. This task provides a more naturalistic and efficient approach to understanding this fundamental aspect of visual perception.

Introduction
The human visual system is continuously processing a vast amount of visual information to help us navigate and interpret the world. To manage this constant influx and to make sense of the external environment, it has evolved several organizing strategies. One such strategy is the integration of information from multiple sources, as in the case of binocular information, where integration of two-dimensional images coming from different eyes plays a crucial role in the extraction of depth information (Burge & Cormack, 2024; Cox, Dougherty, Westerberg, Schall, & Maier, 2019; Harwerth, Fredenburg, & Smith, 2003). Another instance is temporal integration, which refers to the combination of information from successive moments across different timescales. Temporal integration has been extensively measured in the processing of low level properties of vision, such as motion (Bair & Movshon, 2004; Fredericksen, Verstraten, & Van De Grind, 1994; Snowden & Braddick, 1991) and color (Smith, Bowen, & Pokorny, 1984; Uchikawa & Ikeda, 1986). Additionally, visual temporal integration maintains object continuity in spite of the continuous disruptions of the visual flow caused by eye movements and blinks (Melcher & Morrone, 2003) and it also supports higher level cognitive processes such as judgements of causality relationships between spatiotemporally contingent visual events (Deodato & Melcher, 2022; Schlottmann & Shanks, 1992). From a computational perspective, temporal integration could serve the purpose of improving the signal-to-noise ratio by summing sensory evidence over a specific temporal window. Indeed, neurons exhibit considerable variability in their firing rates locked to a stimulus across repeated measurements and it has been shown that integrating responses over time could mitigate this limitation (Goris, Ziemba, Movshon, & Simoncelli, 2018). In this light, temporal integration corresponds to accumulating sensory evidence over time to optimize perceptual judgments in the face of noise (Hyafil et al., 2023). 
Visual temporal integration is closely linked to visual temporal acuity, the ability of the visual system to segregate, rather than integrate, rapidly occurring visual stimuli and events. In other words, this measure describes the shortest sensory sample that can be reliably perceived. There are different cases where temporal integration and segregation are needed to ensure accurate visual perception and the visual system rapidly adapts to these visual task demands, switching between longer or shorter integration windows as needed to ensure accurate perception (Bair & Movshon, 2004; Wutz, Melcher, & Samaha, 2018). Accordingly, it has been shown that temporal integration windows are differentially affected by temporal and spatial attention (Sharp, Melcher, & Hickey, 2019; Sharp, Gutteling, Melcher, & Hickey, 2022; Yeshurun & Levy, 2003). 
Notably, temporal integration windows follow similar developmental trajectories across sensory modalities. Research has shown that visual and auditory integration windows are longer in infants and that older participants have longer windows than young adults (Deodato, Ronconi, & Melcher, 2024; Norman, Lewis, Bryant, & Conn, 2023; Saija, Başkent, Andringa, & Akyürek, 2019). Moreover, unisensory temporal integration has been found to predict multisensory (e.g., audiovisual) integration processes during development (Stevenson, Baum, Krueger, Newhouse, & Wallace, 2018). Overall, research on visual temporal integration mechanisms has been pivotal in understanding multiple aspects of conscious perception and visual processing. Measures of temporal integration have proven to be an extremely valuable marker for ageing and cognitive decline (Curran & Wattis, 2000; Hernández, Setti, Kenny, & Newell, 2019; Kaur, Walia, & Singh, 2020; Landis & Hamwi, 1956; O'Dowd et al., 2023) as well as psychopathologies (Deodato et al., 2024; Giersch et al., 2009; Marsicano, Cerpelloni, Melcher, & Ronconi, 2022) and visual processing disorders (Santoni, Melcher, Franchin, & Ronconi, 2024). The heterogeneous interest around this topic has led to the development of numerous tasks to measure visual temporal integration (Chota et al., 2021; Deodato & Melcher, 2023b; Menétrey, Herzog, & Pascucci, 2023; Norman et al., 2023; Santoni et al., 2024; Wutz et al., 2018). However, these psychophysical methods usually require many trials and preferably highly trained participants. Moreover, these tasks involve prolonged periods of fixation, undermining their ecological validity, and they measure integration as the bridging of a temporal gap between two abrupt successive stimuli, which could involve other confounding factors such as onset and offset responses. Additionally, the repetitiveness and length of the experimental sessions often introduce an element of fatigue that could bias the results and undermine data collection in many specific subpopulations. Recent advances aiming at solving these issues have seen researchers abandoning the canonical trial structure with a stimulus presentation and a forced choice between two alternatives to substitute it instead with continuous tracking of behavioral adjustments to dynamic stimuli (Bonnen, Burge, Yates, Pillow, & Cormack, 2015). For example, this approach has been particularly effective for investigating interocular differences in temporal integration periods and delays and their sensory consequences (Chin & Burge, 2022; Gurman & Reynaud, 2024). 
Here, in line with these efforts for continuous psychophysics, we developed a new task to measure visual temporal integration in a fast and reliable manner. This Continuous Temporal Integration task aims at replacing enforced fixation with free visual exploration and substituting repetitive forced choices with continuous mouse responses. We show in a large sample of participants that our task successfully produces a reliable temporal integration function in a short time and that the estimated length of the integration window is consistent with the previous literature. 
Methods
Participants
Fifty participants (26 women) between 18 and 50 years old (mean = 21.62; std = 5.33) participated in the experiment. Inclusion criteria were normal or corrected-to-normal vision and English fluency. Participants signed an informed consent form before the experiment and received compensation in the form of Amazon Vouchers or course credits. Data were collected in accordance with the Declaration of Helsinki, and the study protocol was approved by the local ethics committee (New York University Abu Dhabi IRB). 
Experimental setup
The experiments took place in a dark room. Stimuli were projected with a PROPixx projector (VPixx Technologies) set to a refresh rate of 100 Hz, displayed on a 65 cm × 36 cm screen. The experiment was implemented using MATLAB (MathWorks, Inc., Natick, MA, USA) and the Psychophysics Toolbox (Brainard, 1997). Participants laid their head on a chin rest at a distance of 85 cm. 
Visual integration task
Participants viewed a stimulus that consisted of a dynamic series of frames. Each frame was realized as follows. First, the image of a horizontal black and white sine wave grating was generated with decreasing contrast along the vertical axis (from 0 to 80 Michelson contrast). Second, noise drawn from a normal distribution (mean = 0; std = 100) was added to the value in every pixel, and the result compared to a threshold value of 250. Third, pixels containing a value above the threshold were painted white and the others black. Finally, the frame was printed on the screen. The thresholding step ensured that while the amount of information retained about the original grating changed as a function of its original contrast, it was also changing stochastically every time the stimulus was generated. The final stimulus displayed information about the sine-wave pattern depending on the original contrast. Thus, contrast was transformed into a measure of information strength about the stimulus. In simpler terms, adding noise to high contrast sine waves resulted in the thresholded white pixels corresponding mostly to the upper phase of the sine wave, while at low contrast adding noise could result in thresholding also pixels in the lower phase of the sine wave (see Figure 1). 
Figure 1.
 
Design of the continuous temporal integration task. Participants were shown a dynamic stimulus consisting of a series of frames. Each frame contained a vertical stimulus displaying a grating (right side of the figure). The stimulus was realized by stacking sine waves of different amplitudes (a). Noise was added to each pixel value (b) and then compared to a threshold (red line in (b)) to generate black and white pixels (c). Participant indicated the vertical location were the grating seemed to disappear into noise by moving a horizontal red bar with a computer mouse (right).
Figure 1.
 
Design of the continuous temporal integration task. Participants were shown a dynamic stimulus consisting of a series of frames. Each frame contained a vertical stimulus displaying a grating (right side of the figure). The stimulus was realized by stacking sine waves of different amplitudes (a). Noise was added to each pixel value (b) and then compared to a threshold (red line in (b)) to generate black and white pixels (c). Participant indicated the vertical location were the grating seemed to disappear into noise by moving a horizontal red bar with a computer mouse (right).
Steps two and three were repeated on every frame of the continuous stimulus, resulting in a dynamic image of a flickering grating. Importantly, on different trials we varied the frame duration (i.e., the interval between generation of different frames or the frequency of stimulus update). The rationale for this was that each time a stimulus frame is generated, a different part of information about the original pattern is made available, due to different pixels turning black and white. Thus integration of successive frames granted more information available to the observer and a clearer recognition of the sine wave pattern. In other words, given a window of temporal integration of a certain length, frames that are shorter than the window can be integrated inside it, while stimuli that exceed the window length would not. Given that information also varies along the stimulus vertical axis, this benefit of temporal integration is more evident on the part of the stimulus that has more sparse information on a single frame (i.e., the part that had initially low contrast). 
Participants had control over the vertical position of a horizontal red bar that was superimposed on the dynamic stimulus and were tasked to move it with a computer mouse to the location where the sine wave pattern became indistinguishable from flickering noise and finally press the mouse button to confirm their response and end the trial. The contrast (or information strength) value corresponding to the vertical position of the bar was saved for subsequent analyses. At the beginning of each trial, the red bar was placed in the center of the screen and on half of the trials the stimuli were presented upside down to avoid response biases. 
The experimental session included 20 trials for each of 8 frame durations (ranging from 10 ms to 150 ms in steps of 20 ms), for a total of 160 trials. The whole procedure lasted ∼20 minutes. 
Analysis
To obtain the temporal integration function, for each participant, responses were averaged over each frame duration and the following exponential function was used to model performance against frame duration:  
\begin{eqnarray*} a{\rm{\ * \ }}{e^{\left( {b{\rm{*}}x} \right)}} + c \end{eqnarray*}
 
The coefficient b represents the exponential growth of the function. This is the most important parameter as it affects how quickly the function approaches its plateau and can be used as a measure of the length of the integration window. This is motivated by the reasoning that performance reaches a negative plateau when the duration of temporal information is longer than the length of the temporal integration window. The scaling factor a affects the overall amplitude of the exponential function, it represents the difference (with a negative sign) between the lowest and highest response, reflecting the perceptual gain provided by the integration of visual information over time. The parameter c vertically shifts the entire function, affecting its baseline. This represents a baseline level of integration efficiency, although this parameter could also reflect some implicit bias or criterion of participants (i.e., a tendency to be more conservative or liberal with respect to the separation between the pattern and the flickering noise). Additionally, for each participant we computed the asymptote of the function as the point in which its derivative was smaller than an arbitrary threshold of 0.0005 (Kingdom & Prins, 2016). 
Results
The fit of the exponential model to single subject data was exceptionally good (mean R2 = 0.98; range = 0.92-0.99), indicating that although this task has less strict requirements, it still provided reliable psychophysical measurements. This exponential function describes the benefit of temporal integration measured as the ability to recognize the pattern at lower information strength for different frame durations. The asymptote point of the function corresponds to the length of the integration window itself. This follows from the fact that when the frames’ duration is equal or greater than the integration window, it is not possible to integrate information from different frames and there is no benefit of integration, thus performance reaches a negative plateau. In our large sample, the temporal integration function reached an average plateau at 106.53 ms (std = 20.71) (see Figure 2), consistent with other findings concerning the length of the human temporal integration window (Chota, Marque, & VanRullen, 2021; Deodato & Melcher, 2023b). Unsurprisingly, the asymptote was linearly correlated across participants with the exponential growth of the function (i.e., the b coefficient) (r(48) = 0.83, p < 0.001). 
Figure 2.
 
Results of the continuous temporal integration task. (Left) Continuous temporal integration function. The figure shows the participants response (i.e., the vertical position of the red bar in Figure 1) against the frame duration of the dynamic stimulus. Lower values on the y axis indicate the ability to see the sine-wave pattern when less visual information is available, thanks to temporal integration of different frames. Shaded lines show single-participant integration functions (N = 50), the blue thick line indicates the average function superimposed on the average responses (the black dots) for each frame duration. Error bars represent the standard error of the mean. The red dot represents the asymptote of the integration function averaged across subjects. (Right) Asymptote distribution of the integration function. The red line represents the average asymptote, corresponding to the red circle in the left figure.
Figure 2.
 
Results of the continuous temporal integration task. (Left) Continuous temporal integration function. The figure shows the participants response (i.e., the vertical position of the red bar in Figure 1) against the frame duration of the dynamic stimulus. Lower values on the y axis indicate the ability to see the sine-wave pattern when less visual information is available, thanks to temporal integration of different frames. Shaded lines show single-participant integration functions (N = 50), the blue thick line indicates the average function superimposed on the average responses (the black dots) for each frame duration. Error bars represent the standard error of the mean. The red dot represents the asymptote of the integration function averaged across subjects. (Right) Asymptote distribution of the integration function. The red line represents the average asymptote, corresponding to the red circle in the left figure.
Discussion
We developed a continuous integration task to measure visual temporal integration in the human visual system. Over the past decades, many tasks have been developed to this purpose. However, our novel task stands on its own for four main reasons: it has no requirements for fixation, it is based on continuous dynamic stimuli, it relies on continuous rather than dichotomous forced responses, and it is short in overall duration. We argue that for these reasons this continuous temporal integration task has overcome at least some of the limitations of the classical psychophysics procedures and is better suited for measurement of temporal integration windows in different populations. 
Research on visual temporal integration has focused mostly on integration performance with respect to few brief stimuli rather than continuous dynamic ones (Chota et al., 2021; Deodato et al., 2024; Lahkar et al., 2023; Sharp, Melcher, & Hickey, 2018). Generally, integration tasks are characterized by presentation of two brief stimuli (<50 ms) separated by a variable inter stimulus interval (ISI) during fixation and participants are asked, more or less directly, if they were able to integrate them. For example, in the two-flash fusion task participants are presented with two consecutive flashes and they're asked to report if they were able to see both of them (segregation) or just one (integration) (Deodato et al., 2024; Deodato & Melcher, 2023b; Samaha & Postle, 2015). Similarly, in temporal order and simultaneity judgement tasks, participants have to report the order of appearance of two stimuli or their simultaneity, under the assumption that incorrect responses are an indication of integration (Chota et al., 2021; Lahkar et al., 2023). Some studies have implemented a variant of the missing-element paradigm that consist of the presentation of two portions of a grid of stimuli divided between two different frames (Di Lollo, 1977). In this case participants are asked to report either an odd element in the grid or a missing one, measuring respectively segregation or integration of the two frames across ISIs (Santoni et al., 2024; Sharp et al., 2018; Sharp et al., 2019; Wutz et al., 2018). However, the mechanisms of temporal integration operate continuously and over multiple time scales that are still under investigation (e.g., Vogelsang, Drissi-Daoudi, & Herzog, 2023). Thus it is not sufficient nor necessary to measure performance with respect to few stimuli presented for short periods of time to quantify the extent of the integration window. Accordingly, a recent study pointed that using only short ISIs (<100 ms) could bias interpretation of the results (Menétrey, Roinishvili, Chkonia, Herzog, & Pascucci, 2024). 
Additionally, in all these tasks performance is affected by state-dependent properties at the moment of stimulus onset, as well as evoked responses linked to an abrupt onset and then offset of a stimulus and also by spatial and temporal attentional factors such has momentary lapses and scanning of stimulus-irrelevant locations. A striking example of these issues is the difference between integration thresholds obtained with two flashes (i.e., two-flash fusion thresholds) and train of flashes (i.e., continuous flicker fusion) (Maley, 1967), which led some to propose that they measure separate functions (King, 1962). Moreover, early processing of a stimulus can interrupt the processing of subsequent or even precedent stimuli (Herzog & Brand, 2015). Although these effects of visual masking have been extensively studied, their influence on visual temporal integration tasks is often not taken into account or considered as a mechanism of integration itself (Menétrey et al., 2024). Finally, the presence of a gap between the two stimuli used in these tasks makes the process of integration more complex as It involves not only the integration of the stimuli but also consideration of the gap itself. This presents an additional challenge: whether the visual system should integrate the gap or exclude it from the integration process, and calls into question whether the nature of these tasks is only visual integration or also “gap detection.” Previous research has shown that introduction of a temporal gap in a plotting sequence actively interferes with integration of different stimuli to increase the perception of clear and distinct perceptual events (Eriksen & Collins, 1968; Hogben & Lollo, 1974). Indeed, it has been suggested that worse performance in the same task with gaps, versus without, could be a consequence of discontinuity detectors (Hogben & Lollo, 1974). Typically, visual integration mechanisms operate on subsequent stimuli that are not separated by gaps, with some exceptions (e.g., occlusions or eye blinks). Therefore the natural tendency of the visual system to integrate contiguous stimuli is disrupted by artificial separations, potentially leading to less accurate and ecological valid measurements of temporal integration windows. 
A notable exception to this issue is the Sequential Metacontrast task (SQM). In the SQM paradigm, a series of vertical lines is displayed, generating the illusion of two diverging motion streams. When one line has a horizontal vernier offset, it causes all the other lines to be perceived as having the same offset, despite being perfectly straight. However, if a vernier with an opposite offset is presented later in the stream the lines appear to be straight again. This is taken as evidence that the two offset are integrated and cancel each other (Menétrey et al., 2023; Vogelsang et al., 2023). However, integration in this task is based on moving stimuli and cannot be easily generalized since integration mechanisms responsible for the perception of motion have been shown to rely on at least partially different neural mechanisms and longer integration periods with respect to static integration (Ronconi, Oosterhof, Bonmassar, & Melcher, 2017a). 
In the continuous temporal integration task, a dynamic stimulus is presented until response and without any temporal gaps or blank frames. By avoiding fixation and the artificial segmentation introduced by ISIs, our task enables a more naturalistic and precise assessment of temporal integration windows. To compute the temporal integration function, we manipulated the amount of information available over a fixed period of time by updating bits of information about the stimulus more or less frequently (i.e., shorter or longer frame duration). This relies on the assumption that less information available in a given temporal integration window is characterized by worse detection performance. In other words, when the information is updated with a period that is longer than the temporal integration window, there is no integration. Consistent with our reasoning we found that performance reaches a negative plateau, which was accurately modelled by an exponential function. 
A popular theory suggests that these integration mechanisms are implemented in the brain through neural oscillations. Specifically, the alpha rhythm (∼10 Hz) could represent cycles of integration which last ∼100 ms, such that stimuli that fall in the same cycle are integrated and otherwise segregated. Indeed, it has been found that integration/segregation of two flashes depends on the phase of alpha oscillations at stimulus onset (Ronconi, Oosterhof, Bonmassar, & Melcher, 2017b; Ronconi, Balestrieri, Baldauf, & Melcher, 2024) and individuals with faster alpha rhythms have shorter two-flash fusion thresholds (Deodato & Melcher, 2023b; Samaha & Postle, 2015). More generally, each alpha cycle could represent a window of evidence accumulation, consistent with the idea that temporal integration could serve as a mechanism for the summation of sensory evidence (Tarasi & Romei, 2024). However, most of the studies testing this integration theory have used brief presentations of stimuli, with the limitations discussed above. Additionally, oscillations may interact with other neural mechanisms (Deodato & Melcher, 2023a) and may not be directly responsible for the windows of integration, as integration windows larger than the alpha cycle have been reported (Menétrey et al., 2024). For example, paradigms involving rapid serial visual presentation (RSVP), visual masking or missing element tasks consistently report different integration thresholds (Karabay & Akyürek, 2017). Additionally, alternative accounts of performance in temporal integration tasks have postulated a decaying sensory trace that makes stimuli available in memory for a short time after their offset (Di Lollo, 1977; Eriksen & Collins, 1968; Hogben & Lollo, 1974). Future research should confirm the relationship between continuous visual integration and rhythmic processes or decaying perceptual traces using a continuous task that does not include blank gaps. 
In conclusion, the novel approach of our continuous integration task overcomes several limitations of traditional tasks that rely on presentation of discrete stimuli separated by gaps during enforced fixation. By using continuous dynamic stimuli and continuous response measures, our task is better suited for capturing the nature of temporal integration in the human visual system. Generally, abandoning the rigid trial structure of psychophysics allows for data collection in less-controlled settings. In this regard, our task shows promise for certain populations but also for online studies because the continuous presentation has fewer timing requirements, and it may be less influenced by dropped frames. Overall, this makes it a valuable tool for investigating visual temporal integration across different populations and conditions. 
Acknowledgments
Supported by the NYUAD Center for Brain and Health, funded by Tamkeen under NYU Abu Dhabi Research Institute grant CG012. 
Raw data and scripts to replicate the analyses are available on github (https://github.com/DeoMiche/-2024-IntegrationTask). 
Commercial relationships: none. 
Corresponding author: Michele Deodato. 
Address: Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates. 
References
Bair, W., & Movshon, J. A. (2004). Adaptive temporal integration of motion in direction-selective neurons in macaque visual cortex. The Journal of Neuroscience, 24(33), 7305–7323, https://doi.org/10.1523/JNEUROSCI.0554-04.2004. [CrossRef]
Bonnen, K., Burge, J., Yates, J., Pillow, J., & Cormack, L. K. (2015). Continuous psychophysics: Target-tracking to measure visual sensitivity. Journal of Vision, 15(3), 14, https://doi.org/10.1167/15.3.14. [CrossRef] [PubMed]
Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433–436, https://doi.org/10.1163/156856897X00357. [CrossRef] [PubMed]
Burge, J., & Cormack, L. K. (2024). Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics. Journal of Vision, 24(5), 4, https://doi.org/10.1167/jov.24.5.4. [CrossRef] [PubMed]
Chin, B. M., & Burge, J. (2022). Perceptual consequences of interocular differences in the duration of temporal integration. Journal of Vision, 22(12), 12, https://doi.org/10.1167/jov.22.12.12. [CrossRef] [PubMed]
Chota, S., Marque, P., & VanRullen, R. (2021). Occipital alpha-TMS causally modulates temporal order judgements: Evidence for discrete temporal windows in vision. NeuroImage, 237, 118173, https://doi.org/10.1016/j.neuroimage.2021.118173. [CrossRef] [PubMed]
Cox, M. A., Dougherty, K., Westerberg, J. A., Schall, M. S., & Maier, A. (2019). Temporal dynamics of binocular integration in primary visual cortex. Journal of Vision, 19(12), 13, https://doi.org/10.1167/19.12.13. [CrossRef] [PubMed]
Curran, S., & Wattis, J. (2000). Critical flicker fusion threshold: A potentially useful measure for the early detection of Alzheimer's disease. Human Psychopharmacology: Clinical and Experimental, 15(2), 103–112, https://doi.org/10.1002/(SICI)1099-1077(200003)15:2<103::AID-HUP149>3.0.CO;2-7. [CrossRef]
Deodato, M., & Melcher, D. (2022). The effect of perceptual history on the interpretation of causality. Journal of Vision, 22(11), 13, https://doi.org/10.1167/jov.22.11.13. [CrossRef] [PubMed]
Deodato, M., & Melcher, D. (2023a). Aperiodic EEG predicts variability of visual temporal processing. Biorkiv.
Deodato, M., & Melcher, D. (2023b). Correlations between visual temporal resolution and individual alpha peak frequency: Evidence that internal and measurement noise drive null findings. Journal of Cognitive Neuroscience, 1–12, https://doi.org/10.1162/jocn_a_01993.
Deodato, M., Ronconi, L., & Melcher, D. (2024). Schizotypal traits and anomalous perceptual experiences are associated with greater visual temporal acuity. Schizophrenia Research, 269, 1–8, https://doi.org/10.1016/j.schres.2024.04.028. [CrossRef] [PubMed]
Di Lollo, V. (1977). Temporal characteristics of iconic memory. Nature, 267(5608), 241–243, https://doi.org/10.1038/267241a0. [CrossRef] [PubMed]
Eriksen, C. W., & Collins, J. F. (1968). Sensory traces versus the psychological moment in the temporal organization of form. Journal of Experimental Psychology, 77(3, Pt.1), 376–382, https://doi.org/10.1037/h0025931. [CrossRef] [PubMed]
Fredericksen, R. E., Verstraten, F. A. J., & Van De Grind, W. A. (1994). Spatial summation and its interaction with the temporal integration mechanism in human motion perception. Vision Research, 34(23), 3171–3188, https://doi.org/10.1016/0042-6989(94)90082-5. [CrossRef] [PubMed]
Giersch, A., Lalanne, L., Corves, C., Seubert, J., Shi, Z., Foucher, J., … Elliott, M. A. (2009). Extended visual simultaneity thresholds in patients with schizophrenia. Schizophrenia Bulletin, 35(4), 816–825, https://doi.org/10.1093/schbul/sbn016. [CrossRef] [PubMed]
Goris, R. L. T., Ziemba, C. M., Movshon, J. A., & Simoncelli, E. P. (2018). Slow gain fluctuations limit benefits of temporal integration in visual cortex. Journal of Vision, 18(8), 8, https://doi.org/10.1167/18.8.8. [CrossRef] [PubMed]
Gurman, D., & Reynaud, A. (2024). Measuring the Interocular Delay and its Link to Visual Acuity in Amblyopia. Investigative Opthalmology & Visual Science, 65(1), 2, https://doi.org/10.1167/iovs.65.1.2.
Harwerth, R. S., Fredenburg, P. M., & Smith, E. L. (2003). Temporal integration for stereoscopic vision. Vision Research, 43(5), 505–517, https://doi.org/10.1016/S0042-6989(02)00653-3. [PubMed]
Hernández, B., Setti, A., Kenny, R. A., & Newell, F. N. (2019). Individual differences in ageing, cognitive status, and sex on susceptibility to the sound-induced flash illusion: A large-scale study. Psychology and Aging, 34(7), 978–990, https://doi.org/10.1037/pag0000396. [PubMed]
Herzog, M. H., & Brand, A. (2015). Visual masking and schizophrenia. Schizophrenia Research: Cognition, 2(2), 64–71, https://doi.org/10.1016/j.scog.2015.04.001. [PubMed]
Hogben, J. H., & Lollo, V. di. (1974). Perceptual integration and perceptual segregation of brief visual stimuli. Vision Research, 14(11), 1059–1069, https://doi.org/10.1016/0042-6989(74)90202-8. [PubMed]
Hyafil, A., de la Rocha, J., Pericas, C., Katz, L. N., Huk, A. C., & Pillow, J. W. (2023). Temporal integration is a robust feature of perceptual decisions. ELife, 12, https://doi.org/10.7554/eLife.84045.
Karabay, A., & Akyürek, E. G. (2017). The effects of Kanizsa contours on temporal integration and attention in rapid serial visual presentation. Attention, Perception, & Psychophysics, 79(6), 1742–1754, https://doi.org/10.3758/s13414-017-1333-6. [PubMed]
Kaur, V., Walia, L., & Singh, R. (2020). Critical Flicker Fusion Frequency: Effect of Age, Gender, Sleep and Display Screens. International Journal of Contemporary Medical Research [IJCMR], 7(6), https://doi.org/10.21276/IJCMR.2020.7.6.20.
King, H. E. (1962). Two-Flash and Flicker Fusion Thresholds for Normal and Schizophrenic Subjects. Perceptual and Motor Skills, 14(3), 517–518, https://doi.org/10.2466/pms.1962.14.3.517. [PubMed]
Kingdom, F. A. A., & Prins, N. (2016). Psychophysics: A Practical Introduction. In Psychophysics: A Practical Introduction. Elsevier, https://doi.org/10.1016/B978-0-12-407156-8.01001-X.
Lahkar, R., Goyal, M., Mishra, P., Rao, B. N., Singh, Y., & Chowdhury, N. (2023). Insights into the perceptual moment theory: Experimental evidence from simultaneity judgment. Attention, Perception, & Psychophysics, 85(4), 1199–1206, https://doi.org/10.3758/s13414-023-02684-7. [PubMed]
Landis, C., & Hamwi, V. (1956). Critical Flicker Frequency, Age, and Intelligence. The American Journal of Psychology, 69(3), 459, https://doi.org/10.2307/1419053. [PubMed]
Maley, M. J. (1967). Two-flash threshold, skin conductance and skin potential. Psychonomic Science, 9(6), 361–362, https://doi.org/10.3758/BF03327848.
Marsicano, G., Cerpelloni, F., Melcher, D., & Ronconi, L. (2022). Lower multisensory temporal acuity in individuals with high schizotypal traits: A web-based study. Scientific Reports, 12(1), 2782, https://doi.org/10.1038/s41598-022-06503-1. [PubMed]
Melcher, D., & Morrone, M. C. (2003). Spatiotopic temporal integration of visual motion across saccadic eye movements. Nature Neuroscience, 6(8), 877–881, https://doi.org/10.1038/nn1098. [PubMed]
Menétrey, M. Q., Herzog, M. H., & Pascucci, D. (2023). Pre-stimulus alpha activity modulates long-lasting unconscious feature integration. NeuroImage, 278, 120298, https://doi.org/10.1016/j.neuroimage.2023.120298. [PubMed]
Menétrey, M. Q., Roinishvili, M., Chkonia, E., Herzog, M. H., & Pascucci, D. (2024). Alpha peak frequency affects visual performance beyond temporal resolution. Imaging Neuroscience, 2, 1–12, https://doi.org/10.1162/imag_a_00107.
Norman, J. F., Lewis, J. L., Bryant, E. N., & Conn, J. D. (2023). Aging and temporal integration in the visual perception of object shape. Scientific Reports, 13(1), 12748, https://doi.org/10.1038/s41598-023-40068-x. [PubMed]
O'Dowd, A., Hirst, R. J., Setti, A., Donoghue, O. A., Kenny, R. A., & Newell, F. N. (2023). The temporal precision of audiovisual integration is associated with longitudinal fall incidents but not sensorimotor fall risk in older adults. Scientific Reports, 13(1), 7167, https://doi.org/10.1038/s41598-023-32404-y. [PubMed]
Ronconi, L., Balestrieri, E., Baldauf, D., & Melcher, D. (2024). Distinct Cortical Networks Subserve Spatio-temporal Sampling in Vision through Different Oscillatory Rhythms. Journal of Cognitive Neuroscience, 36(4), 572–589, https://doi.org/10.1162/jocn_a_02006. [PubMed]
Ronconi, L., Oosterhof, N. N., Bonmassar, C., & Melcher, D. (2017a). Multiple oscillatory rhythms determine the temporal organization of perception. Proceedings of the National Academy of Sciences, 114(51), 13435–13440, https://doi.org/10.1073/pnas.1714522114.
Ronconi, L., Oosterhof, N. N., Bonmassar, C., & Melcher, D. (2017b). Multiple oscillatory rhythms determine the temporal organization of perception. Proceedings of the National Academy of Sciences, 114(51), 13435–13440, https://doi.org/10.1073/pnas.1714522114.
Saija, J. D., Başkent, D., Andringa, T. C., & Akyürek, E. G. (2019). Visual and auditory temporal integration in healthy younger and older adults. Psychological Research, 83(5), 951–967, https://doi.org/10.1007/s00426-017-0912-4. [PubMed]
Samaha, J., & Postle, B. R. (2015). The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception. Current Biology, 25(22), 2985–2990, https://doi.org/10.1016/j.cub.2015.10.007.
Santoni, A., Melcher, D., Franchin, L., & Ronconi, L. (2024). Electrophysiological signatures of visual temporal processing deficits in developmental dyslexia. Psychophysiology, 61(2), https://doi.org/10.1111/psyp.14447.
Schlottmann, A., & Shanks, D. R. (1992). Evidence for a Distinction between Judged and Perceived Causality. The Quarterly Journal of Experimental Psychology Section A, 44(2), 321–342, https://doi.org/10.1080/02724989243000055.
Sharp, P., Gutteling, T., Melcher, D., & Hickey, C. (2022). Spatial attention tunes temporal processing in early visual cortex by speeding and slowing alpha oscillations. The Journal of Neuroscience, JN-RM-0509-22, https://doi.org/10.1523/JNEUROSCI.0509-22.2022.
Sharp, P., Melcher, D., & Hickey, C. (2018). Endogenous attention modulates the temporal window of integration. Attention, Perception, & Psychophysics, 80(5), 1214–1228, https://doi.org/10.3758/s13414-018-1506-y. [PubMed]
Sharp, P., Melcher, D., & Hickey, C. (2019). Different effects of spatial and temporal attention on the integration and segregation of stimuli in time. Attention, Perception, & Psychophysics, 81(2), 433–441, https://doi.org/10.3758/s13414-018-1623-7. [PubMed]
Smith, V. C., Bowen, R. W., & Pokorny, J. (1984). Threshold temporal integration of chromatic stimuli. Vision Research, 24(7), 653–660, https://doi.org/10.1016/0042-6989(84)90206-2. [PubMed]
Snowden, R. J., & Braddick, O. J. (1991). The temporal integration and resolution of velocity signals. Vision Research, 31(5), 907–914, https://doi.org/10.1016/0042-6989(91)90156-Y. [PubMed]
Stevenson, R. A., Baum, S. H., Krueger, J., Newhouse, P. A., & Wallace, M. T. (2018). Links between temporal acuity and multisensory integration across life span. Journal of Experimental Psychology: Human Perception and Performance, 44(1), 106–116, https://doi.org/10.1037/xhp0000424. [PubMed]
Tarasi, L., & Romei, V. (2024). Individual Alpha Frequency Contributes to the Precision of Human Visual Processing. Journal of Cognitive Neuroscience, 36(4), 602–613, https://doi.org/10.1162/jocn_a_02026. [PubMed]
Uchikawa, K., & Ikeda, M. (1986). Temporal integration of chromatic double pulses for detection of equal-luminance wavelength changes. Journal of the Optical Society of America A, 3(12), 2109, https://doi.org/10.1364/JOSAA.3.002109.
Vogelsang, L., Drissi-Daoudi, L., & Herzog, M. H. (2023). Processing load, and not stimulus evidence, determines the duration of unconscious visual feature integration. Communications Psychology, 1(1), 8, https://doi.org/10.1038/s44271-023-00011-2. [PubMed]
Wutz, A., Melcher, D., & Samaha, J. (2018). Frequency modulation of neural oscillations according to visual task demands. Proceedings of the National Academy of Sciences of the United States of America, 115(6), 1346–1351, https://doi.org/10.1073/PNAS.1713318115/-/DCSUPPLEMENTAL. [PubMed]
Yeshurun, Y., & Levy, L. (2003). Transient Spatial Attention Degrades Temporal Resolution. Psychological Science, 14(3), 225–231, https://doi.org/10.1111/1467-9280.02436. [PubMed]
Figure 1.
 
Design of the continuous temporal integration task. Participants were shown a dynamic stimulus consisting of a series of frames. Each frame contained a vertical stimulus displaying a grating (right side of the figure). The stimulus was realized by stacking sine waves of different amplitudes (a). Noise was added to each pixel value (b) and then compared to a threshold (red line in (b)) to generate black and white pixels (c). Participant indicated the vertical location were the grating seemed to disappear into noise by moving a horizontal red bar with a computer mouse (right).
Figure 1.
 
Design of the continuous temporal integration task. Participants were shown a dynamic stimulus consisting of a series of frames. Each frame contained a vertical stimulus displaying a grating (right side of the figure). The stimulus was realized by stacking sine waves of different amplitudes (a). Noise was added to each pixel value (b) and then compared to a threshold (red line in (b)) to generate black and white pixels (c). Participant indicated the vertical location were the grating seemed to disappear into noise by moving a horizontal red bar with a computer mouse (right).
Figure 2.
 
Results of the continuous temporal integration task. (Left) Continuous temporal integration function. The figure shows the participants response (i.e., the vertical position of the red bar in Figure 1) against the frame duration of the dynamic stimulus. Lower values on the y axis indicate the ability to see the sine-wave pattern when less visual information is available, thanks to temporal integration of different frames. Shaded lines show single-participant integration functions (N = 50), the blue thick line indicates the average function superimposed on the average responses (the black dots) for each frame duration. Error bars represent the standard error of the mean. The red dot represents the asymptote of the integration function averaged across subjects. (Right) Asymptote distribution of the integration function. The red line represents the average asymptote, corresponding to the red circle in the left figure.
Figure 2.
 
Results of the continuous temporal integration task. (Left) Continuous temporal integration function. The figure shows the participants response (i.e., the vertical position of the red bar in Figure 1) against the frame duration of the dynamic stimulus. Lower values on the y axis indicate the ability to see the sine-wave pattern when less visual information is available, thanks to temporal integration of different frames. Shaded lines show single-participant integration functions (N = 50), the blue thick line indicates the average function superimposed on the average responses (the black dots) for each frame duration. Error bars represent the standard error of the mean. The red dot represents the asymptote of the integration function averaged across subjects. (Right) Asymptote distribution of the integration function. The red line represents the average asymptote, corresponding to the red circle in the left figure.
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×