Dissociating neural variability related to stimulus quality and response times in perceptual decision-making

2018 ◽  
Vol 111 ◽  
pp. 190-200 ◽  
Author(s):  
Stefan Bode ◽  
Daniel Bennett ◽  
David K. Sewell ◽  
Bryan Paton ◽  
Gary F. Egan ◽  
...  
2018 ◽  
Author(s):  
Yunshu Fan ◽  
Joshua I. Gold ◽  
Long Ding

AbstractDecision-making is often interpreted in terms of normative computations that maximize a particular reward function for stable, average behaviors. Aberrations from the reward-maximizing solutions, either across subjects or across different sessions for the same subject, are often interpreted as reflecting poor learning or physical limitations. Here we show that such aberrations may instead reflect the involvement of additional satisficing and heuristic principles. For an asymmetric-reward perceptual decision-making task, three monkeys produced adaptive biases in response to changes in reward asymmetries and perceptual sensitivity. Their choices and response times were consistent with a normative accumulate-to-bound process. However, their context-dependent adjustments to this process deviated slightly but systematically from the reward-maximizing solutions. These adjustments were instead consistent with a rational process to find satisficing solutions based on the gradient of each monkey’s reward-rate function. These results suggest new dimensions for assessing the rational and idiosyncratic aspects of flexible decision-making.


2021 ◽  
Author(s):  
Hernán Anlló ◽  
Katsumi Watanabe ◽  
Jérôme Sackur ◽  
Vincent de Gardelle

AbstractVerbal hints can bias perceptual decision-making, even when the information they provide is false. Whether individuals may be more or less susceptible to such perceptual influences, however, remains unclear. We asked naive participants to indicate the dominant color in a series of stimuli, after giving them a false statement about which color would likely dominate. As anticipated, this statement biased participants’ perception of the dominant color, as shown by a correlated shift of their perceptual decisions, confidence judgments and response times. Crucially, this perceptual bias was more pronounced in participants with higher levels of susceptibility to social influence, as measured by a standard suggestibility scale. Together, these results indicate that even without much apparatus, simple verbal hints can affect our perceptual reality, and that social steerability can determine how much they do so. Susceptibility to suggestion might thus be considered an integral part of perceptual processing.Statement of relevanceAt a time when fake news soar, understanding the role that simple verbal descriptions play in how we perceive the world around us is paramount. Extensive research has shown that perception is permeable to well-orchestrated manipulation. Comparatively less attention has been paid to the perceptual impact of false information when the latter is imparted simply and straightforwardly, through short verbal hints and instructions. Here we show that even a single sentence suffices to bias perceptual decision-making, and that critically, this bias varies across individuals as a function of susceptibility to social influence. Considering how here perception was biased by a single, plain sentence, we argue that researchers, communicators and policy-makers should pay careful attention to the role that social suggestibility plays in how we build our perceptual reality.


2018 ◽  
Author(s):  
Michael D. Nunez ◽  
Aishwarya Gosai ◽  
Joachim Vandekerckhove ◽  
Ramesh Srinivasan

AbstractEncoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; O’Connell et al., 2012; Philiastides et al., 2014). In this study we directly tested the hypothesis that the latency of the N200 recorded by EEG (a negative peak occurring between 150 and 275 ms after stimulus presentation in human participants) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. We show that N200 latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds, reflecting a linear regression slope of 1. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (including VET) are tracked by the fastest response times. A relationship with a slope of 1 between N200 latencies and VET was found by fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times. A slope of 1 was also found between single-trial N200 latencies and response times. Fitting a novel neuro-cognitive model of decision-making also yielded a slope of 1 between N200 latency and non-decision time in multiple visual noise conditions, indicating that N200 latencies track the completion of visual encoding and the onset of evidence accumulation. The N200 waveforms were localized to the cortical surface at distributed temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus.Significance StatementEncoding of a sensory stimulus is believed to be the first step in perceptual decision making. In this study, we report evidence that visual evoked potentials (EPs) around 200 ms after stimulus presentation track the time of visual figure-ground segregation before the onset of evidence accumulation during decision making. These EP latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds. Hierarchical Bayesian model-fitting was also used to relate these EPs to a specific cognitive parameter that tracks time related to visual encoding in a decision-making model of response time. This work adds to the growing literature that suggests that EEG signals can track the component cognitive processes of decision making.


2020 ◽  
Author(s):  
Joshua Michael Calder-Travis ◽  
Lucie Charles ◽  
Rafal Bogacz ◽  
Nick Yeung

The drift diffusion model (DDM) provides an excellent account of decisions and response times. It also features the optimal property of tracking the difference in evidence between two options. However, the DDM struggles to account for human confidence reports, because responses are triggered when the difference in evidence reaches a set value, suggesting confidence in all decisions should be equal. Previously considered extensions to the DDM fall short of providing an adequate quantitative account of confidence. Possibly because of this, much confidence research has used non-normative models of the decision mechanism. Motivated by the idea that perceptual decision-making will reflect optimal computation, we consider 9 variants of the DDM. Motivated by the idea that the brain will not duplicate the representation of evidence, in all model variants confidence is read out from the decision mechanism. We compare the models to benchmark results, and make 4 qualitative predictions which we verify in a preregistered study. Modelling confidence on a trial-by-trial basis, we find that a subset of model variants provide an excellent account of the precise quantitative effects observed in confidence data. Specifically, models in which confidence reflects a miscalibrated Bayesian readout perform best. These results support the claim that confidence is based on the decision mechanism, which is itself optimal. Therefore, there is no need to abandon the idea that the implementation of perceptual decision-making will reflect optimal computation.


eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Yunshu Fan ◽  
Joshua I Gold ◽  
Long Ding

Decision-making is often interpreted in terms of normative computations that maximize a particular reward function for stable, average behaviors. Aberrations from the reward-maximizing solutions, either across subjects or across different sessions for the same subject, are often interpreted as reflecting poor learning or physical limitations. Here we show that such aberrations may instead reflect the involvement of additional satisficing and heuristic principles. For an asymmetric-reward perceptual decision-making task, three monkeys produced adaptive biases in response to changes in reward asymmetries and perceptual sensitivity. Their choices and response times were consistent with a normative accumulate-to-bound process. However, their context-dependent adjustments to this process deviated slightly but systematically from the reward-maximizing solutions. These adjustments were instead consistent with a rational process to find satisficing solutions based on the gradient of each monkey’s reward-rate function. These results suggest new dimensions for assessing the rational and idiosyncratic aspects of flexible decision-making.


2020 ◽  
Author(s):  
Zoe C. Ashwood ◽  
Nicholas A. Roy ◽  
Iris R. Stone ◽  
Anne K. Churchland ◽  
Alexandre Pouget ◽  
...  

AbstractClassical models of perceptual decision-making assume that animals use a single, consistent strategy to integrate sensory evidence and form decisions during an experiment. Here we provide analyses showing that this common view is incorrect. We use a latent variable modeling framework to show that decision-making behavior in mice reflects an interplay between different strategies that alternate on a timescale of tens to hundreds of trials. This model provides a powerful alternate explanation for “lapses” commonly observed during psychophysical experiments. Formally, our approach consists of a Hidden Markov Model (HMM) with states corresponding to different decision-making strategies, each parameterized by a distinct Bernoulli generalized linear model (GLM). We fit the resulting model (GLM-HMM) to choice data from two large cohorts of mice in different perceptual decision-making tasks. For both datasets, we found that mouse decision-making was far better described by a GLM-HMM with 3 or 4 states than by a traditional psychophysical model with lapses. The identified states were highly consistent across animals, consisting of a single “engaged” state, in which the strategy relied heavily on the sensory stimulus, and multiple biased or disengaged states in which accuracy was low. These states persisted for many trials, suggesting that lapses were not independent, but reflected state dynamics in which animals were relatively engaged or disengaged for extended periods of time. We found that for most animals, response times and violation rates were positively correlated with disengagement, providing independent correlates of the identified changes in strategy. The GLM-HMM framework thus provides a powerful lens for the analysis of decision-making, and suggests that standard measures of psychophysical performance mask the presence of slow but dramatic alternations in strategy across trials.


2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.


Mindfulness ◽  
2021 ◽  
Author(s):  
Sungjin Im ◽  
Maya A. Marder ◽  
Gabriella Imbriano ◽  
Tamara J. Sussman ◽  
Aprajita Mohanty

Sign in / Sign up

Export Citation Format

Share Document