1st TRF Conference: Planning your trip

Dear all,

Thank you to all of you who have submitted an abstract to the 1st TRF Conference. We look forward to welcoming you in Strasbourg and would like to share a few reminders for the conference attendees.

1. Registration
We already have more than 150 registered attendees and would like to encourage everyone to register soon. Please follow the instructions here to complete your registration –
https://trf-strasbourg.sciencesconf.org/resource/page/id/17

2. Accommodation
We have been informed by Anne Giersch that a plenary session of the European Parliament will be taking place in Strasbourg during the same week as the TRF Conference. Therefore, it is imperative that you make the necessary arrangements for your stay sooner rather than later. A list of hotels is provided on the conference website –
https://trf-strasbourg.sciencesconf.org/resource/page/id/15

For those who are interested in sharing a hotel or house with other conference attendees, please let us know soon (email: trf@timingforum.org) and we will compile and share a list to facilitate your search for a potential roommate.

3. Travel
To plan your trip to Strasbourg, please see details on getting there –
https://trf-strasbourg.sciencesconf.org/resource/page/id/22

Any participant who needs a visa to travel to France, please write to Anne Giersch for an invitation letter so that you can secure your visa in good time for the conference.

4. Conference details
The conference program is available here
https://trf-strasbourg.sciencesconf.org/program

We will continue to update the website with more details and also share further information through the various TRF channels.

Wishing you an enjoyable summer,
Sundeep & Argie

Olfactory-Visual Sensory Integration Twists Time Perception

During everyday interactions, our senses are bombarded with different kinds of sensory information, which are processed by dedicated sensory systems operating at different temporal sampling scales to form a coherent percept. The question is whether information from one modality (say olfactory) influences the temporal perception of stimulus from other modality (say vision). Although, previous studies have investigated the effect of auditory stimulus on temporal perception of visual stimuli [1, 2], the evidence for the effect of olfactory stimulus on temporal perception of visual stimuli was lacking. A recent study published in Cerebral Cortex, by Prof. Wen Zhou and her lab members (Dr. Bin Zhou, Guo Feng, and Wei Chen), fills this gap and addresses whether odor influences visual temporal sampling and duration perception.

To study the effect of odor on visual temporal sampling, they used a two alternative forced choice chromatic critical flicker fusion (CFF) task with two isoluminant complimentary color images of banana or apple alternating at different frequencies (15, 20, 22.5 & 25 Hz in different blocks) for duration of 400ms (see figure 1, in original paper). In each trial, there were two 400ms flickering interval each flanked by 200ms mask, and separated by 600ms blank between the two intervals. Out of two, only one interval contained the flickering fruit image (either banana or apple) and participants reported the interval that contained fruit image. Along with visual stimuli, in Exp1 (N=16), participants were also exposed to two different odor stimuli (banana-like, amyl acetate 0.02%v/v in propylene glycol; and apple-like, apple flavor Givaudan, in separate blocks). The idea was to check whether the odor congruency influence the temporal sampling (CFF threshold) for the flickering banana or apple images. Results revealed that participants object detection increased significantly when the odor and the object image content matched, even when the task did not demanded any explicit object discrimination or identification, suggesting that sensory congruency between olfactory and visual inputs boosted the corresponding object visibility around CFF. Another analysis by fitting the psychometric function for the two odor conditions, with frequencies on x-axis and accuracy on y-axis, suggested that olfactory-visual congruency also facilitated the visual temporal sampling.

To establish that the above congruency effect is specific to odor and not just semantic information (or context) provided by the odor, they performed two control experiments. In the first control experiment (Exp2A, N=16), participants performed exactly the same task as Exp1 but instead of actual odors, odorless purified water was used and was suggested to participants as diluted banana or apple odor. In the second control experiment (Exp2B, N=16), semantic textual labels, “banana odor” or “apple odor”, were displayed at the center of the screen. In both the control experiments, they did not observe the odor-visual congruency effect, suggesting that presence of odor is important for such sensory integration.

The next question was to find the neural correlates of the odor-visual congruency effect, emphasizing at what level of visual processing the odor starts modulating it. For this, they performed an EEG experiment (Exp3, N=18) using the same stimuli as in Exp1, but modifying the task a bit. In the modified task, only one flickering interval of 400ms was presented flanked by red-green noise mask of 100ms, and participants reported whether the object was present or absent in that trial. All objects (apple or banana image) were presented to participants’ at subliminal frequency. For nine participants flicker frequency of 22.5Hz was used whereas for other nine participants 25Hz was used. Results from time-frequency analysis, revealed that maximum congruency-induced enhancement (i.e. greater normalized power difference) was observed in electrodes over right temporal regions around 150-300ms post stimulus onset. The difference around this time window suggests that during odor-visual congruency, odor starts influencing vision at the stage of object-level processing. Even source-localization analysis indicated the activation of right temporal region which is again known to be involved in object level representations. Thus, these evidences strongly suggest that odor influences the corresponding visual object at the stage of object-level processing.

From the above experiments, it was evident that the odor-visual congruency modulates visual temporal sampling, so the next logical question was whether it also influences the perceived duration of the visual stimuli. To answer this question, in Exp5 (N=24), they used a 2-Alternative Force Choice (AFC) comparison task, in which one image (either apple or banana) was a standard image (500ms) and the other image (either banana or apple) was test image (of varying durations 300, 350, 400, 450, 500, 550, 600, 650, 700ms). Participants reported which of the two images appeared longer in duration. For half participants (N=12) apple image was standard and banana image was comparison, whereas for other half (N=12) banana image served as standard and apple image served as comparison. Participants in both these groups were exposed to banana-like or apple-like odor in separate blocks. Point of subjective equality (PSE) and difference limen (DL) were measured for both the odor conditions. PSE is the measure of perceived duration whereas DL is the measure of temporal sensitivity. A two way mixed ANOVA on PSE values, with odor (banana-like, apple-like) as within-subjects factor and comparison image (banana image, apple image) as between-subjects factor, showed significant interaction. Further post hoc analysis after Bonferroni correction revealed that participants perceived the duration of the image to be longer when the image content and the odor were congruent compared to when they were incongruent. Similar analysis with DL, did not show any significant difference neither for main effects nor for interaction, suggesting that odor modulates only the perceived duration but not the temporal sensitivity.

Again to confirm that the above congruency effect on perceived duration is due to odor, not just because of semantic information (or context) provided by the odor, they performed two control experiments (Exp5A and Exp5B) similar to Exp2A and Exp2B. In Exp5A (N=24) instead of odor, odorless purified water suggested as diluted banana-like or apple-like odor were presented, whereas in Exp5B (N=24) instead of odor, textual labels (“banana odor” or “apple odor”) were presented on the screen. Neither the purified water nor the textual labels, showed the odor-visual congruency effect of perceived duration as seen in Exp4, suggesting the importance of odor in odor-visual sensory integration to modulate visual temporal perception.

In conclusion, this study provides a convincing evidence for the effect of odor on visual time perception, including temporal sampling and perceived duration. In future, it would be interesting to investigate this effect with other time perception paradigms such as magnitude estimation and measure the slope effect, which might help to know whether odor influences the pacemaker speed or the switch/ gating mechanisms in context of “internal clock model”. Moreover, it would be further interesting to investigate whether such odor-visual congruency effect influence the neural correlate of time perception such as CNV (contingent negative variation) component.

References:

1. Romei, V., De Haas, B., Mok, R. M., & Driver, J. (2011). Auditory stimulus timing influences perceived duration of co-occurring visual stimuli. Frontiers in psychology, 2.

2. Yuasa, K., & Yotsumoto, Y. (2015). Opposite distortions in interval timing perception for visual and auditory stimuli with temporal modulations. PloS one, 10(8), e0135646.

Source article: Zhou, B., Feng, G., Chen, W., & Zhou, W. (2017). Olfaction Warps Visual Time Perception. Cerebral Cortex, 1-11.

—Mukesh Makwana (mukesh@cbcs.ac.in),
Doctoral student,
Centre of Behavioural and Cognitive Sciences (CBCS), India.

The P3 and the subjective experience of time

The P3 (or P300) is an event related potential (ERP) component that has commonly been associated with attentional mechanisms and the updating of expectations. It can be evoked by stimuli in oddball paradigms, where a low-frequency stimulus (the “oddball”) is shown intermixed with high-frequency stimuli (the “standards”). For example, a subject in such a paradigm might be shown a series of words in white font (the standard stimuli), and much more rarely, be shown a word in red font (the oddball). This oddball stimulus commonly elicits a P3 at centro-occipital electrodes, one which is more positive in amplitude compared with the standard stimuli.

Oddball paradigms are also commonly cited in the time perception literature: canonically, the duration of oddball stimuli is overestimated, relative to the standard stimuli. Given this commonality, could the P3 be a neural correlate of this temporal distortion? A recent study by Ernst et al. investigated this question explicitly.

This study used an oddball paradigm as described above, where the standard and oddball stimuli varied in duration (from 600 – 1120 ms). After the presentation of an oddball stimulus, subjects were asked whether this was shorter or longer in duration relative to the preceding standards1. The durations of the stimuli were chosen such that the P3 occurred well before the duration judgement was required (and indeed before the termination of the oddball). This way, it was possible to test whether the amplitude of the relatively early P3 predicted the subsequent duration judgement.

The behavioural data confirmed that time overestimations were observed for the oddball stimuli; the typical temporal oddball effect. Analysis also confirmed the primary hypothesis, larger P3 amplitude led to overestimation of the oddball stimuli. Specifically, the P3 was larger for overestimated, compared to correct trials. (The P3 was also larger for correctly classified “long” judgements compared to correctly classified “short” judgements.) Thus, in trials where the oddball was overestimated, the P3 closely resembled that in correctly judged trials where the oddball was actually longer. In sum, these findings suggest that the P3 tracked the perceived duration of the oddball stimuli.

Average ERP amplitudes at electrode Pz, and scalp distributions, split by duration judgement.
Average ERP amplitudes at electrode Pz, and scalp distributions, split by duration judgement.

The researchers also used a multivariate pattern recognition technique in a supplementary analysis. Here, a classifier was trained to discriminate between standard and oddball stimuli on the basis of the ERP data. Classification accuracy was assessed across the different time windows, and showed above-chance accuracy from 125 ms after oddball presentation, reflecting general ERP differences consistent with the scalp distribution of the P3. By assessing the output of the classifier on trials pooled into correct short, correct long, and overestimated durations, they were able to recapitulate the main findings of the ERP analysis (but for a generated, essentially synthetic, estimated ERP) over the time window of 375 – 600 ms. In essence, because the classifier selectively reproduced an ERP response to oddball stimuli, this analysis lessens the likelihood that some other experimental feature was responsible for the difference in duration perception. Similarly, given the scalp distribution and time window, it also provides some data-driven support that the P3 was responsible for the effect, without specifically pre-selecting for the P32.

Overall, this study provides clear support for the hypothesis that the P3 is a neural correlate of the temporal oddball effect. Given that the P3 has been implicated a rather wide variety of phenomena, these phenomena can now also be interrogated from the perspective of time perception. There is also an ample opportunity for future research to ascertain whether the P3 is either necessary or sufficient for temporal distortions in a wider range of paradigms. In general, this study has delineated the P3 as a valuable component of interest for ongoing time perception research that uses EEG.


Source paper:

Ernst, B., Reichard, S. M., Riepl, R. F., Steinhauser, R., Zimmermann, S. F., & Steinhauser, M. (2017). The P3 and the subjective experience of time. Neuropsychologia, 103, 12–19. http://doi.org/10.1016/j.neuropsychologia.2017.06.033


  1. Notably, there were more trials in which the oddball durations were actually shorter than the standards, ostensibly to increase the number of overestimations. One potential issue is that participants may have tried to balance their proportion of short and long judgements, resulting in some “overestimations” that were due to a decision bias, rather than a perceptual bias. ↩︎
  2. It should be noted, however, that the classifier was trained on all of the electrodes, and not just those located centro-occipitally. ↩︎

Post doc at Kansas State University

Dear Timing Research Forum members,

Please pass along the following job announcement to potentially interested
parties, and apologies for cross posting.

Best wishes,

Kim Kirkpatrick

————————————————————————-

Post-doctoral Fellow Position in the Reward Timing and Decision
Laboratory (Kansas State University).  Applications are invited for a
Post-Doc Research Fellow to work on an NIMH funded R01 grant examining
behavioral interventions to promote self-control in rats. The proposed
experiments will assess the efficacy and mechanism of action of time-
and reward-based interventions designed to mitigate impulsive choice
behavior.  We also aim to begin examining the neural circuitry of
plasticity in decision making induced by the behavioral interventions.
The successful candidate should possess prior experience in conducting
laboratory behavioral analysis techniques, should possess basic
computer programming skills, have formal training in data analysis
skills, and possess good written and oral communication skills. A PhD
in Behavioral Neuroscience, Experimental Psychology, or a related
field is required prior to the time of appointment. Interested
applicants may send in quiries to Dr. Kimberly Kirkpatrick,
kirkpatr@ksu.edu, or may submit an application at:
http://careers.k-state.edu/cw/en-us/job/501727/fellow-post-doc.  For
further details on Dr. Kirkpatricks research program see:
http://www.k-state.edu/psych/research/kirkpatrick/rtdlab/index.html

Implicit variations of temporal predictability: Shaping the neural oscillatory and behavioral response

Anyone accessing this blog probably doesn’t need to be convinced that the ability to predict the timing of upcoming, behaviorally relevant stimuli is important for our ability to perceive and interact with the world. Although I’m quite rhythm-centric, it’s obvious that there are multiple ways in which we can estimate when something important might occur. For example, when the occurrence of an event is inevitable within a specific time window, its probability of occurrence usually increases as a function of time according to what is referred to as a “hazard function” (think of the probability that a car will eventually break down as you keep driving it). However, it’s also possible to engineer distributions for which the probability of occurrence is centered on a particular time point with a small or large amount of variability. The question is then about the neural mechanisms on which this type of temporal predictability (where the event usually occurs after about 1 second, for example) is based.

A recent EEG study by Herbst and Obleser examined the behavioral and neural differences between more and less temporally predictable situations, where temporal predictability had to be learned implicitly by the participants. The task was a pitch categorization task, in which a single tone was presented on each trial, and participants indicated whether it was high or low. The trick was that the time interval between a “cue” that the trial had started and the “target” (to-be-categorized tone – which was importantly embedded in noise to make the task more difficult) was varied according to distributions that made the target more or less temporally predictable. I’ll focus here on their Experiment 2, in which short blocks were presented in randomized order where the target timing was strongly predictable, weakly predictable, or not predictable.

Behaviorally, classical foreperiod effects made it clear that the basic experimental design worked as planned – reaction times decreased with increasing foreperiods (the later the target, the faster the RT). However, the condition-specific behavioral effects (or lack thereof) call into question whether the elegant experimental design (that involved completely implicit learning of temporal predictability) worked as well would have been hoped. The size of the foreperiod effect was indeed larger for temporally predictable compared to unpredictable conditions. However, the critical interaction was actually decidedly nonsignificant. Given that I might have rather expected some benefit at the expected time for the predictable conditions, rather than just a steeper foreperiod effect, I leave it up to the reader to judge whether they are sufficiently convinced by the behavioral results.

However, the some of the neural effects do seem to solidly indicate that implicitly learned temporal predictability was doing something. For example, P2-ish ERP magnitudes decreased with temporal predictability, and a later negative deflection increased in magnitude with temporal predictability. Maybe most interesting, dynamic changes in alpha power seemed to anticipate the expected target onset – alpha power increased briefly after cue onset, then decreased below baseline, and seemed to rebound back to baseline levels in anticipation of target onset. This effect was more obvious for temporally predictable compared to unpredictable conditions. [Of course, this begs the question why getting alpha back to baseline (to a zero-value) would be good for performance.]

For what should have been the most interesting neural dependent measure though, the results are confusing. The authors hypothesized (as I would have), that phase consistency across trials in low-frequency bands (esp. delta, ~0.5–4 Hz) would be higher prior to predictable than undpredictable targets. The reason is that temporal predictability allows low-frequency neural oscillations to get into the right phase at the right time for upcoming stimuli, which might be exactly why we perceive predictable things better than unpredictable things. This goes for paradigms using rhythmic stimuli to entrain low-frequency oscillations as well as more classical foreperiod-style paradigms that vary temporal predictability of a target in a more interval-based fashion. Turns out, Herbst and Obleser observed exactly the opposite – delta phase consistency was reduced for predictable compared to unpredictable schemes (though this difference did occur just after the cue and wasn’t necessarily present leading up to the target when it would have been expected).

With respect to delta phase, there are several possible explanations for the surprising results (that delta phase was less concentrated for predictable situations). First, the authors took great care not to contaminate the pre-target time window with target-evoked responses. By removing the target-evoked ERPS before time–frequency transformation, they may have removed an artifact that has been present in previous studies. Second, the authors took greater care than any paper of which I’m personally aware to not just manipulate foreperiod, but to randomize inter-trial intervals in a way that wouldn’t allow for entrainment to the low-frequency pace of the task itself. To my knowledge, all studies of the neural underpinnings of temporal preparation (except for the one being discussed here) using a fixed or jittered inter-trial interval have never taken such care to abolish an overall experimental pacing. Nonetheless, I still would not have expected opposite phase-consistency results.

In any case, I think the paradigm – where temporal predictability had to be learned entirely implicitly – is remarkably clever and can be used in future work to truly understand the neural mechanisms underlying temporal predictability that is not entrainment-based (i.e., based on rhythm). Given recent work moving in this direction, this work carefully removing rhythmicity (here, of the task itself) and eliminating evoked responses that could contaminate phase-concentration measures, should be used as an example of thoughtful experimental design.

Doctoral and Post Doctoral Positions

We are currently searching for a PhD student to work on temporal
cognition and fMRI-TMS.

Position up to 4 years. Monthly stipend 1900 Euro.

Matlab and some experience with TMS is requested.

http://www.ipsy.ovgu.de/ipsy_media/biologischepsychologie/open_position.pdf

Moreover, we also have an open call for Post-docs:

http://www.cbbs.eu/images/pdf/Ausschreibung_Wissenschaftscampus_2017/CBBS-Wissenschaftscampus_Ausschreibung_2017.pdf

 

Prof. Dr. Toemme Noesselt
Lehrstuhl fuer Biologische Psychologie
Institut fuer Psychologie
Otto-von-Guericke-Universitaet Magdeburg
fernruf:+49-(0)391- 67 18477

Integrating Time & Number: From Neural Bases to Behavioral Processes through Development and Disease

Dear Members of the Timing Research Forum,

I am writing to inform you regarding the launch of a new Research Topic “Integrating Time & Number: From Neural Bases to Behavioral Processes through Development and Disease” that is being co-edited by Dr. Metehan Çiçek, Dr. Karin Kucian, Dr. Trevor Penney and myself as part of Frontiers in Human Neuroscience (cross-listed with Frontiers in Behavioral Neuroscience, Frontiers in Computational Neuroscience, Frontiers in Psychology–Perception Science and Frontiers in Psychiatry–Child and Adolescent Psychiatry).

This research topic aims to consolidate recent developments in the behavioral, psychophysical, neuroimaging, clinical, and theoretical study of interval timing and counting with a specific emphasis on the behavioral, physiological and translational overlaps between these functions. Further details can be found at the following link:

http://journal.frontiersin.org/researchtopic/6387

Manuscripts can be of different article types such as Original Research, Review, Hypothesis & Theory, etc.

The abstract submission deadline is September 29th, 2017.
The manuscript submission deadline is May 4th, 2018.

If you have any questions, please do not hesitate to contact me at fbalci@ku.edu.tr.

Sincerely,

Fuat Balcı, PhD.

Time perception in schizophrenia

The following is a guest post from Sven Thönes at Johannes Gutenberg-Universität Mainz, Germany.

Over the last decades, numerous studies have reported that the perception of time and the basic processing of temporal information is distorted in patients suffering from schizophrenia. The investigation of timing in schizophrenia is of particular interest because the notion of mistimed information transfer by Andreasen, et al. (1999), which is one of the most popular theories on the cognitive impairments and clinical outcomes in schizophrenia, assumes that distorted temporal processing may underlie the patients’ symptoms.

In a recent meta-analytical review published in Clinical Psychology Review, Sven Thoenes (Leibniz Research Centre for Working Environment and Human Factors) and Daniel Oberfeld (Johannes Gutenberg-University Mainz) reevaluated the data from 957 patients with schizophrenia and 1060 healthy control participants, provided by 68 studies on timing in schizophrenia from the past 65 years. The original studies applied a large variety of different temporal tasks, such as time estimation, production, reproduction, and duration discrimination, as well as the detection of temporal gaps and judgments of temporal order or simultaneity. Importantly, the reported behavioral measures represented different aspects of temporal performance, indexing between-group differences either in accuracy (i.e., signed deviation of the duration judgments from the veridical value) or precision (i.e., variability of the judgments or sensitivity in a discrimination task).

In their analyses, Thoenes and Oberfeld differentiated between different duration ranges and different temporal tasks representing either time perception (judgments of time intervals) or basic temporal processing (e.g., judgments of temporal order) as well as between effects of schizophrenia on accuracy and precision.

Interestingly, independent of the specific temporal tasks and interval durations used, the results clearly demonstrate that both time perception and basic temporal processing are less precise (more variable) in patients with schizophrenia (Hedges’ g > 1.00), whereas effects of schizophrenia on the accuracy of time perception are rather small and task-dependent. These results are in accordance with the theoretical assumption of mistimed information transfer and the notion of a more variable internal clock in patients with schizophrenia, but not with a strong effect of schizophrenia on clock speed.

The review suggests that future research needs to investigate to what degree the impairment of temporal precision may be due to clock-unspecific processes, such as general cognitive deficiencies in schizophrenia. Moreover, research should aim at combining established experimental and phenomenological approaches in order to gain a broader understanding of the specific temporal distortions in schizophrenia.

While research on time perception and temporal processing may be interesting as such, the recent meta-analysis shows that it may also be supportive for the generation and testing of theories in adjacent (cognitive) domains, such as clinical psychology and neuropsychology.


Source article:

Thönes, S. & Oberfeld, D. (2017). Meta-analysis of time perception and temporal processing in schizophrenia: Differential effects on precision and accuracy. Clinical Psychology Review, 54, 44-64. http://dx.doi.org/10.1016/j.cpr.2017.03.007

References

Andreasen, N. C., Nopoulos, P., O’Leary, D. S., Miller, D. D., Wassink, T., & Flaum, L. (1999). Defining the phenotype of schizophrenia: Cognitive dysmetria and its neural mechanisms. Biological Psychiatry, 46, 908-20.

2018 Conference on Learning and Memory

Dear TRF colleagues,

I am emailing to invite you to our 2018 International Conference on Learning and Memory in Huntington Beach. The conference will be held in celebration of the 35th anniversary of the Center for the Neurobiology of Learning and Memory at UC Irvine.

When: April 18-22, 2018
Where: Waterfront Beach Resort, A Hilton Hotel, Huntington Beach, CA
The purpose of the conference is to bring together several communities in learning and memory, bridging across animal and human work as well as across fundamental and translational neuroscience. Timing in particular is a topic of major interest in the learning and memory community and have symposia, talks and posters focused on temporal memory, time perception and temporal processing would be most welcome. I would strongly urge you to consider submitting your work to #LEARNMEM2018
The conference is open to scientists, students, clinicians, and community members. Expected attendance is 800-1000. Please feel free to forward this email to any colleagues or lab members who may be interested in presenting and/or attending the meeting.
——————————————————————————————
Abstract Submission, registration, and housing are now OPEN!
——————————————————————————————

We are now accepting proposals for symposia, open papers, lightning talks and posters and will continue to do so until September 1, 2017. However, since space is limited, priority will be given to earlier submissions. We recommend submitting before July 15, 2017.

Highlighted speakers include Carol Barnes, Gyuri Buzsaki, Howard Eichenbaum, Ann Graybiel, Claudia Kawas, Beth Loftus, Gary Lynch, Eleanor Maguire, Jim McGaugh, Bruce McNaughton, Richard Morris, Lynn Nadel, Liz Phelps, Dan Schacter, and Reisa Sperling. Our Keynote Speaker is 2014 Nobel Prize Laureate Edvard Moser.
Discounted registration is available for students/postdocs and other trainees. Southern California has numerous fun attractions and experiences that are family friendly. Come a few days earlier or stay a few days later than the conference (the hotel venue will offer the same discounted lodging rate) and bring the whole family.
I hope TRF colleagues will consider joining us and submitting symposia for the conference. All information is available on the conference website at http://learnmem2018.org
If you are on social media please follow @ucicnlm and #LEARNMEM2018 for updates on the conference