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Brian Maniscalco is a researcher in metacognition and its measurement. With Hakwan Lau, he introduced a method that estimates meta-d-prime from confidence reports and task outcomes. Comparing the estimate with first-order sensitivity asks how much task-relevant information is reflected in self-evaluation. It avoids identifying confidence magnitude alone with accurate monitoring, although the interpretation still depends on the specified signal detection theory model. ^[ref]

Hakwan Lau studies awareness and metacognition. With Brian Maniscalco, he developed a signal detection theory approach for estimating metacognitive sensitivity from confidence ratings. The resulting meta-d-prime expresses confidence-based discrimination using a scale comparable to task sensitivity. The method separates how strongly confidence tracks correctness from a general tendency to use high or low confidence, while retaining assumptions about the modeled decision process. ^[ref]

The first-order sensitivity index that, in a fitted signal detection theory model, would generate the observed confidence discrimination. Model assumptions and sampling uncertainty matter; raw confidence–accuracy association alone does not estimate it.^[ref]

A common computational target is plausible: retrospective confidence evaluates whether an already selected answer is correct. In a Bayesian inference model this can be written $P(\text{selected answer correct}\mid \text{available evidence, action})$. The evidence might be a sensory signal in perception or retrieved information in memory retrieval. A shared target does not require the same evidence representation, parameters or neural implementation in both domains.

A basic signal detection theory account illustrates the common structure. Two alternatives generate different distributions of internal evidence, the decision selects an alternative, and confidence reflects evidence relative to the decision boundary. With equal prior probabilities and two alternatives, let $L$ be the log-likelihood ratio for the observed evidence. For the alternative with the larger posterior probability, confidence as posterior correctness probability givesThis is a normative consequence of the assumed model, rather than a universal empirical law. The same algebra can describe old/new recognition or a perceptual discrimination only when their assumed likelihoods, priors and decision rule justify it. An old/new discrimination also has a different response structure from comparing two simultaneously presented objects.

A different common account is self-consistency theory of confidence: confidence tracks how consistently a choice is supported across sampled representations. Asher Koriat found predictions of this account, previously applied to knowledge judgments, also held in perceptual judgments.^[ref] Its distinctive implication is that confidently repeated errors are possible when misleading evidence consistently favours the wrong choice. Reliable internal agreement need not entail external correctness. This differs from simply identifying subjective confidence with an objectively calibrated posterior.

Important limitations remain. Recognition can be supported by familiarity and recollection, while a perceptual choice may depend on a currently visible stimulus. The content and timing of evidence differ. Confidence can incorporate an additional retrieval attempt, response fluency, or evidence acquired after the initial decision. A common computation can therefore give different levels of metacognitive efficiency across tasks without requiring a wholly different function for confidence. Conversely, similar average rating curves do not prove a single mechanism: quite different latent processes can produce them.

Second-order inference about decisions makes the relation explicit by distinguishing the information supporting the decision from the partially correlated information supporting its evaluation.^[ref] A model in which confidence has exactly the same evidence as the choice is a stronger hypothesis than a model with a shared inferential goal and separate noisy samples. Error detection and later changes of mind are useful tests because they constrain what further information confidence can use.

Strong evidence for a common mechanism would include quantitative predictions across domains, transfer to a new domain after controlled training, and common effects of selective interventions after matching first-order performance. A shared brain region or a cross-task correlation alone is insufficient: task demands and measurement reliability can produce either apparent commonality or apparent dissociation. The most defensible common basis is a family of evidence-based evaluations of correctness, while the claim of one identical process for memory and perception remains substantially stronger.

Practice contributes substantially to compensatory performance after sensory loss, but evidence for selective crossmodal recruitment and causal changes in neural contribution goes beyond an account based solely on accumulated task repetitions. Practice and plasticity are not mutually exclusive: practice can induce neural change, while deprivation can change which circuits practice recruits.

The first requirement is to specify the claimed enhancement. Better tactile discrimination, peripheral visual localization and visual motion detection are different outcomes. Accuracy can improve because of sensory precision, attention, memory, a decision criterion or a slower response. There is no inference from success on one selected task to a universal sharpening of the remaining senses. Sensory loss also differs in onset, completeness, cause and duration. Early blindness, late blindness and temporary blindfolding cannot be treated as equivalent experiments.

A practice account predicts learning with repeated use, specificity to trained inputs and possible transfer to related tasks. A deprivation account predicts changes associated with absent input even when practice is matched. Both can operate, and a statistical difference between groups cannot by itself separate them: daily experience, motivation and selection into a study can differ along with sensory status. Years of Braille use, for example, are not randomly assigned. Comparing blind and sighted experts is more informative than comparing one expert group with novices, but expertise matching does not remove every difference in lifetime experience.

A controlled human study by Lotfi B. Merabet and colleagues used five days of complete blindfolding in normally sighted adults during intensive tactile training, with non-blindfolded training controls. Tactile stimulation increasingly recruited occipital cortex in the deprived group. Disrupting occipital function with repetitive transcranial magnetic stimulation impaired Braille-character discrimination in the blindfolded group on day five, but not in controls; the differential disruption was absent 24 hours after blindfold removal. The imaging response also reversed rapidly.^[ref] The imaging and stimulation arms used different participants, so one should not claim a within-person correlation between their measures.

This combination is more informative than cortical activation alone. Both groups received training, yet the deprived group temporarily depended on a different cortical contribution. Rapid reversibility is compatible with altered weighting or unmasking of existing connections; it does not require wholesale growth of new long-range pathways in five days. The result establishes a functional contribution under the tested state, not that occipital recruitment is sufficient for every tactile gain. Stimulation can affect a network, and the study cannot directly equate sudden reversible deprivation with lifelong blindness. Nor is the persistence of learned skill after the imaging effect disappears paradoxical: skill can remain while the supporting network changes.

A complementary causal result comes from Stephen G. Lomber and colleagues' experiments in congenitally deaf cats. Compared with hearing cats, these animals showed enhanced peripheral visual localization and motion detection on particular tasks. Reversible cooling of posterior auditory cortex removed the localization advantage, whereas cooling a dorsal auditory region removed the motion advantage.^[ref] Cooling provides an experimental contrast between a functioning and temporarily deactivated circuit. The task-specific double dissociation argues for functionally differentiated recruitment, rather than one undifferentiated increase in effort. The evidence is from cats and does not quantify the contribution of practice versus deprivation in humans.

A double dissociation is powerful but not magical. To interpret it, one must check that deactivation does not merely impair movement, alertness or basic visibility, and that tasks are not placed at incomparable difficulty levels. The existence of a recruited circuit identifies part of the implementation, not the entire developmental cause. Extensive experience could help establish that circuit's new role. Thus the strict opposition “neural reorganization or practice” confuses a mechanism with one possible source of the mechanism.

A discriminating program would measure performance before training, randomly assign normally sighted participants to matched training with or without temporary deprivation where appropriate, and follow learning and recovery. For established sensory loss, longitudinal observations and carefully matched comparison groups can constrain onset and experience effects without pretending that sensory loss can be freely randomized. Untrained tasks, different body sites or stimulus dimensions, delayed tests and speed–accuracy measurements distinguish broad transfer from repeated response memorization. Peripheral sensitivity should be checked before attributing every difference to cortex. These are design recommendations, not invented completed studies.

Measurement also needs a mechanistic bridge. Fitting psychometric functions can separate threshold and lapse changes; signal detection theory can distinguish sensitivity from a shifted decision criterion under explicit assumptions. Imaging identifies candidate recruitment, while causal disruption tests contribution. Neither activation magnitude nor a lower threshold alone establishes a “better” brain, and an adaptation useful for one task may create costs elsewhere. Residual input, age of onset and the sensory dimension previously calibrated by the missing modality can all constrain outcomes. Enhancement and impairment can coexist in the same person.

The evidence therefore supports an interaction among altered input, experience and circuit availability. Some compensation is task practice, some reflects state-dependent or developmental changes in neural recruitment, and the strongest explanation specifies how those factors cooperate rather than assigning every gain to only one.

A psychometric function specifies the probability of a measured response as a physical stimulus variable changes. The response might be “present,” “clockwise,” or correct in a two-alternative forced-choice task. These are different dependent variables. Understanding the function's shape is necessary to distinguish sensory changes from decision, attention and measurement effects. A reported threshold without its task, response definition and performance criterion is incomplete. For example, a visual experiment could use calibrated display visual contrasts, timed two-interval presentations, fixation control and button choices; the observer’s choice supplies the dependent variable.

A smooth curve need not mean that a receptor has a smoothly varying conscious threshold. Consider a noisy internal response $R=\mu (x)+\epsilon$, with “yes” reported when $R>c$. Here $c$ is a decision criterion, not necessarily a biological detection limit. If $F_{\epsilon }$ is the noise cumulative distribution function,With Gaussian noise of standard deviation $\sigma$, symmetry of the normal distribution gives a Gaussian psychometric functionThe sigmoid is therefore a distribution tail swept by a changing mean response. At low means, few responses exceed the criterion; near it, many trials switch category; at high means, almost all do. The derivative is $\varphi ((\mu -c)/\sigma ){\mu }^{\prime }(x)/\sigma$, so slope depends on both sensory gain and variability. Moving $c$ shifts the curve without necessarily improving sensitivity. If noise vanished and the mean crossed the criterion sharply, this simple model would approach a step rather than a graded transition.

There is no universal sigmoid in every stimulus coordinate. A nonlinear transduction function changes the curve, and a logarithmic intensity axis changes its appearance. As a counterexample, a toy detector responding whenever at least one event occurs in a Poisson distribution with mean $kx$ has $P(\text{yes}\mid x)=1-e^{-kx}$. This is concave on a linear positive-intensity axis. It follows immediately by subtracting the zero-event probability from one. It is a mathematical example, not evidence that human detection universally uses a one-event rule. Variation in attention or decision criterion across trials can broaden a measured curve even if the within-state sensory noise is unchanged.

Task conventions determine the endpoints. A useful psychometric function with guessing and lapses isHere $\gamma$ is the lower asymptote and $\lambda$ the upper error asymptote. In balanced two-alternative forced-choice performance, $\gamma$ is commonly fixed at $1/2$. A yes/no response probability does not automatically have this lower bound. In this particular cumulative-Gaussian parameterization, $p(\alpha )=\gamma +(1-\gamma -\lambda )/2$; thus $\alpha$ gives 75% correct only for $\gamma =1/2$ and $\lambda =0$. Other function families use different scale parameters. Nor is $\lambda$ automatically the fraction of inattentive trials: if a fraction $r$ of trials involves random two-choice guessing, those trials contribute an upper error probability $r/2$.

The relation to signal detection theory is equally important. Under independent equal-variance Gaussian signal and noise samples, define sensitivity index $d^{\prime }=({\mu }_s-{\mu }_n)/\sigma$. In an ideal two-interval comparison, the response difference has mean ${\mu }_s-{\mu }_n$ and variance $2{\sigma }^2$, givingThis Gaussian forced-choice sensitivity relation follows by standardizing the Gaussian difference. It is a specified decision model, not an identity for every forced-choice experiment. In a yes/no task, hits and false alarms are both needed: under the corresponding equal-variance model, $d^{\prime }={\Phi }^{-1}(H)-{\Phi }^{-1}(F)$. A criterion change can increase both rates while leaving $d^{\prime }$ unchanged. The phenomenological fit above need not have parameters identical to these internal-response parameters.

These distinctions affect experiments. Felix A. Wichmann and N. Jeremy Hill used simulation and statistical modeling to show that unmodeled stimulus-independent errors can bias threshold and slope estimates, even when lapses are uncommon.^[ref] Ignoring high-intensity errors can make a function appear shallower and falsely suggest worse sensory precision. A broad group-average curve can likewise reflect a mixture of individually sharp functions with different locations.

At predetermined stimulus levels, independent trials give counts $k_i\sim \mathrm{Binomial}(n_i,p(x_i))$. The parameter-dependent log-likelihood is ${\sum }_i[k_i\log p_i+(n_i-k_i)\log (1-p_i)]$. Fitting this binomial likelihood, checking goodness of fit and quantifying uncertainty are preferable to drawing a curve through noisy proportions. Easy trials help constrain lapses; levels near the transition constrain location and slope. Randomization, stable calibration and checks for learning or fatigue help distinguish a stationary sensory relation from drift. Adaptive procedures require inference appropriate to their sampling design.

Thus the function is both a description and a testable model. Its shape determines what a threshold means, how an experiment should sample, and which claims about sensory mechanisms are warranted.

Lack of direct access to another animal's subjective experience limits some claims about consciousness, but does not prevent testable explanations of sensory processing, behavioural function or evolutionary history. The essential distinction is between occupying another individual's standpoint and explaining what information it acquires, how it uses that information, and the consequences for survival and reproduction. Thomas Nagel's argument concerns the subjective character of experience, rather than a claim that objective bat biology is inaccessible. ^[ref]

For behavioural biology, Tinbergen's four questions keep the explanatory targets separate. Mechanism asks what sensory and neural processes produce a response; development asks how it changes with maturation and experience; function asks how it affects performance or fitness; evolutionary history asks how it arose and differs among relatives. ^[ref] Not knowing a bat's exact experience does not make the latter questions equivalent to guessing its feelings. It does mean that a functional explanation, even a very successful one, is not by itself an explanation of the qualitative character of consciousness.

Echolocation illustrates substantial knowledge without shared phenomenology. Acoustic recordings and experiments on sensory access linked ultrasonic vocalization and hearing to obstacle avoidance in flying bats. ^[ref] Modern tests can vary echo delay, level or interference while measuring detection or flight responses. A prediction about ranging is available from propagation physics: for sound speed $c$, a round-trip echo delay $\tau$ from a stationary target at distance $r$ gives approximately:An illustrative $2\mathrm{ms}$ delay at $343\mathrm{m}{\mathrm{s}}^{-1}$ corresponds to $0.343\mathrm{m}$. The factor two comes from outward and return travel. Real ranging also depends on target motion, atmospheric conditions, filtering and the timing of emission. None of these testable relationships requires a scientist to experience an echo as the bat does.

The experimental controls matter because a successful animal can exploit an unintended cue. Changing a visual target might also change its acoustic reflection; changing an echo's delay might accidentally change its intensity. Appropriate comparisons manipulate the putative information while holding other cues constant, include sham manipulation, and measure baseline performance. A psychometric function across stimulus levels is more informative than a single success rate. Signal detection theory distinguishes sensitivity from response criterion, helping separate a sensory change from greater willingness to respond. Failure in an unfamiliar apparatus may reflect motivation or the required motor response rather than absence of the sensory capacity.

This approach also prevents treating human perception as the definition of available information. A stimulus invisible to us or outside our hearing range can be salient to the animal. The correct physical description must use the animal's receptors, spatial and temporal resolution, and ecological setting. Human photographs, displays and loudspeakers can omit relevant information or introduce artefacts. Conversely, an animal's failure to categorize objects in our preferred way need not show poor understanding: the experimental categories may not correspond to the distinctions its biology makes useful. Sensory ecology is therefore a substantive constraint on design, even though the animal's qualitative viewpoint remains partly inaccessible.

Causal intervention can connect sensory machinery to behaviour without settling phenomenology. The adult squirrel-monkey opsin-addition experiment demonstrated improved chromatic discrimination following a specified alteration of receptor input. ^[ref] That supports a functional inference about information use. It cannot report the appearance of the new colours to a monkey. Distinguishing these claims makes the experiment more, rather than less, informative: its conclusion is tied to an observable prediction instead of an assumed human-like experience.

Similarly, we can study affect-like influences on decisions without claiming to have measured a feeling directly. In bumblebees, unexpected sucrose reward altered responses to ambiguous cues, with evidence for a dopamine-dependent component and tests of behaviour in more than one context. ^[ref] Such judgement bias is evidence of an internal state influencing decisions; it is not a direct readout of happiness. Changes in activity, hunger, reward valuation or stimulus discrimination can produce superficially similar results and require control. Nor is an unexplained behavioural difference stronger evidence for subjective experience merely because an experimenter finds it familiar.

Evolutionary explanations require an additional bridge. Showing that a bat uses echoes establishes a mechanism; showing improved capture or obstacle avoidance under relevant conditions tests a current function. Demonstrating historical adaptation additionally requires comparisons, inherited variation and an account of the selection pressures or ancestry involved. One can test the prediction that signal structure differs with clutter or prey characteristics while allowing for shared ancestry and body size. A useful hypothesis should predict differences under specified circumstances, rather than explain every outcome retrospectively by invoking what the animal “wanted”.

There are nevertheless real limits. Different internal models can generate the same choice, and an animal cannot necessarily provide the linguistic reports that make some human experiments possible. Success with one discrimination does not reveal the complete content of a representation, and a neural correlate does not uniquely specify an experience. Questions about felt intensity, emotional quality or a reflective awareness of self demand converging behavioural and physiological evidence and may remain underdetermined. This is an epistemic limitation, not evidence that the corresponding experiences are absent.

A productive position avoids both unconstrained anthropomorphism and an equally unsupported denial of mental processing. Specify which sensory information or internal state the model requires, derive predictions, test alternatives and state the level of inference. We can explain much of the evolution and function of animal behaviour through objective relationships among information, decisions and fitness, while remaining uncertain about precisely what those processes feel like to the animal. The residual uncertainty should limit the wording of our conclusions and improve experimental design, rather than stop the research.