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Smart Pediatric Oncology Tracker of Symptoms (SPOTS), a Web-Based Interface for the Pediatric PRO-CTCAE: Development and Usability Study

<strong>Background:</strong> Children undergoing cancer treatment experience a range of treatment-related toxicities that significantly affect quality of life and adherence to therapy. Current methods for symptom reporting rely heavily on clinician interpretation of caregiver or child verbal reports, which can result in incomplete or inaccurate records. The Pediatric Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (Pediatric PRO-CTCAE; National Cancer Institute) provides a validated mechanism for direct symptom reporting by children and caregivers, yet its traditional administration and preselection of questions limit the breadth of symptom capture. <strong>Objective:</strong> This research aimed to co-design and conduct formative usability testing of the Smart Pediatric Oncology Tracker of Symptoms (SPOTS), a novel, web-based interface for the Pediatric PRO-CTCAE to allow children with cancer and their caregivers to comprehensively report symptoms. <strong>Methods:</strong> The research comprised 2 sequential phases: co-design and usability testing. Guided by child-computer interaction theory and participatory design methods, child-caregiver dyads collaborated with the research team to iteratively design and refine the SPOTS prototype. Nine participant dyads engaged in up to 3 co-design sessions that informed system features, layout, and content. During the usability phase, 12 additional dyads (6 with children aged 7-12 years and 6 with adolescents aged 13-17 years, each with a caregiver) completed structured usability tasks using the SPOTS prototype. Task completion, pathway efficiency, and user feedback were recorded through screen capture, field notes, and think-aloud protocols. Quantitative data were analyzed descriptively, and qualitative feedback was analyzed thematically. <strong>Results:</strong> SPOTS was described by users as “very clear” and “easy to navigate.” Participants valued the visual design, the use of a customizable character, and the opportunity for children to report symptoms independently. Key usability challenges included confusing terminology, navigation redundancy, and visual complexities. Quantitative task analyses indicated that while most structured tasks were completed successfully, many required excess steps or assistance. When not directed to use a specific screen, participants’ symptom reporting methods varied, with caregivers and adolescents preferring the Body Parts Screen and younger children favoring the Search Screen. <strong>Conclusions:</strong> The formative development of SPOTS demonstrates the feasibility and value of co-designing pediatric health technologies directly with children and caregivers. SPOTS has the potential to enhance the implementation of the Pediatric PRO-CTCAE by offering an engaging, child-friendly digital format that facilitates more direct symptom reporting. Future work will include a pilot study to further assess real-world usability, the quality of symptom capture (ie, completeness and accuracy), and integration with clinical workflows.
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STAT+: In the battle of sepsis algorithms, performance alone doesn’t predict victory

Five years ago, the bottom fell out of sepsis prediction software. Hundreds of hospitals had adopted an algorithm from electronic health record company Epic that promised to alert physicians to predicted cases of sepsis, a life-threatening reaction to infection that kills more than 350,000 people in the United States every year. 

The AI was a technical flop. Despite its results on paper, the technology failed to perform in the real world, and sent so many alerts that doctors tuned them out or hospitals turned them off. 

Half a decade on, new sepsis models are hitting the scene. Epic released a retooled version of its own algorithm. Startups are testing their models in health systems. A team uses large language models to mine clinical notes for signs of sepsis. And on Tuesday, a sepsis flagging device from Bayesian Health, with origins at Johns Hopkins, announced it has received clearance from the Food and Drug Administration.

Continue to STAT+ to read the full story…

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A global matching model of choice and response times in the Deese–Roediger–Mcdermott semantic and structural false recognition paradigms.

Psychological Review, Vol 133(4), Jul 2026, 771-819; doi:10.1037/rev0000596

One of the most common method of eliciting false memories in the laboratory is the Deese–Roediger–McDermott paradigm (Deese, 1959; Roediger & McDermott, 1995), where participants study a set of items that are all similar to a nonpresented critical lure. A common finding is that false recognition to critical lures is much higher than to other nonpresented items and in some cases is even comparable to true recognition, regardless of whether similarity is semantic or structural (e.g., phonological or orthographic) relations. While there exists a handful of computational models of this paradigm, they have only been applied to semantic but not structural false recognition, they have not been fit at the level of individual participants, and they have not been applied to response times. We present a global matching model that addresses all three of these current gaps. Global similarity of semantic and structural representations drives a pair of linear ballistic accumulators, which are used to produce decisions as well as complete response time distributions. In addition to being able to account for heightened false recognition of critical lures, the model was able to account for differences across both individual participants and items, lower correlations between semantic and structural false recognition than true recognition, differences in false recognition across levels of processing, improved true recognition but not false recognition with higher study time, and heightened false recognition under speed emphasis. The model suggests that semantic and structural false recognition can be explained using only a single retrieval mechanism. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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Within-alternative processing supports transitivity of preferences in multiattribute choice.

Psychological Review, Vol 133(4), Jul 2026, 820-845; doi:10.1037/rev0000587

Transitivity of preference (ToP) is a central axiom of rational choice theory. While violating ToP is rare and subject to debate, there have been reports of such violations (Tsetsos et al., 2016a; Tversky, 1969, but see Iverson & Falmagne, 1985; Regenwetter et al., 2011). If humans indeed violate ToP, an important challenge is understanding the conditions and mechanisms that promote either transitive or intransitive preferences. Here, we report the presence of ToP violations using a data analysis method that was prescribed as statistically adequate (Regenwetter et al., 2011), and an experimental design where each choice is presented on a single visual display to avoid artifacts that can be associated with sequential presentation and aggregation across choice stimuli. We introduce two cognitive heuristics that predict certain violations of ToP and we translate them into probabilistic choice models. Then, in three experiments (one of which is a preregistered replication), we evaluate violations of ToP and we assess the models that predict such violations. We find that, despite pervasive individual differences, the ToP adherence rate is much enhanced when the task was presented in a fashion that facilitates within-alternative integration. We also find that the proposed heuristic models successfully explain those ToP violations that do occur. These findings shed light on the conditions and cognitive mechanisms that support ToP. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.

Psychological Review, Vol 133(4), Jul 2026, 846-863; doi:10.1037/rev0000576

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Different goals require us to engage different control processes, such as adjusting how attentive and cautious we are. Here, we show that performance incurs control adjustment costs when people adjust control to meet changing goals. Across four experiments, we provide evidence of these costs and validate a dynamical systems model explaining the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., being fast or accurate). We modeled control allocation to include a dynamic process of adjusting from one’s current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical finding that people undershoot their target control state more (i.e., exhibit larger adjustment costs) when goals switch rather than remain fixed (Study 1). Further validating our model, we show that the magnitude of this cost is increased when: distances between target states are larger (Study 2), there is less time to adjust to the new goal (Study 3), and goal switches are more frequent (Study 4). Our findings characterize the costs of adjusting control to meet changing goals and show that these costs emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility of goal-directed behavior. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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Integrating dual-process decision making and social dynamics: A formal modeling framework for addiction.

Psychological Review, Vol 133(4), Jul 2026, 864-891; doi:10.1037/rev0000584

Currently, formal models of addiction focus either on the complex individual decision-making processes involved in addiction or on the social dynamics of addiction. They do not integrate these two levels, which has been identified as a key shortcoming of current formal models of addiction. To address this, we propose a nonlinear dynamical modeling framework of addiction integrating both the individual level and social level of addictive behavior. The individual level of our modeling framework is a formalization of a dual-process theory, where one type of process increases the consumption of addictive goods, and another type of process limits consumption. For our formalization, we build on a well-studied model from ecology, originally used to model periodic outbreaks of the spruce budworm population. To this model, we add the process of incentive sensitization at the individual level and at the social level, we incorporate the critical processes of selection homophily and peer influence. We show that our integrated modeling framework can be used to explain key phenomena identified in addiction literature: a gradual transition to heavy use, sudden relapse and sudden quitting, relatively stable use states over time (i.e., abstinence moderate use, and heavy use), social contagion and sudden outbreaks, clustering of users, and social aid in recovery. In addition, we demonstrate how our modeling framework can be extended to include mutualistic, competitive, and more complex interactions between different addictive behaviors. Finally, we show how our framework can lead to new insights and predictions and suggest avenues for future research. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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Cognitive mechanisms of subjective value in multiattribute pricing.

Psychological Review, Vol 133(4), Jul 2026, 892-918; doi:10.1037/rev0000594

Understanding how people assign subjective value to outcomes with multiple attributes, such as risk and delay, is central to understanding the structure and manifestation of economic preferences. However, multiattribute preference has been primarily studied through binary choices. The price at which a person would buy, sell, or equate each prospect offers another measure of subjective value that may diverge from multiattribute choice. In both risky and intertemporal domains, choice and price preferences exhibit systematic preference reversals, where a smaller, sooner, or safer option is chosen while a larger, later, or riskier alternative is assigned a higher price. The present study takes a deep dive into how subjective value is assigned in each case in an attempt to reconcile these diverging measurements and methods of assessing value. To explain how and why preferences change across choice and price, the domains of gains and losses, price frames of buying and selling, and varying levels of time pressure, we develop a two-step neural network–based modeling approach. First, we tested cognitive mechanisms underlying value-based judgments and decisions using a switchboard model comparison. Next, we fit and evaluated individualized joint models, where all data from an individual are modeled using parameters and mechanisms that are specific to their best fitting model structure. While mechanisms like delay discounting and risk aversion are common to both models, our results suggest that anchoring and payoff sensitivity diverged between pricing and choice. Extensive differences across elicitation procedures indicate that a common representation of value may remain elusive. (PsycInfo Database Record (c) 2026 APA, all rights reserved)