Determinants of cue interactions

Wheeler, Daniel S.; Miller, Ralph R.

doi:10.1016/j.beproc.2008.02.002

Cited by 30 publications

(40 citation statements)

References 60 publications

(94 reference statements)

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“…These models are fundamentally similar both in terms of their formal assumptions and in their psychological implications. The merit of this view is seriously weakened in light of the present simulations and in conjunction with empirical results reviewed by others (e.g., Berridge, 2007, 2012; Urcelay & Miller, 2008; Wheeler & Miller, 2008). The intent of the current analysis was to encourage researchers across levels of analysis to develop and test models that do not subscribe to TER.…”

Section: Discussionmentioning

confidence: 43%

“…However, recent discoveries by animal learning researchers appear to challenge the premises of the TER approach and are consistent with a retrieval-focused, LER model (reviewed by Stout & Miller, 2007; Wheeler & Miller, 2008). The purpose of the second part of this review was to use a computational modelling approach to contrast TER and local error reduction (LER) approaches as applied to some of these new cognitive behavioral findings.…”

Section: The Error In Termentioning

confidence: 98%

“…Recent empirical research in our laboratory revealed a surprising and potentially theoretically challenging group of phenomena called counteraction effects (Wheeler & Miller, 2008). Counteraction effects occur when the administration of two cue interaction treatments results in less cue interaction than when either treatment is administered alone.…”

Section: 5 Simulationmentioning

confidence: 99%

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The error in total error reduction

Witnauer

Urcelay

Miller

2014

Neurobiology of Learning and Memory

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Most models of human and animal learning assume that learning is proportional to the discrepancy between a delivered outcome and the outcome predicted by all cues present during that trial (i.e., total error across a stimulus compound). This total error reduction (TER) view has been implemented in connectionist and artificial neural network models to describe the conditions under which weights between units change. Electrophysiological work has revealed that the activity of dopamine neurons is correlated with the total error signal in models of reward learning. Similar neural mechanisms presumably support fear conditioning, human contingency learning, and other types of learning. Using a computational modelling approach, we compared several TER models of associative learning to an alternative model that rejects the TER assumption in favor of local error reduction (LER), which assumes that learning about each cue is proportional to the discrepancy between the delivered outcome and the outcome predicted by that specific cue on that trial. The LER model provided a better fit to the reviewed data than the TER models. Given the superiority of the LER model with the present data sets, acceptance of TER should be tempered.

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Section: Discussionmentioning

confidence: 43%

Section: The Error In Termentioning

confidence: 98%

Section: 5 Simulationmentioning

confidence: 99%

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The error in total error reduction

Witnauer

Urcelay

Miller

2014

Neurobiology of Learning and Memory

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“…Such revisions to empirical benchmarks should be informed by formal modeling procedures as those procedures are best able to reveal the empirical effects that have the potential to differentiate among models. We have used retrospective revaluation (e.g., Kaufman & Bolles, 1981) and counteraction effects (Wheeler & Miller, 2008) to differentiate among models (e.g., Witnauer & Miller, 2011; Witnauer, Urcelay, & Miller, 2014). However, formal, simulation-based comparisons between models are almost absent from the animal learning literature (e.g., Kutlu & Schmajuk, 2012).…”

Section: Model Comparison and Selectionmentioning

confidence: 99%

Methods of comparing associative models and an application to retrospective revaluation

Witnauer

Hutchings

Miller

2017

Behavioural Processes

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Contemporary theories of associative learning are increasingly complex, which necessitates the use of computational methods to reveal predictions of these models. We argue that comparisons across multiple models in terms of goodness of fit to empirical data from experiments often reveal more about the actual mechanisms of learning and behavior than do simulations of only a single model. Such comparisons are best made when the values of free parameters are discovered through some optimization procedure based on the specific data being fit (e.g., hill climbing), so that the comparisons hinge on the psychological mechanisms assumed by each model rather than being biased by using parameters that differ in quality across models with respect to the data being fit. Statistics like the Bayesian information criterion facilitate comparisons among models that have different numbers of free parameters. These issues are examined using retrospective revaluation data.

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“…Animals and humans quickly learn predictive relationships between sensory inputs and their expected outcomes (Gallistel & Matzel, 2013), and if the relationship between sensory inputs and outcomes becomes predictable, neural activity (Alink, Schwiedrzik, Kohler, Singer, & Muckli, 2010) and learning are significantly reduced (Hogarth, Dickinson, Austin, Brown, & Duka, 2008;Kording & Wolpert, 2004;Orban & Wolpert, 2011;Pearce & Hall, 1980;Vanni-Mercier, Mauguiere, Isnard, & Dreher, 2009). This suggests that although the contiguity of events is important (Wheeler & Miller, 2008), the associative relationship between these events is crucial to learning. More specifically, when the relationship between a cue and an outcome is not predictable, but instead is uncertain, learning is enhanced.…”

Section: Introductionmentioning

confidence: 99%

Stimulus uncertainty enhances long-term potentiation-like plasticity in human motor cortex

Sale

Nydam

Mattingley

2017

Cortex

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Plasticity can be induced in human cortex using paired associative stimulation (PAS), which repeatedly and predictably pairs a peripheral electrical stimulus with transcranial magnetic stimulation (TMS) to the contralateral motor region. Many studies have reported small or inconsistent effects of PAS. Given that uncertain stimuli can promote learning, the predictable nature of the stimulation in conventional PAS paradigms might serve to attenuate plasticity induction. Here, we introduced stimulus uncertainty into the PAS paradigm to investigate if it can boost plasticity induction. Across two experimental sessions, participants (n = 28) received a modified PAS paradigm consisting of a random combination of 90 paired stimuli and 90 unpaired (TMS-only) stimuli. Prior to each of these stimuli, participants also received an auditory cue which either reliably predicted whether the upcoming stimulus was paired or unpaired (no uncertainty condition) or did not predict the upcoming stimulus (maximum uncertainty condition). Motor evoked potentials (MEPs) evoked from abductor pollicis brevis muscle quantified cortical excitability before and after PAS. MEP amplitude increased significantly 15 minutes following PAS in the maximum uncertainty condition. There was no reliable change in MEP amplitude in the no uncertainty condition, nor between post-PAS MEP amplitudes across the two conditions. These results suggest that stimulus uncertainty boost may provide a novel means to enhance plasticity induction with the PAS paradigm in human motor cortex. To provide further support to the notion that stimulus uncertainty and prediction error promote plasticity, future studies should further explore the time course of these changes, and investigate what aspects of stimulus uncertainty are critical in boosting plasticity. Highlights• Plasticity can be induced in human cortex by repeated pairing of stimuli.• This form of induced plasticity mimics behavioral associative learning. M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2• We manipulated stimulus uncertainty, which influences learning and plasticity.• When stimulus uncertainty was high, plasticity was enhanced in human motor cortex.

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Determinants of cue interactions

Cited by 30 publications

References 60 publications

The error in total error reduction

The error in total error reduction

Methods of comparing associative models and an application to retrospective revaluation

Stimulus uncertainty enhances long-term potentiation-like plasticity in human motor cortex

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