Lapses in perceptual decisions reflect exploration
Authors: Sashank Pisupati
Anup Khanal
Anne K Churchland
Lital Chartarifsky-Lynn
Journal: eLife (2021)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: audition; computational modeling; decision-making; neuroscience; rat
Affiliations: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States., CSHL School of Biological Sciences, Cold Spring Harbor, New York, United States., University of California, Los Angeles, Los Angeles, United States.

Abstract:  Perceptual decision-makers often display a constant rate of errors independent of evidence strength. These 'lapses' are treated as a nuisance arising from noise tangential to the decision, e.g. inattention or motor errors. Here, we use a multisensory decision task in rats to demonstrate that these explanations cannot account for lapses' stimulus dependence. We propose a novel explanation: lapses reflect a strategic trade-off between exploiting known rewarding actions and exploring uncertain ones. We tested this model's predictions by selectively manipulating one action's reward magnitude or probability. As uniquely predicted by this model, changes were restricted to lapses associated with that action. Finally, we show that lapses are a powerful tool for assigning decision-related computations to neural structures based on disruption experiments (here, posterior striatum and secondary motor cortex). These results suggest that lapses reflect an integral component of decision-making and are informative about action values in normal and disrupted brain states. 

Retracted: No

PubPeer comments:
 In their text, Pisupati et al refer to the subject animals u201cprior beliefs.u201d Information about how the authors quantified these prior beliefs is provided only in Figure 1, which indicates an assumption of u201cflat priors.u201d This figure caption is the only place the nature of the prior is indicated. This seems an oversight u2013 it should have been mentioned in Methods, along with at least a brief discussion of the basis of this choice. It is not an innocuous one; according to Michael Betancourt (2017 https: mc-stan.org users documentation case-studies weakly_informative_shapes.html): u201cAlthough flat priors are often motivated as being u201cnon-informativeu201d, they are actually quite informative and **pull the posterior towards extreme values that can bias our inferences.**u201d This methodological choice is, thus, arguably not properly addressed only in passing, in the fine print of a figure caption. , 
  Human and nonhuman subjects often deviate from these predictions, displaying an additional constant rate of errors independent of the evidence strength known as u2018lapsesu2019, leading to errors even on extreme stimulus levels  Since this model does not take lapses into account, it would predict perfect performance on the easiest stimuli on all conditions  we observed higher lapse rates in the u2018neutralu2019 condition, where trials had higher perceptual uncertainty I m confused as to the distinction being made between errors that are expected due to low stimulus salience or clarity or discriminability, and errors that are identified as  lapses that occur under similar conditions. What is the empirical criterion for the distinction? I understand that comparison with the  ideal observer model fails (this is not a new observation) but what is the basis for taking this predictively ineffective model as a reference standard and framing deviations as  lapses, rather than rejecting it altogether? , 
 #2 Another issue regarding  lapses is that explanations considered include perceptual failures, specifically  inattention, but also other types of causes, such as  motor execution or deliberate selection of options that don t produce rewards:  deliberate propensity to occasionally make random u2018exploratoryu2019 choices togather information about rules and rewards. The authors seem to be treating these as mutually exclusive. Why can t both be in play? This mirrors the ambiguity of the  perceptual decision term itself. It is used both to indicate a putative decision as to the identity of the underlying stimulus as well as a decision of what action to take as a result of this prior  decision. , 
 Performance was assessed using a psychometric curve, that is, the probability of high-rate decisions as a function of stimulus rate. The ideal observer model predicts a relationship between the slope of the psychometric curve and noise in the animalu2019s estimate: the higher the standard deviation (s) of sensory noise, the more uncertain the animalu2019s estimate of the rate and the shallower the psychometric curve. On multisensory trials, the ideal observer should have a more certain estimate of the rate (Figure 1e, visual [blue] and auditory [green] s values are larger than multisensorys [red]), driving a steeper psychometric curve I m not following the train of thought here. Specifically, why should greater uncertainty produce a shallower curve (and vice versa) on a graph in with  high-rate choices on the ordinate? This implies, I think, that the curve would be steeper with greater uncertainty if the ordinate were  low-rate choices. Is there an implied bias toward low rate choices with greater uncertainty? Or am I missing something? , 
 u201cWe assume that the two sensory channels are corrupted by independent Gaussian noise with standard deviations A and sV, respectively, giving rise to conditionally independent observations.u201d With respect to the two modalities being independent, the McGurk effect comes to mind; clearly, there is interaction. Also work by Kubovy (e.g. https: psycnet.apa.org record 2009-22869-013), on cross-modal integration. , 
 Because the authors are citing Green and Swets 1966 (the standard reference) for their SDT modelling, it may be worth noting that invoking  inattention and  guessing (as they do for one of their comparison models) is explicitly excluded by those authors as a factor. As [Swets et al (1961a)] explain:  The fundamental difference between the threshold theory we are considering and decision theory lies in their treatment of false alarm responses. According to the threshold theory, these responses represent guesses determined by nonsensory factors; i.e, pn(A} is independent of the cutoff which is assumed to have a fixed location. Decision theory assumes, on the other hand, that ps (A) varies with the temporary position of a cutoff under the observer s control; that false alarm responses arise for valid sensory reasons, and that therefore a simple correction will not eliminate their effect on PSN(A). (p. 13). The  valid sensory reasons here are non-perceptual According to Swets (1961b): u201cThe observeru2019s report is limited to the two classes of stimulus events u2013 he says either u201cyesu201d (a signal was present) or u201cnou201d (only noise was present). Note that he does not say whether or not he saw or heard the signal; he says whether, under the particular circumstances, he prefers the decision that it was present or the decision that it was absent.u201d This is why inattention and guessing cannot figure as explanations in the original ideal observer conception; and I think this is still the prevailing construct. For Green and Swets, the observer is always guessing on the basis of overlapping probability functions corresponding to the probability of a detector s responding (unidimensionally) given variation in a one-dimensional variable, such as intensity or contrast, given in addition a certain amount of loosely-defined  noise. Since this detector is supposed to be responding, to a degree, given the absence of the stimulus, as well as in its presence, the issue of attention - whether the observer  saw or didn t see the stimulus as a perceptual representation - is moot. Effectively, inattention to the stimulus and  no stimulus present are treated as the same. I don t think [any of this makes very much sense] , but it is what the references support. , 
  Such a softmax decision rule has been used to account for suboptimalities in value-based decisions (Dayan and Daw, 2008), however, it has not been used to account for lapses in perceptual decisions. Choices of rules can have rather dispositive effects on the interpretation of the data. The softmax rule entails a loss of information from the data via a normalization procedure (see below). Authors employing it should arguably justify why this loss is appropriate in their given empirical context. This judgment applies to each research project individually. If a model fits data well by, in effect, fudging data, this should at a minimum be discussed, regardless of whether it has been used before in whatever context. (From Wikipedia) The softmax function takes as input a vector z of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers. That is, prior to applying softmax, some vector components could be negative, or greater than one; and might not sum to 1; but after applying softmax, each component will be in the interval ( 0 , 1 ) {displaystyle (0,1)} , and the components will add up to 1, so that they can be interpreted as probabilities. , 
 #6 The author also seem to be deviating from their lapse rate citation, Wichmann and Hill (2001). Pisupati et al say, on the one hand:  [gamma and lamda] are the lower and upper asymptote of the psychometric function, which parametrize the lapse rates on low and high rates, respectively....Our definition of lapses is restricted to strictly asymptotic errors following Wichmann and Hill,2001... Wichmann and Hill describe gamma and lambda a bit differently: The u201cu03b3u201d term is: u201cinterpreted as the base rate of performance in the absence of a signalu201d and in the 2AFC paradigm assumed to be fixed at 0.5,u201d Only the u201cu03bbu201d term seems linked to a  lapses as defined by them. It: u201ccorresponds to the miss rate, and in n-AFC experiments, it is, similarly, a reflection of the rate at which observers lapse, responding incorrectly regardless of stimulus intensity.u201d Is the apparent difference real or just a technicality? , 
 #8 Sheppard, Raposo and Churchland (2013), one of 4 papers referred to by P et al in regard to the task used, also maintain the distinction between guess and lapse rates that seems to be collapsed by P et al into one category:  u03b3 and u03bb are guessing and lapse rates (i.e., lower and upper asymptotes of the psychometric function... , 
 Abstract:  Perceptual decision-makers often display a constant rate of errors **independent of evidence strength.** These u2018lapsesu2019 are treated as a nuisance arising from noise tangential to the decision, e.g. inattention or motor errors. Here, we use a multisensory decision task in rats to demonstrate that these explanations cannot account for lapsesu2019 **stimulus dependence**. There is either a contradiction here, or the last sentence is a non sequitur due to missing context. At best, the passage is incoherent.  Lapses are first being defined as  independent of evidence strength, and then explanations of them criticized in terms of their (in)ability to explain  stimulus dependence. If  independence from evidence strength is different from  stimulus dependence, then showing that explanations geared toward the former don t work for the latter are irrelevant. If they re the same, then, again, the statements are contradictory. , 
  Human and nonhuman subjects often deviate from these predictions, displaying an additional **constant rate** of errors independent of the evidence strength known as u2018lapsesu2019, But a little further on the authors state:  A number of recent observations have cast doubt on fixed early- or late-stage noise as satisfactory explanations for lapses. For instance, many of these explanations predict that lapses should occur at a **constant rate**, In other words, on the one hand  lapses are being defined as a  constant rate of errors, and on the other, explanations are being dismissed for predicting a constant rate of errors. , 
 A piece of information that seems to be missing is the quality of the  visual flashes and  auditory clicks used as stimuli. An auditory click, for example, may have a range of Hz values. The authors do refer to Hz but only in respect to the number of clicks per second, not the frequency of the sound. Rats have particular sensitivities to sounds at particular frequencies. Since at one point, for example, the authors are comparing conditions using auditory (but not visual) stimuli to neutral conditions, this seems somewhat relevant, aside from the fact that it is a basic factor in methods. , 
 An extensive analysis of this paper (by Lydia Maniatis) may be found here https: psyarxiv.com 5t8ge ,