Carrot and stick 2.0: The benefits of natural and motivational prosody in computer-assisted learning

Wolff, Susann; Brechmann, André

doi:10.1016/j.chb.2014.10.015

Cited by 18 publications

(7 citation statements)

References 58 publications

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“…As feedback stimuli, four positive utterances (ja, “yes”; richtig, “right”; ja, richtig, “yes, right”; stimmt, “correct”) and four negative utterances (nein, “no”; falsch, “wrong”; nein, falsch, “no, wrong”; stimmt nicht, “not correct”) as well as one time-out utterance (zu spät, “too late”) were employed, all spoken in standard German and with a motivational intonation taken from the evaluated prosodic corpus MOTI (Wolff and Brechmann, 2012; see also Wolff and Brechmann, 2015). After 80 trials, a pause of 20 s was introduced and from the next trial on the contingencies were reversed such that the target stimulus required a push of the right instead of the left button.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

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

et al. 2016

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“…As feedback stimuli, we used naturally spoken utterances (e.g., ja, “yes”; nein, “no”) as well as one time-out utterance (zu spät, “too late”) taken from the evaluated prosodic corpus MOTI (Wolff and Brechmann, 2012 , 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, understanding the process leading to an outcome is more important than perfectly fitting a model to a given set of experimental results. Our goal in this regard is to understand the processes underlying human decision-making, not least to aid humans in becoming better at decision-making (Wolff and Brechmann, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Prezenski¹,

Brechmann

Wolff

et al. 2017

Front. Psychol.

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Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

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“…According to Mory 27 (see also Kulhavy and Wager's 16 "feedback triad"), it is essential to separate the function of feedback as a motivator or reinforcer from its function as a unit of information (cf. 28 ). Thus, while feedback may have a rewarding or motivating effect, its main function from an information-processing point of view is providing the user with an opportunity to correct errors, not to administer reward or punishment.…”

Section: Susann Wolff Christin Kohrs Nicole Angenstein and André Brecmentioning

confidence: 99%

Dorsal posterior cingulate cortex encodes the informational value of feedback in human–computer interaction

Wolff

Kohrs

Angenstein

et al. 2020

Sci Rep

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Behavioural results. Table 1 shows the descriptive statistics (means and standard errors) for accuracy rates and reaction times for each of the six experimental conditions. Visual inspection of Q-Q plots, skewness (all |S| < 0.95 , all p > 0.05), kurtosis (all |K| < 1.31 , all p > 0.05), and Shapiro-Wilk tests (all W > 0.91 , all p > 0.05) showed no significant deviations from normal distribution. Neither of the repeated-measures ANOVAs revealed significant main effects or interaction effects of informational content and modality (all F < 3.79 , all p > 0.05).

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Carrot and stick 2.0: The benefits of natural and motivational prosody in computer-assisted learning

Cited by 18 publications

References 58 publications

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

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

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Dorsal posterior cingulate cortex encodes the informational value of feedback in human–computer interaction

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