Reach target selection in humans using ambiguous decision cues containing variable amounts of conflicting sensory evidence supporting each target choice

Coallier, Émilie; Kalaska, John

doi:10.1152/jn.00145.2014

Cited by 16 publications

(31 citation statements)

References 79 publications

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“…However, as discussed in the previous paragraph, several studies using high-contrast stimuli found longer non-decision times when different aspects of the stimulus were relevant for the task or when the stimuli were more ambiguous (Coallier and Kalaska 2014;Ratcliff and Frank 2012;Ratcliff and Smith 2010).…”

Section: Stimulus Contrast Vs Extraction Of Task-relevant Informationmentioning

confidence: 90%

“…Ratcliff and Smith argued that the relevant information for the brightness discrimination task is immediately apparent and does not require detailed extraction. A recent study by Coallier and Kalaska can be used to further test this hypothesis (Coallier and Kalaska 2014). In their task, subjects viewed a fine-grained checkerboard pattern with randomly distributed red, blue, and yellow squares.…”

Section: Psychophysical Findingsmentioning

confidence: 99%

“…The vigor and speed of motor output can depend on properties of the decision, in particular when the response is given via finger press or arm movement. While easy to rule out, for example, by measuring movement onset rather than movement offset (Coallier and Kalaska 2014), this is not common practice in the field. In addition, it might also be highly useful to routinely measure EMG of the relevant muscle groups (Gratton et al 1988).…”

Section: Stimulus Contrast Vs Extraction Of Task-relevant Informationmentioning

confidence: 99%

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The importance of decision onset

Teichert

Grinband

Ferrera

2016

Journal of Neurophysiology

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The neural mechanisms of decision making are thought to require the integration of evidence over time until a response threshold is reached. Much work suggests that response threshold can be adjusted via top-down control as a function of speed or accuracy requirements. In contrast, the time of integration onset has received less attention and is believed to be determined mostly by afferent or preprocessing delays. However, a number of influential studies over the past decade challenge this assumption and begin to paint a multifaceted view of the phenomenology of decision onset. This review highlights the challenges involved in initiating the integration of evidence at the optimal time and the potential benefits of adjusting integration onset to task demands. The review outlines behavioral and electrophysiolgical studies suggesting that the onset of the integration process may depend on properties of the stimulus, the task, attention, and response strategy. Most importantly, the aggregate findings in the literature suggest that integration onset may be amenable to top-down regulation, and may be adjusted much like response threshold to exert cognitive control and strategically optimize the decision process to fit immediate behavioral requirements. diffusion model; integration onset; non-decision time; sequential sampling model A GREAT DEAL OF WORK in perceptual decision making has focused on describing the "stopping rules" that terminate a decision process when integration of further evidence provides little added benefit. It is often neglected that understanding when and how the decision process is initiated may be equally important. In line with sequential sampling models of decision making reviewed below, the decision process can be defined as the integration of evidence over time. Within this framework, decision onset is the time at which evidence integration begins. If integration is initiated too early, i.e., before relevant information is available, it may be hampered by noise or irrelevant information (Laming 1979). Conversely, important information may be lost and/or the decision process may be prolonged unnecessarily if the integration is initiated too late. These issues are further complicated by the fact that the encoding of a stimulus and extraction of task-relevant features take variable amounts of time depending on task demands and stimulus properties. Hence, the optimal time for integration onset may be hard to predict and may vary on a trial-by-trial basis.This review argues that not only is a detailed understanding of the neural mechanisms governing decision onset necessary to provide a comprehensive description of evidence-based decision making but decision onset can have a profound impact on decision outcomes in everyday life. Over the past decade numerous articles have reviewed diverse aspects of decision making (Churchland and Ditterich 2012;Drugowitsch and Pouget 2012;Heekeren et al. 2008;Heitz and Schall 2013;Kepecs and Mainen 2012;Kurniawan et al. 2011;Mulder et al. 2014;Nienborg et al. 2...

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Section: Stimulus Contrast Vs Extraction Of Task-relevant Informationmentioning

confidence: 90%

Section: Psychophysical Findingsmentioning

confidence: 99%

Section: Stimulus Contrast Vs Extraction Of Task-relevant Informationmentioning

confidence: 99%

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The importance of decision onset

Teichert

Grinband

Ferrera

2016

Journal of Neurophysiology

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“…The average RT offset of 20–30 ms is not surprising since instructed trials always provided instantaneous and unambiguous evidence whereas choice trials provided rule-neutral evidence. Higher RTs for choice compared to instruction could be due to: (1) a slower drift rate due to absence of clear evidence in the choice case (Roitman and Shadlen, 2002 ; Hanks et al, 2015 ); (2) higher decision thresholds in the case of symmetric reward choice (Cavanaugh et al, 2006 ; Cavanagh et al, 2011 ; Summerfield and Tsetsos, 2012 ); (3) an increased duration of non-decision time, which delays migration initiation, due to unclear stimuli (Mulder and van Maanen, 2013 ; Coallier and Kalaska, 2014 ); or some combination of these possibilities. Only processes that occur with or after the rule-cue can account for differences between instructed and choice trials since subjects were unaware of the trial type prior to the rule-cue.…”

Section: Discussionmentioning

confidence: 99%

“…Which mechanism is responsible for RT reduction and CP increase in which behavioral context is a topic of ongoing research (Summerfield and Tsetsos, 2012 ). In the context of perceptual decision-making, previous studies showed that prior probability adapts migration distance (Bogacz et al, 2006 ; Simen et al, 2009 ; Mulder et al, 2012 ) while strength of evidence steers drift rate (Roitman and Shadlen, 2002 ; Coallier and Kalaska, 2014 ; Coallier et al, 2015 ; Hanks et al, 2015 ). However, the effect of expected value as been accounted for by different explanations: baseline shift (Maddox, 2002 ; Bogacz et al, 2006 ; Mulder et al, 2012 ) or drift rate change (Diederich and Busemeyer, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

I Plan Therefore I Choose: Free-Choice Bias Due to Prior Action-Probability but Not Action-Value

Suriya-Arunroj

Gail

2015

Front. Behav. Neurosci.

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According to an emerging view, decision-making, and motor planning are tightly entangled at the level of neural processing. Choice is influenced not only by the values associated with different options, but also biased by other factors. Here we test the hypothesis that preliminary action planning can induce choice biases gradually and independently of objective value when planning overlaps with one of the potential action alternatives. Subjects performed center-out reaches obeying either a clockwise or counterclockwise cue-response rule in two tasks. In the probabilistic task, a pre-cue indicated the probability of each of the two potential rules to become valid. When the subsequent rule-cue unambiguously indicated which of the pre-cued rules was actually valid (instructed trials), subjects responded faster to rules pre-cued with higher probability. When subjects were allowed to choose freely between two equally rewarded rules (choice trials) they chose the originally more likely rule more often and faster, despite the lack of an objective advantage in selecting this target. In the amount task, the pre-cue indicated the amount of potential reward associated with each rule. Subjects responded faster to rules pre-cued with higher reward amount in instructed trials of the amount task, equivalent to the more likely rule in the probabilistic task. Yet, in contrast, subjects showed hardly any choice bias and no increase in response speed in favor of the original high-reward target in the choice trials of the amount task. We conclude that free-choice behavior is robustly biased when predictability encourages the planning of one of the potential responses, while prior reward expectations without action planning do not induce such strong bias. Our results provide behavioral evidence for distinct contributions of expected value and action planning in decision-making and a tight interdependence of motor planning and action selection, supporting the idea that the underlying neural mechanisms overlap.

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Sequential sampling from memory underlies action selection during abstract decision-making

Shushruth¹,

Zylberberg²,

Shadlen

2022

Current Biology

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Reach target selection in humans using ambiguous decision cues containing variable amounts of conflicting sensory evidence supporting each target choice

Abstract: Coallier É, Kalaska JF. Reach target selection in humans using ambiguous decision cues containing variable amounts of conflicting sensory evidence supporting each target choice.

Cited by 16 publications

References 79 publications

The importance of decision onset

The importance of decision onset

I Plan Therefore I Choose: Free-Choice Bias Due to Prior Action-Probability but Not Action-Value

Sequential sampling from memory underlies action selection during abstract decision-making

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