An animal rewarding scheme with adaptive methods for measuring sensory, or cognitive, or decision bi- ases in behavioural experiments

Behavioural experiments in animals, in particular psychophysics measures of sensory processing, have been used to demonstrate similar illusory percepts in animals as in humans. These measures suffer from methodological shortcomings that can be argued to create unnecessary biases in the results or interpretation difficulties. In this paper, a method is presented that allows to measure the subjective percept of the tested subject. It combines psychophysics adaptive algorithms with a modified rewarding scheme. It is further argued that this method permits measures of any type of perceptual, cognitive, or response biases that can be of interest to investigate. Introduction Investigations on the visual systems of humans and animals aim at understanding how our and animals’ perceptual world is created on the basis of the brain structures and functions. One of the approaches is through simplified behavioural measures of visual paradigms that target fundamental questions about cognitive and perceptual capacities of the tested subjects, which bring information on cross-species similarities and differences [1-5]. An interesting and useful probe of visual perception are visual illusions or misperceptions. Nowadays, despite differences in neuronal structures and sensory processing between species, multiple types of illusions have been demonstrated in varieties of animals [2,4,610]. These visual effects have become tools for understanding basic neuronal processing of visual information in human [11] or its closest neurophysiological model, the monkey, but also about cross-species general rules of visual information processing [12]. Experiments with illusory percepts, or conditions with misperception effects, are easily carried with human subjects. This is due to the fact that the experimenter can instruct the human subjects about the experiment and task, train the subjects, and receive feedback from the tested subjects in cases of not clear situations or misunderstanding. In animals such measurements are more tricky. The illusory condition creates a situation where the animal’s percept is putatively different from the physical input. Experiments with animals are carried first through a learning procedure that aims to teach them the experimental situation, task, and rewarding scheme, and the animals learn to respond to the physical attributes of the stimuli which are of relevance for the experiment. Thus, a problem appears when one needs to measure illusory or misperception conditions. The animal is rewarded for the physical attribute of the stimulus and this creates cognitive/decision biases in conditions with illusions or misperceptions. Various authors have used clever experimental modifications by introducing low number of trials of illusory conditions or other techniques (e.g. [1,6,7,13]). These methods were sufficient for demonstrating that, indeed, many animal species, not only humans, do have illusory percepts that seem to go in the direction, or in opposition, to human results. But these experimental methods suffer from the difficulty of the rewarding scheme, which can seriously bias or change animal’s decision and learned procedure. In this work, I present a method combining a rewarding scheme with psychophysics adaptive algorithms that allows to measure psychophysical functions also in illusory percepts. I further argue that this method can be applied in some designs without necessity of control trials and also that it allows to measure behavioural biases of any type (sensory, cognitive, decision, etc.) in experiments where adaptive algorithm can be used. Methods in behavioural research and illusory percepts Psychophysical measures of sensory systems, e.g. detection thresholds of sound, light, touch or taste, can be classified in two general methodologies: method of constant stimuli or adaptive methods [14,15]. The experimenter has also the choice between different response types for the subject (detection, discrimination, categorization, etc., see [14,15]). For the case of interest presented here, basic sensory measures where a single physical attribute of the stimulus is varied on a continuum scale, the method of forced-choice with two alternatives together with an adaptive algorithm will be used. When measuring a misperception effect or illusion conditions, the human subject is instructed of the task required by him/her and no feedback about response correctness is allowed during the measures. The no-feedback is done in order to avoid cognitive biases due to perception-feedback interactions and thus allows to measure the subjective perceptual bias when compared to the physical value. A concrete example is the tilt illusion effect, where the orientation of a simple stimulus as a line segment or Gabor patch surrounded by other orientated stimuli is misperceived (Fig.1) [16-20]. This example is used throughout this short method paper for clarifying the important points. In this case, the task of the subject will be to respond whether the orientation of T.Tzvetanov Measuring behavioural biases in animals 2/6 the target in the centre of the stimulus is tilted counterclockwise/clockwise (CCW/CW) from their inner vertical reference, respectively defined as categories A and B. Psychometric function and misperception effects The psychometric function relates the subject responses to the independent variable of the stimulus, the physical dimension that is varied by the experimenter, and in the example it is defined as the physical orientation of the target vs. the proportion of responses "category B" (CW). This function is monotonous and has asymptotes 0 and 1 (if the subject does not have lapses, Fig.2A) [21], its spread defines the discrimination ability of the subject, and its midpoint gives the point of subjective equality, that is, the stimulus value perceived as the reference, here vertical orientation. If the subject has no perceptual or response bias, a control condition will provide the midpoint of the measured function to be at the physical reference value (up to the measurement error). This means that the subject inner vertical reference is the true physical vertical orientation. In the example of Fig.2A the midpoint should be at 0 degrees (black solid curve). When an experimental configuration with misperception effect is taken, the effect on the psychometric function is to shift its midpoint indicating a bias of perception (Fig.2A, red solid curve, corresponding to stimulus design depicted in right panel of Fig.1). It is readily understood when considering the response of the subject to a target with true vertical orientation: in the example the proportion of responses "category B" is about 0.1, demonstrating the bias. Adaptive method for psychometric function measure Adaptive method, or staircase, consists of deciding about staircase’s next presented stimulus given the stimuli and responses of the subject at the present trial or past trials of that algorithm [22,23]. Originally, it was used to target one particular point of proportion “correct” responses (pc) on the psychometric function and its associated stimulus value, but this property is not systematic [24], and now the accumulated data are commonly used for fitting a psychometric function model [21,22]. Many adaptive algorithms are available and here the weighted up-down algorithm is taken [25]. Its rule for a single algorithm is as follows: after each subject response, the stimulus value is modified by an amount Sup toward "category B" if subject answer was "category A" and an amount Sdown toward "category A" if subject response was "category B". The theoretical convergence point on the psychometric function is then pc=Sup/ (Sup+Sdown). Experimenter fixes the starting value of the algorithm, i.e. the first presented stimulus, the required convergence point, one of the two steps, and the stopping criteria; for the tilt illusion example, two staircases with symmetric convergence points are used for each psychometric function measure, with starting points +/8 degrees, pc=0.75 or 0.25, Sdown of 1 or 3 degrees, and stopping criteria was a fixed number of trials n (30) for each independent staircase. It is important to note that, because of the possibility that during the experiment the subject discovers the underlying algorithm [26] and subsequently modifies his response criteria, careful experimental design has to be used when such algorithms are applied. For this reason, multiple adaptive algorithms must be used in parallel and randomly presented from trial-to-trial during the experiment [26], which avoids this learning and criteria change. A second point to consider is the number of responses category A/B of the subject, which if not equal can also create response bias. This is easily solved by designing the experiment such that the number of responses category A/B are equal, for example by using adaptive algorithms converging to symmetrically placed convergence points (if one algorithm targets pc1 then a second algorithm must target pc2=1-pc1). An example of Monte-Carlo simulation of a simultaneous measure of control and tilt illusion condition is presented in Fig.2, with two staircases per psychometric function converging to 0.75 and 0.25 proportion responses, and 30 trials per algorithm. In Fig.2A the solid black and red curves represent the putative discrimination functions of the subject, with the midpoints at 0.5 proportion representing the theoretical target perceived as “vertical”. All four independent staircase algorithms are presented randomly across trials within one experimental session; their Monte-Carlo results are depicted in Fig.2B,C for each condition, respectively. Two general points are of interest. First, any staircase converges to a point on the psychometric function, and wanders around it with an algorithmically controlled Brownian motion, without any prior knowledge from experimenter where the exact transition part of the funct