scholarly journals Probabilistic discrimination of relative stimulus features in mice

2021 ◽  
Vol 118 (30) ◽  
pp. e2103952118
Author(s):  
Dmitry R. Lyamzin ◽  
Ryo Aoki ◽  
Mohammad Abdolrahmani ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Discrimination Task ◽  
Choice Model ◽  
Relevant Information ◽  
Orientation Discrimination ◽  
Specific Information ◽  
Common Belief ◽  
Behavioral Strategies ◽  
Perceptual Decision Making ◽  
Perceptual Decision

During perceptual decision-making, the brain encodes the upcoming decision and the stimulus information in a mixed representation. Paradigms suitable for studying decision computations in isolation rely on stimulus comparisons, with choices depending on relative rather than absolute properties of the stimuli. The adoption of tasks requiring relative perceptual judgments in mice would be advantageous in view of the powerful tools available for the dissection of brain circuits. However, whether and how mice can perform a relative visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task in which the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a typical discrimination acuity of 9°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a probabilistic choice model that explained behavioral strategies in 40 mice and demonstrated that the circularity of the stimulus space is an additional source of choice variability for trials with fixed difficulty. Furthermore, history biases in the model changed with task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice adopt a diverse set of strategies in a task that decouples decision-relevant information from stimulus-specific information, thus demonstrating their usefulness as an animal model for studying neural representations of relative categories in perceptual decision-making research.

scholarly journals Mice can learn a stimulus-invariant orientation discrimination task

2020 ◽  
Author(s):  
Dmitry R Lyamzin ◽  
Ryo Aoki ◽  
Mohammad Abdolrahmani ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Discrimination Task ◽  
Choice Model ◽  
Sensory Information ◽  
Orientation Discrimination ◽  
Decision Variable ◽  
Cognitive State ◽  
Specific Information ◽  
Behavioral Strategies ◽  
Perceptual Decision Making

SummaryUnderstanding how the brain computes choice from sensory information is a central question of perceptual decision-making. Relevant behavioral tasks condition choice on abstract or invariant properties of the stimuli, thus decoupling stimulus-specific information from the decision variable. Among visual tasks, orientation discrimination is a gold standard; however, it is not clear if a mouse – a recently popular animal model in visual decision-making research – can learn an invariant orientation discrimination task and what choice strategies it would use.Here we show that mice can solve a discrimination task where choices are decoupled from the orientation of individual stimuli, depending instead on a measure of relative orientation. Mice learned this task, reaching an upper bound for discrimination acuity of 6 degrees and relying on decisionmaking strategies that balanced cognitive resources with history-dependent biases.We analyzed behavioral data from n=40 animals with the help of a novel probabilistic choice model that we used to interpret individual biases and behavioral strategies. The model explained variation in performance with task difficulty and identified unreported dimensions of variation associated with the circularity of the stimulus space. Furthermore, it showed a larger effect of history biases on animals’ choices during periods of lower engagement.Our results demonstrate that mice can learn invariant perceptual representations by combining decision-relevant stimulus information decoupled from low-level visual features, with the computation of the decision variable dependent on the cognitive state.

scholarly journals Effector-dependent response deterioration by stochastic transformations reveals mixed reference frames for decisions

2017 ◽  
Author(s):  
T. Scott Murdison ◽  
Dominic Standage ◽  
Philippe Lefèvre ◽  
Gunnar Blohm
Keyword(s):  
Decision Making ◽  
Discrimination Task ◽  
Reference Frames ◽  
Button Press ◽  
Perceptual Decision Making ◽  
Motion Direction ◽  
Perceptual Decision ◽  
Direction Discrimination ◽  
Electrophysiological Recordings ◽  
Sensorimotor Transformations

AbstractRecent psychophysical and modeling studies have revealed that sensorimotor reference frame transformations (RFTs) add variability to motor output by decreasing the fidelity of sensory signals. How RFT stochasticity affects the sensory input underlying perceptual decisions, if at all, is unknown. To investigate this, we asked participants to perform a simple two-alternative motion direction discrimination task under varying conditions of head roll and/or stimulus rotation while responding either with a saccade or button press, allowing us to attribute behavioral effects to eye-, head- and shoulder-centered reference frames. We observed a rotation-induced, increase in reaction time and decrease in accuracy, indicating a degradation of motion evidence commensurate with a decrease in motion strength. Inter-participant differences in performance were best explained by a continuum of eye-head-shoulder representations of accumulated decision evidence, with eye- and shoulder-centered preferences during saccades and button presses, respectively. We argue that perceptual decision making and stochastic RFTs are inseparable, consistent with electrophysiological recordings in neural areas thought to be encoding sensorimotor signals for perceptual decisions. Furthermore, transformational stochasticity appears to be a generalized phenomenon, applicable throughout the perceptual and motor systems. We show for the first time that, by simply rolling one’s head, perceptual decision making is impaired in a way that is captured by stochastic RFTs.Significance statementWhen exploring our environment, we typically maintain upright head orientations, often even despite increased energy expenditure. One possible explanation for this apparently suboptimal behavior might come from the finding that sensorimotor transformations, required for generating geometrically-correct behavior, add signal- dependent variability (stochasticity) to perception and action. Here, we explore the functional interaction of stochastic transformations and perceptual decisions by rolling the head and/or stimulus during a motion direction discrimination task. We find that, during visuomotor rotations, perceptual decisions are significantly impaired in both speed and accuracy in a way that is captured by stochastic transformations. Thus, our findings suggest that keeping one’s head aligned with gravity is in fact ideal for making perceptual judgments about our environment.

scholarly journals Priors and Payoffs in Confidence Judgments

2019 ◽  
Author(s):  
Shannon M. Locke ◽  
Elon Gaffin-Cahn ◽  
Nadia Hosseinizaveh ◽  
Pascal Mamassian ◽  
Michael S. Landy
Keyword(s):  
Decision Making ◽  
Orientation Discrimination ◽  
Negative Consequences ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Confidence Judgments ◽  
Expected Gain ◽  
Discrimination Criterion ◽  
Model Observers ◽  
Decision Alternatives

1AbstractPriors and payoffs are known to affect perceptual decision-making, but little is understood about how they influence confidence judgments. For optimal perceptual decision-making, both priors and payoffs should be considered when selecting a response. However, for confidence to reflect the probability of being correct in a perceptual decision, priors should affect confidence but payoffs should not. To experimentally test whether human observers follow this normative behavior, we conducted an orientation-discrimination task with varied priors and payoffs, probing both perceptual and metacognitive decision-making. We then examined the placement of discrimination and confidence criteria according to several plausible Signal Detection Theory models. In the normative model, observers use the optimal discrimination criterion (i.e., the criterion that maximizes expected gain) and confidence criteria that shift with the discrimination criterion that maximizes accuracy (i.e., are not affected by payoffs). No observer was consistent with this model, with the majority exhibiting non-normative confidence behavior. One subset of observers ignored both priors and payoffs for confidence, always fixing the confidence criteria around the neutral discrimination criterion. The other group of observers incorrectly incorporated payoffs into their confidence by always shifting their confidence criteria with the same gains-maximizing criterion used for discrimination. Such metacognitive mistakes could have negative consequences outside the laboratory setting, particularly when priors or payoffs are not matched for all the possible decision alternatives.

scholarly journals Classification images reveal decision variables and strategies in forced choice tasks

2015 ◽  
Vol 112 (23) ◽  
pp. 7321-7326 ◽  
Author(s):  
Lisa M. Pritchett ◽  
Richard F. Murray
Keyword(s):  
Decision Making ◽  
Relevant Information ◽  
Forced Choice ◽  
Internal Variables ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Decision Variables ◽  
Classification Images ◽  
The Difference ◽  
Encode Task

Despite decades of research, there is still uncertainty about how people make simple decisions about perceptual stimuli. Most theories assume that perceptual decisions are based on decision variables, which are internal variables that encode task-relevant information. However, decision variables are usually considered to be theoretical constructs that cannot be measured directly, and this often makes it difficult to test theories of perceptual decision making. Here we show how to measure decision variables on individual trials, and we use these measurements to test theories of perceptual decision making more directly than has previously been possible. We measure classification images, which are estimates of templates that observers use to extract information from stimuli. We then calculate the dot product of these classification images with the stimuli to estimate observers' decision variables. Finally, we reconstruct each observer's “decision space,” a map that shows the probability of the observer’s responses for all values of the decision variables. We use this method to examine decision strategies in two-alternative forced choice (2AFC) tasks, for which there are several competing models. In one experiment, the resulting decision spaces support the difference model, a classic theory of 2AFC decisions. In a second experiment, we find unexpected decision spaces that are not predicted by standard models of 2AFC decisions, and that suggest intrinsic uncertainty or soft thresholding. These experiments give new evidence regarding observers’ strategies in 2AFC tasks, and they show how measuring decision variables can answer long-standing questions about perceptual decision making.

scholarly journals A systematic investigation of differences in perceptual decision-making as a function of the time and method of participant recruitment

2020 ◽  
Author(s):  
Timothy Ballard ◽  
Gina Fisher ◽  
David K. Sewell
Keyword(s):  
Decision Making ◽  
Discrimination Task ◽  
Standard Stimulus ◽  
Brightness Discrimination ◽  
Discrimination Performance ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Speed Accuracy

We examine the extent to which perceptual decision-making processes differ as a function of the time in the academic term in which the participant enrolls in the experiment and whether the participant is an undergraduate who completes the experiment for course credit, a paid participant who completes the experiment in the lab, or a paid participant recruited via Amazon Mechanical Turk who completes the experiment online. In Study 1, we conducted a survey to examine cognitive psychologists' expectations regarding the quality of data obtained from these different groups of participants. We find that cognitive psychologists expect performance and response caution to be lowest among undergraduate participants who enroll at the end of the academic term, and highest among paid in-lab participants. Studies 2 and 3 tested these expectations using two common perceptual decision-making paradigms. Overall, we found little evidence for systematic time-of-term effects among undergraduate participants. The different participant groups responded to standard stimulus quality and speed/accuracy emphasis manipulations in similar ways. Among participants recruited via Mechanical Turk, the effect of speed/accuracy emphasis on response caution was strongest. This group also showed poorer discrimination performance than the other groups in a motion discrimination task, but not in a brightness discrimination task. We conclude that online crowdsourcing platforms can provide high quality perceptual decision-making data, but give recommendations for how data quality can be maximized when using these platforms for recruitment.

scholarly journals Decision Signals in the Local Field Potentials of Early and Mid-Level Macaque Visual Cortex

2020 ◽  
Vol 31 (1) ◽  
pp. 169-183
Author(s):  
Aravind Krishna ◽  
Seiji Tanabe ◽  
Adam Kohn
Keyword(s):  
Decision Making ◽  
Visual Cortex ◽  
Local Field ◽  
Local Field Potentials ◽  
Orientation Discrimination ◽  
Field Potentials ◽  
Perceptual Decision Making ◽  
Neural Basis ◽  
Perceptual Decision ◽  
Area V4

Abstract The neural basis of perceptual decision making has typically been studied using measurements of single neuron activity, though decisions are likely based on the activity of large neuronal ensembles. Local field potentials (LFPs) may, in some cases, serve as a useful proxy for population activity and thus be useful for understanding the neural basis of perceptual decision making. However, little is known about whether LFPs in sensory areas include decision-related signals. We therefore analyzed LFPs recorded using two 48­electrode arrays implanted in primary visual cortex (V1) and area V4 of macaque monkeys trained to perform a fine orientation discrimination task. We found significant choice information in low (0–30 Hz) and higher (70–500 Hz) frequency components of the LFP, but little information in gamma frequencies (30–70 Hz). Choice information was more robust in V4 than V1 and stronger in LFPs than in simultaneously measured spiking activity. LFP-based choice information included a global component, common across electrodes within an area. Our findings reveal the presence of robust choice-related signals in the LFPs recorded in V1 and V4 and suggest that LFPs may be a useful complement to spike-based analyses of decision making.

scholarly journals Superior Colliculus Neuronal Ensemble Activity Signals Optimal Rather Than Subjective Confidence

2017 ◽  
Author(s):  
Brian Odegaard ◽  
Piercesare Grimaldi ◽  
Seong Hah Cho ◽  
Megan A.K. Peters ◽  
Hakwan Lau ◽  
...  
Keyword(s):  
Decision Making ◽  
Superior Colliculus ◽  
Discrimination Task ◽  
Perceptual Decision Making ◽  
Decision Accuracy ◽  
Perceptual Decision ◽  
Decision Confidence ◽  
Brain Signals ◽  
Subjective Confidence ◽  
The Brain

AbstractRecent studies suggest that neurons in sensorimotor circuits involved in perceptual decision-making also play a role in decision confidence. In these studies, confidence is often considered to be an optimal readout of the probability that a decision is correct. However, the information leading to decision accuracy and the report of confidence often co-varied, leaving open the possibility that there are actually two dissociable signal types in the brain: signals that correlate with decision accuracy (optimal confidence) and signals that correlate with subjects’ behavioral reports of confidence (subjective confidence). We recorded neuronal activity from a sensorimotor decision area, the superior colliculus (SC) of monkeys, while they performed two different tasks. In our first task, decision accuracy and confidence co-varied, as in previous studies. In our second task, we implemented a novel motion discrimination task with stimuli that were matched for decision accuracy but produced different levels of confidence as reflected by behavioral reports. We used a multivariate decoder to predict monkeys’ choices from neuronal population activity. As in previous studies on perceptual decision-making mechanisms, we found that neuronal decoding performance increased as decision accuracy increased. However, when decision accuracy was matched, performance of the decoder was similar between high and low subjective confidence conditions. These results show that the SC likely signals optimal decision confidence similar to previously reported cortical mechanisms, but is unlikely to play a critical role in subjective confidence. The results also motivate future investigations to determine where in the brain signals related to subjective confidence reside.Significance StatementConfidence is thought to reflect the rational or optimal belief concerning one’s choice accuracy. Here, we introduce a novel version of the dot-motion discrimination task with stimulus conditions that produce similar accuracy but different subjective behavioral reports of confidence. We decoded decision performance of this task from neuronal signals in the superior colliculus (SC), a subcortical region involved in decision-making. We found that SC activity signaled a perceptual decision for visual stimuli, with the strength of this activity reflecting decision accuracy, but not the subjective level of confidence as reflected by behavioral reports. These results demonstrate an important role for the SC in perceptual decision-making and challenge current ideas about how to measure subjective confidence in monkeys and humans.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

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.

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.

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