Biology subjects

Koss, C.

Publications and source records attributed to Koss, C..

5 recordsLinked to original sources

bioRxiv2026

Stimulus prior and reward probability differentially affect response bias in perceptual decision making

Signal detection theory posits that subjects in two-stimulus, two-choice discrimination tasks decide by comparing random samples of an evidence variable to a static decision criterion. While the core assumptions of the theory have received ample experimental support, it has become evident that the decision criterion is not static but subject to trial-by-trial fluctuations and can be influenced by experimental manipulations. The mechanisms governing the trial-by-trial criterion changes are however not well understood. Here, we report results from five experiments in which we subjected rats to a two-stimulus, two-choice auditory discrimination task. In the first three experiments, we investigated the effects of stimulus presentation ratios and reward ratios and provide clear evidence that the effects of changing reward ratios are more pronounced than those of stimulus presentation ratios. A model-based analysis revealed that this effect was due to more than tenfold higher learning rates when reward ratios were manipulated. In two separate experiments, we investigated the effect of reward density (i.e., global reward rate) on criterion learning but failed to find consistent effects. A systematic comparison of three different trial-by-trial criterion learning models based on detection theory, the matching law, and reinforcement learning showed that no model was able to capture the differential effects of stimulus presentation and reward ratios. We conclude that subjects explicitly represent either prior stimulus probabilities or entire stimulus distributions, and accordingly future models need to represent these factors as well.

animal behavior and cognition↗

bioRxiv2024

Stimulus uncertainty and relative reward rates determine adaptive responding in perceptual decision-making

1In an ever-changing environment, animals must learn to flexibly select actions based on sensory input and the anticipated positive and negative consequences. This type of adaptive behavior is studied using perceptual decision-making (PDM) tasks that feature block-wise changes in stimulus-response-outcome contingencies. Despite extensive research on PDM, there exists no widely accepted mechanistic model of the decision process that captures trial-by-trial adaptation to contingency changes. To address this gap, we first specified three signal detection theory-based models of adaptive PDM. Next, we identified several scenarios in which these models make diverging predictions. For experimental testing, we subjected rats and pigeons to a two-choice auditory discrimination task comprising a sequence of experimental conditions that differed in their stimulusresponse-outcome contingencies. The contingency manipulations were implemented through the concomitant manipulation of reward probabilities, stimulus presentation probabilities and stimulus discriminability across two stimulus-response categories. We find that both rats and pigeons exhibit condition-specific response biases that increase total reward across the entire range of experimental conditions. However, none of the models were able to fit the choice data across all experimental conditions. Through detailed behavior analysis, we demon-strate that learning is driven by the integration of rewards, but not reward omissions. Moreover, model-based analyses reveal that reward integration is influenced by two additional factors, namely perceptual uncertainty and the alignment of steady-state response ratios to relative (rather than absolute) reward differences between the two choices. A model incorporating these factors accounts well for behavioral data across experimental conditions for both species and connects the arguably most influential framework of perception, signal detection theory, with a learning mechanism operating at the level of single trials which, in the steady state, produces behavior consistent with the generalized matching law from animal learning theory. 2 Author summaryHumans and other animals rely on their senses and experience to categorize objects and pursue their goals. For example, a mushroom hunter uses sight, smell and touch as well as knowledge of the local biota to decide whether to pick a particular mushroom. The consequences of erring may be dire - food intoxication if savoring a poisonous exemplar, or a meager dinner if too many palatable mushrooms are rejected. Also, the hunters decision may be influenced by ambient lighting conditions or his estimate of how likely it is to encounter poisonous mushrooms in a certain area. Our work is concerned with the algorithms that animals use to make such decisions and how they adapt when circumstances, such as stimulus discriminability, change. We show that mathematical models that incorporate the animals uncertainty about the type of stimulus currently being perceived make very similar choices as the animals. Furthermore, we find that the animals balance their choices by considering relative, rather than absolute, reward expectations, reflecting a long-standing principle of animal learning theory. Together, these features collectively allow animals to obtain nearly as many rewards as theoretically possible.

animal behavior and cognition↗

bioRxiv2023

Influence of Reinforcement and Its Omission on Trial-by-Trial Changes of Response Bias in Perceptual Decision-Making

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and non-sensory response bias. In the framework of signal detection theory (SDT), these aspects of discrimination performance are quantified through separate measures, sensitivity (d) for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single-trial level as a consequence of reinforcement history. We subjected rats to a two-stimulus two-response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three SDT-based criterion learning models in their ability to fit experimentally observed fluctuations of response bias on a trial-by-trial level. These models shift the criterion by a fixed step (1) after each reinforced response, or (2) after each non-reinforced response, or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady-state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial-based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies.

animal behavior and cognition↗

bioRxiv2020

Measuring Metacognition of Direct and Indirect Parameters of Voluntary Movement

We can make exquisitely precise movements without the apparent need for conscious monitoring. But can we monitor the low-level movement parameters when prompted? And what are the mechanisms that allow us to monitor our movements? To answer these questions, we designed a semi-virtual ball throwing task. On each trial, participants first threw a virtual ball by moving their arm (with or without visual feedback, or replayed from a previous trial) and then made a two-alternative forced choice on the resulting ball trajectory. They then rated their confidence in their decision. We measured metacognitive efficiency using meta-d/d and compared it between different informational domains of the first-order task (motor, visuomotor or visual information alone), as well as between two different versions of the task based on different parameters of the movement: proximal (position of the arm) or distal (resulting trajectory of the ball thrown). We found that participants were able to monitor their performance based on distal motor information as well as when proximal information was available. Their metacognitive efficiency was also equally high in conditions with different sources of information available. The analysis of correlations across participants revealed an unexpected result: while metacognitive efficiency correlated between informational domains (which would indicate domain-generality of metacognition), it did not correlate across the different parameters of movement. We discuss possible sources of this discrepancy and argue that specific first-order task demands may play a crucial role in our metacognitive ability and should be considered when making inferences about domain-generality based on correlations.

neuroscience↗

bioRxiv2019

Response-related signals increase confidence but not metacognitive performance

Confidence judgements are a central tool for research in metacognition. In a typical task, participants first perform perceptual (first-order) decisions and then rate their confidence in these decisions. The relationship between confidence and first-order accuracy is taken as measure of metacognitive performance. Confidence is often assumed to stem from decision-monitoring processes alone, but processes that co-occur with the first-order decision may also play a role in confidence formation. In fact, across a broad range of tasks, trials with quick reaction times to the first-order task are often judged with relatively higher confidence than those with slow responses. This robust finding suggests that confidence could be informed by a readout of reaction times in addition to decision-monitoring processes. To test this possibility, we assessed the contribution of response-related signals to confidence and, in particular, to metacognitive performance (i.e., a measure of the adequacy of these confidence judgements). In a factorial design, we measured the effect of making an overt (vs. covert) decision, as well as the effect of pairing a motor action to the stimulus about which the first-order decision is made. Against our expectations, we found no differences in overall confidence or metacognitive performance when first-order responses were covert as opposed to overt. Further, actions paired to visual stimuli presented led to higher confidence ratings, but did not affect metacognitive performance. These results suggest that some of the relationships between first-order decisional signals and confidence might indeed be correlational, and attributable to an upstream cognitive process, common to the two of them.

neuroscience↗