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Making a decision involves several information processing steps within the time from the presentation of a stimulus to the response. The total time required for the completion of each of these processing steps is the reaction time (RT). Specific processes differ between experimental paradigms, but a minimal set that seems to be agreed upon involves encoding of the choice-relevant features of the stimuli, followed by weighting the evidence for each choice, and initiating a response (Donders, 1868; Ratcliff and McKoon, 2008; Zylberberg et al., 2011; Luce, 1986). Despite being an almost two century-old problem (Helmholtz, 1850), it is unclear how the RT emerges from these putative components.

The answer to this problem has first been hampered by the relatively poor information gained from RT and response-accuracy data alone. Co-registering physiological signals can clarify and extend conclusions about information processing steps in the RT (Turner et al., 2017). Evidence for the putative components that make up RT has been found by registering the electroencephalogram (EEG) during decision tasks. First, a negative deflection in occipital electrodes happening around 200ms after the presentation of a choice, the N200, has been associated with visual encoding of the choice elements by participants (Nunez et al., 2019; Ritter et al., 1979). Second, EEG data has shown that the weighting of evidence toward the alternatives is associated with a positive voltage developing over centro-parietal electrodes after early visual potentials (Kutas et al., 1977). Computational models of decision-making explain the experimental effects observed on these centro-parietal components as an evidence accumulation mechanism (O’Connell et al., 2012; Kelly et al., 2021). Lastly, a component preceding the response has been shown to lateralize with the side of the executed response (Coles et al., 1985). This lateralized readiness potential (LRP) has later been described as arising from an accumulation-to-bound mechanism describing the decision to produce a movement (Schurger et al., 2012).

However, the knowledge gained on the nature and latencies of cognitive processes within the stimulus-response interval from such electrophysiological components is limited by the low signal-to-noise ratio of classical neural measurements. To improve the SNR, researchers usually rely on information derived from the averaging of these signals over many trials. Unfortunately, averaging time-varying signals will result in an average waveform that misrepresents the underlying single-trial events (Luck, 2005; Borst and Anderson, 2024). In the case of decision-making, several studies have shown wide trial-by-trial variation of the timing of cognitively relevant neural events (Vidaurre et al., 2019; Smyrnis et al., 2012; Weindel et al., 2021; Weindel, 2021). Furthermore, averaged components are further distorted by the fact that multiple cognitive processes and associated EEG components are typically present within trials and overlap in time between trials (Woldorff, 1993), forcing researchers to study physiological components in isolation. A few studies have been able to simultaneously investigate multiple EEG components in decision-making using single-trial approaches. As an example, Philiastides et al., 2006 used a classifier on the EEG activity of several conditions to show that the strength of an early EEG component was proportional to the strength of the stimulus, while a later component was related to decision difficulty and behavioral performance (see also Salvador et al., 2022; Philiastides and Sajda, 2006). Furthermore, the authors interpreted that a third EEG component was indicative of the resource allocated to the upcoming decision given the perceived decision difficulty. In their study, they showed that it is possible to use single-trial information to separate cognitive processes within decision-making. Nevertheless, their method requires separate classifiers for each component of interest, limiting the analysis to existing theory of distinct components.

One potential solution mixing both behavior and multivariate analysis of single-trial neural signal to achieve single-trial resolution has emerged through the development of the hidden multivariate methods (Weindel et al., 2024; Anderson et al., 2016). These methods model the neural data of each trial as a sequence of short-lived multivariate cortex-wide events, repeated at each trial, whose timing varies on a trial-by-trial basis and define the RT. In the case of EEG, it is assumed that any cognitive step involved in the RT is represented by a specific topography recurring across trials. The time jitter in the topography is accounted for by estimating, for each of these events, a trial-wise distribution where the expected time of the peak of the topography is given by the time distribution of the previous event’s peak and the expected time distribution of the current event. By constraining, through the recorded behavior, the search for trial-shared sequential activations in the EEG during estimated ranges of time, the hidden multivariate pattern (HMP) model (Weindel et al., 2024) provides an estimation of the number of events and their single-trial latency during each trial. Previous similar approaches have shown that different information processing steps can be extracted from the EEG in a wide range of tasks (Berberyan et al., 2021; Zhang et al., 2018; Anderson et al., 2016; Anderson et al., 2018; Krause et al., 2024). Building on previous work (van Maanen et al., 2021), we expect that the EEG data of a decision-making task will be decomposed into task-relevant intervals indexing the information processing steps in the RT. In the current study, we combine this single-trial modeling strategy with strong theoretical expectations regarding the impact of experimental manipulations on the latent information processing steps during decision-making.

The task of the participants was to answer which of two Gabor patches flanking a fixation cross displayed the highest contrast (Figure 1, top panel). On a trial-by-trial basis, we manipulated the average contrast of both patches but kept the difference between them constant (see the two example trials in Figure 1, one with an average contrast of 5%, and one with an average contrast of 95%, both with a difference of 5%). We hypothesize that this contrast manipulation generates two opposing predictions on encoding and decision processes (Weindel et al., 2022) associated with two of the oldest laws in psychophysics: Piéron’s law (Piéron, 1913) and Fechner’s law (Fechner, 1860).

Piéron’s law predicts that the time to perceive the two stimuli (and thus the choice situation) should follow a negative power law with the stimulus intensity (Figure 1, green curve). In contradistinction, Fechner’s law states that the perceived difference between the two patches follows the logarithm of the absolute contrast of the two patches (Figure 1, yellow curve). As the task of our participants is to judge the contrast difference, Piéron’s law should predict the time at which the comparison starts (i.e., the stimuli become perceptible), while Fechner’s law should implement the comparison, and thus decision, difficulty. Given that Fechner’s law is expected to capture decision difficulty, we connected this law to evidence accumulation models by replacing the rate of accumulation with Fechner’s law in the proportional rate diffusion model of Palmer et al., 2005. This linking with an evidence accumulation model further allows connecting the RT to the proportion of correct responses. To test the generalizability of our findings and allow comparison to standard decision-making tasks, we also included a speed–accuracy manipulation by asking participants to either focus on the speed or the accuracy of their responses in different experimental blocks.

“Behind every investment are people – workers gaining new skills, families recovering after disasters, and communities building their futures,” said California Secretary of Labor Stewart Knox. “This year, California strengthened career pathways, protected workers’ rights, and brought services directly to communities across the state.”

Jobs First: growing jobs across California

The Governor launched the California Jobs First Council, bringing state agencies together to coordinate economic development, workforce, education, and infrastructure investments around a shared strategy for regional job growth. The Council aligns statewide action with 13 region-specific plans developed by local partners to reflect local priorities and labor-market needs.

In August, California Jobs First unveiled an $80 million investment to support 23,000 jobs and 11 projects, bolstering economic progress in communities and industries throughout the state.

Through California Jobs First, the state is training workers and supporting job creation across all 13 economic regions, targeting high-growth industries such as construction, healthcare, education, advanced manufacturing, clean energy, aerospace and defense, life sciences, and agtech.

Master Plan on Career Education: expanding apprenticeships and skills-based pathways

In 2025, California continued to strengthen efforts to connect education, workforce training, and employer needs to create a variety of pathways to good-paying careers. The state moved closer toward Governor Newsom’s goal of serving 500,000 registered apprentices by 2029, serving more than 233,000 Californians in registered apprenticeships and, with interagency support, over 600,000 served through earn and learn programs since 2019. In alignment with the Master Plan on Career Education, the Division of Apprenticeship Standards (DAS) expanded registered apprenticeship and pre-apprenticeship programs statewide by supporting new and expanded programs across traditional and emerging industries. California investments include $15 million to advance 29 apprenticeship programs for young people entering the workforce, and $30 million for Apprenticeship Innovation Funding which benefited 70 apprenticeship programs in expanding sectors, such as health care, advanced manufacturing, and education.

The Employment Training Panel complemented this work by approving 299 training contracts in 2025, supporting 70,544 workers through employer-driven training programs, including $25 million to train more than 22,000 workers through apprenticeship programs that focused on training women, justice-involved individuals, veterans, and people transitioning from unemployment or low-paying jobs.

Clean up and recovery after the Los Angeles firestorms

In response to the devastating firestorms in Los Angeles, Governor Newsom coordinated a rapid, multi-agency response, which included a focus on economic recovery, reemployment, and worker health and safety.

The State delivered $20 million in aid to the LA region to support dislocated workers and critical recovery and rebuilding efforts, with a $10 million Department of Labor National Dislocated Worker Grant for humanitarian aid and cleanup through temporary jobs, and another $10 million for the Los Angeles County Department of Economic Opportunity to provide transitional jobs, training, and supportive services for displaced workers.

Through California Jobs First, the state awarded $3 million to the LA Jobs First Collaborative to support recovery efforts in Altadena and Pacific Palisades, including assistance for local small businesses and tourism-dependent employers and ongoing economic impact reporting and analysis to guide rebuilding.

The Employment Development Department (EDD) activated disaster response protocols to help impacted workers access unemployment insurance and disaster-related assistance, including direct support navigating claims.

At the same time, the Department of Industrial Relations (DIR) prioritized worker protection during cleanup and rebuilding. Cal/OSHA issued wildfire smoke and heat-safety guidance, conducted employer outreach, and enforced safety standards to protect vulnerable workers operating in hazardous conditions.

The Labor and Workforce Development Agency (LWDA) invested $1 million in five organizations focused on providing training and education to day laborers and domestic workers who are engaged in the clean-up and rebuilding of the areas impacted by the wildfires. LWDA coordinated with DIR and Cal/OSHA to collaborate on training and outreach to workers.

Strengthening worker protections

Governor Newsom signed legislation expanding collective bargaining rights and increasing protections for app-based drivers, such as those who work for Uber and Lyft. California also extended Cal/OSHA coverage to additional domestic workers, expanding workplace-safety protections statewide.

As the first state in the nation to adopt enforceable indoor and outdoor heat standards, 2025 marked the first full calendar year under California’s new indoor heat protections. During the 2025 heat season, Cal/OSHA conducted nearly 600 indoor heat inspections statewide and performed 385 targeted high-heat inspection sweeps. Additionally, the division’s criminal referrals tripled, underscoring a strong commitment to worker health and safety.

The Labor Commissioner’s Office strengthened enforcement across industries, recovering more than $65 million in unpaid wages, penalties and interest; issuing $84 million in citations for 687 labor law violations; and correcting 2,279 violations that affected more than 46,000 workers.

The Agricultural Labor Relations Board (ALRB) secured multiple unfair labor practice settlements in 2025. This included a settlement with Bonnie Plants, LLC, requiring $114,834.40 in back pay to eight farmworkers and a settlement with Redwood Empire Vineyard Management, Inc., resulting in $33,548 paid to farmworkers after unlawful retaliation.

Reaching every Californian

Through California’s Rural Strategic Engagement Program, the state is improving how the government serves workers in rural communities by making resources and services easier to access, clearer to navigate, and more responsive when concerns arise.

Through coordinated clinics and service in rural areas, the state is ensuring that when workers reach out, they’re connected to the right support. Multiple state departments work together to operate seven one-stop clinics in rural farm areas throughout the Central Valley, Central Coast, Riverside, and Yolo/Colusa regions. At the clinics, farmworkers can report issues such as unsafe conditions or unpaid wages, and the departments coordinate so the farmworker doesn’t have to navigate multiple systems – simplifying government and connecting working people with the services they need.

Through California’s Rural Strategic Engagement Program, the Labor and Workforce Development Agency and its Departments have begun cross-training staff on skill sets that can enhance their ability to serve agricultural workers, including learning cultural competency skills to engage indigenous farmworkers, presentation and public-speaking skills to better educate workers and employers, and laws and regulations that protect farmworkers even if outside the jurisdiction of the staff.