Matlab 2012a Crack Only
Ainoha Sistek
If you can't convince mex to prepend -L/usr/lib/i386-linux-gnu first, then I think your only other choice is to remove /usr/local/MATLAB/R2012a/sys/os/glnx86/libstdc++.so (just rename it to e.g. libstdc++.so.bak).
It's a late answer, but I believe the cleanest, most Mathworks-approved and least invasive solution is to edit the .matlab7rc.sh script. This is a script used by the matlab script when you start MATLAB under UNIX-like systems. (See )
Copy that script (found under matlabroot/bin) to the root of your project, or to your home directory. Then tell MATLAB to first search in the system directories for the C++ libraries, instead of its own directories. On my system I changed line 191:
This is the value I use for 'LD_LIBRARY_PATH' when compiling my C++ code in Eclipse (I am not using mex files, instead I create an executable of my C++ code in Eclipse and later run it from matlab shell). In my case the value of 'LD_LIBRARY_PATH' is that long because my C++ code uses boost's regex, matlab libraries (libmat, libmx and so on), GSL library and Armadillo. If you don't use all these libraries, setenv('LD_LIBRARY_PATH','') should be enough, I guess.
Then edit .matlab7rc.sh in matlabroot/bin. Delete in the same directory any mexopts.sh file. Restart Matlab. MEX your file from scratch (this will build a new mexopts.sh file with the new settings. Run it from Matlab console.
Before any potentially disruptive changes are merged into the beta branch, wetag and release a new version of Psychtoolbox following the versioning schemePsychtoolbox-3.x.y. Examples of such disruptive changes would be thediscontinuation of support for an operating system, Matlab version, or a computerhardware platform - anything that could introduce functional regressions into yourexisting hardware and software environment. We recommend to stick with the betabranch, unless you have good reason not to do so, as it is the only officiallytested and supported branch.
On Windows: GStreamer versions older than 1.16.0, or any GStreamer MinGW builds. Now only MSVC builds are supported. This was the last version that allowed to avoid installing GStreamer on Windows if one does not need multi-media functionality.
Research on the perception of temporal order uses simultaneity judgment (SJ) tasks or temporal-order judgment (TOJ) tasks. In either case, each trial in a series presents two stimuli with some temporal delay (or stimulus onset asynchrony; SOA). The stimuli may belong to different sensory modalities, and one of them is experimentally defined as the reference, whereas the other is regarded as the test whose delay with respect to the reference is manipulated. Across trials, the onset of the test stimulus may occur from well before the onset of the reference (yielding delays or SOAs that are defined as negative by convention) to well after it (yielding positive delays). The response requested from the observer varies with the task. In the binary SJ2 version of SJ tasks, observers are asked to judge whether stimuli were presented simultaneously (yielding S responses) or asynchronously (yielding A responses); in the ternary SJ3 variant, observers are instead asked to judge whether the reference was presented first (yielding RF responses), the test was presented first (yielding TF responses), or presentation was simultaneous (yielding S responses). In TOJ tasks, observers must simply report whether they judged that the test stimulus was presented before or after the reference, only allowing TF or RF responses and forcing observers to guess when they judge that presentation was simultaneous. Psychometric functions are then fitted to the proportion of responses of each type as a function of delay. Performance measures (e.g., the point of subjective simultaneity; PSS) are also extracted from the fitted functions.
Often, the fitted psychometric functions (usually Gaussian or logistic functions) have the only purpose of describing the path of the data sufficiently accurately to obtain dependable estimates of these performance measures. This strategy seems adequate to establish, for instance, that SJ and TOJ tasks yield significantly different estimates of the PSS, because the fitted curves describe sufficiently adequately the skeleton of the data even if the fit is not good according to some statistic or if the model underlying the fitted psychometric function is not adequate. However, this strategy cannot indicate the cause of these differences, because the parameters of the fitted functions are not linked to the processes presumably governing performance. The functions, then, are mere descriptions of the data and do not permit testing hypotheses about differences in these underlying processes across tasks or across experimental conditions (e.g., in studies of prior entry): Only observed performance is captured in the estimated parameters, but at the cost of an excessively large number of parameters (as many as 24; see García-Pérez & Alcalá-Quintana, 2012a) and with the questionable outcome that the psychometric functions fitted to SJ3 data do not add up to unity.
An alternative approach consists of adopting a generative model that describes the processes underlying judgments and responses in SJ and TOJ tasks, producing psychometric functions that also allow obtaining conventional performance measures but whose parameters capture the underlying processes. Random-walk models (e.g., Heath, 1984) or diffusion models (e.g., Schwarz, 2006) have been proposed to account for performance in TOJ tasks, but these models do not seem to have ever been extended for application to SJ2 or SJ3 tasks (and how to do this is anything but trivial). In contrast, independent-channels models are suitable candidates because they can readily be tailored to SJ2, SJ3, or TOJ tasks and they additionally give rise to psychometric functions that describe the empirical asymmetries and irregularities of S and RF data discussed in the preceding paragraph. Recourse to the alternative strategy based on independent-channels models has shown that errors in the process by which unobservable judgments turn into observed responses account for some empirical characteristics of SJ data that had been regarded as evidence against these models (García-Pérez & Alcalá-Quintana, 2012b) and it has also shown that the discrepancies between performance measures in SJ and TOJ tasks reflect differences in the decisional aspects that determine judgments (García-Pérez & Alcalá-Quintana, 2012a). Unpublished results show that psychometric functions arising from these models also describe adequately the asymmetries and irregularities of the data from individual observers in the experiments reported by Schneider and Bavelier (2003), Keetels and Vroomen (2008), Fujisaki and Nishida (2009), Vroomen and Stekelenburg (2011), and Yates and Nicholls (2011), as well as in data from other yet unpublished studies. In contrast, the fit of conventional logistic or Gaussian functions to these data is generally much poorer, particularly for SJ2 data.
A thorough description of the model is given in García-Pérez and Alcalá-Quintana (2012a, 2012b), but a brief account is given next. The model is a version of the independent-channels model 3 of Sternberg and Knoll (1973) and assumes that the arrival latencies T t and T r of test and reference stimuli are random variables with shifted exponential distributions:
Empirical data show that errors do not always occur in all forms under all tasks and for all observers (García-Pérez & Alcalá-Quintana, 2012a, 2012b), which requires consideration of the full set of models arising for each task by removal of all possible subsets of response error parameters. As can be seen in Table 1, this yields eight variants for the SJ2 task (one case in which all the ε parameters are removed, one case in which all of them are included, three cases in which only one of the ε parameters is removed, and three more cases in which two of them are removed), another eight variants for the SJ3 task (identical to those just described, because removal of an ε parameter also removes its accompanying κ parameter), and four variants for the TOJ task (one case in which the two ε parameters are removed, another case in which they are both included, and two cases in which only one of them is removed). For all tasks, error model 0 is the simplest version, assuming that response errors do not occur. This assumption renders a model with the least number of free parameters: only four (λr, λt, τ, and δ) in SJ2 and SJ3 tasks and five (the previous four plus ξ) in TOJ tasks. Error model 1 is for all tasks the full model described above and renders the largest number of free parameters per task (seven in SJ2 and TOJ tasks and ten in SJ3 tasks, as discussed above). The remaining models for each task represent intermediate cases (in terms of number of free error parameters) and exhaust all the possibilities for each particular task. The routines to be described below are designed so that users can choose between fitting a particular version of the model for the applicable task(s) or requesting that the full set of models be considered and parameter estimates returned for the model that best fits the data according to the Bayesian information criterion (BIC).
Psychometric functions are fitted to counts of responses at each of a number of delays. Figure 1 gives artificial data from SJ2, SJ3, and TOJ tasks that will be used in the examples to follow. A given experimental condition may use only one of these tasks (see, e.g., Yates & Nicholls, 2011; Zampini, Guest, Shore, & Spence, 2005; Zampini, Shore, & Spence, 2003), or it may use two or more of them with the same stimuli and participants (see, e.g., Fujisaki & Nishida, 2009; Schneider & Bavelier, 2003; van Eijk, Kohlrausch, Juola, & van de Par, 2008, 2010). For this reason, separate routines have been written that fit the model separately to data from each individual task, jointly to data from pairs of tasks, and also jointly to data from the three tasks. In the two latter cases, the joint fit implies that parameters describing the distribution of arrival latencies (λ r, λ t, and τ) are regarded as common to all tasks because these distributions must be identical if stimuli and attentional conditions do not change across tasks (for empirical evidence to this effect, see García-Pérez & Alcalá-Quintana, 2012a). Thus, these parameters are forced to take on the same values across tasks, whereas the remaining parameters are separately estimated for each task.Footnote 1
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