Feedback that confirms reward expectation triggers auditory cortex activity

Weis, Tina; Brechmann, André; Puschmann, Sebastian; Thiel, Christiane M.

doi:10.1152/jn.00128.2013

Cited by 25 publications

(24 citation statements)

References 32 publications

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“…Notably, the sensory basis network also learns based on the back-propagated reward prediction error. This is in line with experimental findings indicating that early auditory cortical areas are activated in response to dopaminergic reward signals (Puschmann et al, 2012) and show learning-dependent plastic changes (Weis et al, 2013). …”

Section: Discussionsupporting

confidence: 91%

“…We would expect representations of stimuli that belong to the same task category, i.e., that differ only along dimensions that are non-discriminative with respect to the task, to show much higher correlations among each other than with representations of stimuli that belong to a different task category. While this hypothesis still requires further experimental work, there are indications that task-dependent representations exist in sensory cortices (Ohl et al, 2001; Weis et al, 2013). …”

Section: Discussionmentioning

confidence: 99%

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Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

et al. 2016

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Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden deviations from the monotonicity of its evolution trigger a reset after which another expert subnetwork can be activated—if it has already been established before—or new categories can be recruited and associated with novel behavioral patterns.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

et al. 2016

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“…In conjunction with the presentation of the line graph, a happy or sad sound played if the difficulty level moved up or down, respectively. This feature was included to increase the interactivity of the system and increase subjects engagement with the task, as previous research has shown that engaging the auditory system through reward processing can increase task performance (Weis et al, 2013a,b). …”

Section: Methodsmentioning

confidence: 99%

Combined Action Observation and Motor Imagery Neurofeedback for Modulation of Brain Activity

Friesen

Bardouille

Neyedli

et al. 2017

Front. Hum. Neurosci.

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Motor imagery (MI) and action observation have proven to be efficacious adjuncts to traditional physiotherapy for enhancing motor recovery following stroke. Recently, researchers have used a combined approach called imagined imitation (II), where an individual watches a motor task being performed, while simultaneously imagining they are performing the movement. While neurofeedback (NFB) has been used extensively with MI to improve patients' ability to modulate sensorimotor activity and enhance motor recovery, the effectiveness of using NFB with II to modulate brain activity is unknown. This project tested the ability of participants to modulate sensorimotor activity during electroencephalography-based II-NFB of a complex, multi-part unilateral handshake, and whether this ability transferred to a subsequent bout of MI. Moreover, given the goal of translating findings from NFB research into practical applications, such as rehabilitation, the II-NFB system was designed with several user interface and user experience features, in an attempt to both drive user engagement and match the level of challenge to the abilities of the subjects. In particular, at easy difficulty levels the II-NFB system incentivized contralateral sensorimotor up-regulation (via event related desynchronization of the mu rhythm), while at higher difficulty levels the II-NFB system incentivized sensorimotor lateralization (i.e., both contralateral up-regulation and ipsilateral down-regulation). Thirty-two subjects, receiving real or sham NFB attended four sessions where they engaged in II-NFB training and subsequent MI. Results showed the NFB group demonstrated more bilateral sensorimotor activity during sessions 2–4 during II-NFB and subsequent MI, indicating mixed success for the implementation of this particular II-NFB system. Here we discuss our findings in the context of the design features included in the II-NFB system, highlighting limitations that should be considered in future designs.

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“…Sensory plasticity is however only observed if a cognitive association is formed between the reinforcer and the sensory stimulus (Blake et al, 2006; Puschmann et al, 2013). Recently, several studies in humans have shown that even rewarding outcomes which follow the sensory stimuli activate respective sensory cortices in the absence of the respective sensory input (Pleger et al, 2008, 2009; Weil et al, 2010; Fitzgerald et al, 2013; Weis et al, 2013). In the studies by Pleger et al (2008, 2009) participants had to discriminate somatosensory stimuli applied to an index finger and received a visually presented monetary reward for correct performance.…”

Section: Introductionmentioning

confidence: 99%

Positive and negative reinforcement activate human auditory cortex

Weis¹,

Puschmann²,

Brechmann³

et al. 2013

Front. Hum. Neurosci.

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Prior studies suggest that reward modulates neural activity in sensory cortices, but less is known about punishment. We used functional magnetic resonance imaging and an auditory discrimination task, where participants had to judge the duration of frequency modulated tones. In one session correct performance resulted in financial gains at the end of the trial, in a second session incorrect performance resulted in financial loss. Incorrect performance in the rewarded as well as correct performance in the punishment condition resulted in a neutral outcome. The size of gains and losses was either low or high (10 or 50 Euro cent) depending on the direction of frequency modulation. We analyzed neural activity at the end of the trial, during reinforcement, and found increased neural activity in auditory cortex when gaining a financial reward as compared to gaining no reward and when avoiding financial loss as compared to receiving a financial loss. This was independent on the size of gains and losses. A similar pattern of neural activity for both gaining a reward and avoiding a loss was also seen in right middle temporal gyrus, bilateral insula and pre-supplemental motor area, here however neural activity was lower after correct responses compared to incorrect responses. To summarize, this study shows that the activation of sensory cortices, as previously shown for gaining a reward is also seen during avoiding a loss.

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Feedback that confirms reward expectation triggers auditory cortex activity

Cited by 25 publications

References 32 publications

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

Combined Action Observation and Motor Imagery Neurofeedback for Modulation of Brain Activity

Positive and negative reinforcement activate human auditory cortex

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