Full description Bibliographic Details Other Authors: Frontiers in neuroscience Boca Raton, Fla. Weinberger and Kasia M. How can it be inferred from behavior? White What can different brains do with reward? Wise, and Sarah E. Gottfried and Donald A. Katz and Brian F. Camalier and Jon H. Branch Coslett Neuroanatomy of reward: As a consequence, the focus in the last decade has shifted away from neuronal sensitivity toward the study of perceptual decision-making. It is now asked how and to what degree sensory representations also reflect the behavioral choice Britten et al. Despite these developments, the psychophysical task — at least when used to measure neuronal sensitivities — has, by and large, been considered merely a means to measure responses.
Typically, the minimization of extra-sensory factors is considered as a given. Against the backdrop of the insights gained by SDT half a century ago about the psychological nature of even the simplest sensory detection tasks, it gives cause of concern how little possible effects of extra-sensory factors on the psychometric curve are discussed. Our goal in this review is to remind the reader that all parameters of the psychometric curve depend on the detailed procedure and will, thus, significantly affect the estimation of psychometric sensitivity and thereby the NP ratio.
Each psychophysical task comes with different memory requirements, constraints on information processing, and effects on motivation and bias that limit the use of sensory information. We will start with a brief review of SDT and an analysis of cognitive processes underlying performance in commonly employed psychophysical tasks. SDT offers a broad conceptual framework for the analysis of different psychophysical tasks.
The interested reader is referred to MacMillan and Creelman for further paradigms and discussion. The observer has one response option R available e. The outline of a typical trial is depicted in Figure 2 A. After an inter-trial interval ITI , a stimulus is presented. The biggest advantage of the GNG method is its simplicity. Animals are easily trained on GNG using intense, suprathreshold stimuli.
Consequently, the intensity difference between the stimuli in classes A and B is gradually reduced, until no further improvement is possible see Schwarz et al. Of course, GNG can also be conducted with two stimuli per trial as in two-interval forced choice see below. Rather than two individual stimuli, A and B can be classes of stimuli, e. The outline of a typical trial is depicted in Figure 2 B. If the observer emits the appropriate response within a given time frame response window , reward is delivered; if the incorrect response is emitted, the subject is punished, usually by a brief time-out.
In many monkey studies, the response consists in making a saccade to one of two choice targets. However, use of the YN method is not limited to detection but is employed in studies of discrimination performance as well. Note that, in signal detection theoretical contexts, the YN method is typically understood to employ only two stimuli per block of trials the consequences of departure from this rule are discussed in the main text.
On each trial, a reference stimulus is presented first; then, a second stimulus target is presented. The subject is presented with one of m stimuli in a single interval and has to emit one of m possible responses. The observer is presented with two stimuli, either simultaneously or in succession, and has to judge whether they are the same or different.
The position or the sequence of the two stimuli in a pair is randomized. Unlike YNR, the first stimulus in this task is not identical across trials. This task can take many forms. In tactile psychophysics, a common implementation is the two-interval forced choice task 2-IFC, e. Here, a stimulus is presented for a brief interval of time e. The subject has to decide which of the two stimuli the target is e.
If 2-IFC is used to assess detection performance, one of the stimuli is the null stimulus, the other one is the target. Another implementation of forced choice is the spatial n -alternative forced-choice method n -AFC, Figure 2 D: It is important to note the discordant uses of psychophysical terms in the animal neuro-psychophysics and the psychological literature. In forced choice methods psychological use , the observer is always presented with multiple stimuli per trial, either in temporal succession n -IFC or simultaneously e.
An inconsistency of this terminology is that YN tasks are not commonly called FC, although they do feature a forced choice component they require the observer to emit one of two responses on each trial. This is probably the reason why animal studies often call YN tasks FC, bearing the danger that characteristics of the different tasks that critically relate to the comparison of neurometric and psychometric data slip out of focus and get neglected see main text.
Here we adopt the psychological terminology which is consistent with signal detection theory, see also Section 2. Overview over the most frequently used tasks in animal psychophysics and their properties. Sequence of events in four tasks commonly employed in animal psychophysics. C Two-interval forced choice task. D Spatial two-alternative forced choice task. Signal detection theory starts with the assumption that each presentation of a signal yields a variable internal representation on a hypothetical decision axis.
Similarly, even in the absence of sensory input, the system generates a non-zero, somewhat variable response. In the simplest and most widely used case, the distributions of the internal representation of both stimulus S and noise N are assumed to be normal and their variances identical Figure 3. See main text for details. The task can be conceptualized as a statistical decision problem. The observer is assumed to partition the decision axis into the discrete response options that are available to him: On each trial, there are four possible outcomes: Cases 1 and 4 are correct responses; cases 2 and 3 are false.
Given this experimental setup, a payoff matrix assigns a value to each of the four possible outcomes. Usually, correct responses are equally likely to yield reinforcement, and reinforcers are of the same magnitude for cases 1 and 4. Incorrect responses are usually punished, and again punishments are of the same magnitude for cases 2 and 3. Thus, the probability of hits equals that of correct rejections, and the probability of false alarms equals that of misses.
The discriminability of the two stimuli, N and S, is given by the difference of means of the two stimulus distributions on the decision variable, divided by the common standard deviation SD of the distributions. This separation is of great value because the usual index of performance in psychophysics, percent correct, is known to be highly susceptible to variations in task structure and response bias Green and Swets, A classic example for how SDT can help relate different psychophysical tasks is the relationship between YN and two-interval forced choice 2-IFC; the same applies to spatial two-alternative forced choice, 2-AFC.
In a simple instantiation of 2-IFC, the observer is confronted with two different stimuli per trial; let us assume these are the same two stimuli that have been used previously in the YN task. The observer is presented with both stimuli on each trial, but each stimulus is assigned randomly to one of two successive temporal intervals. Hence, contrary to the YN task, where the subject observes a sample from either the signal or the noise distribution on each trial, here the subject gets one sample from each without knowing which one is presented in which of the two intervals.
The optimal strategy in this case is to take the difference between the two values and base the decision on the sign of the difference. Signal detection theoretical process model of performance in the 2-AFC task. In most neurophysiological experiments, animals are presented with more than two stimuli varying in their discriminability. Hence, each stimulus is assumed to give rise to a Gaussian distribution on the decision axis. How should the subject respond if we showed all stimuli randomized in one and the same block of an experiment?
In Figure 5 A, an observer is confronted with two stimulus categories, S1 and S2. The rightmost panel illustrates overall proportion of correct responses as a function of criterion placement. Illustration how different stimulus presentation probabilities and different ROC-analysis strategies may yield disparate estimates of sensory performance.
A The total stimulus set comprises six different stimuli, five of which correspond to S2 gray distributions, blue distribution is the sum of five individual ones and one corresponds to S1 red. All six stimuli occur with equal probability means: For each possible criterion on the abscissa, the corresponding accuracies for each stimulus can be read off the ordinate.
Vertical line indicates optimal criterion placement. B As in A , but probability of S1 and S2 are equal 0. For the same set of stimuli as in a, the optimal criterion is shifted considerably to the right, and the overall proportion of correct responses drops from 0. E Psychometric functions for different task conditions: Now consider a somewhat different situation: Imagine yet another situation: This case is illustrated for two hardly distinguishable stimuli Figure 5 C and two easily distinguishable stimuli Figure 5 D.
For each of five possible pairs, percentage of correct responses can be calculated and used to construct a psychometric curve Figure 5 E, green. This exercise illustrates an important point: Notably, performance across tasks looks identical when transformed into the same unit of sensitivity, such as. We can use the previous example to make another point. Regarding the NP comparison, it is crucial that the performance of the neurons is considered under the same constraints as the subject. There is, however, also the question of how much of the task and the stimuli is known to the observer.
Assume the subject is confronted with the situation depicted in Figure 5 B — an YN task in which the S1 stimulus presentation probability is the same as that of all S2-stimuli together. In Figure 5 E, this amounts to a transition from the red to the green curve. Still, the neuron would be unfairly favored, since the analysis assumes a sequence of YN tasks with only two stimuli per block, while the observer was faced with six stimuli simultaneously.
Contrary to the experimenter the observer does not know which stimulus is shown on each trial. Thus, the only possible strategy for the observer is to adopt a single decision criterion, as shown in Figure 5 B.
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Optimal performance would accordingly result in the blue psychometric function in Figure 5 E, and this is the correct analysis to apply to the neuronal data: Studies in which this procedure was applied include de Lafuente and Romo and Palmer et al. Given the success of SDT in fitting psychophysical data, it is tempting to think of the calculations involved as actual cognitive processes.
For the YN task, the sequence of steps can be conceptualized as follows: A process model for the GNG task with one stimulus per trial would be identical to that for YN, the difference being that, in YN, the observer has two response options aside from non-task behavior , while in GNG, the observer has only one.
Two-interval forced choice is more complicated because there exist more than one process model for appropriate but also suboptimal behavior. Another strategy c that will give the same performance with regard to percent correct would be to perform two times YN in succession. If the stimulus is detected in neither interval, or if it is falsely detected in both, a random response is produced. Otherwise the interval in which a stimulus was detected is chosen. Yet, there is a fourth, the optimal strategy, d that was already discussed above illustrated in Figure 4.
The important thing to note here is that, for a given psychophysical task, there may be more than one decision strategy to follow. It is often a convenient assumption that subjects follow the optimal strategy, but we must not forget that in most studies that conduct NP comparisons it is only an assumption. Consequently, both the processing required to yield a decision variable and the resulting performance may differ from subject to subject.
Even if the sensory front end as an input to the system is fixed, subjects may use the available information in various, potentially suboptimal, ways. The 2-IFC task can however be adapted to force the animal to pay attention to both stimuli; see Romo and Salinas An analysis of the YNR task is depicted in Figure 6. Here, two stimuli per trial are presented. Again, the task is ambiguous as to its decision strategy. One strategy the optimal one is to ignore R completely and concentrate only on the second stimulus for decision-making.
This assumes, of course, that all stimuli are known exactly to the subject. R is, however, only introduced because the experimenter thinks that this is not the case. One suboptimal strategy that seems likely is hence to 1 encode R, 2 encode the second stimulus, 3 take their difference, and 4 decide according to the sign of the difference; if positive, the second stimulus is deemed more intense Figure 6 B.
Furthermore, because of the ambiguity in task execution, it is unknown which neurometric analysis is most appropriate for this case. A Stimulus distributions along the decision variable under the optimal strategy. B Stimulus distributions along the decision variable under the suboptimal strategy, when subjects decide on the basis of the difference of the first and the second sample. One study actually demonstrated that, in YNR, animals ignore the reference stimulus and thereby follow the optimal strategy. Monkeys were presented with a base stimulus first and a comparison stimulus second, and they had to judge whether the frequency of the comparison stimulus was higher than that of the base stimulus.
Importantly, when the reference stimulus was omitted in control experiments, psychophysical performance did not change, suggesting that the reference stimulus has indeed been ignored by the animals. Also, when conditions were changed such that both base and comparison frequency varied randomly from trial to trial, performance dropped to chance levels, indicating that the animals did not perform the subtraction strategy as delineated above Figure 6 B.
For example, Yeshurun et al. They found, contrary to widespread belief, that the 2-IFC task is not unbiased: That 2-IFC is usually not unbiased was also remarked on by Klein and the topic was recently revisited by Garcia-Perez and Alcala-Quintana in a reanalysis of a large number of datasets. Moreover, sensitivity during the two intervals may differ: Similar observations have been reported and commented on by other authors Nachmias, ; Ulrich and Vorberg, ; Ulrich, This asymmetry could be due to memory limitations, i.
Importantly, Yeshurun et al.
Although their data do not allow a clear interpretation of how the psychometric functions from the different tasks relate to each other, the authors speculate that extra-sensory factors, like sensory memory and spatial attention, have different effects in different tasks. It is noteworthy that these extra-sensory effects are ignored in SDT. On the neurometric side it makes sense to calculate sensitivity using the optimal procedure in order to get an upper bound on the performance that an ideal observer could achieve based on the neural data. We usually also assume that the whole observer behaves optimally when calculating psychometric sensitivity.
We have to be aware, however, that the actual sensitivity of the observer may be higher than observed, since he may be using the information that is available to him in a suboptimal way. The simultaneous measurement of neuronal and behavioral responses is considered the gold standard for conducting the NP comparison, because neuronal responses are not altered by anesthesia, the animal is actively engaged in the task, and stimulus variability across trials affects neurons and observer alike Parker and Newsome, That way, important confounds inherent in comparing neurometric and psychometric data from different animals, such as plasticity of sensory representations during learning Polley et al.
However, as outlined above, simultaneous acquisition of neurometric and psychometric data is not sufficient for conducting valid NP comparisons, because task-specific and -unspecific, see below factors may affect psychophysical performance without affecting neurometric performance. As a consequence, psychophysical performance will frequently fall short of true sensitivity. Psychometric discrimination and detection performance for identical stimuli have been shown to be affected not only by type of task see preceding section , but by a variety of other factors as well.
SDT explicitly acknowledges the role of prior presentation probability and reinforcement history of the stimuli, but there exists a wide range of factors which, we believe, have been largely ignored in previous work. In the following paragraphs, we will review some non-sensory factors that are known to affect psychophysical performance. A short list of important factors in conducting NP comparisons is provided in Table 2. Overview over the most frequent factors potentially affecting the NP comparison. One would expect psychometric functions to change based on learning and this is a good reason to work with highly trained observers and only analyze the responses after their performance does not improve anymore Fine and Jacobs, This is of course the case for most animal experiments, especially those involving monkeys, even though some studies employing rats or mice sometimes stop training when an arbitrary performance criterion of, e.
Nevertheless, even within a session, a highly trained animal may show systematic deviations from stationarity. In order to achieve a high level of motivation, animals in psychophysical studies are usually food- or water deprived. They found that the delivery of larger rewards led to increased performance as measured by the slope of the psychometric function.
Hunger and satiety are known to offset response curves in psychophysical GNG tasks. Boneau and Cole separated response probabilities observed during the first half of an experimental session, when the subject was supposedly most hungry, from the second half of the session, when the animal was arguably less hungry; they observed a substantial decrease in overall response probability from the first to the second half of the session, which showed up at the level of the psychometric function as a shift of threshold.
Similar effects are of course to be expected when the subject gets tired. In order to detect such non-stationarities, one possibility is to compute a rank-biserial correlation between trial number and responses e. Ideally, the correlation should be 0. If the correlation assumes negative values, the number of correct responses is increasing over the duration of the session.
As another means to detect such effects, Wichmann and Hill a , b describe a statistical test that uses the order of the blocks in a constant stimuli design to predict the residuals for the fit of the psychometric function. Animals are presumably inattentive to the task a significant portion of the time. In principle, this problem is unrelated to the psychophysical task employed, but it may be especially detrimental in GNG. Lapses of attention in GNG will tend to yield fewer responses overall and thereby increase the measured threshold.
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In GNG, the experimenter has no way to identify whether the absence of a response in a given trial is indeed based on assessment of the sensory evidence in a trial or due to non-sensory factors such as lapse of attention or decreased motivation. However, even in YN and FC, this may cause problems if the animal does not simply refrain from responding on such trials, but instead presses buttons or makes saccades at random.
To complicate issues further, it could be that, beyond non-sensory influences on response bias, sensitivity itself could be affected by fluctuations of attention. For example, Treue and Martinez Trujillo reported that tuning curves of neurons in MT are gain-controlled by attention. To control for fluctuations in attention within a session, experimenters can follow strategies as suggested in the previous section on motivation and fatigue.
In GNG and YN tasks, working memory is not required in the sense that sensory information needs to be maintained over a short time span, e. If the sequence of the stimuli is seamless, no working memory is needed and discriminability depends on the temporal contrast of the two stimuli.
In this case, the 2-IFC paradigm tests predominantly sensory coding. If, however, the stimuli are separated by a non-zero ISI, storing and retrieving stimulus properties in working memory plays a decisive role. If the ISI is too long, performance decreases Harris et al. It is a welcome recent development in neurophysiology that the mechanisms of sensory working memory are under investigation Romo et al.
The interplay between simple psychophysical paradigms and working memory is certainly a worthwhile field of theoretical and experimental development Machens et al. In any case, it is likely that neurometrics in 2-IFC overestimate performance when sensory neuron responses during the first stimulus period are used, rather than the memory trace of the first stimulus as represented by working memory neurons.
In addition, Busse et al. However, there are indications that the sequential effects are a trace of the mechanisms that produce the observed behavior. For identification tasks such as the YN task it seems likely that the subject needs to store the ideal stimuli in long-term memory and then compares the stimulus on each trial to the stored representations to decide on a response. However, Stewart et al. For detection tasks, Treisman and Williams have argued that the sequential effects arise through an adaptive setting of the criterion based on previous trials.
In many psychological experiments the stimulus range can influence the behavior that one wants to measure, often with unexpected results Poulton, For example, Lages and Treisman show that a task that suggests comparison of a stimulus to a reference stimulus from long-term memory is actually solved by the subject by taking into account the stimulus range without recourse to the reference stimulus at all a possible explanation for this can be found in Treisman and Williams, A related problem is that, in many neurophysiological studies, details of the stimuli e.
However, because this stimulus adaptation has to be done for each single unit recording see Britten et al. For most sensory areas, it is commonly assumed that the neural response to a stimulus is thought to be largely unaffected by stimulus history, as long as some reasonable ISI is provided. Accordingly, neural representation of a given stimulus should remain unaltered by the number of stimuli in a stimulus set, while psychophysical performance is not.
Therefore, experimenters should take care to meet the assumptions of SDT lest the subjects exhibit suboptimal performance. It is often neglected that ideal observer analysis of spike responses using SDT construction of ROC curves requires some assumptions that are frequently not met by experimental conditions. Many neuroscience studies aiming at the NP comparison violate at least one of these assumptions; most often, multiple stimuli are used per experimental block see stimulus range , or the timing of the stimulus is held uncertain e.
This effect is likely due to the added stimulus uncertainty, because performance in the second experiment would be expected to increase according to SDT. Assuming that neural responses were not systematically affected by task type, the NP comparisons would yield different results for the two sets of experiments. Blackwell systematically compared psychophysical methods for measuring visual thresholds in human subjects. He concluded that the 2-IFC method is superior to YN on several indices of quality, including reliability of threshold measurement variability of repeated assessments of threshold , vulnerability of threshold measurement to non-sensory biasing factors i.
For animal subjects, Mentzer has conducted similar comparisons of YN, 2-AFC, and 4-AFC for light detection in pigeons, but could not find any performance differences. Most psychophysical studies employing unit recordings in primates have used the YN method, even though it is usually referred to by another name e.
Vogels and Orban, However, these species can be trained on FC tasks as well pigeons: Blough, ; 4-AFC: Mentzer, ; mice: We know of no study with these species which employed the m -IFC task; still, since rats, mice, and pigeons are known to learn delayed matching-to-sample problems rats: To sum up, while most studies have so far employed YN, other methods seem feasible. It is common understanding in the community of researchers based on anecdotal evidence that GNG is trained faster than YN but see Frederick et al.
More effort is required to make all psychophysical tasks routinely available for future psychophysical research. Spatial m -AFC has the disadvantage that, since several stimuli are presented simultaneously, it is difficult to control for repetitive shifts of attention during the course of a single trial, and to attribute modulations in unit activity to any one stimulus, as opposed to the entire stimulus display.
In addition, all FC variants as well as YNR leave room for different decision strategies see above , which need to be properly assessed before conducting the NP comparison. GNG and YN methods have the advantage that no sequential or simultaneous stimulus presentation is required. Accordingly, no working memory for a sensory stimulus is necessary, which potentially simplifies the task. We believe that the YN method is particularly well suited for NP comparisons. Unlike GNG, lapses of attention, or impulsive responding do not directly contaminate the response measure, compared to FC and YNR, there are less degrees of freedom in terms of strategy to employ, although we regret to say that there are no good data to back up this claim, and these data are badly needed.
The comparison of neurometric and psychometric sensitivity is fraught with problems. We have argued in this review that, in stark contrast to estimation of neurometric sensitivity, problems with the estimation of psychometric sensitivity have been largely ignored in the literature on the physiology of perception. Nevertheless, on both sides significant progress will be needed to make NP measurements more precise. Here we list some recommendations for future work originating from the points raised in this review.
On the neurometric side, we see the research program based on recording single neurons while activating them with sensory stimuli coming to an end. This approach has been invaluable to demonstrate that the neurometric sensitivity of single cells most often reaches close to but hardly surpasses that of the observer, thus fostering a central tenet of theories of sparse coding, as predicted by Barlow and Mountcastle. However, beyond showing sparse coding to be feasible in principle, this approach helps little in elucidating the role of the large neural populations activated even by near-threshold stimulation.
The goal of today must be to characterize the neuronal code of the population of neurons carrying precisely the information leading to behavior. The need to define and access informational bottlenecks renders this a tough task.
Retinal ganglion cells have been spotted to be one such bottleneck and should be exploited further. The creation of bottlenecks by juxtacellular stimulation and soon by optogenetic means will allow carrying this research program further both in rodents and in monkeys. An instructive example has been provided by Roeder in his studies of noctuid moths. These insects use auditory information from just two neurons per ear to decide on different tactics to escape foraging bats Roeder, Insects exhibit complex types of behavior, such as working memory and decision making Menzel and Giurfa, ; Pompilio et al.
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Also, they offer exquisite experimental flexibility in terms of genetic manipulation and optical imaging of neuronal function Briggman et al. Accordingly, invertebrates may serve as valuable model systems to investigate the physiology of perception, and to offer useful insights for the studies of mechanisms of perceptual decision making in mammals. On the psychometric side, the importance of task structure and other non-sensory factors relevant for psychophysical performance must be acknowledged.
More effort is needed to validate the measurements of psychometric sensitivity by deliberate variation of task structure while maintaining a constant stimulus set. For instance, results from YNR or FC studies that allow ambiguous interpretations in terms of underlying cognitive processes can be validated by applying YN tasks.
Formal models of the cognitive processes underlying different tasks need to be refined and pitted against each other both with purely behavioral tests Gomez et al. It is unclear what kind of comparison process underlies perceptual decisions, i. As shown in Figure 5 , psychometric performance for identical stimulus discriminations can be wildly different dependent on presentation strategy. Thus, psychometric performance must be compared with presenting pairs of stimuli vs. Such models need not only isolate sensitivity from response bias Tanner and Swets, ; McCarthy and Davison, ; Busse et al.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. A comparison of neuronal and behavioral detection and discrimination performances in rat whisker system. Chronic cellular imaging of mouse visual cortex during operant behavior and passive viewing.
Deciphering the spike train of a sensory neuron: Fundamentals of Scaling and Psychophysics. MIT Press , — Single units and sensation: Reading a neural code. Neural ensemble codes for stimulus periodicity in auditory cortex. Studies of psychophysical methods for measuring visual thresholds.