Sensory adaptation and pupil-linked arousal support flexible evidence accumulation during perceptual decision making

Perceptual decisions often require the accumulation of uncertain sensory evidence over time. This accumulation process is complicated by the fact that the nature and source of incoming evidence can change even while accumulation is underway. Under these changing conditions, effective decision makers should adjust how sensory information is accumulated so that their perceptual judgments reflect current conditions rather than outdated evidence gathered before a change occurred (Gold and Stocker, 2017). This flexibility can be achieved by adjusting the evidence-accumulation process in several ways, including adopting a leak and/or saturating non-linearity that can be adjusted according to environmental conditions to balance sensitivity to stability versus change (Glaze et al., 2015; Usher and McClelland, 2001). These adaptive forms of evidence accumulation are key features of decision-making behavior in dynamic environments (Glaze et al., 2015; Murphy et al., 2021; Piet et al., 2018) and are often modeled using one or more parameters that control the temporal dynamics of an accumulator (Glaze et al., 2015; Usher and McClelland, 2001; Veliz-Cuba et al., 2016, Figure 1).

Figure 1

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However, unlike these simple models, the brain contains many mechanisms that could, in principle, shape the temporal dynamics of evidence accumulation. Here, we considered a potential role for stimulus-specific sensory adaptation of individual neurons encoding the evidence that is accumulated downstream to form the decision (Figure 1). Adaptation is a hallmark of sensory systems, occurring across modalities (Dalton, 2000; Lampl and Katz, 2017; Laughlin, 1989; Ulanovsky et al., 2004) and species (Brenner et al., 2000; Clarke et al., 2015; Lesica et al., 2007; Nagel and Doupe, 2006; Tolias et al., 2001). Recent theoretical work has framed adaptation as a form of active inference, whereby sensory systems not only modulate their responses to match the statistical structure of incoming signals, but also infer when those statistics have changed and how rapidly they are expected to vary (DeWeese and Zador, 1998; Młynarski and Hermundstad, 2021; Solomon and Kohn, 2014; Wark et al., 2007; Weber et al., 2019). Consistent with this idea, recordings from isolated retinal ganglion cells have shown that the temporal dynamics of recent stimulus history can modulate adaptation (Wark et al., 2009). Yet it remains unknown whether individual neurons in primate sensory cortex undergo similar temporal-context-dependent adaptation, and whether such adaptation contributes to flexible evidence accumulation for perceptual decision making.

To test these ideas, we leveraged decisions about visual motion. These decisions depend on neurons in the middle temporal (MT) area of extrastriate visual cortex (Britten et al., 1992; Britten et al., 1996; Hanks et al., 2006; Newsome and Paré, 1988; Salzman et al., 1990), which exhibit robust stimulus-specific sensory adaptation (Kohn and Movshon, 2003; Van Wezel and Britten, 2002). We manipulated the stability of recent sensory experience by modifying the classic random-dot motion direction-discrimination task to include extended exposure to an adapting stimulus that reversed direction at either a low or high frequency, creating two context-stability conditions (Figure 2A). Immediately following this adapting stimulus, monkeys viewed and reported the direction of a test stimulus, which was identical across conditions, allowing us to isolate the influence of recent stimulus statistics on both behavior and neural activity. We found that monkeys adjusted their decision-making behavior in a manner that was consistent with context-dependent changes in the temporal dynamics of evidence accumulation. These adjustments were based, in part, on differences in how the evidence being accumulated was encoded by individual MT neurons.

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We also examined whether and how these context-dependent behavioral and neural adjustments related to simultaneous measures of pupil diameter. Non-luminance-mediated fluctuations in pupil diameter, an index of arousal, track quantities relevant to adaptive evidence accumulation, including environmental volatility and temporal expectations (Akdoğan et al., 2016; Nassar et al., 2012; Schwiedrzik and Sudmann, 2020). Pupil diameter also relates to perceptual sensitivity, bias, and variability in evidence accumulation (de Gee et al., 2017; de Gee et al., 2014; Keung et al., 2019; Krishnamurthy et al., 2017; Murphy et al., 2014). These findings imply a role for the arousal system in optimizing perceptual decision making, likely via neuromodulatory effects on neural information-processing dynamics (Aston-Jones and Cohen, 2005; Devilbiss et al., 2006; Joshi and Gold, 2020; Navarra et al., 2017; Waterhouse et al., 1998; Wyrick and Mazzucato, 2021). Arousal systems are therefore well positioned to, in principle, influence how sensory information is encoded, accumulated, or both (Figure 1). Our results, which show a dissociation between sensory adaptation- and arousal-related effects on evidence accumulation, provide new insights into the complex, distributed mechanisms that contribute to flexible decision making.

We trained three male rhesus macaques (An, Ch, and Mi) to perform a version of a random-dot motion direction-discrimination task that combined reversals in dot-motion direction (Glaze et al., 2015; Figure 2A) with elements of an adaptation-test paradigm (Van Wezel and Britten, 2002). Each trial consisted of two sequential epochs: an adapting epoch (Epoch 1) and a test epoch (Epoch 2). During the adapting epoch (2400 ms), the random-dot motion stimulus abruptly switched direction at either a low (LSF; 1 switch, occurring at 1200 ms) or high (HSF; 5 switches, occurring every 400 ms) frequency. The two adapting (context-stability) conditions were designed such that the total exposure time to each adapting motion direction was matched for low and high switch frequencies (1200 ms per direction). Immediately following the adapting epoch, the test stimulus was shown for a duration that was sampled independently on each trial from a truncated exponential distribution (100–1200 ms), requiring the monkeys to accumulate evidence over varied and unpredictable timescales. At the end of this duration, the test stimulus and fixation point were extinguished simultaneously, cueing the monkeys to report the final motion direction by making a saccade to the corresponding choice target.

Critically, for both low and high switch-frequency conditions there was a 50% probability that the motion stimulus changed direction between the adapting and test epochs. This design ensured that switch and non-switch trials were equally likely, and thus that the adapting stimulus provided no information about the correct test-stimulus direction. Moreover, the transition between adapting and test epochs was marked by predictable timing and a reduction in motion coherence (to ensure that the task was difficult enough to require sustained evidence accumulation), implying that the monkeys could, in principle, learn to ignore the adapting stimulus altogether. Instead, as detailed below, systematic effects of context stability on behavior, neural activity, and pupil modulations point to inherent ways in which the brain uses recent stimulus history to shape perception.

Context stability shapes sensory adaptation in MT

We recorded activity from 155 MT single units while the three monkeys performed the task (Monkey An = 55, Ch = 13, Mi = 87). As expected, given that stimulus properties were tailored to each neuron (see Methods), the activity of both individual units (Figure 3A) and the recorded population (Figure 3B) were robustly modulated by motion direction, showing strong responses to motion in each cell’s preferred direction and weak responses in the opposite, anti-preferred (null) direction that followed stimulus switching dynamics. Given the lack of responsiveness to null motion and the unbalanced exposure to the final motion direction on non-switch trials (i.e., an additional 800 ms at low relative to high switch frequency), unless otherwise noted the analyses of MT neural activity were restricted to preferred-motion switch trials, which were most informative for characterizing context-dependent evidence encoding.

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Temporal-context stability systematically affected responses to preferred motion during the test epoch, with reduced activity following an adaptor that switched at a high versus low frequency (Figure 3C; individual animals in Figure 3—figure supplement 1A). Differences in neural activity between the two context-stability conditions were best characterized by a change in overall response magnitude rather than a change in temporal dynamics (Figure 3—figure supplement 1B). Moreover, these differences varied systematically with direction selectivity. MT single units with stronger direction selectivity, defined as larger differences between responses to preferred and null motion, showed greater differences in activity between switch-frequency conditions (Figure 3D). In other words, neurons that encoded motion evidence more strongly and more selectively were also more sensitive to the temporal statistics and stability of that evidence, exhibiting larger context stability-dependent adjustments.

These context-dependent differences in MT neural activity did not reflect baseline differences in responsiveness that persisted throughout the low and high switch-frequency blocks. Rather, they emerged over the course of each trial, consistent with stimulus-specific sensory adaptation. Specifically, the average population responses to the first presentation of preferred motion during the adapting stimulus were initially matched between conditions (Figure 4A; although one animal showed an early difference, possibly reflecting learned expectation about switching dynamics within blocks, Figure 4—figure supplement 1). Context-dependent differences developed with repeated stimulus presentation within a trial, culminating in robust differences in neural responses to the test stimulus between low and high switch-frequency conditions (Figure 4A, B).

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The effect of context stability on evidence encoding was consistent across distinct patterns of adaptation. Whereas most MT single units showed persistent response decrements with repeated preferred-motion presentations (Figure 4C, bottom), some exhibited response increments (Figure 4C, top). We classified these two groups of neurons as ‘adapting’ and ‘facilitating’, respectively, based on changes in baseline-subtracted, normalized responses between the first presentation of preferred motion and preferred motion during the test stimulus (see Materials and methods). Regardless of whether neurons were adapting or facilitating, both groups showed comparable magnitudes of response change as a function of preferred-motion stimulus presentation (Figure 4D). Notably, the majority of this response change, particularly for adapting neurons, occurred between the first and second presentations of preferred motion, with relatively little additional change across subsequent presentations. This pattern suggests that adaptation dynamics are shaped by temporal features of the adapting stimulus, such as the frequency of direction switches, as opposed to the number of preferred-motion stimulus presentations. Moreover, regardless of whether MT neurons were adapting or facilitating, both groups exhibited robust context-stability effects with diminished activity at high versus low switch frequency during the test stimulus (Figure 4E, F). Thus, context-specific differences in evidence encoding arose from stimulus-specific sensory adaptation that unfolded over repeated preferred-motion exposure and was expressed across MT neurons with different response profiles.

Context-dependent evidence encoding in MT relates to evidence-accumulation behavior

To assess the behavioral relevance of context-dependent differences in MT neural responses, we first quantified how well individual neurons discriminated between preferred and null motion at low versus high switch frequency using ROC area (Figure 5A). Across the population, ROC area tended to be lower for high versus low switch frequency (Figure 5B; individual animals in Figure 5—figure supplement 1A), indicating decreased evidence discriminability in MT when the test stimulus was preceded by a more unstable adapting context. We then tested whether, across sessions, these differences in ROC area predicted context-dependent behavioral differences. Because ROC analyses were restricted to switch trials (which, unlike non-switch trials, have balanced exposure to the final adapting motion direction), we also restricted the behavioral analyses to switch trials, using percent correct instead of psychometric slope (which was based on all trials). We focused on trials 375–600 ms in duration, because that time bin exhibited robust context-dependent differences in accuracy that remained reliably above chance, but below ceiling, for both conditions (Figure 2D, Figure 2—figure supplement 3B). We found that the differences in MT evidence discriminability (LSF minus HSF ROC area, in the epoch 200–400 ms following test-stimulus onset that showed the largest context-dependent differences in neural activity) correlated with context-stability differences in behavioral performance (LSF minus HSF percent correct for trials ending 375–600 ms following test-stimulus onset, Figure 5C), such that greater differences in discriminability between low and high switch-frequency conditions predicted larger differences in performance. This effect was significant across animals but driven primarily by Monkey An (Figure 5—figure supplement 1B).

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To further explore this relationship, we divided sessions based on evidence-accumulation behavior. For sessions in which the monkeys had shallower psychometric slopes at high relative to low switch frequency (Figure 5D), MT neurons exhibited a corresponding decrease in firing rate and discriminability on switch trials (pairwise comparisons for firing rates in Figure 5—figure supplement 2). Conversely, for all other sessions (Figure 5E), we did not identify any reliable context-stability differences in MT evidence encoding or discriminability (pairwise comparisons for firing rates in Figure 5—figure supplement 2). This relationship was specific to correct trials and absent for incorrect trials, which showed no modulation based on evidence-accumulation behavior (Figure 5—figure supplement 3). A similar, behavior-dependent pattern in discriminability was also evident on non-switch trials (Figure 5—figure supplement 4). However, these context-dependent differences were restricted to a smaller and earlier time window (50–200 ms), likely reflecting activity driven primarily by the adapting rather than the test stimulus. Together, these results suggest that adaptation-driven differences in MT evidence encoding contribute, at least in part, to context-dependent evidence-accumulation behavior.

Context-dependent evoked pupil modulations relate to evidence-accumulation behavior

To probe whether processes beyond local sensory adaptation contribute to adaptive evidence accumulation, we examined both baseline and evoked pupil diameter as proxies for arousal-related modulations that have been shown to encode key features of adaptive perceptual decision making (Cheadle et al., 2014; Krishnamurthy et al., 2017; Murphy et al., 2021). Trial-wise baseline pupil diameter was correlated with the time required for monkeys to obtain fixation (Figure 6—figure supplement 1A), a relationship that was robust across sessions and animals (Figure 6—figure supplement 1B). However, baseline pupil diameter did not differ systematically between low and high switch-frequency conditions (Figure 6—figure supplement 1C), suggesting that it captured global as opposed to context-dependent fluctuations in arousal or task engagement. We therefore focused on task-evoked, as opposed to baseline, pupil responses in subsequent analyses.

Evoked pupil responses were modulated by context stability. Following a brief, stereotyped constriction caused by the abrupt luminance increase at motion-stimulus onset, differences in pupil diameter emerged between low and high switch-frequency conditions (Figure 6A). To quantify these context-dependent effects, we used sliding-window linear regressions (Equation 2) to estimate differences in evoked pupil diameter (LSF–HSF), while controlling for baseline pupil diameter (because evoked and baseline diameter tend to be inversely related; Mathôt et al., 2018). For both monkeys (Monkey Ch was excluded from pupil-related analyses; see Methods), the effect of context-stability on evoked pupil diameter increased over the course of the adapting stimulus (Figure 6B; individual animals in Figure 6—figure supplement 2A). This result is consistent with the idea that evoked pupil responses track context-dependent expectations or anticipatory processes associated with behaviorally relevant stimuli.

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Context-dependent differences in evoked pupil diameter were relevant to behavior. Specifically, differences in evoked pupil diameter (LSF–HSF) showed correlations with behavioral sensitivity (LSF–HSF psychometric slope) that varied over the course of the trial and peaked just before the onset of the test stimulus (Figure 6C). Around this peak (−500 to 0 ms before test-stimulus onset), average context-dependent differences in evoked pupil diameter reliably predicted subsequent differences in behavioral sensitivity across sessions (Figure 6D; individual animals in Figure 6—figure supplement 2B). As such, sessions with larger differences in evoked pupil diameter also tended to show larger context-dependent differences in behavioral sensitivity.

Adaptation and arousal-related mechanisms are jointly and differentially recruited across sessions

To directly compare the relative contributions of MT sensory adaptation and arousal-related mechanisms to adaptive evidence-accumulation behavior, we separately incorporated trial-wise measures of MT neural activity and evoked pupil diameter into the psychometric functions (Equation 3). This approach allowed us to quantify how strongly each signal modulated behavioral sensitivity as a function of context stability (LSF–HSF) on a session-by-session basis.

Both neural and pupil terms improved model fits, as evidenced by increased explanatory power relative to the behavior-only model in both animals (Figure 7A). However, the relative improvement attributable to neural activity versus evoked pupil diameter differed between monkeys, with Monkey An showing a larger difference between neural versus pupil terms than Monkey Mi (Wilcoxon rank-sum test on neural–pupil differences in Tjur’s pseudo-R²; p < 0.001, Figure 7A), implying joint, but flexible recruitment of adaptation and arousal-related mechanisms.

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This flexible recruitment was evident across sessions, where the influence of context-dependent MT neural activity and evoked pupil diameter on behavioral sensitivity varied considerably, with neither term reliably predominating over the other (Figure 7B). Session-wise, context-dependent contributions from neural and pupil terms were uncorrelated with each other, consistent with the two signals contributing separately rather than redundantly or in opposition (Figure 7B). In further support of this idea, context-dependent modulations of pupil size were not related systematically to context-dependent modulations of MT neural activity or discriminability (Figure 6—figure supplement 2C, D). Together, these findings suggest that sensory adaptation and pupil-linked arousal both contribute to adaptive evidence accumulation, likely operating at different processing stages, with their relative weighting varying substantially across sessions and animals.

Flexible evidence accumulation is critical for effective decision making in dynamic environments. Many algorithmic accounts propose that this flexibility arises from context-dependent adjustments to the process of evidence accumulation itself. However, the brain contains many adaptive mechanisms at different stages of the decision process that could contribute to this behavioral flexibility. Motivated by this idea, we manipulated the temporal stability of recent sensory experience and simultaneously measured both neural activity in area MT and pupil-linked arousal. We identified two complementary mechanisms that could support flexible evidence accumulation, one involving sensory adaptation and the other involving arousal-related neuromodulation (Figure 1). Our results suggest that these mechanisms operate separately, likely at different processing stages, providing a new view of the distributed neural substrates for flexible evidence accumulation in dynamic environments.

Our MT findings indicate that context-dependent changes in how choice accuracy evolves as a function of viewing time, which is a behavioral signature of flexible evidence accumulation, can arise, at least in part, from context-dependent differences in sensory encoding. Both motion-evidence encoding and discriminability of MT responses during the test-stimulus tended to be reduced following exposure to more temporally unstable adapting stimuli (Figure 3A–C). These reductions were not sustained differences throughout the two context-stability conditions but instead emerged on each trial through repeated presentations of preferred motion at different switch frequencies, consistent with stimulus-specific sensory adaptation (Figure 4A, B). This stimulus-specific adaptation varied systematically across individual units, such that more direction-selective cells were more affected by differences in stimulus temporal statistics, which is consistent with previous findings that more active MT neurons adapt more strongly (Figure 3D; Van Wezel and Britten, 2002).

It is well established that sensory adaptation can depend on stimulus statistics such as the mean, variance, and salience of sensory inputs (Dean et al., 2005; Fairhall et al., 2001) and even changes in those statistics (Kastner and Baccus, 2011; Tikidji-Hamburyan et al., 2015). Here, we extend these findings by demonstrating that adaptation can also depend on the rate of change of the stimulus statistics. This kind of temporal dynamic-dependent adaptation has been observed in retinal ganglion cells (Wark et al., 2009). Our work extends those findings to non-human primate cortical neurons with causal links to behavior (Hanks et al., 2006; Newsome and Paré, 1988), in particular showing that context stability-dependent adaptation in MT shapes the sensory evidence available for time-evolving perceptual decisions.

We also observed heterogeneity in how MT neural activity changed over the course of the adapting stimulus, with some cells exhibiting progressive response attenuation and others facilitation (Figure 4C, D). A plausible mechanism for this facilitation is that adaptation alters the local excitation-inhibition balance (Higley and Contreras, 2007; Solomon and Kohn, 2014). In particular, repeated presentation of null motion may weaken opponent input, producing disinhibition of preferred-motion responses (Kohn and Movshon, 2003). Consistent with this account, facilitating neurons showed increased responses to preferred motion following null-motion adaptation (Figure 4—figure supplement 2). This diversity of adaptation profiles mirrors observations in retinal ganglion cells (Kastner and Baccus, 2011; Kastner and Baccus, 2013), primary auditory cortex (Seay et al., 2020), and somatosensory cortex (Adibi et al., 2013; Cohen-Kashi Malina et al., 2013; Derdikman et al., 2006; Kheradpezhouh et al., 2017), and recent work suggests that whether a neuron adapts or facilitates may itself be a flexible property that depends on recent sensory experience (Dobler et al., 2024). Nevertheless, regardless of the adaptation profile, context stability shaped MT activity and discriminability in ways that corresponded to context-dependent differences in evidence-accumulation behavior across sessions.

These adaptation-driven changes in MT evidence encoding were relevant to behavior but not fully prescriptive of context-dependent adjustments in evidence accumulation. For instance, for sessions in which the monkeys showed behavioral evidence of context-dependent evidence-accumulation (i.e., reduced sensitivity as a function of viewing time at high relative to low switch frequency; Figure 5D), there was a corresponding change in MT evidence encoding (e.g., reduced neural activity at high relative to low switch frequency; Figure 5D). However, sessions showing the opposite pattern of behavior did not show opposite neural effects (Figure 5E). Thus, while sensory adaptation supports flexible evidence accumulation by shaping the fidelity of sensory input, additional mechanisms likely contribute to how that information is dynamically weighed and accumulated over time.

Consistent with this idea, fluctuations in task-evoked pupil size, an index of arousal-related neuromodulation, were also related systematically to temporal-context stability. Differences in evoked pupil diameter emerged between low and high switch-frequency conditions over the course of the adapting epoch, peaking just before test-stimulus onset (Figure 6B). This time course suggests that evoked pupil dynamics may be tracking expectations about environmental statistics to prepare downstream decision circuits for the behaviorally relevant stimulus, consistent with previous work (Filipowicz et al., 2020). Across sessions, the magnitude of context-dependent differences in evoked pupil diameter predicted corresponding differences in behavioral sensitivity (Figure 6C, D). Specifically, larger pupil diameter corresponded to less-efficient evidence accumulation, a result consistent with previous work relating larger pupil diameter to increased variability (Murphy et al., 2014) and individual differences in the temporal dynamics of evidence accumulation (Keung et al., 2019). These findings join a growing body of work showing that pupil-linked arousal contributes to adaptive mechanisms that shape behavior to match current environmental demands.

Our findings further suggest that these pupil-linked arousal mechanisms operate separately from sensory adaptation to support flexible evidence accumulation. Adding trial-wise MT neural activity or evoked pupil diameter each increased explanatory power relative to behavior-only models, demonstrating that both signals provided behaviorally relevant information (Figure 7). However, the fact that their relative contributions were uncorrelated across sessions and that evoked pupil responses were unrelated to MT activity suggests that sensory adaptation and pupil-linked arousal may shape behavior through distinct mechanisms at different processing stages. That said, given the general unreliability of trial-wise data, particularly pupil responses, we cannot rule out the possibility that the lack of correlation between adaptation and arousal-related contributions results, in part, from this inherent variability as opposed to true independence of the underlying mechanisms.

One possible interpretation of these arousal-related signals is that they are related to neuromodulatory mechanisms that control the efficiency or leakiness of downstream accumulator circuits, such as those in the lateral intraparietal area, frontal eye field, or superior colliculus, that read out MT activity. Alternatively, or in addition, both MT adaptation and pupil-linked arousal could be independently regulated by prefrontal regions, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, or orbitofrontal cortex, that monitor task performance and environmental state (Behrens et al., 2007; McGuire et al., 2014; O’Reilly et al., 2013) and project both to brainstem arousal systems (Breton-Provencher and Sur, 2019; Schwarz and Luo, 2015) and to sensory cortex (Banerjee et al., 2020; Gilbert and Li, 2013; Murphy et al., 2021). Either possibility supports a framework in which sensory adaptation provides a ‘bottom-up’ mechanism that modulates evidence encoding based on recent stimulus statistics, whereas pupil-linked arousal provides a ‘top-down’ signal that adjusts how that encoded evidence is accumulated over time according to expectations about environmental stability. Together, these complementary processes may jointly implement the context-dependent non-linearities described by normative models of evidence accumulation in dynamic environments (Glaze et al., 2015; Murphy et al., 2021; Figure 1), with variability in their relative contributions across sessions and animals suggesting flexible recruitment that may depend on factors such as training, experience, or strategy.

Our conclusions are tempered by several limitations. First, we do not know the exact algorithmic nature of the context-dependent effects we measured on evidence accumulation behavior (i.e., differences in how accuracy evolves as a function of viewing time), which could involve a leak, saturating non-linearity, and/or other forms of adjustment. Disentangling these alternatives necessitates additional behavioral testing and analyses (e.g., reverse-correlation analyses, which would require interspersing lower-coherence test stimuli than were used in this study) under a broader range of conditions. Second, we do not know where the context-dependent adaptation measured in MT originates. In principle, these effects could, at least in part, be inherited from upstream visual areas, including areas V1 (Born and Bradley, 2005; Churchland et al., 2005; Movshon and Newsome, 1996; Rodman et al., 1989) and V2 (DeYoe and Van Essen, 1985), where adaptation to motion is also known to occur. Third, our recordings sampled single units across sessions and thus did not capture population-level changes in MT activity. Adaptation can influence activity beyond single neurons, including changes in correlated variability within neural populations that can affect decision making (Solomon and Kohn, 2014; Whitmire and Stanley, 2016). Moreover, correlated variability in cortex can covary with pupil size and LC–NE activity (Joshi and Gold, 2022; Vinck et al., 2015), providing another potential link between arousal and sensory encoding that merits further study. Fourth, downstream cortical (Ding and Gold, 2012; Kim and Shadlen, 1999; Shadlen and Newsome, 2001) and subcortical (Ding and Gold, 2013; Horwitz and Newsome, 1999; Lovejoy and Krauzlis, 2010) areas that read out MT activity to form the decision may also contribute to flexible adjustments to the process of evidence accumulation. Future work combining further behavioral testing and simultaneous recordings and/or causal manipulations across sensory, decision, and arousal-related regions will be essential to disentangle how multiple distributed mechanisms implement adaptive evidence accumulation to support flexible decision making.

In summary, this work contributes to a broader reconceptualization of classic, hierarchical views of sensorimotor pathways that cast primary sensory cortex as a static encoder supplying input to flexible downstream decision processes that guide behavior. Instead, a growing body of evidence suggests that these early sensory areas can provide adaptive, context-dependent signals shaped by recent experience and environmental structure that can impact behavior (Waiblinger et al., 2022; Waiblinger et al., 2026). Our demonstration of separable, behaviorally relevant contributions from sensory adaptation in MT and pupil-linked arousal supports this view, indicating that flexible decision making in dynamic environments emerges from distributed mechanisms that include intrinsically adaptive signals in early sensory cortex that complement adjustments in downstream processes.

Three adult male rhesus monkeys (Macaca mulatta, age range: 16–21) participated in this study. Two of the monkeys (An and Ch) had extensive prior training on a standard (i.e., non-switching) version of the random-dot motion direction-discrimination task. All training, surgery, and experimental procedures conformed to the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the University of Pennsylvania Institutional Animal Care and Use Committee (protocol #806027).

The data are available on Dryad: https://doi.org/10.5061/dryad.jh9w0vtsr. The code is available on GitHub: https://github.com/TheGoldLab/ms_2026_mcgaughey_gold_elife, copy archived at Gold, 2026.

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   Data from: Sensory adaptation and pupil-linked arousal support flexible evidence accumulation during perceptual decision making.

   https://doi.org/10.5061/dryad.jh9w0vtsr

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Author details

1. Kara D McGaughey

   1. Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
   2. Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, United States
   3. Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Software, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8670-7303

2. Joshua I Gold

   1. Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
   2. Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, United States
   3. Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   jigold@pennmedicine.upenn.edu

   Competing interests

   Senior editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-6018-0483

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