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Brief Communications

Temporal Expectation Improves the Quality of Sensory Information

Gustavo Rohenkohl, André M. Cravo, Valentin Wyart and Anna C. Nobre
Journal of Neuroscience 13 June 2012, 32 (24) 8424-8428; DOI: https://doi.org/10.1523/JNEUROSCI.0804-12.2012
Gustavo Rohenkohl
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André M. Cravo
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Valentin Wyart
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Anna C. Nobre
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    Figure 1.

    Schematic illustration of the task structure. Each trial consisted of a stream of stimuli presented foveally either with a fixed (regular condition) or jittered (irregular condition) SOA. Notice that the intervals surrounding the targets were exactly the same for the regular and irregular condition. The targets were visual gratings (Gabors) tilted 45° clockwise or counter-clockwise and presented at seven contrast levels to yield a range of performance levels spanning from near chance to near perfect. Participants were asked to respond to the orientation of the target (i.e., left or right) whenever a target was presented. Target presentation was always indicated by a change in the placeholder color to prevent responses to standard stimuli.

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    Figure 2.

    Behavioral consequences of rhythmic temporal expectation. a, Psychometric functions describing performance on regular (red line) and irregular (gray line) temporal expectation conditions as a function of target contrast. Bar plots show the average values of slope and threshold in the regular and irregular conditions. b, Scatterplots showing the effect of temporal expectation on slope and threshold values for each participant. Blue dots indicate the group average. Points below the identity line indicate a gain effect of temporal expectation. c, Average reaction times for correct responses from the regular and irregular conditions as a function of target contrast. Error bars indicate SEM.

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    Figure 3.

    Comparison between behavioral results and diffusion-model predictions. a, Fitted diffusion model to psychometric and chronometric functions at the target contrasts. All three free parameters were fitted separately for the regular (red) and irregular (gray) conditions using maximum-likelihood estimation. b, Hierarchical model comparing tests between nested models to determine which of the three model parameters (the accumulation rate k, the decision-bound A, and the residual time constant tR) could explain the observed difference between regular and irregular conditions significantly better than a null model for which the three parameters had fixed values across the two conditions. c, Comparison of changes in threshold predicted by each model with the behavioral data. *p < 0.05; ns, nonsignificant effect.

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The Journal of Neuroscience: 32 (24)
Journal of Neuroscience
Vol. 32, Issue 24
13 Jun 2012
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Temporal Expectation Improves the Quality of Sensory Information
Gustavo Rohenkohl, André M. Cravo, Valentin Wyart, Anna C. Nobre
Journal of Neuroscience 13 June 2012, 32 (24) 8424-8428; DOI: 10.1523/JNEUROSCI.0804-12.2012

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Temporal Expectation Improves the Quality of Sensory Information
Gustavo Rohenkohl, André M. Cravo, Valentin Wyart, Anna C. Nobre
Journal of Neuroscience 13 June 2012, 32 (24) 8424-8428; DOI: 10.1523/JNEUROSCI.0804-12.2012
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  • Re:Temporal expectation may affect the onset, not the rate, of evidence accumulation
    Gustavo Rohenkohl
    Published on: 05 July 2012
  • Temporal expectation may affect the onset, not the rate, of evidence accumulation
    Sander Nieuwenhuis
    Published on: 25 June 2012
  • Published on: (5 July 2012)
    Page navigation anchor for Re:Temporal expectation may affect the onset, not the rate, of evidence accumulation
    Re:Temporal expectation may affect the onset, not the rate, of evidence accumulation
    • Gustavo Rohenkohl, Postdoctoral researcher
    • Other Contributors:
      • Andre M. Cravo, Valentin Wyart, and Anna C. Nobre

    In their letter, Nieuwenhuis et al. suggest that temporal expectation may affect the onset, rather than the rate, of evidence accumulation. They correctly pointed out a discrepancy between our results and previous findings, in which temporal expectation benefits performance by reducing the time needed for stimulus encoding (Bausenhart et al., 2010; Jepma et al., 2011; Seibold et al., 2011). Their main argument is that the...

    Show More

    In their letter, Nieuwenhuis et al. suggest that temporal expectation may affect the onset, rather than the rate, of evidence accumulation. They correctly pointed out a discrepancy between our results and previous findings, in which temporal expectation benefits performance by reducing the time needed for stimulus encoding (Bausenhart et al., 2010; Jepma et al., 2011; Seibold et al., 2011). Their main argument is that the model used in our study (Palmer et al., 2005) is not appropriate for tasks in which the duration of evidence accumulation is limited by backward masking. Though we concede that it is important to find a reason for the divergence between findings, we disagree with their line of argumentation, mainly because we do not think that our stimuli were backward masked to any significant extent.

    In our study, targets were presented for 50 ms and were followed by a standard (irrelevant) stimulus at a fixed interval of 400 ms (identical in regular and irregular conditions). During the inter-stimulus interval, a gray screen with a light-gray circle positioned around target area was presented, with no noise or masking of any kind. Thus, only the next stimulus could have masked the target. However, the long interval between stimuli precludes any significant effects of backward masking. In general, backward masking is known to be strongly SOA-dependent, peaking before 100 ms and decaying long before 400 ms (Breitmeyer and Ogmen, 2006). Similarly, modeling studies have also shown that masking should not disrupt the diffusion process after 200-300 ms following target onset (see Smith and Ratcliff, 2009 for a review). In particular, although one could argue that the subsequent stimulus could have disrupted the representation of the target in visual short-term memory (VSTM), previous findings suggest that the strength of the VSTM trace has reached its asymptote after 400 ms in conditions similar to ours (Smith and Ratcliff, 2009). Therefore, it is unlikely that the presentation of the standard stimulus has any influence on the dynamics of the ongoing diffusion process.

    Nevertheless, we agree with Nieuwenhuis et al. that the discrepancy between our results and earlier findings calls for an explanation. We believe that a major contributing factor may be the methods used to induce temporal expectations. In our task, temporal expectation was generated by the regular isochronic rhythmic presentation of stimuli. In the previous experiments mentioned by Nieuwenhuis and colleagues, temporal expectation was induced by foreperiod effects or voluntary temporal attention (Jepma et al., 2011; Seibold et al., 2011). In Jepma et al., for example, temporal expectation was manipulated by having a fixed cue-target delay in a blocked design (experiment 1), or by using symbolic cues in a simple reaction-time task (experiment 2). The mechanisms supporting different types of temporal expectation may not always be the same. For example, dissociations have been noted in mechanisms supporting temporal expectations generated by rhythm versus symbolic cues (Coull and Nobre, 2008; Rohenkohl et al., 2011). Additionally, invasive studies have found that cortical oscillations in early sensory regions become entrained to external rhythms as a mechanism of selective attention. If these oscillations reflect structured fluctuations in cortical excitability, then it is plausible that they can lead to more efficient processing of sensory information occurring at expected moments. As ever, further experimentation using different methods for generating temporal expectations in diverse modalities and under text demands will be useful to conciliate or adjudicate between competing explanations.

    References:

    Bausenhart KM, Rolke B, Seibold VC, Ulrich R (2010) Temporal preparation influences the dynamics of information processing: evidence for early onset of information accumulation. Vision research 50:1025-1034.

    Breitmeyer BG, Ogmen H (2006) Visual Masking: Time Slices Through Conscious And Unconscious Vision. Oxford University Press.

    Coull J, Nobre A (2008) Dissociating explicit timing from temporal expectation with fMRI. Current opinion in neurobiology 18:137-144.

    Jepma, M., Wagenmakers, E.-J., and Nieuwenhuis, S. (2012). Temporal expectation and information processing: A model-based analysis. Cognition, 122, 426-441.

    Palmer J, Huk AC, Shadlen MN (2005) The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of Vision 5:376-404.

    Rohenkohl G, Coull JT, Nobre AC (2011) Behavioural dissociation between exogenous and endogenous temporal orienting of attention. PloS one 6:e14620.

    Seibold, V. C., Bausenhart, K. M., Rolke, B., and Ulrich, R. (2011). Does temporal preparation increase the rate of sensory information accumulation? Acta Psychologica, 137, 56-64.

    Smith PL, Ratcliff R (2009) An integrated theory of attention and decision making in visual signal detection. Psychological review 116:283- 317.

    Conflict of Interest:

    None declared

    Show Less
    Competing Interests: None declared.
  • Published on: (25 June 2012)
    Page navigation anchor for Temporal expectation may affect the onset, not the rate, of evidence accumulation
    Temporal expectation may affect the onset, not the rate, of evidence accumulation
    • Sander Nieuwenhuis
    • Other Contributors:
      • , Marieke Jepma and Eric-Jan Wagenmakers

    In this article, Rohenkohl et al. report data showing that increased temporal expectation, caused by rhythmic structure in the stimulus sequence, leads to increased accuracy and reduced response times to targets embedded in the sequence. The main goal of the authors was to determine how increased temporal expectation improved the quality of sensory information: by speeding up early stimulus encoding (prior to the decisi...

    Show More

    In this article, Rohenkohl et al. report data showing that increased temporal expectation, caused by rhythmic structure in the stimulus sequence, leads to increased accuracy and reduced response times to targets embedded in the sequence. The main goal of the authors was to determine how increased temporal expectation improved the quality of sensory information: by speeding up early stimulus encoding (prior to the decision process), enhancing the rate of evidence accumulation, or (less likely given the observed data) a change in decision threshold. Rohenkohl et al. fit their data with a Palmer diffusion model and found that the improved performance could be accounted for by an increased evidence- accumulation rate, but not by a reduction in the time needed for stimulus encoding or a change in decision threshold.

    However, recent studies using sequential-sampling models (including Ratcliff's diffusion model; Jepma et al., 2012) and other methods (Bausenhart et al., 2010; Seibold et al., 2011) have shown that in other paradigms the performance benefits of increased temporal expectations are due to a reduction in the time needed for stimulus encoding (so that evidence accumulation can start earlier), not to a change in evidence- accumulation rate. The discrepancy between these earlier results and those of Rohenkohl et al. calls for an explanation.

    A plausible explanation is that the use of the Palmer diffusion model, and in particular its formula describing accuracy, is inappropriate for tasks as that used by Rohenkohl et al., in which the duration of evidence accumulation is limited through backward masking of the target. Under such conditions, a reduction in encoding time (due to increased temporal expectation) acts to lengthen the time available for accumulating evidence, and, consequently, may lead to an increase in accuracy. This scenario is not within the purview of the Palmer diffusion model. Hence, we believe that the diffusion model analysis of Rohenkohl et al. may not be able to discriminate between an early onset and a higher rate of evidence accumulation. Additional work using non-masked stimuli is required to assess the extent to which the results from Rohenkohl et al. are truly inconsistent with earlier work.

    References:

    Jepma, M., Wagenmakers, E.-J., and Nieuwenhuis, S. (2012). Temporal expectation and information processing: A model-based analysis. Cognition, 122, 426-441.

    Seibold, V. C., Bausenhart, K. M., Rolke, B., and Ulrich, R. (2011). Does temporal preparation increase the rate of sensory information accumulation? Acta Psychologica, 137, 56-64.

    Bausenhart, K. M., Rolke, B., Seibold, V. C., and Ulrich, R. (2010). Temporal preparation influences the dynamics of information processing: Evidence for early onset of information accumulation. Vision Research, 50, 1025-1034.

    Conflict of Interest:

    None declared

    Show Less
    Competing Interests: None declared.

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