Integrating neuronal coding into cognitive models: Predicting reaction time distributions

Oram, Mike W.

doi:10.1080/09548980500445039

Cited by 6 publications

(3 citation statements)

References 123 publications

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“…Previous investigations fitted accumulator models to behavior and then compared the predicted trajectories with neural activity (Boucher et al, 2007;Ratcliff et al, 2003Ratcliff et al, , 2007, but model input (drift rate) was defined by free parameters. Other investigations used accumulated spike rates to predict RT but did not compare model and neural output (e.g., Bundesen et al, 2005;Cook & Maunsell, 2002;Oram, 2005). Our models integrate neural data to make predictions, and we compare those predictions with neural data.…”

Section: Relation To Other Modelsmentioning

confidence: 99%

Neurally constrained modeling of perceptual decision making.

Purcell

Heitz

Cohen

et al. 2010

Psychological Review

345

459

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Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to test alternative models of how evidence is combined in the accumulation process. The models were evaluated on their ability to predict both response time distributions and movement neuron activity observed in monkeys performing a visual search task. Models that assume gating of perceptual evidence to the accumulating units provide the best account of both behavioral and neural data. These results identify discrete stages of processing with anatomically distinct neural populations and rule out several alternative architectures. The results also illustrate the use of neurophysiological data as a model selection tool and establish a novel framework to bridge computational and neural levels of explanation. Keywordsperceptual decision making; stochastic accumulator models; mental chronometry; frontal eye field Mathematical psychology has converged on a general framework to explain the time course of perceptual decisions. Models that assume perceptual information accumulates to a response threshold provide excellent accounts of decision-making behavior (Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006;Nosofsky & Palmeri, 1997;Palmeri, 1997;Ratcliff & Rouder, 1998;Smith & Van Zandt, 2000;Usher & McClelland, 2001). These accumulator models entail at least two distinct processes: (a) A stimulus must be encoded with respect to the current task to represent perceptual evidence, and (b) some mechanism must accumulate that evidence to reach a decision. Models that assume very different decisionmaking architectures can account for many of the same behavioral phenomena (S. Brown & Heathcote, 2005;S. D. Brown & Heathcote, 2008;. Recently, the observation that the pattern of activity of certain neurons resembles an accumulation to threshold sparked a synthesis of mathematical psychology and neurophysiology (Beck et al., 2008;Boucher, Palmeri, Logan, & Schall, 2007;Bundesen, Habekost, & Kyllingsbaek, 2005;Carpenter, Reddi, & Anderson, 2009;Ditterich, 2006b;Mazurek, Roitman, Ditterich, & Shadlen, 2003;Niwa & Ditterich, 2008;Ratcliff, Cherian, & Segraves, 2003;Ratcliff, Hasegawa, Hasegawa, Smith, & Segraves, 2007;Schall, 2004;Wang, 2002;Wong, Huk, Shadlen, & Wang, 2007;Wong & Wang, 2006). This synthesis is powerful because neurophysiology can constrain key assumptions about the representation of perceptual evidence, the mechanisms that accumulate evidence to threshold, and how the two interact.Correspondence concerning this article should be addressed to Thomas J. Palmeri, Department of Psychology, Vanderbilt University, PMB 407817, 2301 Vanderbilt Place, Nashville, TN 37240-7817. thomas.j.palmeri@vanderbilt.edu. NIH Public Access Author ManuscriptPsychol Rev. Author manuscript; availa...

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Section: Relation To Other Modelsmentioning

confidence: 99%

Neurally constrained modeling of perceptual decision making.

Purcell

Heitz

Cohen

et al. 2010

Psychological Review

345

459

View full text Add to dashboard Cite

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“…Gawne (2000) and Gawne et al (1996) found that first-spike latency and spike rate differentially code for the contrast and orientation of visual line stimuli, respectively, whereas Victor and Purpura (1996) found that visual contrast is coded at a higher temporal resolution than visual texture. Meanwhile, Oram (2005) used a model of visual processing based on the differential coding of contrast and orientation to successfully predict reaction time distributions in visual recognition experiments. It is possible that the multiplexing of information in different codes within single neuron spike trains is a general mechanism of increasing information capacity in the brain.…”

Section: Multiplexing Of Information Within a Single Spike Trainmentioning

confidence: 99%

Cues for Sound Localization Are Encoded in Multiple Aspects of Spike Trains in the Inferior Colliculus

Chase

Young²

2008

Journal of Neurophysiology

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To localize sound, information from three cues--interaural timing differences (ITDs), interaural level differences (ILDs), and spectral notch cues (SNs)--must be properly integrated. The inferior colliculus (IC) receives convergent input from neurons encoding all three cues. Using virtual space stimuli and information theoretic techniques, we investigated the coding of the various localization cues in single neurons of the IC under different encoding schemes. Here we focus on the analysis of information encoded by first-spike latency, in comparison to previous results on discharge rate and ongoing spike timing. The results show that the localization cues converge to different degrees in particular neurons. ITD information is conveyed most strongly by spike rate, with small amounts of independent information in latency and ongoing spike timing. ILD information shows a similar pattern, with larger mutual information values for all three cues. For these cues, ongoing spike timing does not typically contribute independent information over that captured by a joint rate/first-spike latency code. SNs are coded by both rate and first-spike latency, but ongoing spike timing significantly enhances their representation in a best frequency-dependent manner, as long as the temporal envelope of the stimulus can be used in the decoder. The differential coding of the localization cues suggests that information about multiple cues could be multiplexed onto the responses of single neurons.

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“…To explain the orientation dependency of the familiar configuration effects, Peterson and colleagues appealed to a proposal by Ashbridge et al, (2000)—that evidence accumulates faster in a neural population representing an object when the object appears in its typical upright orientation rather than in an inverted orientation [ 41 ] (cf. [ 12 , 42 , 43 ]). Ashbridge et al showed that the cumulative response in such a neural population at any point in time would be larger for objects viewed in their typical upright orientation.…”

Section: The Present Experimentsmentioning

confidence: 99%

Semantic Expectation Effects on Object Detection: Using Figure Assignment to Elucidate Mechanisms

Skocypec

Peterson

2022

Vision

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Recent evidence suggesting that object detection is improved following valid rather than invalid labels implies that semantics influence object detection. It is not clear, however, whether the results index object detection or feature detection. Further, because control conditions were absent and labels and objects were repeated multiple times, the mechanisms are unknown. We assessed object detection via figure assignment, whereby objects are segmented from backgrounds. Masked bipartite displays depicting a portion of a mono-oriented object (a familiar configuration) on one side of a central border were shown once only for 90 or 100 ms. Familiar configuration is a figural prior. Accurate detection was indexed by reports of an object on the familiar configuration side of the border. Compared to control experiments without labels, valid labels improved accuracy and reduced response times (RTs) more for upright than inverted objects (Studies 1 and 2). Invalid labels denoting different superordinate-level objects (DSC; Study 1) or same superordinate-level objects (SSC; Study 2) reduced accuracy for upright displays only. Orientation dependency indicates that effects are mediated by activated object representations rather than features which are invariant over orientation. Following invalid SSC labels (Study 2), accurate detection RTs were longer than control for both orientations, implicating conflict between semantic representations that had to be resolved before object detection. These results demonstrate that object detection is not just affected by semantics, it entails semantics.

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Integrating neuronal coding into cognitive models: Predicting reaction time distributions

Cited by 6 publications

References 123 publications

Neurally constrained modeling of perceptual decision making.

Neurally constrained modeling of perceptual decision making.

Cues for Sound Localization Are Encoded in Multiple Aspects of Spike Trains in the Inferior Colliculus

Semantic Expectation Effects on Object Detection: Using Figure Assignment to Elucidate Mechanisms

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