Successive discrimination reversal performance as a function of level of drive and incentive

Feldman, Jerome M.

doi:10.3758/bf03342516

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…This increased efficiency of performance in serial reversal tasks cannot be explained by learning mechanisms which are solely based on the initially formed associations of complex stimulus features to categorical responses, since this leads to decreased efficiency due to interference effects from the previously learned contingencies (Pubols, 1957; Clayton, 1962; Gossette and Inman, 1966; Feldman, 1968; Gossette and Hood, 1968; Kulig and Calhoun, 1972; Garner et al, 1996; Bathellier et al, 2013; Kangas and Bergman, 2014). …”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Hence, when the reversal occurred earlier the subjects had received relatively few reinforcers, were hungrier and perhaps more resistant to extinction; conversely, when the reversal occurred later, they had received more reinforcers, were less hungry and perhaps less resistant to extinction. However, studies of the effect of satiation on reversal learning have repeatedly found that performance seems unrelated to drive level (Bruner et al, 1958; Feldman, 1968; Meyer, 1951).…”

Section: Discussionmentioning

confidence: 99%

Constantly timing, but not always controlled by time: Evidence from the midsession reversal task.

Santos¹,

Vasconcelos²,

Machado³

2021

Journal of Experimental Psychology: Animal Learning and Cogniti

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We used a midsession reversal task to investigate how temporal and situational cues may combine to determine choice in frequently changing environments. Pigeons learned a simultaneous discrimination with 2 stimuli: S1 and S2. Choices of S1 were reinforced only during the first trials, and choices of S2 were reinforced only during the last trials of the session, that is, the reinforcement contingencies reversed once during the session. To weaken the temporal cue (time into the session) that signaled the reversal trial, we varied the location of reversal trial randomly across sessions; to weaken the situational cue (the outcome of the previous trials that might support a win-stay/loose shift strategy), we varied the payoff probabilities associated with S1 and S2. Performance was consistent neither with the exclusive use of a timing strategy, nor with the exclusive use of a situational, win-stay/lose-shift strategy. Instead, choice seemed to be under joint control of both cues. The relative influence of these cues was dynamic: When payoff was higher for S1 than S2, behavior was less time-controlled than when the payoff was higher for S2 that S1, or when they were equal. We advance a descriptive mixture model of joint control for the midsession reversal task.

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Neurobiological Fundamentals of Strategy Change: A Core Competence of Companion-Systems

Schulz

Woldeit

Brosch

et al. 2017

Cognitive Technologies

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Successive discrimination reversal performance as a function of level of drive and incentive

Cited by 7 publications

References 17 publications

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

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

Constantly timing, but not always controlled by time: Evidence from the midsession reversal task.

Neurobiological Fundamentals of Strategy Change: A Core Competence of Companion-Systems

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