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The role of consolidation for perceptual learning in temporal discrimination within and across modalities
Acta Psychologica 147 (2014) 75–79
Contents lists available at ScienceDirect
Acta Psychologica journal homepage: www.elsevier.com/ locate/actpsy
The role of consolidation for perceptual learning in temporal discrimination within and across modalities☆ Daniel Bratzke ⁎, Hannes Schröter, Rolf Ulrich Cognition and Perception, Department of Psychology, University of Tübingen, Schleichstrasse 4, 72076 Tübingen, Germany
a r t i c l e
i n f o
Article history: Received 10 January 2013 Received in revised form 19 June 2013 Accepted 27 June 2013 Available online 30 July 2013 PsycINFO classification: 2320 2343 Keywords: Perceptual learning Consolidation Temporal processing Interval timing
a b s t r a c t Training people on temporal discrimination can substantially improve performance in the trained modality but also in untrained modalities. A pretest–training–posttest design was used to investigate whether consolidation plays a crucial role for training effects within the trained modality and its transfer to another modality. In the pretest, both auditory and visual discrimination performance was assessed. In the training phase, participants performed only the auditory task. After a consolidation interval of either 5 min or 24 h, participants were again tested in both the auditory and visual tasks. Irrespective of the consolidation interval, performance improved from the pretest to the posttest in both modalities. Most importantly, the training effect for the trained auditory modality was independent of the consolidation interval whereas the transfer effect to the visual modality was larger after 24 h than after 5 min. This finding shows that transfer effects benefit from extended consolidation. © 2013 Elsevier B.V. All rights reserved.
1. Introduction It has been repeatedly shown that the efficiency of the perceptual system can improve with training. These beneficial effects of training have been termed perceptual learning (for a review, see Goldstone, 1998). It has been argued that the time course of perceptual learning can be divided into two stages: (a) a fast, within-session improvement, and (b) a slowly-developing improvement that occurs during a consolidation phase lasting at least 6–8 h (for a review, see Karni & Bertini, 1997; see also Atienza, Cantero, & Dominguez-Marin, 2002). Fast within-session improvements may reflect stimulus learning and conceptual learning, as for example, learning of the procedure and the task (Ortiz & Wright, 2009). Slow improvements may rather reflect a modification of long-term memory representations (Atienza et al., 2002; Karni & Sagi, 1993). The present study examined the role of consolidation for perceptual learning in temporal discrimination. Ortiz and Wright (2010) provided first evidence that consolidation may play an important role for perceptual learning in temporal discrimination. In their study, participants were tested in an interaural time differences (ITD) discrimination task. In the training phase, participants either received training on the ITD or an interaural level difference discrimination ☆ This study was supported by the Deutsche Forschungsgemeinschaft (UL 116/12-1). We thank Teresa Birngruber and Linda Idelberger for assistance in data acquisition. ⁎ Corresponding author. Tel.: +49 7071 29 74512; fax: +49 7071 29 2410. E-mail address: [email protected] (D. Bratzke). 0001-6918/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.actpsy.2013.06.018
task. Performance in the ITD task was then assessed in a posttest immediately, 10 h, or 24 h after the training. Irrespective of the training task, participants' performance in the ITD task improved from the pretest to the posttest without any consolidation interval. Most importantly, performance showed further improvement both 10 and 24 h after the training, suggesting that perceptual learning benefits from consolidation. In the present study we investigated the influence of different consolidation intervals on perceptual learning in auditory temporal discrimination and its cross-modal generalization to visual stimuli. First attempts to demonstrate cross-modal transfer from auditory training to visual stimuli were unsuccessful (Grondin & Ulrich, 2011; Lapid, Ulrich, & Rammsayer, 2009; but see Nagarajan, Blake, Wright, Bly, & Merzenich, 1998, for cross-modal transfer from the somatosensory to the auditory modality). In a recent study, however, Bratzke, Seifried, and Ulrich (2012) reported asymmetric cross-modal transfer of training on a temporal discrimination task from audition to vision. Specifically, these authors employed a pretest–training–posttest design including a control group that performed only the pretest and the posttest. Participants in the training groups were trained with an empty interval of 100 ms duration, which was either marked by two visual or by two auditory stimuli. This training was distributed across several days. Participants in the control group did not receive such training. In the pre- and posttest, all participants were tested with different durations (100 and 200 ms) in the two modalities. Trained participants showed a larger performance improvement from pretest to posttest than the control group for the trained but not for the untrained duration. This training effect transferred to the other modality only for those participants
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who had been trained with auditory stimuli but not for those who had been trained in the visual modality. Thus, this pattern of results demonstrates an asymmetric cross-modal transfer of perceptual learning in temporal discrimination from audition to vision. According to Bratzke et al. (2012), the absence of cross-modal transfer effects in previous studies (Grondin & Ulrich, 2011; Lapid et al., 2009) can be explained by non-optimal conditions to reveal cross-modal transfer, including fatigue due to massive training, absence of feedback, and possible training effects in multiple pretest sessions. In addition, Grondin and Ulrich (2011) trained and tested their participants in a single session lasting 3.5 h with potential breaks of only 5 min between the different parts of the experiment. In contrast, in the study of Bratzke et al. (2012) the pre-and posttests as well as the training sessions were distributed across several days. Thus, these studies differed not only with respect to the distribution and the amount of training during the acquisition phase but also with respect to the length of the consolidation interval between training and posttest. Thus, it is possible that the relatively long consolidation interval in the study of Bratzke et al. (2012) was crucial for the cross-modal transfer from audition to vision to occur. In the present study, we used a pretest–training–posttest design similar to the one of Bratzke et al. (2012). During the training phase, participants were trained exclusively with an auditory 100-ms interval. Before and after the training phase (pre- vs. posttest), participants were tested with the trained auditory interval and a visual transfer interval of the same length. Since Bratzke et al. (2012) observed generalization of perceptual learning from the auditory to the visual modality but not vice versa, an auditory training interval and a visual transfer interval were employed. To assess the influence of consolidation on perceptual learning, the posttest was administered either 5 min or 24 h after the training phase. Based on previous findings (Ortiz & Wright, 2010) we expected larger performance improvements from the pretest to the posttest for the group of participants with the longer consolidation interval. If consolidation is especially important for the cross-modal transfer of perceptual learning to occur, one would expect that the transfer of perceptual learning from the auditory to the visual modality benefits more from an extended consolidation interval than perceptual learning within the trained auditory modality. 2. Method 2.1. Participants Forty-eight volunteers participated in the study. Participants reported normal hearing and normal or corrected-to-normal vision. They were randomly assigned to one of two groups (5-min vs. 24-hr consolidation interval). Seven participants of the original sample were replaced by additional participants because they produced virtually flat psychometric functions. Furthermore, 3 participants were replaced because their performance deviated from mean performance in at least one of the test conditions in the pre- or posttest by more than 3 SDs. In the final sample, both the 5-min consolidation group (mean age: 23.8 years, SD = 5.8) and the 24-hr consolidation group (mean age: 22.0 years, SD = 2.7) consisted of 7 males and 17 females each. 2.2. Apparatus and stimuli Participants were tested individually in a sound-attenuated, dimly illuminated booth. They sat at a distance of approximately 50 cm from a circular red LED with a diameter of 0.57°, wore headphones, and responded with two external response keys. The experiment was written in Matlab (The MathWorks, 2007), using the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) version 3.0.8, and was run on an Apple iMac with OS X. As in Bratzke et al. (2012), the stimuli were auditory and visual empty intervals of 100 ms duration
(A100 vs. V100). In the auditory condition, intervals were marked by 15-ms beeps (1 kHz) with 5-ms sinusoidal onset and offset ramps and were presented binaurally via headphones at an intensity level of 70 dB SPL. In the visual condition, intervals were marked by two 15-ms light pulses produced by the LED. The length of the interval was defined as the time interval from the offset of the first marker to the onset of the second marker. 2.3. Procedure Following Bratzke et al. (2012), a single stimulus protocol was used (see also Karmarkar & Buonomano, 2003). At the beginning of each block of trials, the respective standard interval was presented ten times. In each trial, participants were then presented with one of 8 possible comparison intervals that were symmetrically distributed around the standard interval (A100: 79, 85, 91, 97, 103, 109, 115, 121 ms; V100: 65, 75, 85, 95, 105, 115, 125, 135 ms). Participants were instructed to respond as accurately as possible with their left (right) index finger when the comparison interval was shorter (longer) than the initially presented standard interval. Subsequent trials were separated by one of four randomly distributed foreperiods (800, 900, 1000, or 1100 ms). In case of an erroneous response, feedback (tactile stimulation of the right lower leg for 500 ms) was provided during the training phase. No feedback was provided in the pre- and posttests. In both the pre- and posttest, participants were tested in each of the two conditions (A100, V100). The order of the conditions within the pre- and the posttest was counterbalanced across participants. Each test consisted of 2 blocks (one for each testing condition) of 160 trials. The training phase followed immediately after the pretest and consisted of 5 blocks of 160 trials each. In this phase participants were trained exclusively with the A100 condition. The posttest was administered either 5 min (5-min consolidation) or 24 h (24-hr consolidation) after the end of the training phase. 2.4. Data analysis For each participant and test condition, a psychometric function was traced, plotting the eight comparison intervals on the x-axis and the relative frequency of responding “long” on the y-axis. A logistic function was fitted to the resulting curves by the method of maximum likelihood. From these fitted curves the estimate of the difference limen (DL) was obtained. Specifically, the DL was estimated as being half the interquartile range of this fitted function—that is, DL = (x.75–x.25)/2, where x.25 and x.75 denote the values of the comparison that yield 25% and 75% “longer” responses, respectively. The Weber fraction (DL/standard duration) was then computed for each test condition. Finally, in order to test for differences in perceptual learning between the two consolidation groups and conditions, we calculated difference scores between the Weber fractions obtained in the pretest and the postest. 3. Results Fig. 1 depicts Weber fractions for the two consolidation groups and test conditions as a function of block. Both consolidation groups showed similar learning curves for the trained auditory condition. Fig. 1 suggests that performance improvements for the auditory test condition mainly occurred from the pretest to the first training block (Block 2). To test whether there was still a significant improvement from the end of the training phase to the posttest, we conducted a mixed-model ANOVA with the factors consolidation interval (5 min vs. 24 h) and block (Block 6 vs. posttest). This ANOVA revealed a significant main effect of block, F(1, 46) = 8.42, p = .006, η2p = .16, but no main effect of consolidation group, F(1, 46) = 0.88, p = .354, η2p = .02, or interaction of consolidation group and block, F(1, 46) = 1.38, p = .245, η2p = .03. These results indicate that perceptual learning for the auditory
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Fig. 1. Mean Weber fraction as a function of block, test condition (auditory vs. visual) and consolidation interval (5 min vs. 24 h). Error bars indicate ±1 SE.
condition further improved from the end of the training phase to the posttest. However, this improvement did not benefit from a prolonged consolidation phase. Fig. 1 also suggests differences in pretest performance for the visual transfer condition. We therefore compared the pretest Weber fractions between the two consolidation groups with a mixed-model ANOVA with the between-subjects factor consolidation interval (5-min vs. 24-hr) and the within-subjects factor modality (auditory vs. visual). This analysis revealed no main effect of consolidation interval, F(1, 46) = 1.09, p = .303, η2p = .02, or interaction of consolidation interval and modality, F(1, 46) = 1.45, p = .235, η2p = .03. The main effect of modality was significant, F(1, 46) = 57.93, p b .001, η2p = .56, reflecting the common finding of larger Weber fractions for visual (0.25) than for auditory (0.14) stimuli. An additional Welch test for pretest Weber fractions only in the visual transfer condition also revealed no significant effect of consolidation interval, t(40) = 1.27, p = .213. Thus, statistical analyses did not show reliable differences in pretest performance for the visual transfer condition. Since baseline performance did not differ between the two consolidations groups, we analyzed the difference scores of the Weber fractions (pretest minus posttest) in the subsequent analysis. Fig. 2 depicts the Weber fraction improvement from pretest to postest as a function of consolidation interval and modality. These difference scores were positive in all conditions (all ps b .045; one-tailed Welch tests) reflecting a general improvement from the pretest to the posttest. An ANOVA with the between-subjects factor consolidation interval and the within-subjects factor modality on the difference scores revealed no significant main effect of consolidation interval, F(1, 46) = 1.28, p = .264, η2p = .03, or modality, F(1, 46) = 0.14, p = .708, η2p b .01. Importantly, the interaction of consolidation interval and modality was significant, F(1, 46) = 4.18, p = .047, η2p = .08. As can be seen in Fig. 2, the difference scores for the trained auditory interval were virtually identical for the two consolidation intervals. For the visual transfer condition, however, the performance improvement was larger for the 24-hr than for the 5-min consolidation interval. This was confirmed by a one-tailed Welch test, t(44) = 1.68, p b .05. We conducted additional analyses to test whether perceptual learning in the visual transfer condition was associated with discrimination performance in the auditory training condition. This was assessed in two ways. First, the improvement in visual performance was regressed against the auditory pretest performance (Fig. 3, left panel). Correlation analysis revealed a positive relationship between these two measures (r = .33, p = .021). An ANCOVA with auditory
Fig. 2. Mean Weber fraction improvement (pretest minus posttest) as a function of test condition (auditory vs. visual), and consolidation interval (5-min vs. 24-hr). Error bars indicate ±1 SE.
pretest performance as a covariate and visual improvement as the dependent variable (see Vickers, 2001; Vickers & Altman, 2001) indicated again a stronger improvement for the 24-hr than for the 5-min consolidation group (p = .077). Second, the improvement in visual performance was regressed against the improvement in auditory performance (Fig. 3, right panel). Again, both measures were positively correlated (r = .26, p = .071), and an ANCOVA revealed a stronger improvement for the 24-hr than for the 5-min consolidation group (p = .053). In conclusion, these additional analyses are in line with the assumption that the participants with worse pretest performance gain an especially large performance improvement in the transfer condition. Furthermore, the ANCOVA results are in agreement with the results of the ANOVA indicating that the performance improvement in the visual transfer condition was larger for the 24-hr than for the 5-min consolidation interval. 4. Discussion The present study examined the role of consolidation for perceptual learning in temporal discrimination of brief intervals. Discrimination performance generally increased from the pretest to the posttest indicating perceptual learning. Most importantly, the length of the consolidation interval differentially affected performance improvements in the trained auditory and the visual transfer conditions. The amount of performance improvement in the trained auditory condition was virtually identical for a 5-min and a 24-hr consolidation interval. In contrast, performance in the visual transfer condition improved substantially more after a consolidation interval of 24 h as compared to 5 min. This novel finding suggests that consolidation plays an important role for cross-modal transfer of perceptual learning. The present results replicate the cross-modal transfer effect from the auditory to the visual modality previously reported by Bratzke et al. (2012). Thus, our findings provide further evidence for the notion that cross-modal transfer of perceptual learning in temporal discrimination can occur from audition to vision. Other studies failed to observe such asymmetric audio–visual transfer effects (e.g., Grondin & Ulrich, 2011; Lapid et al., 2009). As outlined in the introduction, the experiment of Grondin and Ulrich (2011) did include massive
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Fig. 3. Left panel: visual Weber fraction improvement (pretest minus posttest) as a function of auditory pretest Weber fraction for the 5-min and the 24-hr consolidation groups. Right panel: visual Weber fraction improvement (pretest minus posttest) as a function of auditory Weber fraction improvement (pretest minus posttest) for the 5-min and the 24-hr consolidation groups. Each data point represents a single subject. Fitted lines represent regression lines from the ANCOVA.
training but no consolidation interval before the posttest. This may have counteracted potential transfer effects in their study. Additionally, massive training may have impaired performance at the end of their experiment due to fatigue. Fatigue might also explain why in the present study performance in the transfer condition was better after the long than the short consolidation interval. Such an explanation for the present results, however, seems unlikely to us because the training phase was much shorter than in the study of Grondin and Ulrich (2011). Furthermore, performance in the trained condition of the present study still showed an improvement at the end of the experiment. The absence of a consolidation interval, however, cannot explain why Lapid et al. (2009) did not observe cross-modal transfer. In their study, other factors such as the absence of feedback during the training phase or extensive training of the transfer condition in the pretest might have prevented cross-modal transfer effects to occur (see also Bratzke et al., 2012). The beneficial effect of consolidation on temporal discrimination performance in the present study is consistent with a two-stage model of perceptual learning (e.g., Atienza et al., 2002; Karni & Sagi, 1993). According to this model, in the second stage of perceptual learning, information acquired during training is transferred to and stored in long-term memory. Since this second stage needs up to several hours, performance should generally benefit from a rather long consolidation interval. Accordingly, the two-stage model would predict that both the trained and the transfer conditions benefit from extended consolidation. In contrast to this prediction, we observed such a benefit only in the transfer condition. This finding fits well with the results of a previous study on perceptual learning in auditory temporal–interval discrimination (Wright, Wilson, & Sabin, 2010). This study reported that learning of the trained stimulus occurred before generalization to other stimuli. It is important to note, however, that Wright et al. (2010) distributed the training sessions across several days and investigated generalization within the auditory modality. Under these conditions, generalization to an auditory stimulus with an untrained frequency only emerged after 4 days of training. The finding of an exclusive benefit from extended consolidation for the transfer condition might be explained by a memory-related account for perceptual learning in temporal discrimination (Bratzke et al., 2012). This account assumes that participants perform the temporal discrimination task by comparing an internal reference with the comparison interval in each trial. The internal reference is based on all previously experienced intervals and is updated in a trial-by-trial fashion (Dyjas, Bausenhart, & Ulrich, 2012). During training within one modality, participants might build up a stable modality-specific
reference and also a rather noisy amodal reference. Accordingly, if the trained modality is tested immediately after training, the modality-specific representation can be used for comparison with the test stimuli. If, however, a transfer modality is tested, comparison has to rely on the yet unstable amodal reference. During consolidation, the amodal reference is stabilized and thus performance in the transfer condition can be further improved. Alternatively, only a modality-specific reference might be available following the training phase. This reference is then transformed into an amodal reference during the consolidation phase. It should be noted that there are two limitations of the present study. First, it lacks a control group receiving no training. Without such a control group, it is possible that the present findings reflect a modality-dependent consolidation effect due to learning in the pretest rather than a genuine consolidation effect on the cross-modal transfer of perceptual learning. This possibility, however, appears to be rather unlikely given the results of Bratzke et al. (2012). These authors found that a control group without training did not show a performance improvement in the visual transfer condition. Second, sleep was not controlled for in the present study. It is widely agreed that sleep plays an important role for memory consolidation (for a review, see Born & Wilhelm, 2012). In addition, some studies have shown that consolidation of perceptual learning benefits from sleep (e.g., Censor, Karni, & Sagi, 2006; Fenn, Nusbaum, & Margoliash, 2003). Consolidation of perceptual learning, however, may also occur during wakefulness (Ashley & Pearson, 2012; Ortiz & Wright, 2010). Further studies should therefore investigate more specifically the role of sleep for the consolidation of perceptual learning in temporal discrimination. In conclusion, the present study provides further evidence for cross-modal transfer of perceptual learning in temporal discrimination from the auditory to the visual modality. The magnitude of such cross-modal transfer, however, may critically depend on the long-term consolidation of the trained temporal information. The present study thus highlights the role of consolidation for perceptual learning in temporal discrimination. References Ashley, S., & Pearson, J. (2012). When more equals less: Overtraining inhibits perceptual learning owing to lack of wakeful consolidation. Proceedings of the Royal Society B: Biological Sciences, 279, 4143–4147. Atienza, M., Cantero, J. L., & Dominguez-Marin, E. (2002). The time course of neural changes underlying auditory perceptual learning. Learning & Memory, 9, 138–150. Born, J., & Wilhelm, I. (2012). System consolidation of memory during sleep. Psychological Research, 76, 192–203.
D. Bratzke et al. / Acta Psychologica 147 (2014) 75–79 Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436. Bratzke, D., Seifried, T., & Ulrich, R. (2012). Perceptual learning in temporal discrimination: Asymmetric cross-modal transfer from audition to vision. Experimental Brain Research, 221, 205–210. Censor, N., Karni, A., & Sagi, D. (2006). A link between perceptual learning, adaptation and sleep. Vision Research, 46, 4071–4074. Dyjas, O., Bausenhart, K. M., & Ulrich, R. (2012). Trial-by-trial updating of an internal reference in discrimination tasks: Evidence from effects of stimulus order and trial sequence. Attention, Perception, & Psychophysics, 74, 1819–1841. Fenn, K. M., Nusbaum, H. C., & Margoliash, D. (2003). Consolidation during sleep of perceptual learning of spoken language. Nature, 425, 614–616. Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49, 585–612. Grondin, S., & Ulrich, R. (2011). Duration discrimination performance: No cross-modal transfer from audition to vision even after massive perceptual learning. In A. Vatakis, A. Esposito, M. Giagkou, M. F. Cummins, & G. Papadelis (Eds.), Multidisciplinary aspects of time and time perception (pp. 92–100). Berlin: Springer. Karmarkar, U. R., & Buonomano, D. V. (2003). Temporal specificity of perceptual learning in an auditory discrimination task. Learning & Memory, 10, 141–147. Karni, A., & Bertini, G. (1997). Learning perceptual skills: Behavioral probes into adult cortical plasticity. Current Opinion in Neurobiology, 7, 530–535. Karni, A., & Sagi, D. (1993). The time course of learning a visual skill. Nature, 365, 250–252.
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Lapid, E., Ulrich, R., & Rammsayer, T. (2009). Perceptual learning in auditory temporal discrimination: No evidence for a cross-modal transfer to the visual modality. Psychonomic Bulletin & Review, 16, 382–389. Nagarajan, S. S., Blake, D. T., Wright, B. A., Bly, N., & Merzenich, M. M. (1998). Practice-related improvements in somatosensory interval discrimination are temporally specific but generalize across skin location, hemisphere, and modality. The Journal of Neuroscience, 18, 1559–1570. Ortiz, J. A., & Wright, B. A. (2009). Contributions of procedure and stimulus learning to early, rapid perceptual improvements. Journal of Experimental Psychology: Human Perception and Performance, 35, 188–194. Ortiz, J. A., & Wright, B. A. (2010). Differential rates of consolidation of conceptual and stimulus learning following training on an auditory skill. Experimental Brain Research, 201, 441–451. Pelli, D. G. (1997). The video toolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437–442. Vickers, A. J. (2001). The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: A simulation study. BMC Medical Research Methodology, 1, 6. Vickers, A. J., & Altman, D. G. (2001). Analysing controlled trials with baseline and follow up measurements. British Medical Journal, 323, 1123–1124. Wright, B. A., Wilson, R. M., & Sabin, A. T. (2010). Generalization lags behind learning on an auditory perceptual task. The Journal of Neuroscience, 30, 11635–11639.
Contents lists available at ScienceDirect
Acta Psychologica journal homepage: www.elsevier.com/ locate/actpsy
The role of consolidation for perceptual learning in temporal discrimination within and across modalities☆ Daniel Bratzke ⁎, Hannes Schröter, Rolf Ulrich Cognition and Perception, Department of Psychology, University of Tübingen, Schleichstrasse 4, 72076 Tübingen, Germany
a r t i c l e
i n f o
Article history: Received 10 January 2013 Received in revised form 19 June 2013 Accepted 27 June 2013 Available online 30 July 2013 PsycINFO classification: 2320 2343 Keywords: Perceptual learning Consolidation Temporal processing Interval timing
a b s t r a c t Training people on temporal discrimination can substantially improve performance in the trained modality but also in untrained modalities. A pretest–training–posttest design was used to investigate whether consolidation plays a crucial role for training effects within the trained modality and its transfer to another modality. In the pretest, both auditory and visual discrimination performance was assessed. In the training phase, participants performed only the auditory task. After a consolidation interval of either 5 min or 24 h, participants were again tested in both the auditory and visual tasks. Irrespective of the consolidation interval, performance improved from the pretest to the posttest in both modalities. Most importantly, the training effect for the trained auditory modality was independent of the consolidation interval whereas the transfer effect to the visual modality was larger after 24 h than after 5 min. This finding shows that transfer effects benefit from extended consolidation. © 2013 Elsevier B.V. All rights reserved.
1. Introduction It has been repeatedly shown that the efficiency of the perceptual system can improve with training. These beneficial effects of training have been termed perceptual learning (for a review, see Goldstone, 1998). It has been argued that the time course of perceptual learning can be divided into two stages: (a) a fast, within-session improvement, and (b) a slowly-developing improvement that occurs during a consolidation phase lasting at least 6–8 h (for a review, see Karni & Bertini, 1997; see also Atienza, Cantero, & Dominguez-Marin, 2002). Fast within-session improvements may reflect stimulus learning and conceptual learning, as for example, learning of the procedure and the task (Ortiz & Wright, 2009). Slow improvements may rather reflect a modification of long-term memory representations (Atienza et al., 2002; Karni & Sagi, 1993). The present study examined the role of consolidation for perceptual learning in temporal discrimination. Ortiz and Wright (2010) provided first evidence that consolidation may play an important role for perceptual learning in temporal discrimination. In their study, participants were tested in an interaural time differences (ITD) discrimination task. In the training phase, participants either received training on the ITD or an interaural level difference discrimination ☆ This study was supported by the Deutsche Forschungsgemeinschaft (UL 116/12-1). We thank Teresa Birngruber and Linda Idelberger for assistance in data acquisition. ⁎ Corresponding author. Tel.: +49 7071 29 74512; fax: +49 7071 29 2410. E-mail address: [email protected] (D. Bratzke). 0001-6918/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.actpsy.2013.06.018
task. Performance in the ITD task was then assessed in a posttest immediately, 10 h, or 24 h after the training. Irrespective of the training task, participants' performance in the ITD task improved from the pretest to the posttest without any consolidation interval. Most importantly, performance showed further improvement both 10 and 24 h after the training, suggesting that perceptual learning benefits from consolidation. In the present study we investigated the influence of different consolidation intervals on perceptual learning in auditory temporal discrimination and its cross-modal generalization to visual stimuli. First attempts to demonstrate cross-modal transfer from auditory training to visual stimuli were unsuccessful (Grondin & Ulrich, 2011; Lapid, Ulrich, & Rammsayer, 2009; but see Nagarajan, Blake, Wright, Bly, & Merzenich, 1998, for cross-modal transfer from the somatosensory to the auditory modality). In a recent study, however, Bratzke, Seifried, and Ulrich (2012) reported asymmetric cross-modal transfer of training on a temporal discrimination task from audition to vision. Specifically, these authors employed a pretest–training–posttest design including a control group that performed only the pretest and the posttest. Participants in the training groups were trained with an empty interval of 100 ms duration, which was either marked by two visual or by two auditory stimuli. This training was distributed across several days. Participants in the control group did not receive such training. In the pre- and posttest, all participants were tested with different durations (100 and 200 ms) in the two modalities. Trained participants showed a larger performance improvement from pretest to posttest than the control group for the trained but not for the untrained duration. This training effect transferred to the other modality only for those participants
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who had been trained with auditory stimuli but not for those who had been trained in the visual modality. Thus, this pattern of results demonstrates an asymmetric cross-modal transfer of perceptual learning in temporal discrimination from audition to vision. According to Bratzke et al. (2012), the absence of cross-modal transfer effects in previous studies (Grondin & Ulrich, 2011; Lapid et al., 2009) can be explained by non-optimal conditions to reveal cross-modal transfer, including fatigue due to massive training, absence of feedback, and possible training effects in multiple pretest sessions. In addition, Grondin and Ulrich (2011) trained and tested their participants in a single session lasting 3.5 h with potential breaks of only 5 min between the different parts of the experiment. In contrast, in the study of Bratzke et al. (2012) the pre-and posttests as well as the training sessions were distributed across several days. Thus, these studies differed not only with respect to the distribution and the amount of training during the acquisition phase but also with respect to the length of the consolidation interval between training and posttest. Thus, it is possible that the relatively long consolidation interval in the study of Bratzke et al. (2012) was crucial for the cross-modal transfer from audition to vision to occur. In the present study, we used a pretest–training–posttest design similar to the one of Bratzke et al. (2012). During the training phase, participants were trained exclusively with an auditory 100-ms interval. Before and after the training phase (pre- vs. posttest), participants were tested with the trained auditory interval and a visual transfer interval of the same length. Since Bratzke et al. (2012) observed generalization of perceptual learning from the auditory to the visual modality but not vice versa, an auditory training interval and a visual transfer interval were employed. To assess the influence of consolidation on perceptual learning, the posttest was administered either 5 min or 24 h after the training phase. Based on previous findings (Ortiz & Wright, 2010) we expected larger performance improvements from the pretest to the posttest for the group of participants with the longer consolidation interval. If consolidation is especially important for the cross-modal transfer of perceptual learning to occur, one would expect that the transfer of perceptual learning from the auditory to the visual modality benefits more from an extended consolidation interval than perceptual learning within the trained auditory modality. 2. Method 2.1. Participants Forty-eight volunteers participated in the study. Participants reported normal hearing and normal or corrected-to-normal vision. They were randomly assigned to one of two groups (5-min vs. 24-hr consolidation interval). Seven participants of the original sample were replaced by additional participants because they produced virtually flat psychometric functions. Furthermore, 3 participants were replaced because their performance deviated from mean performance in at least one of the test conditions in the pre- or posttest by more than 3 SDs. In the final sample, both the 5-min consolidation group (mean age: 23.8 years, SD = 5.8) and the 24-hr consolidation group (mean age: 22.0 years, SD = 2.7) consisted of 7 males and 17 females each. 2.2. Apparatus and stimuli Participants were tested individually in a sound-attenuated, dimly illuminated booth. They sat at a distance of approximately 50 cm from a circular red LED with a diameter of 0.57°, wore headphones, and responded with two external response keys. The experiment was written in Matlab (The MathWorks, 2007), using the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) version 3.0.8, and was run on an Apple iMac with OS X. As in Bratzke et al. (2012), the stimuli were auditory and visual empty intervals of 100 ms duration
(A100 vs. V100). In the auditory condition, intervals were marked by 15-ms beeps (1 kHz) with 5-ms sinusoidal onset and offset ramps and were presented binaurally via headphones at an intensity level of 70 dB SPL. In the visual condition, intervals were marked by two 15-ms light pulses produced by the LED. The length of the interval was defined as the time interval from the offset of the first marker to the onset of the second marker. 2.3. Procedure Following Bratzke et al. (2012), a single stimulus protocol was used (see also Karmarkar & Buonomano, 2003). At the beginning of each block of trials, the respective standard interval was presented ten times. In each trial, participants were then presented with one of 8 possible comparison intervals that were symmetrically distributed around the standard interval (A100: 79, 85, 91, 97, 103, 109, 115, 121 ms; V100: 65, 75, 85, 95, 105, 115, 125, 135 ms). Participants were instructed to respond as accurately as possible with their left (right) index finger when the comparison interval was shorter (longer) than the initially presented standard interval. Subsequent trials were separated by one of four randomly distributed foreperiods (800, 900, 1000, or 1100 ms). In case of an erroneous response, feedback (tactile stimulation of the right lower leg for 500 ms) was provided during the training phase. No feedback was provided in the pre- and posttests. In both the pre- and posttest, participants were tested in each of the two conditions (A100, V100). The order of the conditions within the pre- and the posttest was counterbalanced across participants. Each test consisted of 2 blocks (one for each testing condition) of 160 trials. The training phase followed immediately after the pretest and consisted of 5 blocks of 160 trials each. In this phase participants were trained exclusively with the A100 condition. The posttest was administered either 5 min (5-min consolidation) or 24 h (24-hr consolidation) after the end of the training phase. 2.4. Data analysis For each participant and test condition, a psychometric function was traced, plotting the eight comparison intervals on the x-axis and the relative frequency of responding “long” on the y-axis. A logistic function was fitted to the resulting curves by the method of maximum likelihood. From these fitted curves the estimate of the difference limen (DL) was obtained. Specifically, the DL was estimated as being half the interquartile range of this fitted function—that is, DL = (x.75–x.25)/2, where x.25 and x.75 denote the values of the comparison that yield 25% and 75% “longer” responses, respectively. The Weber fraction (DL/standard duration) was then computed for each test condition. Finally, in order to test for differences in perceptual learning between the two consolidation groups and conditions, we calculated difference scores between the Weber fractions obtained in the pretest and the postest. 3. Results Fig. 1 depicts Weber fractions for the two consolidation groups and test conditions as a function of block. Both consolidation groups showed similar learning curves for the trained auditory condition. Fig. 1 suggests that performance improvements for the auditory test condition mainly occurred from the pretest to the first training block (Block 2). To test whether there was still a significant improvement from the end of the training phase to the posttest, we conducted a mixed-model ANOVA with the factors consolidation interval (5 min vs. 24 h) and block (Block 6 vs. posttest). This ANOVA revealed a significant main effect of block, F(1, 46) = 8.42, p = .006, η2p = .16, but no main effect of consolidation group, F(1, 46) = 0.88, p = .354, η2p = .02, or interaction of consolidation group and block, F(1, 46) = 1.38, p = .245, η2p = .03. These results indicate that perceptual learning for the auditory
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Fig. 1. Mean Weber fraction as a function of block, test condition (auditory vs. visual) and consolidation interval (5 min vs. 24 h). Error bars indicate ±1 SE.
condition further improved from the end of the training phase to the posttest. However, this improvement did not benefit from a prolonged consolidation phase. Fig. 1 also suggests differences in pretest performance for the visual transfer condition. We therefore compared the pretest Weber fractions between the two consolidation groups with a mixed-model ANOVA with the between-subjects factor consolidation interval (5-min vs. 24-hr) and the within-subjects factor modality (auditory vs. visual). This analysis revealed no main effect of consolidation interval, F(1, 46) = 1.09, p = .303, η2p = .02, or interaction of consolidation interval and modality, F(1, 46) = 1.45, p = .235, η2p = .03. The main effect of modality was significant, F(1, 46) = 57.93, p b .001, η2p = .56, reflecting the common finding of larger Weber fractions for visual (0.25) than for auditory (0.14) stimuli. An additional Welch test for pretest Weber fractions only in the visual transfer condition also revealed no significant effect of consolidation interval, t(40) = 1.27, p = .213. Thus, statistical analyses did not show reliable differences in pretest performance for the visual transfer condition. Since baseline performance did not differ between the two consolidations groups, we analyzed the difference scores of the Weber fractions (pretest minus posttest) in the subsequent analysis. Fig. 2 depicts the Weber fraction improvement from pretest to postest as a function of consolidation interval and modality. These difference scores were positive in all conditions (all ps b .045; one-tailed Welch tests) reflecting a general improvement from the pretest to the posttest. An ANOVA with the between-subjects factor consolidation interval and the within-subjects factor modality on the difference scores revealed no significant main effect of consolidation interval, F(1, 46) = 1.28, p = .264, η2p = .03, or modality, F(1, 46) = 0.14, p = .708, η2p b .01. Importantly, the interaction of consolidation interval and modality was significant, F(1, 46) = 4.18, p = .047, η2p = .08. As can be seen in Fig. 2, the difference scores for the trained auditory interval were virtually identical for the two consolidation intervals. For the visual transfer condition, however, the performance improvement was larger for the 24-hr than for the 5-min consolidation interval. This was confirmed by a one-tailed Welch test, t(44) = 1.68, p b .05. We conducted additional analyses to test whether perceptual learning in the visual transfer condition was associated with discrimination performance in the auditory training condition. This was assessed in two ways. First, the improvement in visual performance was regressed against the auditory pretest performance (Fig. 3, left panel). Correlation analysis revealed a positive relationship between these two measures (r = .33, p = .021). An ANCOVA with auditory
Fig. 2. Mean Weber fraction improvement (pretest minus posttest) as a function of test condition (auditory vs. visual), and consolidation interval (5-min vs. 24-hr). Error bars indicate ±1 SE.
pretest performance as a covariate and visual improvement as the dependent variable (see Vickers, 2001; Vickers & Altman, 2001) indicated again a stronger improvement for the 24-hr than for the 5-min consolidation group (p = .077). Second, the improvement in visual performance was regressed against the improvement in auditory performance (Fig. 3, right panel). Again, both measures were positively correlated (r = .26, p = .071), and an ANCOVA revealed a stronger improvement for the 24-hr than for the 5-min consolidation group (p = .053). In conclusion, these additional analyses are in line with the assumption that the participants with worse pretest performance gain an especially large performance improvement in the transfer condition. Furthermore, the ANCOVA results are in agreement with the results of the ANOVA indicating that the performance improvement in the visual transfer condition was larger for the 24-hr than for the 5-min consolidation interval. 4. Discussion The present study examined the role of consolidation for perceptual learning in temporal discrimination of brief intervals. Discrimination performance generally increased from the pretest to the posttest indicating perceptual learning. Most importantly, the length of the consolidation interval differentially affected performance improvements in the trained auditory and the visual transfer conditions. The amount of performance improvement in the trained auditory condition was virtually identical for a 5-min and a 24-hr consolidation interval. In contrast, performance in the visual transfer condition improved substantially more after a consolidation interval of 24 h as compared to 5 min. This novel finding suggests that consolidation plays an important role for cross-modal transfer of perceptual learning. The present results replicate the cross-modal transfer effect from the auditory to the visual modality previously reported by Bratzke et al. (2012). Thus, our findings provide further evidence for the notion that cross-modal transfer of perceptual learning in temporal discrimination can occur from audition to vision. Other studies failed to observe such asymmetric audio–visual transfer effects (e.g., Grondin & Ulrich, 2011; Lapid et al., 2009). As outlined in the introduction, the experiment of Grondin and Ulrich (2011) did include massive
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Fig. 3. Left panel: visual Weber fraction improvement (pretest minus posttest) as a function of auditory pretest Weber fraction for the 5-min and the 24-hr consolidation groups. Right panel: visual Weber fraction improvement (pretest minus posttest) as a function of auditory Weber fraction improvement (pretest minus posttest) for the 5-min and the 24-hr consolidation groups. Each data point represents a single subject. Fitted lines represent regression lines from the ANCOVA.
training but no consolidation interval before the posttest. This may have counteracted potential transfer effects in their study. Additionally, massive training may have impaired performance at the end of their experiment due to fatigue. Fatigue might also explain why in the present study performance in the transfer condition was better after the long than the short consolidation interval. Such an explanation for the present results, however, seems unlikely to us because the training phase was much shorter than in the study of Grondin and Ulrich (2011). Furthermore, performance in the trained condition of the present study still showed an improvement at the end of the experiment. The absence of a consolidation interval, however, cannot explain why Lapid et al. (2009) did not observe cross-modal transfer. In their study, other factors such as the absence of feedback during the training phase or extensive training of the transfer condition in the pretest might have prevented cross-modal transfer effects to occur (see also Bratzke et al., 2012). The beneficial effect of consolidation on temporal discrimination performance in the present study is consistent with a two-stage model of perceptual learning (e.g., Atienza et al., 2002; Karni & Sagi, 1993). According to this model, in the second stage of perceptual learning, information acquired during training is transferred to and stored in long-term memory. Since this second stage needs up to several hours, performance should generally benefit from a rather long consolidation interval. Accordingly, the two-stage model would predict that both the trained and the transfer conditions benefit from extended consolidation. In contrast to this prediction, we observed such a benefit only in the transfer condition. This finding fits well with the results of a previous study on perceptual learning in auditory temporal–interval discrimination (Wright, Wilson, & Sabin, 2010). This study reported that learning of the trained stimulus occurred before generalization to other stimuli. It is important to note, however, that Wright et al. (2010) distributed the training sessions across several days and investigated generalization within the auditory modality. Under these conditions, generalization to an auditory stimulus with an untrained frequency only emerged after 4 days of training. The finding of an exclusive benefit from extended consolidation for the transfer condition might be explained by a memory-related account for perceptual learning in temporal discrimination (Bratzke et al., 2012). This account assumes that participants perform the temporal discrimination task by comparing an internal reference with the comparison interval in each trial. The internal reference is based on all previously experienced intervals and is updated in a trial-by-trial fashion (Dyjas, Bausenhart, & Ulrich, 2012). During training within one modality, participants might build up a stable modality-specific
reference and also a rather noisy amodal reference. Accordingly, if the trained modality is tested immediately after training, the modality-specific representation can be used for comparison with the test stimuli. If, however, a transfer modality is tested, comparison has to rely on the yet unstable amodal reference. During consolidation, the amodal reference is stabilized and thus performance in the transfer condition can be further improved. Alternatively, only a modality-specific reference might be available following the training phase. This reference is then transformed into an amodal reference during the consolidation phase. It should be noted that there are two limitations of the present study. First, it lacks a control group receiving no training. Without such a control group, it is possible that the present findings reflect a modality-dependent consolidation effect due to learning in the pretest rather than a genuine consolidation effect on the cross-modal transfer of perceptual learning. This possibility, however, appears to be rather unlikely given the results of Bratzke et al. (2012). These authors found that a control group without training did not show a performance improvement in the visual transfer condition. Second, sleep was not controlled for in the present study. It is widely agreed that sleep plays an important role for memory consolidation (for a review, see Born & Wilhelm, 2012). In addition, some studies have shown that consolidation of perceptual learning benefits from sleep (e.g., Censor, Karni, & Sagi, 2006; Fenn, Nusbaum, & Margoliash, 2003). Consolidation of perceptual learning, however, may also occur during wakefulness (Ashley & Pearson, 2012; Ortiz & Wright, 2010). Further studies should therefore investigate more specifically the role of sleep for the consolidation of perceptual learning in temporal discrimination. In conclusion, the present study provides further evidence for cross-modal transfer of perceptual learning in temporal discrimination from the auditory to the visual modality. The magnitude of such cross-modal transfer, however, may critically depend on the long-term consolidation of the trained temporal information. The present study thus highlights the role of consolidation for perceptual learning in temporal discrimination. References Ashley, S., & Pearson, J. (2012). When more equals less: Overtraining inhibits perceptual learning owing to lack of wakeful consolidation. Proceedings of the Royal Society B: Biological Sciences, 279, 4143–4147. Atienza, M., Cantero, J. L., & Dominguez-Marin, E. (2002). The time course of neural changes underlying auditory perceptual learning. Learning & Memory, 9, 138–150. Born, J., & Wilhelm, I. (2012). System consolidation of memory during sleep. Psychological Research, 76, 192–203.
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