scholarly journals Good Me Bad Me: Prioritization of the Good-Self During Perceptual Decision-Making

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Chuan-Peng Hu ◽  
Yuxuan Lan ◽  
C. Neil Macrae ◽  
Jie Sui
Keyword(s):  
Decision Making ◽  
The Self ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Association Task ◽  
Identity Based ◽  
Core Identity ◽  
Shape Categorization

People display systematic priorities to self-related stimuli. As the self is not a unified entity, however, it remains unclear which aspects of the self are crucial to producing this stimulus prioritization. To explore this issue, we manipulated the valence of the self-concept (good me vs. bad me) — a core identity-based facet of the self — using a standard shape-label association task in which participants initially learned the associations (e.g., circle/good-self, triangle/good-other, diamond/bad-self, square/bad-other), after which they completed shape-label matching and shape-categorization tasks, such that attention was directed to different aspects of the stimuli (i.e., self-relevance and valence). The results revealed that responses were more efficient to the good-self shape (vs. other shapes), regardless of the task that was undertaken. A hierarchical drift diffusion model (HDDM) analysis indicated that this good-self prioritization effect was underpinned by differences in the rate of information uptake. These findings demonstrate that activation of the good-self representation exclusively facilitates perceptual decision-making, thereby furthering understanding of the self-prioritization effect.

scholarly journals Good Me Bad Me: Prioritization of the Good-Self During Perceptual Decision-Making

2019 ◽  
Author(s):  
Chuan-Peng Hu ◽  
Yuxuan Lan ◽  
Neil Macrae ◽  
Jie Sui
Keyword(s):  
Decision Making ◽  
The Self ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Association Task ◽  
Identity Based ◽  
Core Identity ◽  
Shape Categorization

People display systematic priorities to self-related stimuli. As the self is not a unified entity, however, it remains unclear which aspects of the self are crucial to producing this stimulus prioritization. To explore this issue, we manipulated the valence of the self-concept (good me vs. bad me) — a core identity-based facet of the self — using a standard shape-label association task in which participants initially learned the associations (e.g., circle/good-self, triangle/good-other, diamond/bad-self, square/bad-other), after which they completed shape-label matching and shape-categorization tasks, such that attention was directed to different aspects of the stimuli (i.e., self-relevance and valence). The results revealed that responses were more efficient to the good-self shape (vs. other shapes), regardless of the task that was undertaken. A hierarchical drift diffusion model (HDDM) analysis indicated that this good-self prioritization effect was underpinned by differences in the rate of information uptake. These findings demonstrate that activation of the good-self representation exclusively facilitates perceptual decision-making, thereby furthering understanding of the self-prioritization effect.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

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.

scholarly journals Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

scholarly journals Alertness modulates perceptual decision-making

2020 ◽  
Author(s):  
Sridhar R. Jagannathan ◽  
Corinne A. Bareham ◽  
Tristan A. Bekinschtein
Keyword(s):  
Decision Making ◽  
Neural Dynamics ◽  
External Information ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Diffusion Modelling ◽  
Human Participants ◽  
Multivariate Pattern ◽  
Work Done

ABSTRACTThe ability to make decisions based on external information, prior knowledge and context is a crucial aspect of cognition and it may determine the success and survival of an organism. Despite extensive and detailed work done on the decision making mechanisms, the understanding of the effects of arousal remain limited. Here we characterise behavioural and neural dynamics of decision making in awake and low alertness periods to characterise the compensatory signatures of the cognitive system when arousal decreases. We used an auditory tone-localisation task in human participants under conditions of fully awake and low arousal. Behavioural dynamics analyses using psychophysics, signal detection theory and drift-diffusion modelling showed slower responses, decreased performance and a lower rate of evidence accumulation due to alertness fluctuations. To understand the modulation in neural dynamics we used multivariate pattern analysis (decoding), identifying a shift in the temporal and spatial signatures involved. Finally, we connected the computational parameters identified in the drift diffusion modelling with neural signatures, capturing the effective lag exerted by alertness in the neurocognitive system underlying decision making. These results define the reconfiguration of the brain networks, regions and dynamics needed for the implementation of perceptual decision making, revealing mechanisms of resilience of cognition when challenged by decreases in arousal.

scholarly journals Cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change in speech

2016 ◽  
Vol 116 (5) ◽  
pp. 2023-2032 ◽  
Author(s):  
Daniel R. Lametti ◽  
Leonie Oostwoud Wijdenes ◽  
James Bonaiuto ◽  
Sven Bestmann ◽  
John C. Rothwell
Keyword(s):  
Decision Making ◽  
Speech Perception ◽  
Perceptual Learning ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Perceptual Change ◽  
Healthy Humans ◽  
Speech Timing ◽  
Cerebellar Tdcs

Neuroimaging studies suggest that the cerebellum might play a role in both speech perception and speech perceptual learning. However, it remains unclear what this role is: does the cerebellum help shape the perceptual decision, or does it contribute to the timing of perceptual decisions? To test this, we used transcranial direct current stimulation (tDCS) in combination with a speech perception task. Participants experienced a series of speech perceptual tests designed to measure and then manipulate (via training) their perception of a phonetic contrast. One group received cerebellar tDCS during speech perceptual learning, and a different group received sham tDCS during the same task. Both groups showed similar learning-related changes in speech perception that transferred to a different phonetic contrast. For both trained and untrained speech perceptual decisions, cerebellar tDCS significantly increased the time it took participants to indicate their decisions with a keyboard press. By analyzing perceptual responses made by both hands, we present evidence that cerebellar tDCS disrupted the timing of perceptual decisions, while leaving the eventual decision unaltered. In support of this conclusion, we use the drift diffusion model to decompose the data into processes that determine the outcome of perceptual decision-making and those that do not. The modeling suggests that cerebellar tDCS disrupted processes unrelated to decision-making. Taken together, the empirical data and modeling demonstrate that right cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change. The results provide initial evidence in healthy humans that the cerebellum critically contributes to speech timing in the perceptual domain.

scholarly journals A flexible framework for simulating and fitting generalized drift-diffusion models

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Maxwell Shinn ◽  
Norman H Lam ◽  
John D Murray
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Cognitive Neuroscience ◽  
Software Package ◽  
Planck Equation ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Flexible Framework ◽  
Decision Making Model

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

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