How Dopamine Shapes Representations 
in Auditory Cortex
Sebastian Puschmann1
Tina Weis2
Christiane M. Thiel3
Carl von Ossietzky Universität Oldenburg, Germany
Abstract. The neural representation of  sound in the auditory cortex is not invariably predetermined by its 
acoustical properties, but it is constantly reshaped while the listener acquires new experiences. Such plastic 
changes are a prerequisite for lifelong learning and allow some degree of  rehabilitation after brain injuries. Several 
neurotransmitter systems modulate these plastic changes. In this paper, we focus on how the neurotransmitter 
dopamine modulates learning-related plasticity in auditory cortex, and how animal and human research can 
complement each other in providing an experimental approach that has relevance for studying mechanisms of  
recovery of  function.
Keywords. dopamine, auditory, learning, plasticity, neuroimaging.
Actualidades en Psicología, 28(117), 2014, 67-78 ISSN 2215-3535
1 Sebastian Puschmann. Biological Psychology, Department of  Psychology, European Medical School; and Cluster of  Excellence 
“Hearing4all”, Carl-von-Ossietzky Universität Oldenburg, Oldenburg, Germany. E-mail: sebastian.puschmann@uni-oldenburg.de
2 Tina Weis. Cognitive and Developmental Psychology, Technical University Kaiserslautern; and Cluster of  Excellence “Hearing4all”, Carl 
von Ossietzky Universität Oldenburg, Oldenburg, Germany. E-mail: tina.weis@uni-oldenburg.de
3 Christiane M. Thiel, Biological Psychology, Department of  Psychology, European Medical School; Cluster of  Excellence “Hearing4all”; and 
Research Center Neurosensory Science, Carl-von-Ossietzky, Universität Oldenburg, Oldenburg, Germany. Postal addressE-mail: christine.
thiel@uni-oldenburg.de
DOI: http://dx.doi.org/10.15517/ap.v28i117.14137
Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-SinDerivar 4.0 Internacional.
68 Puschmann, Weis and Thiel
Actualidades en Psicología, 28(117), 2014, 67-78
Introduction
Adaptation to a changing environment is a 
prerequisite for lifelong learning and recovery after 
damage to the central nervous system. Over one 
hundred years ago, the psychologist William James 
suggested that “organic matter, especially nervous 
tissue, seems endowed with a very extraordinary degree 
of  plasticity” (James, 1890). Nowadays, neuroscientific 
methods, which range from single cell recordings 
of  receptive fields in animals to the assessment of  
hemodynamic changes by means of  functional imaging 
in humans, allow to investigate the neurobiological 
basis of  such plasticity. In auditory cortex learning-
related changes have been demonstrated in a variety 
of  associative learning paradigms in animals and 
men (for reviews see Schreiner & Polley, 2014; Thiel, 
2007; Weinberger, 2007). In this paper we provide 
an overview of  how the neurotransmitter dopamine 
modulates such learning-related plasticity, and how 
animal and human research can complement each 
other in providing an experimental approach that has 
relevance to studying mechanisms of  recovery and 
treatment effects in patients with injuries.
Learning-related plasticity in auditory cortex: animal 
and human evidence
The neural representation of  sound in the auditory 
cortex is not always predetermined by its acoustical 
properties, but it is constantly reshaped while the 
listener acquires new experiences (Froemke & Jones, 
2011; Pienkowski & Eggermont, 2011). If  a sound 
gains behavioral relevance in the actual environment, 
neuronal receptive fields in auditory cortex can adjust 
rapidly, resulting in increased neuronal responses 
to this specific sound (Bakin, South, & Weinberger, 
1996; Edeline, Pham, & Weinberger, 1993). The fact 
that this re-tuning may improve both the detection 
and the recognition of  relevant stimuli has been 
suggested (Froemke et al., 2013). Such plastic changes 
are a prerequisite for lifelong learning and allow some 
degree of  rehabilitation after brain injuries (Albert & 
Kesselring, 2012; May, 2011). In the auditory system 
neuronal plasticity can be observed in different time 
scales. Professional musicians, who have experienced 
an extensive lifelong auditory training, show profound 
structural changes in auditory sensory areas, affecting 
both the size and the cortical organization (Meyer, 
Elmer, & Jancke, 2012; Schneider et al., 2002). 
Following the insertion of  a cochlear implant, the 
auditory cortex of  a formerly deaf  patient is reshaped 
by the newly available auditory input, resulting in an 
extensive restoration of  sensory abilities only a few 
months after implantation (Fallon, Irvine, & Shepherd, 
2008; Kral & Sharma, 2012; Moore & Shannon, 2009). 
In addition to these progressively developing structural 
changes, the auditory cortex also shows more rapidly 
evolving forms of  functional plasticity, which modulate 
the actual representation of  stimuli in auditory cortex 
(Ohl & Scheich, 2005; Scheich et al., 2011; Spierer et 
al., 2011). Experimental data suggest that the latter type 
of  plasticity may play a role in adjusting the auditory 
system to current needs by facilitating the cortical 
processing of  behavioral relevant stimuli (Bao, Chang, 
Woods, & Merzenich, 2004; Froemke et al., 2013; Liu 
& Schreiner, 2007).
The formation of  changes in auditory cortex activity 
is often observed in associative learning paradigms in 
animals, in which subjects have to learn to relate a 
specific sound to some kind of  reward or punishment 
(Blake, Heiser, Caywood, & Merzenich, 2006; Blake, 
Strata, Churchland, & Merzenich, 2002; Condon & 
Weinberger, 1991; Diamond & Weinberger, 1986; 
Ohl, Scheich, & Freeman, 2001). In such experiments, 
learning the significance of  a sound is frequently 
associated with a re-tuning of  neuronal receptive fields, 
resulting in an increased cortical representation of  the 
relevant stimulus (Bieszczad & Weinberger, 2010; Polley, 
Steinberg, & Merzenich, 2006; Weinberger, 2007). This 
re-tuning can occur rapidly within only a few stimulus 
presentations and has been shown to consolidate 
after the experiment (Edeline et al., 1993; Galvan & 
Weinberger, 2002). Hence, changes in receptive fields 
can persist up to several days in the absence of  further 
training (Weinberger, Javid, & Lepan, 1993). The effect 
can, however, be neutralized rapidly if  the stimulus is 
repeatedly presented in a neutral context; thus, it loses 
its relevance (Diamond & Weinberger, 1986).
How Dopamine Shapes Representations in Auditory Cortex 
Actualidades en Psicología, 28(117), 2014, 67-78
69
Complementing these findings, previous 
neuroimaging work in humans also revealed the rapid 
formation of  learning-related changes in auditory cortex 
activity (Kluge et al., 2011; Morris, Friston, & Dolan, 
1998; Thiel, Bentley, & Dolan, 2002; Thiel, Friston, & 
Dolan, 2002; van Wassenhove & Nagarajan, 2007). In 
line with animal data that show increased representations 
of  the behaviorally relevant sound in auditory cortex, 
functional magnetic resonance imaging (fMRI) data in 
humans show increased BOLD responses to sounds, 
which have been paired with an electric shock to the foot 
in a classical conditioning experiment (Thiel, Bentley, 
et al., 2002; Thiel, Friston, et al., 2002). On the other 
hand, some studies have reported decreased auditory 
cortex activity following stimulus discrimination training 
(Brechmann & Scheich, 2005; Jancke, Gaab, Wustenberg, 
Scheich, & Heinze, 2001). Although the majority of  
animal studies report increased responses, there are a 
few experiments also showing decreased responses to 
relevant sounds after learning (Ohl & Scheich, 1996, 
1997). It has been suggested that these discrepancies 
reflect the use of  experimental paradigms with differing 
complexity, requiring different cortical representations 
of  the relevant sound (Scheich et al., 2011).
The cholinergic system and the learning-related auditory 
cortex plasticity
Research in both animals and humans has provided 
compelling evidence suggesting that the cholinergic 
neurotransmitter system plays a crucial role in promoting 
the formation of  learning-related changes in cortical 
representation of  sounds (Weinberger, 2004). Pairing a 
tone presentation with direct electrical stimulation of  the 
nucleus basalis, a region containing high concentrations 
of  cholinergic projection neurons, induces changes in 
auditory cortex receptive fields, which result in increased 
neuronal responses to the paired stimulus (Bakin & 
Weinberger, 1996; Kilgard & Merzenich, 1998; Kilgard, 
Vazquez, Engineer, & Pandya, 2007). This effect can 
be abolished by administering atropine, a cholinergic 
antagonist which blocks muscarinic acetylcholine 
receptors (Miasnikov, McLin, & Weinberger, 2001). In 
humans, the cholinergic modulation in auditory cortex 
has been investigated by Thiel and colleagues (2002a; 
2002b) with pharmacological fMRI in an aversive classical 
conditioning experiment in which one of  two different 
pure tones was repeatedly paired with an electric shock to 
the foot. In a placebo condition, this procedure resulted 
in enhanced auditory cortex responses to the conditioned 
stimulus, the so called CS+, whereas responses to the 
neutral stimulus, the so called CS-, which was never paired 
with the shock, were not affected. However, if  participants 
received scopolamine, a muscarinic antagonist, before the 
experiment, no conditioning-related changes in BOLD 
responses could be observed (Thiel et al., 2002b). This 
suggests that blockade of  cholinergic neurotransmission 
reduces learning-related plasticity in human auditory 
cortex, which would be in line with the animal findings. 
Similarly, the administration of  physostigmine, a 
cholinesterase inhibitor that enhances cholinergic activity, 
before the conditioning phase resulted in no differences 
in BOLD signal between the conditioned and the 
neutral tone (Thiel et al., 2002a). Under physostigmine, 
however, responses to both stimuli were enhanced after 
conditioning as compared to the pre-conditioning phase, 
indicating that boosting the cholinergic system results 
in changes not only for conditioned but also for neutral 
sounds (Thiel, 2007).
The dopaminergic system and learning-related auditory 
cortex plasticity
Even though the majority of  pharmacological 
approaches focused on the cholinergic system, there is 
also some evidence indicating that noradrenaline and 
dopamine may affect the development of  functional 
changes in auditory cortex. Manunta and Edeline 
(1997, 1998, 1999) demonstrated that the application 
of  noradrenaline can lead to a decrease of  both evoked 
and spontaneous activity of  auditory cortex neurons. 
Furthermore, pairing a tone with the administration of  
noradrenaline changes neuronal frequency tuning curves 
in auditory cortex resulting in decreased responses 
to the paired stimulus (Manunta & Edeline, 2004). 
First evidence indicating a dopaminergic influence on 
learning-dependent plasticity came from a study by Stark 
and Scheich (1997), who used in vivo microdialysis in 
gerbils to study dopaminergic activity in auditory cortex 
during electric shock avoidance training. During the 
70 Puschmann, Weis and Thiel
Actualidades en Psicología, 28(117), 2014, 67-78
experiment animals learned to avoid an electric shock to 
the foot by changing the compartment of  a shuttle box 
in response to an auditory target stimulus. Their data 
showed an increased concentration of  homovanillic 
acid, a metabolite of  dopamine, during initial learning 
but not during later re-training phases. Based on this 
observation, the authors suggested that dopamine may 
be important for the initial formation of  an association 
between a tone and a behavioral response, but not 
during later re-training of  this association. Moreover, 
several studies show that pairing a tone with a direct 
electric microstimulation of  the ventral tegmental area, 
a midbrain region containing a high concentration of  
dopaminergic projection neurons, results in an increased 
spatial representation of  this stimulus in primary 
auditory cortex (Bao, Chan, & Merzenich, 2001; Hui 
et al., 2009; Kisley & Gerstein, 2001). No changes in 
the representation of  the paired stimulus were observed 
when dopaminergic D1 and D2 receptor antagonists 
were administered to the animals before the initiation of  
the pairing procedure (Bao et al., 2001).
Pharmacological functional magnetic resonance imaging 
(fMRI) studies in humans investigating the role of  
dopamine in learning-related auditory plasticity
In order to investigate whether the dopaminergic 
neurotransmitter system modulates learning-related 
plasticity in human auditory cortex, we performed 
two fMRI studies using an auditory operant appetitive 
conditioning paradigm since it is known that dopamine 
plays a major role in reward learning (Puschmann, 
Brechmann, & Thiel, 2013; Weis, Puschmann, 
Brechmann, & Thiel, 2012). In the paradigm, 
participants had to learn to associate a specific category 
of  auditory input with the chance to gain a monetary 
reward in a subsequent reaction time task. Previous 
work using such tasks, in which a reward was associated 
with a visual or auditory cue, found increased neural 
activity in dopaminergic brain areas not only during 
the reward delivery but also during reward anticipation, 
when the reward-predicting stimulus was presented 
(Knutson, Fong, Adams, Varner, & Hommer, 2001; 
Schultz, Dayan, & Montague, 1997; Wittmann et al., 
2005). Several animal studies investigating dopaminergic 
midbrain activity during associative learning tasks 
showed that before the animals learned a given stimulus-
reward association, dopaminergic neurons responded 
during reward delivery (Ljungberg, Apicella, & Schultz, 
1992; Schultz, Apicella, & Ljungberg, 1993). After 
learning, however, dopaminergic activity was observed 
in response to the reward-predicting stimulus but no 
longer during reward delivery. Based on these findings, 
we hypothesized that in our experiment, learning the 
relevance of  the auditory cue should result in increased 
dopaminergic responses to this cue, which might then, 
in turn, lead to learning-induced changes in the auditory 
cortex representation of  this stimulus.
The operant conditioning paradigm is depicted in 
Figure 1. In each trial, participants had to indicate via 
key press whether a number presented on a screen was 
larger or smaller than five. In half  of  the trials (CS+ 
trials), fast and accurate responses resulted in a reward 
of  50 Euro cent, whereas wrong or slow responses 
led to neutral feedback. In the other half  (CS- trials), 
however, participants always received neutral feedback, 
independent of  their response. To indicate which trials 
were potentially rewarded, a frequency modulated (FM) 
tone was presented at the beginning of  each trial. The 
FM tones differed in several sound features, including 
frequency range, loudness, modulation rate, modulation 
direction, and duration. Participants were instructed that 
a specific class of  sounds predicted a reward chance in 
the upcoming trial, but they had to learn the relationship 
between tone and reward by trial and error during fMRI 
measurements. We used the sound duration as the 
reward-predicting cue. For half  of  the subjects, long 
FM tones (800 ms) indicated a reward chance; for the 
other half, short FM tones (400 ms) were associated 
with the potential monetary reward in the reaction time 
task.  After each tone, participants had to  state  their 
reward expectation for the upcoming trial via a key 
press, allowing us to gauge their individual learning 
progress. To slow down the progress of  learning during 
the experiment, the reaction time threshold leading to a 
reward in the reaction time task was adjusted individually 
so that participants received only about 80% of  the 
potential reward. Consequently, most participants did not 
identify the correct association between tone and reward 
How Dopamine Shapes Representations in Auditory Cortex 
Actualidades en Psicología, 28(117), 2014, 67-78
71
instantaneously at the beginning of  the experiment, but 
only after an initial phase of  trial and error.
To investigate if  learning-related plasticity occurs 
in appetitive conditioning paradigms in humans, we 
analyzed BOLD activity during reward anticipation and 
compared the signal to FM tones in CS+ trials with 
BOLD activity to FM tones in CS- trials. This differential 
activity was analyzed separately for participants who 
learnt the correct stimulus-reward association and in 
non-learners, who did not show any learning behavior 
in the course of  the experiment. We were particularly 
interested in whether learning-induced changes occurred 
exclusively in learners, and whether such changes in 
auditory cortex activity are paralleled by similar effects 
in parts of  the dopaminergic system, which would 
provide a first indication for a dopaminergic influence 
on learning-related plasticity in human auditory cortex.
We studied thirty-nine participants with the above 
task in an fMRI setting (Puschmann et al., 2013). Sixteen 
participants learned the correct association between the 
presented FM tones and the chance to gain a monetary 
reward. On the average, the learning performance of  
this group was at chance level during the first quarter of  
the experiment and reached ceiling level (i.e., over 90% 
of  FM tones were assigned correctly) in the last quarter 
of  the experiment (see Figure 2). Comparing reaction 
times between the unlearned (i.e., the first quarter) and 
learned (i.e., fourth quarter) phases of  the experiment, 
we observed a significant decrease in reaction times in 
potentially rewarded trials after learning. At the same 
time, this group showed a significant learning-dependent 
difference in BOLD responses to the reward-predicting 
(CS+) and neutral sounds (CS-) in the left auditory cortex 
(see Figure 3A). At the end of  the experiment, BOLD 
responses to reward-predicting stimuli were significantly 
increased as compared to neutral tones, demonstrating 
learning-dependent changes. In contrast, no differences 
between categories were observed at the beginning of  
the experiment. Our functional imaging data also showed 
a learning-related difference in BOLD responses in large 
parts of  the dopaminergic neurotransmitter system, in 
particular within the dopaminergic midbrain (ventral 
tegmental area/substantia nigra) and the nucleus 
accumbens (see Figure 3B and C). No significant 
changes, either on the behavioral level or regarding 
BOLD responses, were observed in the group of  non-
learners (n=9), who showed no learning progress and 
stayed at chance level during all parts of  the experiment. 
A third group of  subjects (n=10), who showed some 
Figure 1. Appetitive operant conditioning paradigm: Each trial started with an FM tone. Participants were instructed that a specific 
category of  tones (CS+) predicted a reward chance in the upcoming reaction time task and had to learn the correct categorization 
scheme by trial and error. To gauge the participants’ learning progress they had to indicate after each tone whether they expected a 
reward in the upcoming trial or not. Subsequently, participants had to indicate whether a number presented on a screen was smaller 
or larger than five. In CS+ trials, fast and correct responses were rewarded with fifty Euro cent. Slow or false responses resulted in a 
neutral feedback. In the other half  of  trials (CS-), responses always led to neutral feedback. Figure from (Weis et al., 2012).
72 Puschmann, Weis and Thiel
Actualidades en Psicología, 28(117), 2014, 67-78
learning behavior but did not reach a high categorization 
performance, was not analyzed in the experiment.
While similar effects in auditory cortex and 
dopaminergic midbrain regions suggest that dopamine 
may contribute to learning-related plasticity, causality can 
only be demonstrated with pharmacological approaches. 
We, therefore, performed a second study where we 
combined fMRI with a pharmacological challenge (Weis 
et al., 2012). The dopaminergic precursor L-dopa (100 
mg; n=27) or placebo (n=28) were administered to 
human volunteers before they performed the appetitive 
operant conditioning paradigm described above. 
Behaviorally, we found no difference in learning curves 
under L-dopa and placebo suggesting that dopamine 
did not affect learning the stimulus-reward association. 
Dopaminergic stimulation had however an impact on 
the speed of  responding, especially in unrewarded trials, 
which were significantly slowed down over the course of  
the experiment. Differences in BOLD activity between 
reward-predicting (CS+) and neutral sounds (CS-) 
were found in the nucleus accumbens, dopaminergic 
midbrain regions, and left insula (see Figure 4A). In 
contrast to our expectations and results of  the first 
study, we found no evidence for learning-related 
changes, i.e. higher responses to CS+ as compared to 
CS- sounds, in auditory cortex which may have been due 
to the faster learning in this second study (see Weis et al 
2012 for further discussion). Dopaminergic stimulation, 
as compared to placebo, increased BOLD activity in 
left auditory cortex; Broca’s area and anterior cingulate 
cortex (see Figure 4B). Note that this increase occurred 
for both reward predicting (CS+) and neutral (CS-) FM 
tones. The left sided increase in neural activity is in line 
with findings of  Brechmann and Scheich (2005) who 
showed an involvement of  the left auditory cortex, 
when participants had to categorize FM tones according 
to their duration. Thus, the dopaminergic modulation of  
activity in auditory cortex occurred in a region involved 
Figure 2. Learning curves in the appetitive operant conditioning paradigm. Data is derived from the volunteers’ indication in the 
reward anticipation phase. Dotted line indicates the lower border of  above-chance-performance. Learners were defined as those 
participants, showing a clear increase in the percentage of  correct responses over time and reaching a stable plateau of  at least 90 % 
correct responses within the first 6 time bins. Non-learners were defined as those participants never reaching at least a level of  66.4 % 
correct responses. Figure from (Puschmann et al., 2013) with permission of  Wiley Periodicals. © 2012 Wiley Periodicals, Inc.
How Dopamine Shapes Representations in Auditory Cortex 
Actualidades en Psicología, 28(117), 2014, 67-78
73
in categorizing the specific reward-predicting feature (i.e. 
duration) of  the auditory stimuli. Furthermore, neural 
activity in this brain region correlated with L-dopa 
plasma levels and learning rate. Hence, dopaminergic 
stimulation may be beneficial to increase neural activity 
in auditory cortex in a stimulus-unspecific way. Note 
that no effects of  dopaminergic stimulation were found 
at the time point where the reward was delivered, even 
though a reactivation of  auditory cortex was present at 
this time (Weis, Brechmann, Puschmann, & Thiel, 2013).
Clinical Relevance
Understanding the mechanisms of  neuroplasticity 
is of  clinical relevance for the recovery of  sensory 
and motor function. Previous studies in humans 
already indicated that administration of  the dopamine 
precursor L-dopa facilitates novel word learning, 
improves motor cortex plasticity in healthy human 
subjects and motor recovery after stroke (Knecht et 
al., 2004; Monte-Silva, Liebetanz, Grundey, Paulus, 
Figure 3. BOLD activity during the reward anticipation phase. Left Side: Regions showing a conditioning by time interaction, i.e. 
increases to the reward-predicting CS+ as compared to the neutral CS- FM tone over the course of  the experiment. Right side: 
Average time-courses of  the signal within the brain regions in the learner and non-learner group. Figure from (Puschmann et 
al., 2013) with permission of  Wiley Periodicals. © 2012 Wiley Periodicals, Inc.
74 Puschmann, Weis and Thiel
Actualidades en Psicología, 28(117), 2014, 67-78
& Nitsche, 2010; Scheidtmann, 2004). With respect 
to auditory rehabilitation, data in a few subjects who 
received a cochlear implant and amphetamine together 
with aural rehabilitation therapy showed increased 
speech tracking skills and auditory cortex activity in 
these amphetamine treated subjects (Tobey et al., 
2005). Given our own human data (Puschmann et 
al., 2013; Weis et al., 2012) and the available animal 
evidence (Stark and Scheich 1997, Bao et al. 2001), 
future clinical studies should further investigate the role 
of  dopaminergic stimulation in auditory rehabilitation.
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Received: March 4th, 2014
Accepted: August 20th, 2014