Dissociating neuronal signatures of spatial attention and behavioural state in the primary vibrissal cortex of mice

Reviewer #1 (Public review):

The paper uses a passive whisker detection task in mice to identify a behavioral phenomenon that can reasonably be interpreted as spatial attentional capture. The attentional effect occurs transiently after a successful whisker stimulus detection yields reward, and lasts for a few trials before subsiding. The attentional effect is to the right or left whiskers, depending on whether right or left whiskers are rewarded; no finer spatial resolution for attention was tested. By recording whisker-evoked spiking from single units in S1, the authors show that this form of spatial attention increases the gain of whisker-evoked neuronal responses in S1 for a large subset of S1 units. In contrast, neural responses are not modulated by overall task engagement. Together, these findings show a neural signature of spatial attention in S1 cortex. Because whisker or facial movements were not tracked, it is not clear whether this represents covert attention or whisker movement in response to previously rewarded stimuli, which would be a form of overt attention.

Substantial attentional modulation of neural responses was observed for a subset of whisker-responsive S1 units, but the effect size was small on average for the total unit population. The top 25% of units showed a ~12% attentional response modulation (relative to firing rate range for each unit), but the median unit showed only a 1.3% response modulation. It would have been useful to analyze the magnitude or prevalence of attentional modulation across layers or in fast-spiking vs. regular spiking units, but this was not reported.

Major

(1) It is hard to interpret the underlying causes of the attentional modulation of neural activity without having measured whisker and facial movement. This is a particular issue in S1, where whisker movement against the stimulation grid can alter the mechanical efficiency of stimulus delivery. Such movements would represent overt attention, which would engage an entirely different neural mechanism than covert attention.

(2) An interesting debate is whether the behavioral phenomenon is best described as attention or as dynamic learning of the stimulus-response association for that block. In Posner-type cued attention tasks, and also in many block-type attention tasks in rodents, animals receive reward for successfully detecting either cued or uncued stimuli, and thus attention (higher response probability or improved psychometric sensitivity for cued stimuli) is at least partially dissociated from the stimulus-reward contingency. That is not the case here. The fact that mice have difficulty learning the contingency reversal suggests that the phenomenon is better explained by attention than by learning the contingency; however, to prove this clearly, the existence of the attentional effect on neural activity in Block 1 vs. Block 2 would have to be shown.

(3) Some of the graphical representations of the attentional modulation of neural activity are unclear. The single-unit example of attentional modulation is quite strong (Figure 3d). The mean response for the top 25% of units is also visually clear (Figure 3f). But the effect is not apparent at all in Figure 3e, which the figure legend says shows every unit. What is the yellow point and line in this figure? Why isn't the attentional effect visible in this panel? Perhaps I am misunderstanding Figure 3e, but it is not clear to me why it compares Pref>0.5 to Pref<0.5, when the intended analysis suggests it should be Pref>0 to Pref<0? Also in Figure 3, it is critical for the reader to know whether panels 3g-3h represent the top 25% of units or all units. Neither the results text nor the legend is clear on this.

(4) There is a missed opportunity to quantify attentional modulation across cortical layers, since laminar probes and Neuropixels probes were used for the recordings. In addition, there is no separation of fast-spiking from regular-spiking units, and no quantitative metrics are provided to assess the quality of single units. This could reveal key aspects of cortical processing of attentional signals.

Reviewer #2 (Public review):

Summary:

Dyce et al investigate the modulation of sensory responses in the somatosensory 'barrel' cortex during a novel whisker vibration detection task in head-fixed mice, aiming to find correlates of spatial attention in both the animals' behavior and their neuronal activity.

Strengths:

The authors produced an extensive and parameterized dataset of both behavioral responses and neuronal activity, with >3000 single units of which >1400 were responsive.

Weaknesses:

In my view, the main conclusions of the manuscript are not currently well supported by the data.

The authors effectively define "spatial attention" as a state where an animal responds more to a stimulus that gives more rewards (out of two possible stimuli presented on different sides of the snout, i.e., segregated spatially). If one defines spatial attention purely in these terms, then their findings do show neuronal correlates of spatial attention. However, those neuronal correlates can be explained by known aspects of neuronal responses in the barrel cortex.

This plays out in several different ways:

From the behavioral point of view, greater attention may correlate with an increased hit rate to stimuli on the rewarded side, but in the absence of other supporting measurements, the relationship could well be the opposite: an animal could pay more (rather than less) attention to the stimulus delivered on the unrewarded side, to make sure it suppresses the incorrect response. It is impossible to tell, as the data don't provide an independent measurement of whether the animal is paying greater attention to, or is more aware of, one side than the other, nor do they provide an independent measurement of neuronal tuning on either side. There is no separate measurement of arousal either (e.g., via pupillometry or locomotion).

The experimental design involved two blocks on each daily task session, with the second block reversing the side on which rewarded stimuli were delivered. Reinforcing one's doubts about the behavior and its interpretation, mice had much poorer performance on each day's second block, to the extent that perceptual sensitivity (d') was the same for both sides: d' did not increase after reward reversal for stimuli on the initially unrewarded side. This further emphasizes the lack of a separate demonstration of focused "spatial attention".

Much of the data (both behavioral and neuronal) could be accounted for, e.g., by a strategy where the mouse keeps a token in working memory of what side seems to be driving rewards, while maintaining equally strong sensory drive on both sides, but with no attentional shift at all. The policy would be to respond more whenever the stimulated side matches the token in memory (thus also reinforcing the token, thus enhancing performance next time). This would be easily implemented with a disinhibitory reward-modulation signal such as the one multiple researchers have found carried by VIP neurons (e.g., Szadai et al DOI: 10.7554/eLife.78815).

Similarly, the fact that "attended trials" (Pref > 0) produced greater responses than "unattended trials" appears to be explainable as follows. Here, "attended" trials are those where the contralateral stimulus is presented (and, if responded to, is rewarded), "unattended" trials are those where the stimulus is ipsilateral (and not rewarded). The animal responds more (at least in the first block) to stimuli delivered to the contralateral pad - i.e., rewarded as opposed to unrewarded ones. Beyond the knowledge mentioned above that cortex-wide VIP sensitivity to rewards can drive disinhibition in general, activity modulation dependent on rewards and outcomes (and stimulus value) has been established specifically in the barrel cortex (e.g., Lacefield et al DOI: 10.1016/j.celrep.2019.01.093, Bale et al DOI: 10.1016/j.cub.2020.10.059, Banerjee et al DOI: 10.1038/s41586-020-2704-z, Chereau et al 10.1038/s41467-020-17005-x). The reward- and value-evoked activity demonstrated in those papers would suffice to predict more activity at the contralateral electrode on "attended" trials, along the lines of the findings in Ramamurthy et al (DOI: 10.1038/s41467-025-60592-w) and consistent also with the enhanced "attentional modulation" on hit trials.

Other aspects of the analysis and terminology lead to confusing outcomes. For example, in the analysis in Figure 3, Performance averaged in a set of trials around a given trial is defined as the mean rate of responses to stimulation on either side - regardless of whether those responses are correct (since the stimuli can be on either side, but only one side is correct and gets rewarded and putatively reinforced). Thus, this definition of "Performance" can increase with the rate of incorrect licks to the wrong side and is at odds with the normal use of the word. On trials where this Perf = 1 and the stimuli are balanced on either side, this corresponds to a true performance (and reward rate) of only 0.5 - what one would normally consider random discrimination between the sides. Thus, Perf = 1 trials may still give a low reward rate and, if responses scale with reward, a small effect of reward. Hence, based on known properties of reward dependence, greater correlation of neuronal activity with "Preference" than with "Performance" would be expected, rather than reflecting a new aspect of "spatial attention". A definition of performance more in line with established practice and measuring side-to-side discrimination (corresponding more closely to the authors' "Preference" parameter) would have shown this more clearly.

Author response:

(1) Introduction & Roadmap

We are grateful to the Reviewers for engaging with outstanding questions relating to our findings’ connections to multiple subdisciplines of cognitive neuroscience. Noting that Reviewers 1 and 2 interpreted our findings differently, we welcome the opportunity to engage in what Reviewer 1 characterised as “an interesting debate”. To promote a shared understanding and discussion of our findings, we have organised our response to address more technical comments first.

Our provisional response is organised as follows: Section 2 addresses selected technical comments relating to our Results. Section 3 addresses comments related to the design of our behavioural paradigm. Section 4 focuses on the broader interpretation of our findings. Section 5 concludes our provisional response with potential future directions and a summary of the significance of our findings.

(2) Selected technical comments related to our Results

We apologise to Reviewer 2 for the confusion in relation to the meaning of “attended” and “unattended” trials. What we said was “Positive Pref values indicate a higher response rate to the contralateral side than the ipsilateral side (relative to the electrode)” (Figure 3c caption), “we indexed all contralateral whisker vibrations according to their associated Perf and Pref” (Results text), and “we divided trials into (contralaterally) attended (Pref_C/L: Pref>0) and unattended (Pref_I/L: Pref<0) groups” (Results text). We can confirm that we defined an “unattended trial” (Pref<0) as a contralateral stimulus trial in the centre of an epoch (10-15 trials) within which the mouse responded (licked) more frequently to ipsilateral stimuli. Critically, we did not define an unattended trial as an ipsilateral stimulus trial. Furthermore, attention thus defined (i.e. Pref>0) can vary independently of the whisker stimulus associated with rewards. Indeed, while we initially did not include this result in our paper for the sake of brevity, even unrewarded “attended” trials (Pref>0) evoked significantly greater neuronal responses than unrewarded “unattended” (Pref<0) trials. We note that this is an analysis suggested by Reviewer 1, and we will include and discuss this result in our revised manuscript (e.g. in relation to literature suggested by Reviewer 2). For additional clarity, we use “Performance” (Perf) in relation to overall stimulus detection, consistent with the analysis of Lee et al. (2020), which found this measure was correlated with pupil diameter in a vibrissal target detection task.

We thank Reviewer 1 for noticing that the axes on Figure 3e should be labelled “Pref>0” (Y axis) and “Pref<0” (X axis), as suggested by the figure caption. We will correct this in our revised submission. The yellow point on Fig 3e shows the unit from Fig 3d, while the yellow line in Fig 3e shows the magnitude of that unit’s (non-normalised) gain modulation. While this is alluded to in the Results text (“The example unit in Figure 3d is in the 93rd percentile of units for raw modulation depth (ΔHits(attended – unattended) = 3.3 spikes/second; yellow line in Fig.3e)”, this should be explained in the Figure caption, and it will be in our revised manuscript. We would also like to clarify that Figures 3g–3h display results for all units, not just the top 25%. We agree this is not sufficiently clear and we will rectify this in our revised manuscript. Addressing Reviewer 2, while we acknowledge that mice responded less to both stimuli in the second block, they also meaningfully adjusted their behaviour to the reversal in reward contingencies: their responses to the previously rewarded stimulus reduced significantly more than those to the previously unrewarded stimulus.

(3) Design of the behavioural paradigm

We made a deliberate design choice to maximise the ecological validity of our behavioural paradigm, and note that there are advantages to doing so. For example, our paradigm can be used to show that even unrewarded “attended” trials (Pref>0) evoke significantly greater neuronal responses than unrewarded “unattended” (Pref<0) trials (see Section 2, above). Indeed, it is precisely this finding that makes our paradigm uniquely suited to the investigation of value-driven attentional capture (Anderson et al., 2011): in this instance attention directed to stimuli that are no longer rewarded despite equal availability of rewarded stimuli. This finding also demonstrates that our paradigm dissociates attention from stimulus-reward contingency at least as well as other paradigms which have been successfully used to study spatial attention in mice. As noted in Section 2, we will discuss this result in relation to other relevant research (e.g. Ramamurthy et al., 2025) in our revised manuscript.

Briefly, the direct manipulation of reward contingencies is one of two noteworthy methodological distinctions between our own paradigm and that of Ramamurthy and colleagues (2025). The task of Ramamurthy et al. (2025) associated all whisker stimuli with rewards and delivered stimuli to different whiskers on a single whisker pad. These methodological distinctions may have reduced the relevance of the spatial differences between stimuli to the mice undertaking the task. Indeed, it is not certain that a mouse would treat the unilateral variation in whisker stimulation Ramamurthy and colleagues delivered as primarily spatial or featural. The psychophysical and neural differences between spatial and featural attention in humans suggest dissociable underlying mechanisms, and the same may be true in mice. Thus, our own paradigm may more effectively isolate spatial attention from featural attention. Conversely, to the extent that the findings of Ramamurthy and colleagues do reflect spatial attention, our combined findings and paradigms help elucidate the associated mechanisms across spatial scales in mice.

We acknowledge that spatial cueing is well-suited to isolating the effects of covert attention from other forms of attention. However, it should be noted that spatial cueing in rodents is subject to its own challenges, including limitations in trial numbers due to the required manipulation of stimulus intensity (Reynolds et al., 2000; Herrmann et al., 2010), cue validity and associated trial probabilities (Peterson & Gibson, 2011; Girardi et al., 2013). Such experiments are further complicated by the duration and efficacy of training (i.e. the number of mice that learn the task; Wang & Krauzlis, 2018; Hu & Dan, 2022). It is also worth noting that trial probability manipulations introduce the same limitation in trial numbers with block-type attention tasks (You & Mysore, 2020; Kanamori & Mrsic-Flogel, 2022).

While there are clear differences between our own paradigm and those mentioned above, there are also important similarities. First, these tasks are all goal-directed, stimulus-driven, and reliant on learned task contingencies (e.g. Peterson & Gibson, 2011; Girardi et al., 2013). Furthermore, these paradigms are all operant conditioning protocols which leverage learned stimulus-reward contingencies to train attention-related behaviours in mice. A noteworthy similarity between our findings and those of authors using block-type attention tasks in particular (e.g. You & Mysore, 2020; Kanamori & Mrsic-Flogel, 2022) is the observation of apparent attentional biases in behavioural responses independent of the experimental manipulations (i.e. stimulus probability / reward contingency).

(4) Comments relating to the broader interpretation and discussion of our findings

Fundamentally, attention involves dedicating limited processing resources to some stimulus events at the expense of others. The design of our behavioural paradigm was informed by existing literature on spatial attention in humans, non-human primates, and mice. Our choice of behavioural and neuronal measures as proxies for attention in mice is consistent with this literature. It is technically possible “an animal could pay ‘more’ (rather than less) attention to the stimulus delivered on the unrewarded side, to make sure it suppresses the incorrect response”, but this seems unlikely given what is known about how attention is typically allocated in such tasks, based on the previously mentioned literature.

With respect to the interpretation and discussion of our findings, Reviewer 1 describes them as “a behavioral phenomenon that can reasonably be interpreted as spatial attentional capture” but suggests they do not clearly distinguish whether this attentional capture is covert or overt. We respectfully disagree for three reasons. First, as discussed in our paper, whisker motion during detection tasks has consistently been associated with reduced detection performance (Ollerenshaw et al., 2012; Kyriakatos et al., 2017; Vandevelde et al., 2023), suggesting that a “receptive” strategy (Diamond & Arabzadeh, 2013) of whisker immobilisation is more applicable to the current data than a “generative” strategy of asymmetric whisker movement (O'Connor et al., 2010; Dominiak et al., 2019). Second, if our behavioural and neuronal findings were due to the mice moving their whiskers to maximise contact with the meshes, we would expect increased evoked neuronal responses to be associated with greater Perf, not just with greater Pref. This pattern was not observed. Of course, the mice might have employed different whisker movement strategies during epochs of high Pref and Perf, but this seems unlikely and is not a parsimonious explanation for our findings. Third, as noted in the Methods section of the paper, we deliberately positioned the meshes close to the base of the whiskers, limiting the impact of whisker movements on stimulus detectability and the incentive to make them.

In contrast, Reviewer 2 questions the interpretation of our findings as evidence of spatial attention and suggests they might reflect working memory instead. Current research suggests attention and working memory are intimately related integrative brain functions. Indeed, some researchers have even proposed that working memory might be a form of internally directed attention (Awh & Jonides, 2001; Chun, 2011; Gazzaley & Nobre, 2012; Kiyonaga & Egner, 2013; or vice versa: Libedinsky & Fernandez, 2019). Consistent with the comments of Reviewer 2, more recent work seems to emphasise the coordination of attention and working memory (e.g. Joe & Kim, 2023; Zhu et al., 2026; for reviews see Huynh Cong & Kerzel, 2021; van Ede & Nobre, 2023), along with shared mechanisms (Kiyonaga et al., 2021; Panichello & Buschman, 2021), and nuanced dissociations (Liu et al., 2025). Attention is difficult to dissociate from working memory partly because there are multiple definitions (and/or types) of attention. We did not discuss the various definitions and/or forms of attention at length in our paper, but we will briefly discuss this in the revised manuscript.

The “interesting debate” to which Reviewer 1 refers could also be described as vigorous, despite approximately three decades of research. This debate broadly relates to the degree to which attentional control is driven by exogenous (e.g. colour contrast) versus endogenous factors (e.g. the focus of spatial attention, see Fig.2 in Belopolsky et al., 2007; see also: Liesefeld & Mueller, 2020; Manini et al., 2021; Beffara et al., 2022), and the degree to which this is a function of experimental context. The review article by Luck et al. (2021) entitled “Progress toward resolving the attentional capture debate” provides a striking illustration of this debate, as do the twenty-two commentaries (and three commentary responses) associated with it. Admittedly, this debate largely revolves around human attention experiments, and human cognition may be more complex than mouse cognition. However, the complexity of human cognition may also be easier to study and appreciate because complex behavioural experiments can be explained to, understood, and performed by human participants with relative ease.

(5) Comments relating to future directions and the significance of our findings

The complexity of the attentional capture debate underscores the importance of developing accessible and scalable animal experiments which can be used to provide mechanistic insights. If the human attention literature is any indication, a diversity of rodent experimental paradigms will be necessary to thoroughly map the neuronal implementation of spatial attention. Returning to our paradigm, Reviewer 1 noted that valuable insights into the mechanisms of vibrissal spatial attention might be obtained from comparing the magnitude of attentional modulation we observed between putative regular and fast-spiking categories of units, and between units located in different cortical layers. We agree it is important to understand spatial attention with cell-type and circuit (including laminar) specificity. However, because we could not persuasively cluster our units based on waveform width, and because of the lack of histological data, segregating units on the basis of such variables is not feasible. Despite our assertion that our findings reflect the effects of covert attention (contra Reviewer 1), we agree that future experiments will be required to conclusively rule out overt attention. Noting the proximity of the meshes to the base of the whiskers in our paradigm, and the difficulty of tracking whiskers in this context, Botulinum toxin injections (as in Ramamurthy et al., 2025) might be a means of achieving this.

The above notwithstanding, our findings provide multiple contributions to the literature on spatial attention (and perhaps working memory). We detected significant attentional gain modulation across a population of 1461 responsive units. While the gain modulation exhibited by the median unit was modest (albeit statistically significant), the top 25% of responsive units showed a ~12% response modulation (relative to firing rate range for each unit), and ~21% of responsive units were suppressed by the average vibrissal stimulus in the unattended state. Our experimental framework offers an accessible platform for future studies leveraging genetic and circuit-level interventions to dissect the cell-type specific mechanisms of spatial attention. Our work is timely, noting the recent focus of human research on the nexus of attention, selection history, and valence (e.g. Serences, 2008; Della Libera & Chelazzi, 2009; Della Libera et al., 2011; van den Berg et al., 2014; Kim & Anderson, 2019, 2023). Our work is also uniquely poised to stimulate new interdisciplinary research into the circuit mechanisms of value-driven attentional capture, with translational relevance to psychopathologies such as ADHD, addiction, and depression; where value-driven attentional capture is altered (for a review see Anderson, 2021).

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