Response outcomes gate the impact of expectations on perceptual decisions

Hermoso-Mendizabal, Ainhoa; Hyafil, Alexandre; Rueda-Orozco, Pavel E.; Jaramillo, Santiago; Robbe, David; Rocha, Jaime de la

doi:10.1101/433409

Cited by 10 publications

(16 citation statements)

References 39 publications

(84 reference statements)

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“…To understand how subjects adapt to constancy and flux across trials, classic 2AFC experiments have been extended to include correlated cross-trial choices (Fig. 2b) where both evidence accumulated during a trial, and probabilistic reward provide information that can be used to guide subsequent decisions 16,29 . When a Markov process 30 (Fig.…”

Section: Subjects Account For Correlations Between Trials By Biasing mentioning

confidence: 99%

“…Thus, optimal models predict that observers should, on average, respond more quickly, but not more accurately 28 . Empirically, humans 12,35,36 and other animals 29 do indeed often respond faster on repeat trials, which can often be modeled by per trial adjustments in initial belief. Furthermore, this bias can result from explicit feedback or subjective estimates, as demonstrated in studies where no feedback is provided (Fig.…”

Section: Subjects Account For Correlations Between Trials By Biasing mentioning

confidence: 99%

“…Can this inherent bias be overcome through training? The answer may be attainable by extending the training periods of humans or nonhuman primates 5,9 , or using novel auditory decision tasks developed for rodents 6,29 . Ultimately, high throughput experiments may be needed to probe how ecologically adaptive evidence accumulation strategies change with training.…”

Section: Subjects Account For Correlations Between Trials By Biasing mentioning

confidence: 99%

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Optimal models of decision-making in dynamic environments

Kilpatrick

Holmes

Eissa

et al. 2019

Current Opinion in Neurobiology

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Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.

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Section: Subjects Account For Correlations Between Trials By Biasing mentioning

confidence: 99%

Section: Subjects Account For Correlations Between Trials By Biasing mentioning

confidence: 99%

Section: Subjects Account For Correlations Between Trials By Biasing mentioning

confidence: 99%

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Optimal models of decision-making in dynamic environments

Kilpatrick

Holmes

Eissa

et al. 2019

Current Opinion in Neurobiology

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“…Choice biases are extensively studied in relation to how current decisions are influenced by previous decisions, what is known as choice history biases (Abrahamyan et al 2016;Akaishi et al 2014;Fründ, Wichmann, and Macke 2014;Urai, Braun, and Donner 2017;Fischer and Whitney 2014;Braun, Urai, and Donner 2018;St John-Saaltink et al 2016;Fritsche, Mostert, and de Lange 2017;Hermoso-Mendizabal, Hyafil, and Rueda-Orozco 2019). Depending on the perceptual scenario, people tend to repeat their previous choice (Akaishi et al 2014;Braun, Urai, and Donner 2018), alternate between choices (Fritsche, Mostert, and de Lange 2017) or idiosyncratically repeat or alternate (Urai, Braun, and Donner 2017;Abrahamyan et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…We will refer to these as global choice biases. The existence of these biases is acknowledged (Gold and Ding 2013;Kingdom and Prins 2016;Michael J. Morgan et al 2012;García-Pérez and Alcalá-Quintana 2013;Peters, Ro, and Lau 2016) and they are included in current models of perceptual decision making (Abrahamyan et al 2016;Akaishi et al 2014;Urai, Braun, and Donner 2017;Braun, Urai, and Donner 2018;Hermoso-Mendizabal, Hyafil, and Rueda-Orozco 2019), but whether they reflect sensory or decisional processes has not been, to our knowledge, assessed 1 . The problem to identify their origin is that, in standard perceptual paradigms, the tendency to choose one alternative more often is consistent with a biased sensory representation, but also with a bias in the decision process (García-Pérez and Alcalá-Quintana 2013; Gold and Ding 2013).…”

Section: Introductionmentioning

confidence: 99%

Decoupling sensory from decisional choice biases in perceptual decision making

Linares

Aguilar-Lleyda

López‐Moliner

2016

Preprint

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Choice behavior in detection tasks demonstrates that perceptual decision-making not only depends on sensory evidence, but also on choice biases: the tendency to choose one alternative over the others independently of the sensory evidence. To assess whether choice biases pervade other perceptual decision scenarios, we asked humans to perform a simple common perceptual discrimination task with two symmetric alternatives. We found that participants did not choose the two alternatives equally often, which is consistent with the occurrence of choice biases, but also with the occurrence of sensory biases. To test these possibilities, participants performed the task reversing the mapping between perception and the category of the alternatives. With this simple manipulation, participants reversed the frequency of choosing the two alternatives, which supports that the biased choice behavior was caused by biased sensory evidence. We also found consistent estimates of the sensory biases using a task with two asymmetric alternatives. Perceptual decision-making in simple tasks, thus, might be entirely based on the representation of sensory information.

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Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior

Mochol

Kiani

Moreno‐Bote

2020

Preprint

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Goal-directed behavior requires integrating sensory information with prior knowledge about the environment. Behavioral biases that arise from these priors could increase positive outcomes when the priors match the true structure of the environment, but mismatches also happen frequently and could cause unfavorable outcomes. Biases that reduce gains and fail to vanish with training indicate fundamental suboptimalities arising from ingrained heuristics of the brain. Here, we report systematic, gain-reducing choice biases in highly-trained monkeys performing a motion direction discrimination task where only the current stimulus is behaviorally relevant. The monkey's bias fluctuated at two distinct time scales: slow, spanning tens to hundreds of trials, and fast, arising from choices and outcomes of the most recent trials. Our finding enabled single trial prediction of biases, which influenced the choice especially on trials with weak stimuli. The pre-stimulus activity of neuronal ensembles in the monkey prearcuate gyrus represented these biases as an offset along the decision axis in the state space. This 2 offset persisted throughout the stimulus viewing period, when sensory information was integrated, leading to a biased choice. The pre-stimulus representation of historydependent bias was functionally indistinguishable from the neural representation of upcoming choice before stimulus onset, validating our model of single-trial biases and suggesting that pre-stimulus representation of choice could be fully defined by biases inferred from behavioral history. Our results indicate that the prearcuate gyrus reflects intrinsic heuristics that compute bias signals, as well as the mechanisms that integrate them into the oculomotor decision-making process.Addressing these questions requires developing a task in which heuristic biases are not rewarding. Biases that increase reward rate encourage alteration of decision strategies based on task structure, complicating generalization of results across tasks.But systematic biases that persist with training and are non-rewarding (or even reduce gain) provide an opportunity to explore the heuristics that shape history-dependent biases. Addressing our questions also requires single trial quantification of the magnitude of bias, as well as recording from neural ensembles in brain regions that represent both the bias and the decision-making process. Single trial quantification of 3 the magnitude of bias necessitates development of behavioral models that can accurately predict the bias on individual trials. Although many studies attempted to do so (Gold et al., 2008a; Jasper et al., 2019; Lueckmann et al., 2018), there are few comprehensive models that achieve sufficient accuracy, often because they ignore one or more key factors that shape the bias. Additionally, past studies on the neural representation of bias focused largely on single neuron activity (Eskandar and Assad, 1999; Hanks et al., 2011; Lueckmann et al., 2018; Nogueira et al., 2017; Padoa-Schioppa, 2013; Shadlen and Ne...

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Response outcomes gate the impact of expectations on perceptual decisions

Cited by 10 publications

References 39 publications

Optimal models of decision-making in dynamic environments

Optimal models of decision-making in dynamic environments

Decoupling sensory from decisional choice biases in perceptual decision making

Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior

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