Mechanisms underlying dependencies of performance on stimulus history in a two-alternative forced-choice task

Cho, Raymond Y.; Nystrom, Leigh E.; Brown, Eric; Jones, A. D.; Braver, Todd S.; Holmes, Philip; Cohen, Jonathan D.

doi:10.3758/cabn.2.4.283

Cited by 128 publications

(269 citation statements)

References 31 publications

(92 reference statements)

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“…Numerous studies have investigated the computational principles that underlie the ability to perform clustering and categorization (11,(46)(47)(48)(49), and the value of stimulus order has also been observed in many experimental contexts (1,4,6,8,19,29,30,43,50). However, a systematic investigation of how stimulus order interacts with the learning of latent clusters is only beginning to emerge in the field.…”

Section: Discussionmentioning

confidence: 99%

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Learning bundles of stimuli renders stimulus order as a cue, not a confound

Qian

Aslin

2014

Proc. Natl. Acad. Sci. U.S.A.

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The order in which stimuli are presented in an experiment has long been recognized to influence behavior. Previous accounts have often attributed the effect of stimulus order to the mechanisms with which people process information. We propose that stimulus order influences cognition because it is an important cue for learning the underlying structure of a task environment. In particular, stimulus order can be used to infer a "stimulus bundle"-a sequence of consecutive stimuli that share the same underlying latent cluster. We describe a clustering model that successfully explains the perception of streak shooting in basketball games, along with two other cognitive phenomena, as the outcome of finding the statistically optimal bundle representation. We argue that the perspective of viewing stimulus order as a cue may hold the key to explaining behaviors that seemingly deviate from normative theories of cognition and that in task domains where the assumption of stimulus bundles is intuitively appropriate, it can improve the explanatory power of existing models.H uman cognition typically takes place in an environment where information becomes available over time. The sequential nature of information availability implies that stimulus order-the order in which a sequence of stimuli is presentedmay strongly influence the interpretation of a task environment. Consider the weather patterns in Fig. 1 as an example. If we were to record the dominant weather phenomenon in each month of a year, order 1, as shown in Fig. 1, would likely represent a typical city in upstate New York, such as Rochester. Given this sequence of stimuli, it is relatively easy for an observer to infer the existence of "seasons" underlying the weather pattern-that is, winter seems to be in effect from November to April, followed by spring/summer, and then by fall. However, if we reorder this set of stimuli from order 1 to order 2 as shown in Fig. 1, the inference of seasons becomes much less apparent. Even when the exact same set of stimuli is presented in both sequences, the difference in stimulus order has led to a dramatically different interpretation about its underlying structure. Why does stimulus order affect human cognition in this interesting way?The effect of stimulus order on cognition has often been viewed as a consequence of the way that the human mind processes information (1-8). For example, Elliott and Anderson (5) accounted for the effect of stimulus order on category learning by incorporating the mechanisms of memory decay into a categorization model. Memory decay intuitively captures the assumption that the human mind tends to be influenced by nearby stimuli more than by distant ones. It has also been hypothesized that when a sequence of stimuli is ordered in such a way that the similarity between successive stimuli is maximized, it offers a processing benefit because the stimuli being kept in working memory will be similar to each other and thus allow for easier abstraction of concepts (4). In addition, the effects of stimulus ord...

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Section: Discussionmentioning

confidence: 99%

“…The HGP mixture model builds upon many previous ideas in both cognitive science and computer science (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Detailed Fig.…”

Section: Inferring Stimulus Bundles In the Inputmentioning

confidence: 99%

Learning bundles of stimuli renders stimulus order as a cue, not a confound

Qian

Aslin

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…This problem has been explored in the context of motor control (e.g., Körding, Beierholm, et al, 2007) and reinforcement learning (e.g., Cho et al, 2002;Gallistel et al, 2001), where it is typically cast as a problem of detecting changes in the statistics of the local environment (change detection). Our…”

Section: Resultsmentioning

confidence: 99%

“…Even looking beyond speech perception into the burgeoning literature on learning in the face of (latently) non-stationary statistics (e.g., Cho et al, 2002;Gallistel et al, 2001), most existing models cannot account for some of the basic properties of speech perception discussed here. 21 We return to this point below, as it motivates the proposal we lay out in this second part of the article.…”

Section: Recognize the Familiarmentioning

confidence: 99%

Perception in a Variable but Structured World: The Case of Speech Perception

Kleinschmidt¹

2017

Preprint

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Perceptual systems have to make sense out of a world that is not only noisy and ambiguous, but that also varies from situation to situation. Human speech perception is a perceptual domain where this problem has long been acknowledged: individual talkers vary substantially in how they produce linguistic units using acoustic cues. Yet, how the speech system solves this problem of talker variability remains poorly understood. This thesis presents a computational framework---the ideal adapter---for understanding this problem and how the speech perception system solves it. The basic insight of this framework is that variability in speech is not arbitrary but rather structured: talkers are reasonably consistent in the way they produce cues, and individual talkers tend to cluster into groups by gender, regional background, etc. This structure means that listeners can use their previous experience with other talkers to guide perception of unfamiliar talkers, as well as familiar talkers that they encounter again. This framework unifies a large and messy literature on how listeners cope with talker variability, leads to quantitative models that provide good fits to human behavior in a variety of situations, and makes specific, testable predictions that open up new frontiers in understanding speech perception. This framework also applies to perception in general, and highlights how speech perception can serve as a model organism for understanding how perceptual systems cope with a variable but structured world.

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“…Hence, a dose of nicotine can potentiate the phasic DA response to rewarding stimuli and evokes such a signal by itself [29,31,32] . The phasic DA in turn instructs the learning in the action-selection machinery [33,34] identified with dorsal nigro-striatal-cortical loops [35,36] . This plasticity is governed in the model by a Hebbian learning rule that is gated by the tonic DA.…”

Section: Large-scale Neurodynamical Framework For Nicotine Action In mentioning

confidence: 99%

Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms

Graupner

Gutkin

2009

Acta Pharmacol Sin

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Neuromodulator action has received increasing attention in theoretical neuroscience. Yet models involving both neuronal populations dynamics at the circuit level and detailed receptor properties are only now being developed. Here we review recent computational approaches to neuromodulation, focusing specifically on acetylcholine (ACh) and nicotine. We discuss illustrative examples of models ranging from functional top-down to neurodynamical bottom-up. In the top-down approach, a computational theory views ACh as encoding the uncertainty expected in an environment. A different line of models accounts for neural population dynamics treating ACh as toggling neuronal networks between read-in of information and recall of memory. Building on the neurodynamics idea we discuss two models of nicotine's action with increasing degree of biological realism. Both consider explicitly receptor-level mechanisms but with different scales of detail. The first is a large-scale model of nicotine-dependent modulation of dopaminergic signaling that is capable of simulating nicotine self-administration. The second is a novel approach where circuit-level neurodynamics of the ventral tegmental area (VTA) are combined with explicit models of the dynamics of specific nicotinic ACh receptor subtypes. We show how the model is constructed based on local anatomy, electrophysiology and receptor properties and provide an illustration of its potential. In particular, we show how the model can shed light on the specific mechanisms by which nicotine controls dopaminergic neurotransmission in the VTA. This model serves us to conclude that detailed accounts for neuromodulator action at the basis of behavioral and cognitive models are crucial to understand how neuromodulators mediate their functional properties.

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Mechanisms underlying dependencies of performance on stimulus history in a two-alternative forced-choice task

Cited by 128 publications

References 31 publications

Learning bundles of stimuli renders stimulus order as a cue, not a confound

Learning bundles of stimuli renders stimulus order as a cue, not a confound

Perception in a Variable but Structured World: The Case of Speech Perception

Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms

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