Department of Psychology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria 3800, Australia
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Abstract |
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Key Words: plasticity, sharpness of tuning, tonotopic organization
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Introduction |
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In the auditory study, Recanzone et al. (1993) reported that in owl monkeys trained on a frequency discrimination task the area of representation of the training frequencies in AI was enlarged (by a factor of
7 to
9, depending on frequency) relative to untrained (control) monkeys, and that within the group of trained animals, larger areas of representation were associated with superior discrimination performance. The frequency tuning of multi-neuron clusters in these enlarged areas of representation was sharper, and response latency was longer, relative to that in the areas in which the same frequency ranges were represented in control animals. Expansion of the representation of training frequencies must presumably occur at the expense of the representation of adjacent frequencies. In accordance with this expectation, Recanzone et al. (1993
) reported that in two animals in which frequency discrimination was tested using a range of frequencies above the target frequency (+
F discrimination) there was a decreased cortical representation of a range of frequencies below the target frequency.
In experimental studies, improvement in performance on a perceptual task is likely to involve improvement in performance of the task used to assess perceptual performance (commonly termed procedural learning) in addition to possible improvement in perceptual discrimination per se (perceptual learning) (Robinson and Summerfield, 1996). Although these two forms of learning presumably occur concurrently, procedural learning is generally assumed to be reflected in a rapid initial phase of improvement in performance, while perceptual learning is assumed to depend on extensive training and to be a more gradual process. Procedural learning would also be expected to generalize to any other perceptual discrimination tested with the same procedures, while the specificity or generalizability of perceptual learning is an empirical issue. In accordance with these views, Recanzone et al. (1993
) reported that a fast initial phase of improvement in the performance of their monkeys on the auditory frequency discrimination task, which they described in terms of conceptual learning of the task, generalized to other (untrained) frequencies. In contrast, the slower gradual phase of learning did not generalize, but was associated with a decrement in performance at adjacent frequencies.
We have studied the effect of perceptual learning at one training frequency on the representation of that frequency in AI of cats that showed perceptual learning and reached asymptotic performance levels on a frequency discrimination task. In order to establish the specificity of perceptual learning on this task, we also examined the extent to which improvements generalized to distant and adjacent frequencies. Although we found some evidence suggestive of small changes in the breadth of frequency tuning and the response latency of neurons in the cortical area of representation of the training frequencies, we found no evidence of changes in the area of cortex devoted to these or adjacent frequencies, or of decrements in performance at distant or adjacent frequencies. A preliminary report of some of these data has been published (Brown et al., 2002).
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Materials and Methods |
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Six domestic cats trained on the frequency discrimination task were housed together from 8 to 12 weeks of age, and were handled daily and given ad lib food and water up until the beginning of training (at 12 weeks). During the training period cats were weighed daily and fed measured quantities of dry food after their training session and on days which they were not trained, so that they maintained, or gained in, body weight. Four (untrained) cats used as controls were tested electrophysiologically at between 6 and 12 months of age. All procedures carried out on these animals were approved by the Monash University Department of Psychology Animal Ethics Committee.
Psychophysics
The effects of perceptual learning on the frequency organization of AI were examined using a +F frequency discrimination task at 8 kHz, because this frequency was one of those used by Recanzone et al. (1993
) and in most cats is represented on the surface of the middle ectosylvian gyrus (MEG), where accurate mapping is possible. In most cats, lower frequencies are represented in the rostral bank of the posterior ectosylvian sulcus (PES), in which it is much more difficult to map with the required degree of precision the area of representation of a particular frequency. One group of trained cats (n = 3) was initially trained to a near-asymptotic level on the frequency discrimination task at 3 kHz to ensure that procedural learning was complete before training was commenced at 8 kHz. This procedure also enabled an assessment of any effect on performance at this frequency of the subsequent training and perceptual leaning at 8 kHz. The other group of trained cats (n = 3) was extensively trained on only the 8 kHz task and in the course of training was tested on interpolated trials on discrimination of 8 kHz using lower-frequency stimuli (a
F task) and on discrimination at 3 kHz.
The psychophysical training methods were based on those reported by May et al. (1995), with some modification of stimulus presentation parameters. At 12 weeks of age, each cat was trained on an auditory association task by giving it small amounts of a highly desirable food whenever an auditory click was presented. The cats behaviour was then shaped such that it was required to perform a bar press, which initiated the presentation of the click followed by the food reward. This behaviour was further shaped into a holdrelease paradigm (see below), suitable for use on an operant platform, as described by May et al. (1995
).
All frequency discrimination training was carried out in a sound attenuated room (>70 dB at 8.0 kHz). Training began when the cat could sit on the operant platform and perform the holdrelease task. The cat initiated a trial by pressing and holding down a large paw pedal. At a randomly variable time (between 1 and 4 s) after initiation of the bar press, a sequence of comparison tones (S1), randomly variable in number between 2 and 8, was presented. The S1 tones were either 8.0 or 3.0 kHz tone pips, 600 ms in duration with 5 ms rise/fall intervals, and a 400 ms inter-stimulus interval. All tonal stimuli were generated by Tucker Davis Technologies System 2 hardware, which was controlled by custom designed software. A difference tone (S2), different in frequency but with otherwise the same stimulus parameters as the S1 tones, was presented 400 ms after the offset of the last S1 stimulus. The cat was required to release the paw pedal on detection of the S2 stimulus to receive a food reward delivered through a spout positioned directly in front of and at the level of its mouth. The food reward (14 ml) consisted of liquidized and filtered tinned cat food diluted 50% in water. The cat was restrained on the operant platform such that if it did not have its head in a forward position with its mouth directed at the food spout, the reward would fall to a collecting dish out of reach. The spout position ensured that the cats head was directed toward the speaker positioned 1 m directly in front of and 20° above the cats interaural horizontal plane. The position of the speaker was elevated to reduce the filtering effects of the outer ear (Rice et al., 1992
).
The cat was required to release the paw pedal in the interval 201000 ms after the onset of the S2 stimulus. Releases that occurred between the start of the first S1 tone pip and 20 ms after the start of the second S1 tone pip ended the trial, and were recorded as pre false positives (pre-FP). Releases after the pre-FP period and prior to 20 ms after the start of the S2 stimulus ended the trial and were recorded as false positives (FP). Releases after the FP period and prior to 400 ms after the cessation of the S2 stimulus completed the trial and were recorded as hits. Non-releases and releases after the hit period were recorded as misses. During the early part of the frequency discrimination training, and at various times throughout the training, pre-FP, FP and misses were followed by a time-out period of 4 s, during which another trial could not be initiated. Animals were video-monitored during training to ensure they were attentive to the task and not distressed.
All discrimination training was carried out with S2 at a higher frequency than S1 (i.e. with +F), but as described earlier, some interpolated 8 kHz test trials were carried out using S2 values lower than S1 (
F). The frequency range over which S2 varied, and the intervals between S2 values, were adjusted throughout each cats training so that performance within a session was maintained near 7090% correct. During each session, response performance across all S2 stimuli was monitored online so that time-out periods and reward volume could be adjusted to help maintain the cats performance. Cats performed between 50300 trials per session; any training session in which a cat completed considerably less trials than its daily average was repeated later in the day. Cats were trained for 912 months with one or two sessions per day, 35 days a week, up to the day prior to electrophysiological mapping of AI.
The stimulus sound pressure level (SPL) was sampled at 1.0, 1.05 and 1.1 m from the free-field speaker (Vifa, Model P25WO-00) using a Brüel and Kjær 0.5 inch condenser microphone and measuring amplifier (Brüel and Kjær Type 4133 and Type 2606). These locations encompassed the range over which the cats head could be located while attending to the stimuli. The SPL of the stimuli across the three locations varied by no more than 5 dB at any given frequency in the range of S1 and S2 frequencies used in training. At a given training frequency, the average of the SPLs across the three locations was entered into a calibration table used by the computer to generate stimuli at the required SPL. To eliminate any cues for discrimination based on loudness differences between stimuli, the stimulus SPL was randomly varied over the range of 60 ± 5 dB.
The number of S1 repeats within a trial varied randomly between 2 and 8. The probability of the S2 occurring after any of the S1 stimuli was equal and remained constant for all training sessions. The probability of chance performance for the modified variable holdrelease paradigm therefore decreased with successive S1 stimuli within a trial. The overall chance level of performance for this training paradigm is 0.14. For the determination of minimum discrimination thresholds a 0.5 critical level of performance was set, which is well above chance, and therefore gave a conservative estimate of minimum discrimination thresholds.
Sessions were divided into blocks comprising an approximately equal number of trials for the analysis of frequency discrimination, which was measured in terms of the just detectable frequency difference (F). Psychometric functions were plotted from the hit rate for each S2 after correction for the FP rate. The hit rate is defined as the number of hits for each S2 divided by the number of trials on which the S2 was presented. The FP rate served as an estimate of the cats guess rate and as an indicator that cats were under stimulus control. The total FP rate (FPtr) is the number of false positives within a block of trials divided by the total number of S1 presentations in that block. The FP correction is therefore 1-FPtr. Discrimination performance was then calculated by multiplying the hit rate for each S2 stimulus by 1-FPtr. Four-parameter sigmoidal functions (Hill functions) were fitted to the discrimination functions (see Fig. 1). The parameters from the sigmoidal function were then used to calculate the frequency at a performance level of 0.5.
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Procedures for mapping AI were generally as described previously (e.g. Rajan et al., 1993) except that rather than mapping the entire frequency representation a fine-resolution map of the region over which neurons responded to 8 kHz was obtained. Cats were fasted for 18 h prior to the induction of surgical anesthesia with sodium pentobarbitone (Nembutal, @ 45 mg/kg). Throughout surgery and during recording, supplemental doses of anesthetic (Nembutal) were delivered via a slow i.v. infusion pump at a rate of 25 mg/kg/h or as required. The level of anesthesia was monitored using ECG, pedal reflex and ocular dilation, and the cats core body temperature was maintained at around 37.5°C using a DC heating pad controlled by a rectal thermistor. Following tracheal cannulation the cat was placed in a head holding frame, and the bullae were opened to allow stainless-steel spring electrodes to be placed on the round windows. Polyethylene tubes (0.38 mm I.D.) were placed into the bullae to allow static pressure equalization, and the bullae were then sealed with dental cement. Auditory compound action potential (CAP) audiograms were obtained by measuring the N1 threshold for stimulus frequencies between 2 and 40 kHz (5 ms duration; 0.4/3.0 ms rise/fall interval), using signal averaging (10 ± 1 µV criterion). The left auditory cortex was exposed and a calibrated digital photograph of the cortex obtained. The position of each electrode penetration was plotted onto the photograph based on cortical vasculature. The xy coordinate of each penetration could then be determined from the coordinate of the central pixel under each penetration position. A modified Davies chamber with its top parallel to the gyral surface was positioned over the exposure, secured to the skull, filled with sterile saline, and sealed with a glass plate. Electrode penetrations were thus made approximately orthogonal to the surface of the MEG, using a hydraulic micromanipulator mounted in the glass plate on top of the Davies chamber. Recordings were made using 12 M
glass-insulated tungsten microelectrodes. The electrode was advanced 500700 µm into the cortex before searching for tone-evoked multi-unit (neuronal cluster) activity. Once a cluster containing clearly-defined action potentials was obtained, the characteristic frequency (CF; frequency at which threshold is lowest) was determined audiovisually (just detectable increase in spike rate, ±5 dB SPL; ±0.1 kHz). A response area was obtained for each cluster by presenting, under computer control, a frequencyintensity matrix (FI) that was centred about the audiovisually determined CF and varied over an SPL range from below threshold to
60 dB suprathreshold. The FI stimuli were 50 ms pure tone bursts with 5 ms rise/fall intervals, presented at a rate of 2 Hz. Frequencyintensity combinations were presented pseudorandomly across the matrix, and the complete matrix was presented five times in a different pseudorandom order. For the collection of these quantitative data, a Schmitt trigger was set at a level well above the noise floor and exceeded only by clearly defined action potentials, which produced Schmitt trigger output pulses timed by the computer with an accuracy of 10 µs. The software reported the number of spikes within a specified count window and the mean first-spike latency at each FI combination. The CF, best frequency (BF; the frequency which elicits the largest number of action potentials at an intensity within the FI matrix range), Q20 (CF/bandwidth 20 dB above CF threshold) and the CF-L20 (latency 20 dB above threshold at CF) were obtained from the response area. Following the FI, an 8 kHz inputoutput function, over a 7080 dB range, was obtained for each cluster.
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Results |
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The psychometric frequency-discrimination functions from which threshold measures were derived are illustrated in Figure 1 by the functions for selected blocks of trials throughout the period of 8.0 kHz training for cat 01-19. The threshold (50% performance) delta frequency (Ft) decreases over the period of training. This is reflected in an increase in the slope of the psychometric function, with the greatest change occurring in the early period of training. The frequency discrimination thresholds over the training period of all cats are shown in Figure 2. For all cats, the initial few thousand trials in which the animals were learning the task were collected over many session and did not generate measurable discrimination thresholds. For the three cats (00-11, 00-21 and 00-22: Fig. 2D, E and F, respectively) given extensive training on the 3.0 kHz task prior to training at 8.0 kHz, only the data for the later stages of training at 3 kHz, at which performance levels were near-asymptotic, are shown. The transfer from 3 to 8 kHz required the presentation of discrimination trials at intervening S2 frequencies, which did not generate measurable discrimination thresholds; hence the gap in the frequency discrimination threshold functions for these animals.
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For each of the five cats whose thresholds at 8 kHz improved with training, false alarm rates did not change systematically in the course of training but remained approximately constant (varying in the range 0.63.8%), indicating that the improvement did not reflect a change in criterion. As a further assessment of whether the improvement reflected a change in perceptual sensitivity, d' values were calculated for each cat for the discrimination of 8 from 10 kHz (i.e. for S2 = 10 kHz) for a block of trials early and at the end of training. These data are presented in Figure 4. For each of the cats, d' increased over the training period, indicating an increase in sensitivity to that frequency difference. Although this analysis is qualified by the fact that the calculation of d' from a single pair of hit and false alarm rates assumes that the signal and noise distributions are normal and of equal variance (Gescheider, 1997), it supports the interpretation of the improved performance as reflecting perceptual learning. The fact that the increase in d' was larger in the animals trained only at 8 kHz (mean increase = 0.87) than in the animals trained first at 3 kHz (mean increase = 0.28) is also in accordance with the view that in the latter group perceptual learning was complete at 3 kHz and had generalized to a large extent to 8 kHz, so that less perceptual learning at 8 kHz occurred in this group.
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Further evidence on the specificity of improvements in discrimination to the training frequency and on the effects of training at one frequency range on discrimination at other frequencies is provided by the data derived from the blocks of test trials on a 3.0 kHz and/or a F 8 kHz discrimination task that were interpolated in the 8 kHz training period. The number of interpolated trials on these tasks was kept to the minimum needed to obtain an accurate determination of performance. All cats tested showed an improvement in performance on the 3.0 kHz task throughout the training period. For those cats initially trained to asymptotic levels at 3 kHz (Fig. 2E,F), the trials interpolated late in 8 kHz training showed a further slight improvement in performance. For the three cats that were not initially trained at 3 kHz (Fig. 2AC), performance on the interpolated trials was similar to that of the better-performing animals trained at 3 kHz (Fig. 2E,F) and improved across the two blocks of interpolated trials. The mean improvement in
Ft for the interpolated 3.0 kHz task was 0.33 kHz (0.15 octaves).
For cats that were tested on the F 8 kHz task (01-18, 01-19 and 01-20), the trials occurred early and toward the end of the training period (Fig. 2AC). All three cats showed an improvement in discrimination performance from the initial to the final
F testing, and performance on
F trials after training with +
F was comparable (Fig. 2A) or superior (Fig. 2B,C) to that on +
F trials. The mean improvement on the
F 8 kHz task was 0.62 kHz (0.11 octaves).
These observations provide no evidence for a decrement in performance at other frequencies as a consequence of training and improvement in discrimination thresholds at 8 kHz, and indicate that learning at that frequency has in fact generalized substantially to the other frequencies tested.
Electrophysiology
The CAP audiograms for all of the trained cats were within the normal range (Rajan et al., 1991). Tonotopic maps of AI of sufficient detail to provide information on cortical map changes were obtained from five trained cats and four normal (untrained) cats. The number of CF determinations made in AI of one trained cat (01-18) did not allow isofrequency contours (see text below) to be fitted to the penetration points and a quantitative map could therefore not be generated for this animal. The cortical map and neuronal response property data obtained from trained cat 00-11, which did not show perceptual learning at 8 kHz, have not been included in the group analyses for the trained cats. The tonotopic maps cover an area that extends 34 mm rostrocaudally and 45 mm mediolaterally over AI, with a frequency range from
4 kHz to 16 kHz. Some maps were limited on their caudal, low frequency edge by the PES. The number of penetrations in each map ranged from 40 to 88, with a mean of 71. This produced a sampling resolution of 250500 µm across AI in the frequency range of interest.
The CF cortical map from an untrained animal is shown in Figure 5A. Isofrequency contours were fitted to the CF cortical maps using an inverse distance method (SigmaPlot, interpolated 3-D mesh plot; X/Y intervals = 15; distance weight = 6) with contour intervals set at 0.5 kHz. The isofrequency contours in Figure 5A range from 6.25 to 12.75 kHz and run mediolaterally across the cortex. The orientation of the CF axis in this map is 10.9° from the rostrocaudal plane, and across all other animals it varied between 7.3° and 26.4°. In determining the area representing these 0.5 kHz bandwidths, the lateromedial extent of the cortical map was limited by a 3 mm wide band, parallel to the frequency axis and centred on the region of the map where the isofrequency contours were approximately parallel. The area between contour lines (representing 0.5 kHz bandwidths) and within the 3 mm band was calculated from the number of underlying pixels in the calibrated digital photograph. The area of each 0.5 kHz CF band in cat 01-17 is shown in Figure 5B. It is apparent that the area of these 0.5 kHz bands varies substantially, in this case by a factor of almost 3 (compare bands centred on 7.5 and 10.5 kHz). Similar but idiosyncratic variation in the area of frequency band strips was seen in all normal cats.
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It could be argued that perceptual learning resulting from frequency discrimination training might alter cortical cluster response latency at the discrimination frequency rather than at the clusters CF. To investigate this possibility the first spike latency of the 8.0 kHz response at 20 dB above the 8.0 kHz threshold (8.0 kHz-L20; derived from the 8.0 kHz I/O functions) was compared across the untrained and trained groups. An ANOVA on these data (Fig. 13C) showed no significant main effect for either frequency (F = 0.84; df = 4; P > 0.05) or group (F = 2.92; df = 1; P > 0.05), but a significant frequency x group interaction (F = 2.46; df = 4; P < 0.05). The mean 8.0 kHz-L20 of the trained group is significantly shorter than that of the untrained group for the 8- and 10 kHz bands (two-tailed t test; t = 2.51; P < 0.02 and t = 2.98; P < 0.01 respectively). The differences in the other frequency bins were not significant. These data, together with the CF-L20 data suggest that response latency of AI clusters in some frequency bands has become shorter as a result of frequency discrimination training.
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Discussion |
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Behavioural Results
The asymptotic performance levels achieved by the cats in this study are in good agreement with those reported by other investigators using similar appetitive training procedures (Hienz et al., 1993). As can be seen in Figure 14, our
F value at 3 kHz (based on the three cats trained originally at that frequency) is almost identical to that reported by Hienz et al. (1993
), and our 8 kHz value (based on the five cats that showed perceptual learning at that frequency) lies close to the extension of their curve. The much lower values for cats reported by Elliott et al. (1960
) (see Fig. 14) in an early study using shock avoidance conditioning almost certainly reflect the cats use of a very lax criterion in an aversive paradigm in which false alarms were apparently not penalised. The
F values achieved by our cats at 8 kHz (5101850 Hz) are much larger than those achieved by Recanzone et al.s monkeys at that frequency (120328 Hz). However, just as our values are in accord with those of Hienz et al. (1993
), Recanzone et al.s values are in accord with those of other studies of frequency discrimination in non-human primates (Sinnott et al., 1985
, 1987; Prosen et al., 1990
). As discussed by Hienz et al. (1993
), there is clearly a species difference between cats and monkeys in frequency discrimination ability.
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Recanzone et al. (1993) also reported that in one monkey tested with
F in widely spaced sessions during training with +
F, performance on the
F discrimination became worse in the course of training, and presented similar but less complete data in another animal tested with
F at the end of training. In contrast, in the three cats we tested with
F at various times during training,
F discrimination improved in parallel with +
F discrimination, and
F thresholds were consistently better than those with +
F (Fig. 2AC), as has also been reported for humans (Sinnott et al., 1987
). These data are in agreement with the findings in an experiment on human frequency discrimination, in which
F thresholds were obtained before and after extensive training with +
F and showed similar improvements (Irvine et al., 2004
).
It is unclear whether these different patterns of results with respect to generalization are attributable to species differences or to differences in procedures, although it seems unlikely that generalization to other frequencies and from +F to
F discrimination should occur in cats and humans but not in owl monkeys.
Neurophysiological Results
The fact that we found no evidence of the enlarged representation of training frequencies reported by Recanzone et al. (1993) might be a consequence of the use of different species and/or of different stimulus configurations and training paradigms. As noted in the preceding section, one difference between the two species is in the level of frequency discrimination performance of which they are capable. Notwithstanding this difference, the critical fact is that the cats in our study exhibited perceptual learning and achieved asymptotic performance levels, without any change in auditory cortical topography. Another possible difference between cats and monkeys is the extent to which frequency discrimination depends on auditory cortex in the two species. Early evidence concerning the effects of auditory cortical lesions on frequency discrimination was equivocal in both species, the different results reflecting differences in tasks and in the nature of the stimulus sequences employed (for a review see Elliott and Trahiotis, 1972
). It should also be emphasised that all of the lesion data are derived from experiments involving large lesions of most or all of auditory cortex, and none of them bear directly on the role of AI per se. The most recent evidence for monkeys (Harrington et al., 2001
) indicates that such lesions result in a small increase in frequency discrimination thresholds, and thus that normal frequency discrimination depends at least partly on auditory cortex. In the case of cats, the weight of evidence suggests that auditory cortex is probably not necessary for frequency discrimination (Elliott and Trahiotis, 1972
; Heffner and Heffner, 1998
), although it is noteworthy that in the only study that involved a discrimination similar to that used in our experiment (namely detection of S2 after a train of S1 stimuli), cats with large lesions of auditory cortex were unable to relearn the task (Meyer and Woolsey, 1952
). Even if auditory cortex were found not to be necessary for frequency discrimination in cats, if perceptual learning on the task involved an enlarged representation of the training frequencies at lower levels of the lemniscal auditory pathway, this change in topography would be reflected in AI and would have been detected in our experiments. Our data therefore indicate that such learning in cats does not result in such enlarged representations either at AI or at any level of the lemniscal pathway prior to AI.
The possible effects of stimulus factors are indicated not only in the review of the early lesion results by Elliott and Trahiotis (1972) but also by the report of Kilgard et al. (2001
) that the form of plasticity in AI neural response properties produced by pairing of stimuli with basal forebrain stimulation is differentially dependent on stimulus parameters. In our study, cats were presented with a train of 28 tone pulses and were required to detect a change from S1 (8 kHz) to S2 (8 kHz +
F). In the experiment of Recanzone et al. (1993
), monkeys were presented with a train of tone-pulse pairs, and were required to detect a change from S1 (both pulses the same frequency; e.g. 8 kHz) to S2 (the first pulse at that frequency and the second at that frequency +
F). If the difference in the two studies reflects this difference in stimulus parameters, it would indicate that the neural changes observed by Recanzone et al. (1993
) were the consequence of a particular stimulus configuration rather than of improvements in frequency discrimination capacity per se.
Our cortical data differ in two further respects from those reported by Recanzone et al. (1993). As noted above, they reported that in the mapping data for the monkey tested most thoroughly on a
F discrimination there were no locations in AI at which the CF fell in the range of the
F stimuli. In contrast, there was no suggestion that the area of representation of frequencies below 8 kHz in our trained cats was smaller than that in the untrained cats, and the mean areas for the frequency range 6.257.75 kHz did not differ between the two groups. Recanzone et al. (1993
) also reported that multi-neuron clusters in the area of enlarged representation of the training frequencies had sharper frequency tuning (i.e. higher Q10 values) and longer response latencies relative to those of clusters in the areas in which the same frequency ranges were represented in control animals. In contrast to these results we observed a (non-significant) tendency for broader frequency tuning for neurons in two 1 kHz frequency bands immediately above 8 kHz, and significantly shorter response latencies in at least one of these frequency bands.
Our finding that improved frequency discrimination in the trained animals was associated with a tendency for broader frequency tuning (lower Q20 values) in clusters with CF in the 2 kHz band above the training frequency at first sight appears counter-intuitive. If frequency discrimination depended on the activation of different populations of neurons in AI or a subcortical nucleus providing input to AI (i.e. on a simple place coding mechanism), it would be expected that improvement in discrimination would be associated with sharper tuning (resulting in less overlap of the activated populations). However, it is possible that frequency discrimination depends on differences in the distributed pattern of activity in overlapping populations of neurons (for a discussion of the various aspects of overlapping excitation patterns that might contribute to discrimination, see McKay et al., 1999). If this were the case, it might be that an increase in the breadth of tuning of neurons with CF above the discrimination frequency could contribute to differentiation of the patterns of activity evoked by the test and comparison stimuli. It is well established that frequency discrimination thresholds in humans decrease with increasing SPL (e.g. Wier et al., 1977
; Nelson et al., 1983
; Wakefield and Nelson, 1985
; Freyman and Nelson, 1991
). At low frequencies, this might reflect better phase-locking at higher SPLs, but at higher frequencies the improvement would be associated with broadening of the frequency selectivity of most cortical neurons at higher SPLs, and thus greater overlap of excited populations.
Comparison with Studies of Visual Perceptual Learning
Our finding that perceptual learning on an auditory frequency discrimination task was associated with no change in AI topography and subtle or no changes in the response properties of neurons in restricted regions of AI is in accordance with the results of a number of recent studies of changes in V1 associated with perceptual learning on various visual discrimination tasks. Schoups et al. (2001) and Ghose et al. (2002
) trained monkeys on orientation discrimination tasks. In neither case was there any change in retinotopy in V1 (or in V2 in the Ghose et al. study) or in the proportion of units tuned to the training orientation (i.e. in the cortical orientation map). Schoups et al. (2001
) reported that neurons tuned to the training orientation exhibited lower discharge rates than neurons tuned to other orientations, and that there was a significant change in the slope of the orientation tuning curves at the training orientation of neurons with preferred orientations approximately 20° from the training orientation. In the context of our finding of a tendency towards broader frequency tuning, it is of interest that Schoups (2002
) has also described the change in slope of orientation tuning curves as an increase in breadth of tuning. Ghose et al. (2002
) reported no effect of training on the RF properties of neurons in either V1 or V2, but found a small but statistically significant decrease in the population response in V1 to the trained orientation at the trained location, which reflected a slight decrease in the number of neurons responding best to the trained orientation. In a third study, Crist et al. (2001
) trained monkeys on a three-line bisection task. They found no change in the retinotopic organization of V1 or in the RF and orientation tuning properties of neurons in the region of V1 activated by the training stimuli. The only effect of training was a significant change in the extent to which the responses of neurons in this region to stimuli in their RF were modified by contextual stimuli. This effect was observed only for contextual stimuli that were present in the training task, and only when monkeys were performing the bisection task.
In Figure 15, our data are plotted on a modified version of figure 15 of Ghose et al. (2002) to illustrate the relationship between their data on cortical topography and RF changes and those reported by Recanzone et al. (1992
, 1993) in their somatosensory and auditory system studies. Like Ghose et al.s data, our data point lies close to the intersection of the axes corresponding to no change in either topography or RFs, and as noted above, the data points for Schoups et al. (2001
) and Crist et al. (2001
) would also be clustered around this point. Whatever the reasons for the discrepancy between our results and those of Recanzone et al. (1993
), our data are in agreement with these recent studies of visual discrimination learning, in indicating that perceptual learning can be associated with only small changes in the response properties of neurons in primary sensory cortex and with no change in primary cortical topography.
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Notes |
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Correspondence to be sent to Dr M. Brown, Department of Psychology, Monash University, Victoria 3800, Australia. Email: mel.brown{at}med.monash.edu.au.
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References |
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---|
Buonomano DV, Merzenich MM (1998) Cortical plasticity: From synapses to maps. Annu Rev Neurosci 21:149186.[CrossRef][ISI][Medline]
Calford MB, Webster WR, Semple MN (1983) Measurement of frequency selectivity of single neurons in the central auditory pathway. Hear Res 11:395401.[CrossRef][ISI][Medline]
Crist RE, Li W, Gilbert CD (2001) Learning to see: experience and attention in primary visual cortex. Nat Neurosci 4:519525.[ISI][Medline]
Dosher BA, Lu Z-L (1999) Mechanisms of perceptual learning. Vision Res 39: 31973221.[CrossRef][ISI][Medline]
Elliott DN, Trahiotis C (1972) Cortical lesions and auditory discrimination. Psychol Bull 7:198222.
Elliott DN, Stein L, Harrison MJ (1960) Determination of absolute-intensity thresholds and frequency-difference thresholds in cats. J Acoust Soc Am 32:380384.[ISI]
Freyman RL, Nelson DA (1991) Frequency discrimination as a function of signal frequency and level in normally-hearing and hearing-impaired listeners. J Speech Hear Res 34:13711386.[ISI][Medline]
Gescheider GA (1997) Psychophysics: the fundamentals, 3rd edn. Mahwah, NJ: Lawrence Erlbaum.
Ghose GM, Yang T, Maunsell JHR (2002) Physiological correlates of perceptual learning in monkey V1 and V2. J Physiol (Lond) 87:18671888.
Gilbert CD (1998) Adult cortical dynamics. Physiol Rev 78:467485.
Harrington IA, Heffner RS, Heffner, HE (2001) An investigation of sensory deficits underlying the aphasia-like behavior of macaques with auditory cortex lesions. Neuroreports 12:12171221.[CrossRef][ISI][Medline]
Heffner HE, Heffner RS (1998) Hearing. In Comparative psychology, a handbook (Greenberg G, Haraway MM, eds), pp. 290303. New York: Garland.
Hienz RD, Sachs MB, Aleszczyk CM (1993) Frequency discrimination in noise: comparison of cat performances with auditory-nerve models. J Acoust Soc Am 93:462469.[ISI][Medline]
Irvine DRF, Martin RL, Klimkeit E, Smith R (2000) Specificity of perceptual learning in a frequency discrimination task. J Acoust Soc Am 108:29642968.[CrossRef][ISI][Medline]
Irvine DRF, Brown M, Park VN, Martin, RL (2004) Perceptual learning and cortical plasticity. In: Auditory cortex: towards a synthesis of human and animal research (Heil P, König R, Budinger E, eds). Mahwah, NJ: Lawrence Erlbaum (in press).
Kaas JH (2000) The reorganization of sensory and motor maps after injury in adult mammals. In: The new cognitive neurosciences (Gazzaniga MS, ed.), pp. 223236. Cambridge, MA: MIT Press.
Karni, A and Bertini, G (1997) Learning perceptual skills: behavioral probes into adult cortical plasticity. Curr Opin Neurobiol 7:530535.[CrossRef][ISI][Medline]
Karni A, Sagi D (1991) Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proc Natl Acad Sci USA 88:49664970.[Abstract]
Kilgard MP, Pandya PK, Vazquez J, Gehi A, Schreiner CE, Merzenich MM (2001) Sensory input directs spatial and temporal plasticity in primary auditory cortex. J Neurophysiol 86:326338.
McKay CM, OBrien A, James CJ (1999) Effect of current level on electrode discrimination in electrical stimulation. Hear Res 136:159164.[CrossRef][ISI][Medline]
May BJ, Huang AY, Aleszczyk CM, Hienz RD (1995) Design and conduct of sensory experiments for domestic cats. In: Methods of comparative acoustics (Dooling R, Fay R, eds.), pp. 95108. Basel: Birkhauser.
Meyer DR, Woolsey CN (1952) Effects of localized cortical destruction on auditory discriminative conditioning in cat. J Neurophysiol 15:149162.
Mollon JD, Danilova MV (1996) Three remarks on perceptual learning. Spat Vision 10:5158.[ISI][Medline]
Nelson DA, Stanton ME, Freyman RL (1983) A general equation describing frequency discrimination as a function of frequency and sensation level. J Acoust Soc Am 73:21172123.[ISI][Medline]
Prosen CA, Moody DB, Sommers MS, Stebbins WC (1990) Frequency discrimination in the monkey. J Acoust Soc Am 88:21522158.[ISI][Medline]
Rajan R, Irvine DRF, Cassell JF (1991) Normative N1 audiogram data for the barbiturate-anaesthetised domestic cat. Hear Res 53:153158.[CrossRef][ISI][Medline]
Rajan R, Irvine DRF, Wise LZ, Heil P (1993) Effect of unilateral partial cochlear lesions in adult cats on the representation of lesioned and unlesioned cochleas in primary auditory cortex. J Comp Neurol 338:1749.[ISI][Medline]
Ramachandran VS, Braddick O (1973) Orientation-specific learning in stereopsis. Perception 2:371376.[ISI][Medline]
Recanzone GH, Merzenich MM, Jenkins WM, Grajski KA, Dinse HR (1992) Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained in a frequency- discrimination task. J Neurophysiol 67:10311056.
Recanzone GH, Schreiner CE, Merzenich MM (1993) Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J Neurosci 13:87103.[Abstract]
Rice JJ, May BJ, Spirou GA, Young ED (1992) Pinna-based spectral cues for sound localization in cat. Hear Res 58:132152.[CrossRef][ISI][Medline]
Robinson, K and Summerfield, AQ (1996) Adult auditory learning and training. Ear Hear 17:5165.
Schoups A (2002) Electrophysiological correlates of perceptual learning. In: Perceptual learning (Fahle M, Poggio T, eds), pp. 8393. Cambridge, MA: MIT Press.
Schoups A, Vogels R, Qian N, Orban G (2001) Practising orientation identification improves orientation coding in V1 neurons. Nature 412:549553.[CrossRef][ISI][Medline]
Sinnott JM, Owren MJ, Petersen MR (1987) Auditory frequency discrimination in primates: species differences (Cercopithecus, Macaca, Homo). J Comp Psychol 101:126131.[CrossRef][ISI]
Sinnott JM, Petersen MR, Hopp SL (1985) Frequency and intensity discrimination in humans and monkeys. J Acoust Soc Am 78:19771985.[ISI][Medline]
Wakefield GH, Nelson DA (1985) Extension of a temporal model of frequency discrimination: intensity effects in normal and hearing-impaired listeners. J Acoust Soc Am 77:613619.[ISI][Medline]
Wang J, McFadden SL, Caspary D, Salvi R (2002) Gamma-aminobutyric acid circuits shape response properties of auditory cortex neurons. Brain Res 944:219231.[CrossRef][ISI][Medline]
Weinberger NM (1995) Dynamic regulation of receptive fields and maps in the adult sensory cortex. Annu Rev Neurosci 18:129158.[CrossRef][ISI][Medline]
Wier CC, Jesteadt W, Green DM (1977) Frequency discrimination as a function of frequency and sensation level. J Acoust Soc Am 61:178184.[ISI][Medline]