Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making.

Optimality analysis of value-based decisions in binary and multi-alternative choice settings predicts that reaction times should be sensitive only to differences in stimulus magnitudes, but not to overall absolute stimulus magnitude. Yet experimental work in the binary case has shown magnitude sensitive reaction times, and theory shows that this can be explained by switching from linear to multiplicative time costs, but also by nonlinear subjective utility. Thus disentangling explanations for observed magnitude sensitive reaction times is difficult. Here for the first time we extend the theoretical analysis of geometric time-discounting to ternary choices, and present novel experimental evidence for magnitude-sensitivity in such decisions, in both humans and slime moulds. We consider the optimal policies for all possible combinations of linear and geometric time costs, and linear and nonlinear utility; interestingly, geometric discounting emerges as the predominant explanation for magnitude sensitivity.

Quantifying the benefits of using decision models with response time and accuracy data.

Response time and accuracy are fundamental measures of behavioral science, but discerning participants' underlying abilities can be masked by speed-accuracy trade-offs (SATOs). SATOs are often inadequately addressed in experiment analyses which focus on a single variable or which involve a suboptimal analytic correction. Models of decision-making, such as the drift diffusion model (DDM), provide a principled account of the decision-making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioral variables. For plausible parameters of a typical between-groups experiment, we simulate experimental data, for both real and null group differences in participants' ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time, and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modeling, but to use established decision models to demonstrate and quantify the benefits of decision modeling for experimentalists. We show, in terms of reduction of required sample size, how decision modeling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed-accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment.

Single-trial dynamics explain magnitude sensitive decision making.

BACKGROUND: Previous research has reported or predicted, on the basis of theoretical and computational work, magnitude sensitive reaction times. Magnitude sensitivity can arise (1) as a function of single-trial dynamics and/or (2) as recent computational work has suggested, while single-trial dynamics may be magnitude insensitive, magnitude sensitivity could arise as a function of overall reward received which in turn affects the speed at which decision boundaries collapse, allowing faster responses as the overall reward received increases. RESULTS: Here, we review previous theoretical and empirical results and we present new evidence for magnitude sensitivity arising as a function of single-trial dynamics. CONCLUSIONS: The result of magnitude sensitive reaction times reported is not compatible with single-trial magnitude insensitive models, such as the statistically optimal drift diffusion model.

Autistic Traits in the Neurotypical Population do not Predict Increased Response Conservativeness in Perceptual Decision Making.

Recent research has shown that adults and children with autism spectrum disorders have a more conservative decision criterion in perceptual decision making compared to neurotypical individuals, meaning that autistic participants prioritise accuracy over speed of a decision. Here, we test whether autistic traits in the neurotypical population correlate with increased response conservativeness. We employed three different tasks; for two tasks we recruited participants from China ( N = 39) and for one task from the United Kingdom ( N = 37). Our results show that autistic traits in the neurotypical population do not predict variation in response criterion. We also failed to replicate previous work showing a relationship between autistic traits and sensitivity to coherent motion and static orientation. Following the argument proposed by Gregory and Plaisted-Grant, we discuss why perceptual differences between autistic and neurotypical participants do not necessarily predict perceptual differences between neurotypical participants with high and low autistic traits.

Toward an Atlas of Canonical Cognitive Mechanisms.

A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists start focusing on simpler explananda that will enable them to chart an atlas of elementary cognitive operations. Looking forward, the next challenge for Cognitive Science will be to understand how these elementary cognitive processes are pieced together to explain complex behavior.

Computational Scientific Discovery in Psychology.

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

Magnitude-sensitivity: rethinking decision-making.

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

The illusion of absence: how a common feature of magic shows can explain a class of road accidents.

The purpose of the present note is to draw attention to the potential role of a recently discovered visual illusion in creating traffic accidents. The illusion consists in a compelling and immediate experience that the space behind an occluding object in the foreground is empty. Although the illusion refers to a region of space, which is invisible due to occlusion (a blind spot), there is evidence to suggest that it is nevertheless driven by visual mechanisms and that it can be just as deceptive and powerful as ordinary visual illusions. We suggest that this novel illusion can make situations involving blind spots in a road user's field of view even more dangerous than one would expect based on the lack of visibility by itself. This could be because it erroneously makes the road user feel that (s)he has actually seen everything there is to see, and thus has verified that the blind spot is empty. This hypothesis requires further testing before definitive conclusions can be drawn, but we wish to make researchers and authorities involved in the analysis of traffic accidents and on-the-spot crash investigations aware of its potential role in order to encourage registration of relevant data and facilitate further research.

Evidence for the speed-value trade-off: human and monkey decision making is magnitude sensitive.

Complex natural systems from brains to bee swarms have evolved to make adaptive multifactorial decisions. Recent theoretical and empirical work suggests that many evolved systems may take advantage of common motifs across multiple domains. We are particularly interested in value sensitivity (i.e., sensitivity to the magnitude or intensity of the stimuli or reward under consideration) as a mechanism to resolve deadlocks adaptively. This mechanism favours long-term reward maximization over accuracy in a simple manner, because it avoids costly delays associated with ambivalence between similar options; speed-value trade-offs have been proposed to be evolutionarily advantageous for many kinds of decision. A key prediction of the value-sensitivity hypothesis is that choices between equally-valued options will proceed faster when the options have a high value than when they have a low value. However, value-sensitivity is not part of idealised choice models such as diffusion to bound. Here we examine two different choice behaviours in two different species, perceptual decisions in humans and economic choices in rhesus monkeys, to test this hypothesis. We observe the same value sensitivity in both human perceptual decisions and monkey value-based decisions. These results endorse the idea that neural decision systems make use of the same basic principle of value-sensitivity in order to resolve costly deadlocks and thus improve long-term reward intake.

Understanding perceptual judgment in autism spectrum disorder using the drift diffusion model.

OBJECTIVE: Two-alternative forced-choice tasks are widely used to gain insight into specific areas of enhancement or impairment in individuals with autism spectrum disorder (ASD). Data arising from these tasks have been used to support myriad theories regarding the integrity, or otherwise, of particular brain areas or cognitive processes in ASD. The drift diffusion model (DDM) provides an account of the underlying processes which give rise to accuracy and reaction time (RT) distributions, and parameterizes these processes in terms which have direct psychological interpretation. Importantly, the DDM provides further insight into the origin of potential group differences in task performance. Here, for the first time, we used the DDM to investigate perceptual decision making in ASD. METHOD: Adults with (N = 25) and without ASD (N = 32) performed an orientation discrimination task. A drift diffusion model was applied to the full RT distributions. RESULTS: Participants with ASD responded more slowly than controls, the groups did not differ in accuracy. Modeled parameters indicated that: (a) participants with ASD were more cautious than controls (wider boundary separation); (b) nondecision time was increased in ASD; and (c) the quality of evidence extracted from the stimulus (drift rate) did not vary between groups. CONCLUSIONS: Taking the behavioral data in isolation would suggest reduced perceptual sensitivity in ASD. However, DDM results indicated that despite response slowing, there was no evidence of differential perceptual sensitivity between participants with and without ASD. Future use of the DDM in investigations of perception and cognition in ASD is highly recommended. (PsycINFO Database Record