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Inter-subject signal sensitivity estimation?

Inter-subject signal sensitivity estimation?


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Factor loading background

I am using paired comparison methods to estimate estimate respondents' sensitivity to distinctions in words used to characterize the construct of agreeableness. (e.g, Undemanding = .34 versus Peaceful = .72). The contrast between the words' factor loadings is the signal to be detected. The stronger loading was alternated between being on the first and second word. I did find that the contrast predicted probability of correct detection (r = .20*).

Experiment background

The stimulus is paired comparison words with each word having a different factor loading (from prior research) based on it's measurement of agreeableness. So each word taps the construct more or less deeply than the other. The contrast between the words in terms of their factor loading is the signal. A set of 122 such paired comparison tasks was developed and the task before the respondent is to select the more agreeable word.

The respondents are students recruited for the research having varying degrees of sensitivity. Each word in the pair only has a factor loading as the estimate of signal and the contrast should be the salient feature in the task.

How to get sensitivity estimate?

I have been using the number of correct “larger” loading detections as the respondent's score. How might I estimate respondents' sensitivity to the presented signals?


Publications

Cox, G. & Criss, A.H. (in press). Parametric supplements to systems factorial analysis: Identifying interactive parallel processing using systems of accumulators. Journal of Mathematical Psychology, 92, 102-247., https://doi.org/10.1016/j.jmp.2019.01.004 [pdf] [data & code]

Wilson, J.H., Kellen, D., Criss, A.H. (in press). Mechanisms of output interference in cued recall. Memory & Cognition, https://doi.org/10.3758/s13421-019-00961-1 [pdf] [data]

Starns, J.J., Cataldo, A.M., Rotello, C.M., et al (in press). Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245919869583 [pdf] [data]

Lee, M.D., Criss, A.H., Devezer, B., Donkin, C., Etz, A., Leite, F., Matzke, D., Rouder, J., Trueblook, J., White, C.N., Vandekerckhove, J. (2019). Robust Modeling in Cognitive Science. Computational Brain & Behavior, 3-4, 141-153. https://doi.org/10.1007/s42113-019-00029-y [pdf]

Vandekerckhove, J. White, C.N., Trueblook, J., Rouder, J., Matzke, D., Leite, F., Etz, A. Donkin, C., Devezer, B., Criss, A.H., & Lee, M.D. (2019). Robust Diversity in Cognitive Science. Computational Brain & Behavior, 3-4, 271–276. https://doi.org/10.1007/s42113-019-00066-7 [pdf]

Koop, G.J., Criss, A.H., Pardini, A. (2019). A strength-based mirror effect persists even when criterion shifts are unlikely. Memory & Cognition, 47, 842- 854. https://doi.org/10.3758/s13421-019-00906-8 [pdf] [data]

Selker, R., van den Bergh, D., Criss, A.H., Wagenmakers, E.J., (2019). Parsimonious estimation of signal detection models from confidence ratings. Behavior Research Methods, 51 (5), 1953-1967. https://doi.org/10.3758/s13428-019-01231-3 [pdf] [code & data]

Chen, S., Malmberg, K.J., Prince, M., Peckoo, S., & Criss, A.H. (2019). The effect of perceptual information on output interference. Psychonomic Bulletin & Review, 26 (1), 269-278. https://doi.org/10.3758/s13423-018-1521-y. [pdf] [data]

Aue, W.R., Fontaine, J.M., & Criss, A.H. (2018). Examining the role of context variability in memory for items and associations. Memory & Cognition , 46(6), 940-954. doi:10.3758/s13421-018-0813-9. [pdf] [data]

Vempaty, A., Varshney, L.R., Koop, G.J., Criss, A.H., and Varshney, P.K. (2018) Experiments and Models for Decision Fusion by Humans in Inference Networks. IEEE Transactions on Signal Processing, 66(11), 2960 – 2971. doi: 10.1109/TSP.2017.2784358 [pdf]

Cox, G., Hemmer, P., Aue, W.R., & Criss, A.H (2018). Information and Processes Underlying Semantic and Episodic Memory Across Tasks, Items, and Individuals. Journal of Experimental Psychology: General, 147(4):545-590. doi: 10.1037/xge0000407 [pdf] [data, code, & stimuli]

Criss, A.H., Salomão, C., Malmberg, K.J., Aue, W.A., Kılıç, A., & Claridge, M. (2018). Release from Output Interference in Recognition Memory: A Test of the Attention Hypothesis. Quarterly Journal of Experimental Psychology, 71(5):1081-1089. doi: 10.1080/17470218.2017.1310265 [pdf] [data]

Wilson, Criss, Spangler, Walukevich, & Hewett (2017) Analysis of acute Naproxen administration on memory in young adults: A randomized double-blind placebo–controlled study, Journal of Psychopharmacology, 31(10):1374-1376. doi: 10.1177/0269881117724406 [pdf] [data]

Cox, G. & Criss, A.H. (2017). Parallel interactive retrieval of item and associative information from event memory. Cognitive Psychology, 97, 31-61. doi 10.1016/j.cogpsych.2017.05.004 [pdf] [data and code]

Wilson, J.H., & Criss, A.H. (2017). The list strength effect in cued recall. Journal of Memory and Language, 95, 78-88. https://doi.org/10.1016/j.jml.2017.01.006 [pdf] [data]

Aue, W.R., Criss, A.H., & Novak, M.D. (2017). Evaluating mechanisms of proactive facilitation in cued recall. Journal of Memory and Language, 94, 103-118. http://dx.doi.org/10.1016/j.jml.2016.10.004 [pdf] [data]

Kilic, A., Criss, A.H., Malmberg, K.J., & Shiffrin, R.M. (2017). Models that allow us to perceive the world more accurately also allow us to remember past events more accurately via differentiation. Cognitive Psychology, 92, 65-86. http://dx.doi.org/10.1016/j.cogpsych.2016.11.005[pdf] [data]

Kellen, D., Erdfelder, E., Malmberg, K.J., Dube, C., & Criss, A.H. (2016). The Ignored Alternative: An Application of Luce’s Low-threshold Model to Recognition Memory. Journal of Mathematical Psychology, 75, 86-95. http://dx.doi.org/10.1016/j.jmp.2016.03.001[pdf]

Koop, G.J. & Criss, A.H. (2016). The Response Dynamics of Recognition Memory: Sensitivity and Bias. Journal of Experimental Psychology: Learning, Memory, & Cognition, 42(5), 671-685. http://dx.doi.org/10.1037/xlm0000202 [pdf] [data]

Annis, J., Lenes, J.G., Westfall, H., Criss, A.H., & Malmberg, K.J. (2015). The list-length effect does not discriminate netween models of recognition memory. Journal of Memory and Language, 85, 27-41. https://doi.org/10.1016/j.jml.2015.06.001 [pdf]

Aue, W.R., Criss, A.H., Prince, M. (2015). Dynamic memory searches: Selective output interference for the memory of facts. Psychonomic Bulletin & Review, 22, 1798-1908. doi: 10.3758/s13423-015-0840-5 [pdf][data]

Howard, M.W., Shankar, K.H., Aue, W.R., & Criss, A.H. (2015). A quantitative model of internal time in memory. Psychological Review, 122(1), 24-53. doi: 10.1037/a0037840 [pdf]

Koop, G.J., Criss, A.H., & Malmberg, K.J. (2015). The role of mnemonic processes in pure-target and pure-foil recognition memory. Psychonomic Bulletin & Review, 22(2), 509-516. doi: 10.3758/s13423-014-0703-5 [pdf][data]

Criss, A.H., Aue, W.R., & Kilic, A. (2014). Age and response bias: Evidence from the strength based mirror effect. Quarterly Journal of Experimental Psychology, 67, 1910-1924. doi: 10.1080/17470218.2013.874037 [pdf] [data]

Malmberg, K.J., Lehman, M., Annis, J., Criss, A.H., & Shiffrin, R.M. (2014). Consequences of testing memory. Psychology of Learning & Motivation – Advances in Research and Theory, 61, 285-313. https://doi.org/10.1016/B978-0-12-800283-4.00008-3 [pdf]

Hemmer, P. & Criss, A.H. (2013). The shape of things to come: Evaluating word frequency as a continuous variable in recognition memory. Journal of Experimental Psychology: Learning, Memory, & Cognition, 39, 1947-1952. doi: 10.1037/t19791-000 [pdf] [data]

Annis, J., Malmberg, K.J., Criss, A.H., & Shiffrin, R.M. (2013). Sources of interference in recognition testing. Journal of Experimental Psychology: Learning, Memory, & Cognition, 39, 1365-1376. doi: 10.1037/a0032188 [pdf]

Criss, A.H., Wheeler, M.E., & McClelland, J.L. (2013). A differentiation account of recognition memory: Evidence from fMRI. Journal of Cognitive Neuroscience, 25, 421-435. doi:10.1162/jocn_a_00292 [pdf]

Kilic, A., Criss, A.H., and Howard, M.W. (2013). A causal contiguity effect that persists across time scales. Journal of Experimental Psychology: Learning, Memory, & Cognition, 39, 297-303. doi: 10.1037/a0028463 [pdf] [data]

Malmberg, K.J., Criss, A.H., Gangwani, T. & Shiffrin, R.M. (2012). Overcoming the negative consequences of interference that results from recognition memory testing. Psychological Science, 23, 115-119. https://doi.org/10.1177/0956797611430692 [pdf] [data]

Wagenmakers, E., Krypotos, A., Criss, A.H., & Iverson, G. (2012). On the iterpretation of uninterpretable interactions: A survey of the field 32 years after Loftus. Memory & Cognition 40, 145-160. [pdf]

Aue, W.R., Criss, A.H., Fischetti, N. (2012). Associative information in memory: Evidence from cued recall. Journal of Memory and Language, 66, 109-122. [pdf] [data]

Criss, A.H., Aue, W.R., & Smith, L. (2011). The effects of word frequency and context variability in cued recall. Journal of Memory and Language, 64, 119-132. [pdf] [data]

Criss, A.H., Malmberg, K.J., & Shiffrin, R.M. (2011). Output interference in recognition memory. Journal of Memory and Language, 64(4), 316-326. [pdf] [data]

Criss, A.H. (2010). Differentiation and response bias in episodic bemory: Evidence from reaction time distributions. Journal of Experimental Psychology: Learning, Memory, & Cognition, 484-499. [pdf] [data]

Criss, A.H. (2009). The distribution of subjective memory strength: List strength and response bias. Cognitive Psychology, 297-319. [pdf] [data]

Criss, A.H. & Malmberg, K.J. (2008). Evidence in support of the elevated attention hypothesis of recognition memory. Journal of Memory & Language, 59, 331-345. [pdf]

Criss, A.H. (2006). The consequences of differentiation in episodic memory: Similarity and the strength based mirror effect. Journal of Memory & Language: Special Issue on Computational Models of Memory, 55, 461-478. [pdf]

Criss, A.H. and McClelland, J.L. (2006). Differentiating the differentiation models: A comparison of the retrieving effectively from memory model (REM) and the subjective likelihood model (SLiM). Journal of Memory & Language: Special Issue on Computational Models of Memory, 55, 447-460. [pdf]

Criss, A.H. and Shiffrin, R.M. (2005). List discrimination and representation in associative recognition. Journal of Experimental Psychology: Learning, Memory, & Cognition, 31(6), 1199-1212. [pdf]

Criss, A.H. and Shiffrin, R.M. (2004c). Pairs do not suffer interference from other types of pairs or single items in associative recognition. Memory & Cognition, 32(8), 1284-1297. [pdf]

Criss, A.H. and Shiffrin, R.M. (2004b). Interactions between study task, study time, and the low frequency hit rate advantage in recognition memory. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30(4), 778-786. [pdf]

Criss, A.H. and Shiffrin, R.M. (2004a). Context noise and item noise jointly determine recognition memory: A comment on Dennis & Humphreys (2001). Psychological Review, 111(3), 800-807. [pdf]

Other Publications

Kilic, A. & Criss, A.H. (2018). Methods for Studying Memory Differences Between Younger and Older Adults. In: Otani, H.. & Schwartz, B. (Eds.) Handbook on Research Methods in Human Memory Research. https://doi.org/10.4324/9780429439957 [pdf]

Criss (2018). In Vivo commentary on Basic Parameter Estimation chapter of Farrell & Lewandowsky Computational Modeling in Cognition. Cambridge University Press https://doi.org/10.1017/CBO9781316272503 [pdf]

Vempaty, Aditya, Varshney, Lav R, Koop, Gregory J, Criss, Amy H, Varshney, Pramod K (2015). Decision fusion by people: Experiments, models, and sociotechnical system design. 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 83-87. [pdf]


Background

Melancholia is frequently conceptualised as a biological disorder encompassing disturbances of mood, motor function, thinking, cognition and perception [1, 2]. Whilst cognitive impairments in melancholia have been investigated in detail [3, 4], definitive identification of selective neurocognitive impairments has not been achieved. Given the pressing need to examine underlying perceptual and inferential processes in heterogeneous illnesses such as depression [5], it is increasingly recognised that a range of methodological approaches should be utilised in the analysis of neurocognitive data to more accurately capture the nature of disturbances across differing depressive syndromes. Such refined approaches have direct utility in enhancing understanding of group-specific psychophysical processes across sub-types of depression.

There is typically great inter-subject variability on tests of neuropsychological function in the major psychiatric illnesses [6]. Meaningful interpretations of brain function in specific disorders is difficult given such variability. This is further compounded by summarising an individual’s position on a performance continuum (as with summing errors on a task) in order to infer the presence or absence of cognitive impairments. Furthermore, commonly utilised neuropsychological tests in those with depressive disorders typically rest upon broad construct-level approaches (e.g. tests of ‘executive function’ or ‘attentional control’) that do not facilitate the development of theories regarding specific psychophysical disturbances in individuals. Despite such drawbacks in assessing cognition in psychiatric illnesses, significant advances have been made over the past 20 years in explaining human perceptual inference and action [7–9] using probabilistic statistical principles such as those developed through a Bayesian-based approach [10]. The Bayesian statistical modelling approach has been applied to individual and group cognitive data across multiple cognitive domains, including signal detection that is viewed as encompassing the processes of attention, decision-making and executive functioning [11, 12]. Formally, the signal detection capacity of an individual can be influenced by prior beliefs (or internal models of the world) and the incoming sensory stream, generating that individual’s response profiles. This, in turn, provides an ideal platform through which to measure perception and inference. In the analysis of cognitive data, signal detection theory or SDT [13] allows modelling of the optimal detection of stimuli, through estimating discriminability and bias [14]. SDT gives rise to measures of discriminability – how easily signal (response) and noise (non-response) trials are distinguished – and bias, reflecting how well the decision-making criterion relates to the optimal criterion. Both constructs reflect an individual’s internal model of the sensorium and their prior contextual beliefs. Signal and noise trials of a task can be represented along a perceptual strength construct in SDT, referring to the strength of inference made to a particular stimulus – that is, the probability that a conclusion (decision/action) is true given its premises. Inferences during streams of trials are continuously monitored through sensory experience and evaluation, and may then be used to update decision criteria for subsequent task performance. Rouder and Lu [15] suggest it is reasonable to expect that on such tasks there will be significant participant-level variability in signal detection sensitivity, creating a need for statistical models that capture individual processes.

Inter-subject variability is rarely modelled in neuropsychological studies of depressed individuals. Moreover, commonly used aggregation methods have the potential to lead to statistical effect estimates that may poorly represent group heterogeneity [15]. Bayesian statistics offer the ability to pre-specify prior knowledge through the specification of priors [16]. A Bayesian approach to data analysis is also appealing in the setting of decision- making in the face of uncertainty because it embodies the same type of assumptions – and hence represents the same constructs – as emerging models of human decision-making [17, 18]. When considering group data using SDT, individual subject variability can be modelled using hierarchical Bayesian techniques [15], allowing estimation of data-driven posteriors of mean bias and discriminability as well as their variance or precision (the inverse of variance) [11, 12]. When cognition is variably disrupted, as arguably is the case in depression, inter-subject estimates of bias and optimal judgement may be influenced, which can ideally be modelled through hierarchical Bayesian SDT analyses. There are several reasons as to why such an approach may offer significant benefit.

In health, cognitive ‘priors’ can be viewed as personal beliefs that are optimised towards the most likely value of a given percept [19]. In depression, however, such processes may be suboptimal in different ways across individuals, extending variously across perceptual, inferential and performance domains. It has been suggested that depression is associated with distorted inference (e.g. “arbitrary inference”) at certain levels of severity (e.g. psychotic depression [20]), yet despite recent theoretical research with Bayesian modelling in depression [5, 21] no studies have employed Bayesian statistics to model cognitive capabilities in depressive illnesses such as melancholia. Most studies to date have attempted to delineate underlying mechanisms of negative cognitive biases [22] based on the notion that depressed individuals have a characteristically negative view of the self, world and future [20, 23]. Several studies have shown that attention is selectively drawn to negative information (e.g. [24]), and that memory of negative information is enhanced [25]. However, few studies have provided a formal quantitative framework for modelling individual level disturbances from empirical psychophysical data. While some studies (e.g. [26]) have established evidence for neurobiological correlates of response bias, it remains to be seen whether cognitive biases extend across depression as a whole or whether they are specific to given individuals or sub-set diagnostic groups. From the findings to date it is evident that there is an unmet need in elucidating basic mechanisms of neurocognitive dysfunction across individuals with depression.

We propose that biases in emotional stimulus processing in depression can be accurately captured through investigation of different depressive sub-types using a hierarchical Bayesian emotional SDT framework. Employing an emotional word ‘go/no-go’ task, which requires responding and inhibition of responding to serially presented, randomly sequenced positive, negative and neutral words, we hypothesised that each depressed sub-set would show less optimal responding (poorer discriminability) across emotional signal conditions as compared to the control group, but that the melancholic sub-set would show more difficulty in detecting true signal trials from noise trials, particularly on emotional signal conditions (i.e. lower sensitivity) compared with non-melancholic and control participants.


Visuocortical changes during a freezing-like state in humans

June 12th, 2018

Neuroimage (2018) Maria Lojowska, Sam Ling, Karin Roelofs, Erno Hermans

An adaptive response to threat requires optimized detection of critical sensory cues. This optimization is thought to be aided by freezing - an evolutionarily preserved defensive state of immobility characterized by parasympathetically mediated fear bradycardia and regulated by the amygdala-periaqueductal grey (PAG) circuit. Behavioral observations in humans and animals have suggested that freezing is also a state of enhanced visual sensitivity, particularly for coarse visual information, but the underlying neural mechanisms remain unclear. We induced a freezing-like state in healthy volunteers using threat of electrical shock and measured threat-related changes in both stimulus-independent (baseline) and stimulus-evoked visuocortical activity to low- vs. high-spatial frequency gratings, using functional MRI. As measuring immobility is not feasible in MRI environments, we used fear bradycardia and amygdala- PAG coupling in inferring a freezing-like state. An independent functional localizer and retinotopic mapping were used to assess the retinotopic specificity of visuocortical modulations. We found a threat- induced increase in baseline (stimulus-independent) visuocortical activity that was retinotopically nonspecific, which was accompanied by increased connectivity with the amygdala. A positive correlation between visuocortical activity and fear bradycardia (while controlling for sympathetic activation), and a concomitant increase in amygdala-PAG connectivity, suggest the specificity of these findings for the parasympathetically dominated freezing-like state. Visuocortical responses to gratings were retinotopically specific but did not differ between threat and safe conditions across participants. However, individuals who exhibited better discrimination of low-spatial frequency stimuli showed reduced stimulus-evoked V1 responses under threat. Our findings suggest that a defensive state of freezing involves an integration of preparatory defensive and perceptual changes that is regulated by a common mechanism involving the amygdala.


Acknowledgements

The authors thank X. Peng and M. Li for technical assistance. Data set II was provided by the Human Connectome Project, WU-Minn Consortium (principal investigators, D. Van Essen and K. Ugurbil 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. Data set III was provided by the Brain Genomics Superstruct Project of Harvard University and the Massachusetts General Hospital (principal investigators, R.L.B., J. Roffman and J. Smoller), with support from the Center for Brain Science Neuroinformatics Research group, the Athinoula A. Martinos Center for Biomedical Imaging, and the Center for Human Genetic Research. Twenty investigators at Harvard and MGH contributed data to the overall project. This work was supported by NIH grants K25NS069805 (H.L.), R01NS091604 (H.L.), P50MH106435 (R.L.B. and H.L.), K01MH099232 (A.J.H.), R01HD067312 (G.L.), P41EB015902 (G.L.), OeNB Nr. 15929 (G.L.), Medical Imaging Cluster of the Medical University of Vienna (G.L.), National Basic Research Program of China Grant 2011CB504100 (X.W.), National Science Foundation of China Grant 61473169 (B.H.) and National Program on Key Basic Research Projects of China Grant 2011CB933204 (B.H.).


Exploitation and paternalism

Yet some still worry. Bioethicists Emily Largent and Franklin Miller wrote in a recent paper that a payment might “unfairly" exploit “those U.S. residents who have lost jobs … or slipped into poverty during the pandemic," which could leave them feeling as if they have “no choice but to be vaccinated for cash." Others have noted that vaccine hesitancy is higher in nonwhite communities, where incomes tend to be lower, as is trust in the medical establishment.

Ethicists and policymakers should indeed focus on the poorest members of our community and seek to minimize racial disparities in both health outcomes and wealth. But there is no evidence that offering money is actually detrimental to such populations. Receiving money is a good thing. To suggest that we have to protect adults by denying them offers of money may come across as paternalism.

Some ethicists also argue that the money is better spent elsewhere to increase participation. States could spend the money making sure vaccines are convenient to everyone, for example, by bringing them to community events and churches. Money could also support various efforts to fight misinformation and communicate the importance of getting the shot.


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Abstract

How should we measure metacognitive (“type 2”) sensitivity, i.e. the efficacy with which observers’ confidence ratings discriminate between their own correct and incorrect stimulus classifications? We argue that currently available methods are inadequate because they are influenced by factors such as response bias and type 1 sensitivity (i.e. ability to distinguish stimuli). Extending the signal detection theory (SDT) approach of Galvin, Podd, Drga, and Whitmore (2003), we propose a method of measuring type 2 sensitivity that is free from these confounds. We call our measure meta-d′, which reflects how much information, in signal-to-noise units, is available for metacognition. Applying this novel method in a 2-interval forced choice visual task, we found that subjects’ metacognitive sensitivity was close to, but significantly below, optimality. We discuss the theoretical implications of these findings, as well as related computational issues of the method. We also provide free Matlab code for implementing the analysis.

Highlights

► Signal detection theory (SDT) predicts that task performance affects metacognition. ► Current measures of metacognition do not account for these confounds. ► Our new SDT method measures metacognitive performance without these confounds. ► Applying the method to data, we find observers are below SDT-optimal metacognition. ► We provide free Matlab code for performing the analysis.


Signal detection theory

A theory in psychology which characterizes not only the acuity of an individual's discrimination but also the psychological factors that bias the individual's judgments. Failure to separate these two aspects of discrimination had tempered the success of theories based upon the classical concept of a sensory threshold. The theory provides a modern and more complete account of the process whereby an individual makes fine discriminations.

The theory of signal detection has two parts of quite different origins. The first comes from mathematical statistics and is a translation of the theory of statistical decisions. The major contribution of this part of the theory is that it permits a determination of the individual's discriminative capacity, or sensitivity, that is independent of the judgmental bias or decision criterion the individual may have had when the discrimination was made. The second part of the theory comes from the study of electronic communications. It provides a means of calculating for simple signals, such as tones and lights, the best discrimination that can be attained. The prediction is based upon physical measurements of the signals and their interfering noise.

This opportunity to compare the sensitivity of human observers with the sensitivity of an “ideal observer” for a variety of signals is of considerable usefulness, and of growing interest, in sensory psychology. Signal detection theory has been applied to several topics in experimental psychology in which separation of intrinsic discriminability from decision factors is desirable. Included are attention, imagery, learning, conceptual judgment, personality, reaction time, manual control, and speech.

The analytical apparatus of the theory has been of value in the evaluation of the performance of systems that make decisions based on uncertain information. Such systems may involve only people, or people and machines together, or only machines, Examples come from medical diagnosis, where clinicians may base diagnostic decisions on a physical examination, or on an x-ray image, or where machines make diagnoses, perhaps by counting blood cells of various types.


Psychophysics

Psychophysics quantitatively investigates the relationship between physical stimuli and the sensations and perceptions they produce. Psychophysics has been described as "the scientific study of the relation between stimulus and sensation" [1] or, more completely, as "the analysis of perceptual processes by studying the effect on a subject's experience or behaviour of systematically varying the properties of a stimulus along one or more physical dimensions". [2]

Psychophysics also refers to a general class of methods that can be applied to study a perceptual system. Modern applications rely heavily on threshold measurement, [3] ideal observer analysis, and signal detection theory. [4]

Psychophysics has widespread and important practical applications. For example, in the study of digital signal processing, psychophysics has informed the development of models and methods of lossy compression. These models explain why humans perceive very little loss of signal quality when audio and video signals are formatted using lossy compression.


PS3012: Advanced Research Methods Lecture 9: Psychophysics, psychophysical methods, and signal detection theory - PowerPoint PPT Presentation

PS3012: Advanced Research Methods Lecture 9: Psychophysics, psychophysical methods, and signal detection theory Jonas Larsson Department of Psychology &ndash PowerPoint PPT presentation

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