scholarly journals Influence of expected reward on perceptual decision making

2018 ◽  
Author(s):  
Mohsen Rakhshan ◽  
Vivian Lee ◽  
Emily Chu ◽  
Lauren Harris ◽  
Lillian Laiks ◽  
...  

AbstractPerceptual decision making is influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing and/or later stages of decision making. To address this question, we conducted two experiments in which human subjects made saccades to what they perceived to be the first or second of two visually identical but asynchronously presented targets, while we manipulated expected reward from correct and incorrect responses on each trial. We found that unequal reward caused similar shifts in target selection (reward bias) between the two experiments. Moreover, observed reward biases were independent of the individual’s sensitivity to sensory signals. These findings suggest that the observed reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing and thus, are more compatible with response bias rather than perceptual bias. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that during a temporal judgment task, the influence of reward information on perceptual choice is more compatible with changing later stages of decision making rather than early sensory processing.

2020 ◽  
Vol 32 (4) ◽  
pp. 674-690 ◽  
Author(s):  
Mohsen Rakhshan ◽  
Vivian Lee ◽  
Emily Chu ◽  
Lauren Harris ◽  
Lillian Laiks ◽  
...  

Perceptual decision-making has been shown to be influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing, later stages of decision-making, or both. To address this question, we conducted two experiments in which human participants made saccades to what they perceived to be either the first or second of two visually identical but asynchronously presented targets while we manipulated expected reward from correct and incorrect responses on each trial. By comparing reward-induced bias in target selection (i.e., reward bias) during the two experiments, we determined whether reward caused changes in sensory or decision-making processes. We found similar reward biases in the two experiments indicating that reward information mainly influenced later stages of decision-making. Moreover, the observed reward biases were independent of the individual's sensitivity to sensory signals. This suggests that reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our experimental observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that, during a temporal judgment task, reward exerts its influence via changing later stages of decision-making (i.e., response bias) rather than early sensory processing (i.e., perceptual bias).


eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jochem van Kempen ◽  
Gerard M Loughnane ◽  
Daniel P Newman ◽  
Simon P Kelly ◽  
Alexander Thiele ◽  
...  

The timing and accuracy of perceptual decision-making is exquisitely sensitive to fluctuations in arousal. Although extensive research has highlighted the role of various neural processing stages in forming decisions, our understanding of how arousal impacts these processes remains limited. Here we isolated electrophysiological signatures of decision-making alongside signals reflecting target selection, attentional engagement and motor output and examined their modulation as a function of tonic and phasic arousal, indexed by baseline and task-evoked pupil diameter, respectively. Reaction times were shorter on trials with lower tonic, and higher phasic arousal. Additionally, these two pupil measures were predictive of a unique set of EEG signatures that together represent multiple information processing steps of decision-making. Finally, behavioural variability associated with fluctuations in tonic and phasic arousal, indicative of neuromodulators acting on multiple timescales, was mediated by its effects on the EEG markers of attentional engagement, sensory processing and the variability in decision processing.


2017 ◽  
Author(s):  
Laura Gwilliams ◽  
Jean-Rémi King

AbstractModels of perceptual decision making have historically been designed to maximally explain behaviour and brain activity independently of their ability to actually perform tasks. More recently, performance-optimized models have been shown to correlate with brain responses to images and thus present a complementary approach to understand perceptual processes. In the present study, we compare how these approaches comparatively account for the spatio-temporal organization of neural responses elicited by ambiguous visual stimuli. Forty-six healthy human subjects performed perceptual decisions on briefly flashed stimuli constructed from ambiguous characters. The stimuli were designed to have 7 orthogonal properties, ranging from low-sensory levels (e.g. spatial location of the stimulus) to conceptual (whether stimulus is a letter or a digit) and task levels (i.e. required hand movement). Magneto-encephalography source and decoding analyses revealed that these 7 levels of representations are sequentially encoded by the cortical hierarchy, and actively maintained until the subject responds. This hierarchy appeared poorly correlated to normative, drift-diffusion, and 5-layer convolutional neural networks (CNN) optimized to accurately categorize alpha-numeric characters, but partially matched the sequence of activations of 3/6 state-of-the-art CNNs trained for natural image labeling (VGG-16, VGG-19, MobileNet). Additionally, we identify several systematic discrepancies between these CNNs and brain activity, revealing the importance of single-trial learning and recurrent processing. Overall, our results strengthen the notion that performance-optimized algorithms can converge towards the computational solution implemented by the human visual system, and open possible avenues to improve artificial perceptual decision making.


2017 ◽  
Author(s):  
Amitai Shenhav ◽  
Mark A. Straccia ◽  
Jonathan D. Cohen ◽  
Matthew M. Botvinick

AbstractDecision-making is typically studied as a sequential process from the selection of what to attend (e.g., between possible tasks, stimuli, or stimulus attributes) to the selection of which actions to take based on the attended information. However, people often gather information across these levels in parallel. For instance, even as they choose their actions, they may continue to evaluate how much to attend other tasks or dimensions of information within a task. We scanned participants while they made such parallel evaluations, simultaneously weighing how much to attend two dynamic stimulus attributes and which response to give based on the attended information. Regions of prefrontal cortex tracked information about the stimulus attributes in dissociable ways, related to either the predicted reward (ventromedial prefrontal cortex) or the degree to which that attribute was being attended (dorsal anterior cingulate, dACC). Within dACC, adjacent regions tracked uncertainty at different levels of the decision, regarding what to attend versus how to respond. These findings bridge research on perceptual and value-based decision-making, demonstrating that people dynamically integrate information in parallel across different levels of decision making.Naturalistic decisions allow an individual to weigh their options within a particular task (e.g., how best to word the introduction to a paper) while also weighing how much to attend other tasks (e.g., responding to e-mails). These different types of decision-making have a hierarchical but reciprocal relationship: Decisions at higher levels inform the focus of attention at lower levels (e.g., whether to select between citations or email addresses) while, at the same time, information at lower levels (e.g., the salience of an incoming email) informs decisions regarding which task to attend. Critically, recent studies suggest that decisions across these levels may occur in parallel, continuously informed by information that is integrated from the environment and from one’s internal milieu1,2.Research on cognitive control and perceptual decision-making has examined how responses are selected when attentional targets are clearly defined (e.g., based on instruction to attend a stimulus dimension), including cases in which responding requires accumulating information regarding a noisy percept (e.g., evidence favoring a left or right response)3-7. Separate research on value-based decision-making has examined how individuals select which stimulus dimension(s) to attend in order to maximize their expected rewards8-11. However, it remains unclear how the accumulation of evidence to select high-level goals and/or attentional targets interacts with the simultaneous accumulation of evidence to select responses according to those goals (e.g., based on the perceptual properties of the stimuli). Recent work has highlighted the importance of such interactions to understanding task selection12-15, multi-attribute decision-making16-18, foraging behavior19-21, cognitive effort22,23, and self-control24-27.While these interactions remain poorly understood, previous research has identified candidate neural mechanisms associated with multi-attribute value-based decision-making11,28,29 and with selecting a response based on noisy information from an instructed attentional target3–5. These research areas have implicated the ventromedial prefrontal cortex (vmPFC) in tracking the value of potential targets of attention (e.g., stimulus attributes)8,11 and the dorsal anterior cingulate cortex (dACC) in tracking an individual’s uncertainty regarding which response to select30–32. It has been further proposed that dACC may differentiate between uncertainty at each of these parallel levels of decision-making (e.g., at the level of task goals or strategies vs. specific motor actions), and that these may be separately encoded at different locations along the dACC’s rostrocaudal axis32,33. However, neural activity within and across these prefrontal regions has not yet been examined in a setting in which information is weighed at both levels within and across trials.Here we use a value-based perceptual decision-making task to examine how people integrate different dynamic sources of information to decide (a) which perceptual attribute to attend and (b) how to respond based on the evidence for that attribute. Participants performed a task in which they regularly faced a conflict between attending the stimulus attribute that offered the greater reward or the attribute that was more perceptually salient (akin to persevering in writing one’s paper when an enticing email awaits). We demonstrate that dACC and vmPFC track evidence for the two attributes in dissociable ways. Across these regions, vmPFC weighs attribute evidence by the reward it predicts and dACC weighs it by its attentional priority (i.e., the degree to which that attribute drives choice). Within dACC, adjacent regions differentiated between uncertainty at the two levels of the decision, regarding what to attend (rostral dACC) versus how to respond (caudal dACC).


2018 ◽  
Vol 38 (24) ◽  
pp. 5632-5648 ◽  
Author(s):  
Nuttida Rungratsameetaweemana ◽  
Sirawaj Itthipuripat ◽  
Annalisa Salazar ◽  
John T. Serences

2018 ◽  
Author(s):  
Yasmin K. Georgie ◽  
Camillo Porcaro ◽  
Stephen D. Mayhew ◽  
Andrew P. Bagshaw ◽  
Dirk Ostwald

AbstractWe present a neuroimaging data set comprising behavioural, electroencephalographic (EEG), and functional magnetic resonance imaging (fMRI) data that were acquired from human subjects performing a perceptual decision making task. EEG data were acquired both independently and simultaneously with fMRI data. Potential data usages include the validation of biocomputational accounts of human perceptual decision making or the empirical validation of simultaneous EEG/fMRI data processing algorithms. The dataset is available from the Open Science Framework and organized according to the Brain Imaging Data Structure standard.


2021 ◽  
Author(s):  
Ren Paterson ◽  
Yizhou Lyu ◽  
Yuan Chang Leong

AbstractPeople are biased towards seeing outcomes that they are motivated to see. For example, sports fans of opposing teams often perceive the same ambiguous foul in favor of the team they support. Here, we test the hypothesis that amygdala-dependent allocation of visual attention facilitates motivational biases in perceptual decision-making. Human participants were rewarded for correctly categorizing an ambiguous image into one of two categories while undergoing fMRI. On each trial, we used a financial bonus to motivate participants to see one category over another. The reward maximizing strategy was to perform the categorization task accurately, but participants were biased towards categorizing the images as the category we motivated them to see. Heightened amygdala activity preceded motivation consistent categorizations, and participants with higher amygdala activation exhibited stronger motivational biases in their perceptual reports. Trial-by-trial amygdala activity was associated with stronger enhancement of neural activity encoding desirable percepts in sensory cortices, suggesting that amygdala-dependent effects on perceptual decisions arose from biased sensory processing. Analyses using a drift diffusion model provide converging evidence that trial-by-trial amygdala activity was associated with stronger motivational biases in the accumulation of sensory evidence. Prior work examining biases in perceptual decision-making have focused on the role of frontoparietal regions. Our work highlights an important contribution of the amygdala. When people are motivated to see one outcome over another, the amygdala biases perceptual decisions towards those outcomes.Significance StatementForming accurate perceptions of the environment is essential for adaptive behavior. People however are biased towards seeing what they want to see, giving rise to inaccurate perceptions and erroneous decisions. Here, we combined behavior, modeling, and fMRI to show that the bias towards seeing desirable percepts is related to trial-by-trial fluctuations in amygdala activity. In particular, during moments with higher amygdala activity, sensory processing is biased in favor of desirable percepts, such that participants are more likely to see what they want to see. These findings highlight the role of the amygdala in biasing visual perception, and shed light on the neural mechanisms underlying the influence of motivation and reward on how people decide what they see.


2019 ◽  
Vol 121 (6) ◽  
pp. 1977-1980 ◽  
Author(s):  
Alexander J. Simon ◽  
Jessica N. Schachtner ◽  
Courtney L. Gallen

A large body of work has investigated the effects of attention and expectation on early sensory processing to support decision making. In a recent paper published in The Journal of Neuroscience, Rungratsameetaweemana et al. (Rungratsameetaweemana N, Itthipuripat S, Salazar A, Serences JT. J Neurosci 38: 5632–5648, 2018) found that expectations driven by implicitly learned task regularities do not modulate neural markers of early visual processing. Here, we discuss these findings and propose several lines of follow-up analyses and experiments that could expand on these findings in the broader perceptual decision making literature.


2020 ◽  
Vol 124 (2) ◽  
pp. 497-509
Author(s):  
Amélie J. Reynaud ◽  
Clara Saleri Lunazzi ◽  
David Thura

Recent hypotheses propose that choices and movements share optimization principles derived from economy, possibly implemented by one unique context-dependent regulation signal determining both processes’ speed. In the present behavioral study conducted on human subjects, we demonstrate that action properties indeed influence perceptual decision-making, but that decision duration and action vigor are actually independently set depending on the difficulty of the movement executed to report a choice.


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